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10.1371/journal.pntd.0003441 | Type 3 Secretion System Cluster 3 Is a Critical Virulence Determinant for Lung-Specific Melioidosis | Burkholderia pseudomallei, the bacterial agent of melioidosis, causes disease through inhalation of infectious particles, and is classified as a Tier 1 Select Agent. Optical diagnostic imaging has demonstrated that murine respiratory disease models are subject to significant upper respiratory tract (URT) colonization. Because human melioidosis is not associated with URT colonization as a prominent presentation, we hypothesized that lung-specific delivery of B. pseudomallei may enhance our ability to study respiratory melioidosis in mice. We compared intranasal and intubation-mediated intratracheal (IMIT) instillation of bacteria and found that the absence of URT colonization correlates with an increased bacterial pneumonia and systemic disease progression. Comparison of the LD50 of luminescent B. pseudomallei strain, JW280, in intranasal and IMIT challenges of albino C57BL/6J mice identified a significant decrease in the LD50 using IMIT. We subsequently examined the LD50 of both capsular polysaccharide and Type 3 Secretion System cluster 3 (T3SS3) mutants by IMIT challenge of mice and found that the capsule mutant was attenuated 6.8 fold, while the T3SS3 mutant was attenuated 290 fold, demonstrating that T3SS3 is critical to respiratory melioidosis. Our previously reported intranasal challenge studies, which involve significant URT colonization, did not identify a dissemination defect for capsule mutants; however, we now report that capsule mutants exhibit significantly reduced dissemination from the lung following lung-specific instillation, suggesting that capsule mutants are competent to spread from the URT, but not the lung. We also report that a T3SS3 mutant is defective for dissemination following lung-specific delivery, and also exhibits in vivo growth defects in the lung. These findings highlight the T3SS3 as a critical virulence system for respiratory melioidosis, not only in the lung, but also for subsequent spread beyond the lung using a model system uniquely capable to characterize the fate of lung-delivered pathogen.
| Respiratory melioidosis is a lethal disease presentation of the bacterium Burkholderia pseudomallei, which is found in tropical regions worldwide. Respiratory melioidosis has also been highlighted as a concern in the biodefense community given the potential for weaponization of B. pseudomallei. This study demonstrates that respiratory melioidosis models can significantly vary in their disease presentations in mice, depending on whether the upper respiratory tract represents an initial site of infection. We have demonstrated that lung-specific infections of mice, which avoid nasal cavity colonization, result in a course of disease with greater maturation of pneumonia and systemic spread, and we propose that this represents a critical advance in the field of studying respiratory melioidosis. We further characterize that the capsule virulence determinant, previously considered important for respiratory melioidosis, has reduced significance when characterized in the context of lung-specific disease, while the Type 3 Secretion System cluster 3 is a critical virulence determinant for B. pseudomallei required for efficient colonization of the lung as well as spread to other tissues.
| Burkholderia pseudomallei is the Tier 1 Select Agent bacterial pathogen responsible for the disease melioidosis. B. pseudomallei is found in moist tropical soils worldwide, but has been long characterized to be endemic to Southeast Asia and northern Australia [1]. Naturally acquired disease typically involves percutaneous inoculation or inhalation of pathogen by susceptible hosts, with risk factors including diabetes and alcoholism [2]. Under exceptional conditions, such as natural disasters, otherwise healthy individuals are also susceptible to melioidosis [3], [4], [5], [6], suggesting that dose and route of inoculation are key elements to determining whether or not a healthy individual acquires disease. The ability of B. pseudomallei to establish a lethal respiratory disease, combined with its inherent resistance to numerous classes of antibiotics, highlights the importance of characterizing respiratory melioidosis for the purposes of biodefense. Importantly, no licensed vaccine exists for melioidosis, nor for glanders, which is caused by the very closely related pathogen Burkholderia mallei.
Respiratory melioidosis has been well studied in surrogate animal models for both basic science investigations as well as therapeutic studies. B. pseudomallei has a significant lung tropism, irrespective of the route of acquisition [2], and is able to spread to other tissues to cause a lethal systemic disease. Bioluminescent B. pseudomallei strains have been generated which allow for the temporal assessment of disease progression in individual animals using optical diagnostic imaging. In the first of such studies, a non-lethal intranasal challenge revealed that B. pseudomallei prominently colonizes the upper respiratory tract (URT) of mice, leading to a rapid development of meningitis within 24 hr, likely resulting from spread to the olfactory bulbs via olfactory nerve endings [7]. Interestingly, the symptoms of a prominent URT colonization (rhinitis, sinusitis, tonsillitis, laryngitis, and otitis media) have not been described as common presentations of melioidosis [8], [9], suggesting that URT infections do not play the prominent role in humans as the one observed in murine models. Indeed, a large clinical sampling study of throat swabs revealed no carriage of B. pseudomallei in healthy volunteers, while melioidosis patients had culturable B. pseudomallei from the throat in 36.1% of cases [10] – less than the presentation rate of pneumonia at 50% [11]. Additionally, paired analyses of throat and sputum carriage from the same patient demonstrated that B. pseudomallei culture from the throat is underrepresented relative to sputum culture, suggesting that presence of B. pseudomallei in the lung is not a direct result of a descending infection from a colonized throat [10]. These clinical trends, combined with the relatively rare presentation of meningitis at an incident rate of 4–5% [11], [12], suggest that murine models in which B. pseudomallei is delivered intranasally over-represent the incidence of URT disease and CNS involvement in studies of respiratory melioidosis. Significant URT colonization was also observed by diagnostic optical imaging in a lethal intranasal murine model of respiratory melioidosis [13], suggesting that the URT colonization phenotype is not specific to sub-acute disease.
The role of murine URT colonization on studies of respiratory melioidosis is poorly understood, though may have a significant impact on both basic and translational studies. A recent study investigating an intratracheal instillation of B. pseudomallei directly into the lungs successfully demonstrated that avoidance of initial URT colonization could limit both CNS involvement and late stage URT colonization [14]. This finding is consistent with other studies demonstrating the mechanism by which melioidosis-associated meningitis arises from spread to the brain from the nasal cavity within 24 hr using both olfactory and trigeminal nerves [15]. Because intratracheal delivery is capable of avoiding such URT and CNS involvement, an unanswered question is what impact these cephalic disease presentations have on disease outcome of pneumonic and systemic disease. We have improved upon other published non-surgical approaches to deliver bacteria directly into the lung as a novel instillation strategy termed intubation-mediated intratracheal (IMIT) inoculation, which we have validated to provide >98% efficiency in pulmonary delivery [16], and we therefore used this highly accurate lung inoculation approach to study the impact of lung-specific melioidosis on dissemination and disease outcome. Additionally, we investigated whether lung-specific administration of capsular polysaccharide and Type 3 Secretion mutants exhibit modified courses of disease relative to previous characterization in other respiratory murine models. Using a combination of optical diagnostic imaging, targeted lung-specific delivery of B. pseudomallei, and previously characterized virulence system mutants, we demonstrate that the presence or absence of URT infection in murine models exhibits significant disease outcome differences, with potential impacts on both basic and translational studies.
Burkholderia pseudomallei strains were routinely cultured in Lennox Broth (LB) at 37°C. In preparation for infection studies, B. pseudomallei strains were subcultured 1∶25 from overnight LB cultures into dialyzed and chelated Trypticase Soy Broth (TSBDC [17]) supplemented with 50 µM monosodium glutamate and grown for 3 hr at 37°C with shaking. Antibiotics were used at the following concentrations: kanamycin, 25 µg/ml; polymyxin B, 50 µg/ml; and streptomycin, 100 µg/ml.
Luminescent B. pseudomallei strains JW280 and JW280 Δwcb were generated and described elsewhere [13]. JW280 ΔsctUBp3 was generated by allelic exchange by using the S17-1/pKAS46-araPtolClux construct [13] to add the luxCDABE operon to DD503 ΔsctUBp3 [18], as previously described.
These studies were approved by the University of Louisville Institutional Animal Care and Use Committee (Protocol numbers 10073 and 13053) in agreement with NIH guidelines and the “Guide for the Care and Use of Laboratory Animals” (NRC). In-house breeding of mice was conducted on Protocol 11113.
Animal studies were conducted under Biosafety Level 3 conditions using eight to ten-week-old female or male albino C57BL/6J mice (B6(Cg)-Tyrc-2J/J, Jackson Laboratories and in-house breeding). Freshly grown bacteria were washed into phosphate buffered saline (PBS) to appropriate concentrations for infection using OD600-based calculations. Intranasal infections were carried out as previously described using 30 µl B. pseudomallei suspensions [19]. Intubation-mediated intratracheal (IMIT) instillation was performed, as previously described, to facilitate non-surgical lung-specific disease [16], [20]. Briefly, mice were isoflurane-anesthetized and 2% lidocaine was applied to the back of the mouth as a local anesthetic. Mice were intubated using a 20 gauge catheter, and catheter placement was confirmed by flow meter. Bacterial suspensions were directly instilled into the lung using a 22 gauge blunt needle inserted through the catheter. Optical diagnostic imaging was conducted with an IVIS Spectrum (Caliper Life Sciences) as described previously [13], [21], with once to twice-daily imaging through day 5, and once daily thereafter until the study completion at day 14. Animals were euthanized at the onset of moribund disease which was defined by loss of righting reflex.
Infected animals were euthanized and necropsied to enumerate bacteria from infected tissues. Blood samples were collected by cardiac puncture, while bronchoalveolar lavage (BAL) was collected in 1 ml of PBS. Necropsied tissues were subjected to bioluminescence imaging (IVIS Spectrum) in a 24 well black plate before serial dilution enumeration of bacterial burden from tissue homogenate, as described elsewhere [21]. We have defined correlations between tissue cps and CFU specifically for each tissue as follows: Lung, log(cps) = 1.219log(CFU) – 2.999 (R2 = 0.68); Liver, log(cps) = 1.238log(CFU) – 3.502 (R2 = 0.99); Spleen, log(cps) = 1.088log(CFU) – 1.741 (R2 = 0.98).
Student T-test, one-way ANOVA, two-way ANOVA and survival analyses (Mantel-Cox test and Gehan-Breslow-Wilcoxon test) were conducted in GraphPad Prism. Probit analysis (Finney Method, StatPlus 2009 Professional) was used to calculate LD50 +/− standard error, which was subsequently subjected to Student T-test analysis to investigate significant differences of LD50 values of different B. pseudomallei strains.
Opportunistic URT colonization by B. pseudomallei is associated with high rates of meningitis in murine respiratory models [7], [22], and avoidance of bacterial deposition on the nasal mucosa by intratracheal instillation is associated with reduced CNS involvement [14]. We decided to investigate whether the URT colonization typical of current respiratory melioidosis models impacts the moribund disease presentation. To investigate this, we employed a non-surgical approach that would facilitate direct instillation of bacteria directly into the lungs of mice with >98% efficacy [16], termed intubation-mediated intratracheal (IMIT) delivery [20]. Albino C57BL/6J mice were used as a model system in these studies given the myriad of transgenic tools available in the C57BL/6J background, and the importance of coat color in optimizing detection of bioluminescent bacterial pathogens [21]. Thus, albino C57BL/6J mice were infected with 104 CFU of luminescent B. pseudomallei strain, JW280, using either intranasal or IMIT delivery and monitored twice daily by optical diagnostic imaging. While challenge with 104 CFU by both routes of inoculation resulted in moribund disease over a similar time frame, the foci of disease in moribund animals was dramatically different (Fig. 1). As observed in our previous work with a BALB/c model [13], C57BL/6J mice infected intranasally developed a significant URT infection with a reduced bioluminescent signal associated with the thoracic cavity/lung (Fig. 1). Interestingly, mice infected in a lung-specific manner by IMIT developed a pulmonary infection earlier than by intranasal delivery of the same inoculum, consistent with estimates that 10% of an intranasal inoculum is delivered to the lung for other respiratory pathogens [23]. Importantly, the early involvement of the lung in the IMIT model led to a more mature pneumonia than that observed in the intranasal model, with subsequent systemic spread not previously observed in the intranasal model. This suggests that mice are capable of sustaining an advanced systemic disease than previously thought possible in the intranasal model, and therefore that URT colonization by B. pseudomallei directly contributes to the host morbidity of the intranasal model. These data indicate that avoiding initial deposition of bacteria in the upper respiratory mucosa represents an important modification for studying mature pneumonia development and subsequent systemic spread.
We decided to examine whether the kinetics of respiratory disease vary as a function of the method of delivery in order to investigate whether the involvement of different foci of infection impacts the median time to death (MTTD). Mice were infected by either intranasal or IMIT instillation and both respiratory disease models were able to establish an acute course of disease in C57BL/6J mice with a typical MTTD of ∼4 days (Fig. 2). We observed typical dose response disease susceptibility in the intranasal model with a transition from 100% survivors to 100% fatality over a 32 fold dose range (Fig. 2A). Interestingly, we observed a much sharper dose transition in host susceptibility to disease in the IMIT-infected groups, which occurred over a single ten-fold dose range (Fig. 2B). We calculated the LD50 of the intranasal C57BL/6J model to be 12.1±2.4×103 CFU, while the IMIT model significantly lowered the LD50 to 5.4±2.0×103 CFU (P = 0.04). Thus, targeting of B. pseudomallei directly into the lungs of mice resulted in a lowering of the LD50 and resolved host susceptibility into a more discrete dose range transition from host susceptibility to clearance of pathogen. Importantly, the small 2.2 fold change in the LD50 and similar MTTD at equivalent doses suggest that the ultimate course of disease takes place over a similar time frame, regardless of the method of inoculation, but that the disease presentation is dramatically altered dependent on whether B. pseudomallei spreads specifically from the lung, or whether initial inoculation prominently colonizes the URT. We conclude that intranasal and IMIT respiratory infections therefore cause very different morbidity in the host as a result of either primarily URT or systemic endpoints, respectively.
To further investigate the role of URT colonization on disease endpoints, we subsequently investigated the bacterial burdens of tissues isolated from moribund mice to further characterize differences in bacterial dissemination. Tissues were necropsied from moribund mice infected with ∼LD100 doses of JW280 by either the intranasal or IMIT routes of infection, and we compared the tissue burdens of moribund animals for the lung, liver, spleen, BAL and blood of mice infected with (i.n.) or without (IMIT) involvement of the URT. Consistent with the findings of Fig. 1, we found that IMIT delivery of B. pseudomallei facilities significant disease maturation in all monitored tissues, both at the primary site of infection in the lung, as well as in the disseminated infection of the liver and spleen (Fig. 3). Further, we observe a >3 log CFU difference of bacterial dissemination through the blood, indicating that moribund mice exhibit a greater degree of septicemia in the IMIT model relative to the i.n. model. Consistent with in vivo diagnostic imaging, bacterial tissue burden analysis reveals that B. pseudomallei infections involving prominent URT colonization result in host morbidity associated with reduced disease maturation in core body sites, suggesting that the host morbidity of the i.n. model is directly influenced by the bacterial colonization of the nasal cavity.
Given a recent focus in the scientific community on understanding disease progression in both male and female model systems [24], we additionally performed survival analyses in male albino C57BL/6J mice to investigate whether sex differences impact susceptibility to lung-specific respiratory melioidosis. Male disease progression closely mirrored that observed in the female models (Fig. 2 and 4), where in both cases, the 100% minimally lethal dose was observed at 104.2 CFU by IMIT, with a 91 hr MTTD. We calculated the LD50 for lung-specific melioidosis in male mice as 1.9±1.2×103 CFU, which was 2.9 fold reduced relative to the female LD50 (P = 0.25). Thus, male mice are not significantly different in their susceptibility to respiratory melioidosis relative to female mice in the C57BL/6J IMIT model system.
We observed that URT colonization impacts disease outcome in the murine respiratory melioidosis model, and we therefore hypothesized that URT colonization could impact our basic understanding of the role of virulence determinants in mediating B. pseudomallei pathogenesis. Previous murine studies demonstrated that a capsule mutant LD50 is attenuated 101.8 fold in a respiratory melioidosis model [19], and that T3SS3 is also required for the full virulence in an equivalent dose challenge [25]. We therefore examined the response of albino C57BL/6J mice infected with increasing doses of either a luminescent capsular polysaccharide mutant (JW280 Δwcb) or a T3SS3 mutant (JW280 ΔsctUBp3). We found that a capsule mutant inoculated by IMIT was not significantly attenuated relative to the wild type strain with a capsule mutant LD50 calculated to be 104.57 CFU (6.8 fold attenuation, P = 0.60). We observed a MTTD of 72 hr at a ∼LD100 challenge (Fig. 5a), which represents a faster course of disease than the a ∼LD100 dose of wild type at 91 hr, albeit with a larger challenge dose. Thus, the JW270 capsular polysaccharide mutant is not significantly attenuated in the lung-specific IMIT model, contrasting with our prior findings in the i.n. model. In the IMIT model, the T3SS3 mutant had a calculated LD50 of 106.19 CFU, which represents a significant attenuation of 102.5 fold (P = 0.004). The course of disease of the T3SS3 was observed to have a MTTD of 79 hr at the minimally lethal dose (Fig. 5b), which like the capsule mutant strain was faster than the wild type MTTD, albeit with a larger challenge dose. Thus, in a lung-specific respiratory melioidosis model, T3SS3 is a critical virulence determinant for B. pseudomallei in the lung, whereas the capsular polysaccharide appears to play a more minor role.
We decided to further investigate whether abrogation of URT colonization in our lung-specific disease studies impacts the dissemination potential of the capsule and T3SS3 mutants. We performed optical diagnostic imaging of ∼LD100 infections of the wild type strain as well as both the capsule and T3SS3 mutants to characterize bacterial burdens at moribund disease. We found that the wild type strain is capable of dissemination beyond the lung to colonize all sites of the body at high titer (Fig. 6). The capsule and T3SS3 mutants developed significant bacterial pneumonia yet exhibited a spread deficiency with minimal bacterial burden outside of the lung (Fig. 6). We further characterized the dissemination defects of these mutants by enumerating bacteria from the lung, liver and spleen, from mice infected with minimally lethal doses of each strain. We found that while all tested strains established bacterial pneumonias of 108–109 CFU per tissue, the capsule and T3SS3 mutants exhibited significant dissemination defects to the liver and spleen in both tissues (Fig. 7). These data demonstrate that a capsule mutant exhibits reduced fitness to disseminate from the lung, consistent with the previously characterized role of capsular polysaccharide in mediating complement protection [26]. Thus, a capsule mutant is capable of producing a lethal pneumonia with a similar LD50 as wild type, yet without the wild type-ability to spread beyond this organ. In contrast, the T3SS3 virulence determinant exhibits a significantly reduced fitness in the lung by LD50 analysis, and this reduced fitness is also associated with a reduced dissemination potential to the liver and spleen. Thus, both the capsule and T3SS3 mutants are spread deficient when delivered specifically to the lung, highlighting a major difference between the current lung-specific respiratory melioidosis model versus our previous work with i.n. models. This finding suggests that the differences in respiratory melioidosis models, with respect to URT involvement in dissemination and endpoint, dramatically influences our interpretation of basic science investigation of the role of B. pseudomallei virulence determinants.
We hypothesized that the reduced fitness characterized at moribund disease would similarly be associated with a reduced fitness throughout the course of disease. We therefore performed optical imaging of mice infected at a ∼LD100 dose of JW280, JW280 Δwcb, or JW280 ΔsctUBp3 and quantified the in vivo bioluminescence until moribund endpoints were reached. We identified an in vivo logarithmic increase in bioluminescence of all strains in the lung, but observed that the T3SS3 mutant exhibited a reduced fitness relative to both wild type and capsule mutant strains (Fig. 8). The bioluminescence doubling rate was calculated for all strains and we found non-significant differences of the doubling rates of the wild type and capsule mutant strains of 9.88 and 9.24 hr, respectively (One-way ANOVA/Tukey not significant). However, the T3SS3 mutant bioluminescence doubled at a significantly reduced rate of 13.64 hr (P<0.001), suggesting that the T3SS3 mutant is less fit to grow in host niches and/or is subject to enhanced clearance by the host. This finding is consistent with our study data, highlighting T3SS3 as a critical virulence determinant for B. pseudomallei lung colonization whereas the capsular polysaccharide plays a lesser role in disease of the lung.
We have previously developed an optical diagnostic imaging model of intranasal respiratory melioidosis and observed that the URT of mice infected in this manner are subject to prominent infection [13]. URT colonization is associated with infection of the nasal-associated lymphoid tissue (NALT) as well as infection of the olfactory bulbs/CNS [7], [15]. As discussed above, descriptions of disease states associated with URT infections have not been described in human melioidosis, and paired analysis of cultures sputum and throat swabs suggests that pneumonia gives rise to presence of B. pseudomallei at the top of the respiratory tract rather than URT carriage seeding a primary infection which descends to the lung [10]. The over-representation of these symptoms in mice have led us, and others, to investigate alternatives to the standard approaches of inoculating mice with B. pseudomallei through the nares. A recently developed intratracheal model of respiratory melioidosis succeeded in abrogating CNS infections, suggesting that URT colonization is directly responsible for the high levels of meningitis reported in the murine model [14], [27]. Our current studies focused on advancing these findings by identifying whether URT infection in the mouse impacts the overall course of disease and ask whether these impacts might influence both basic and translational studies of respiratory melioidosis.
Importantly, we found that inoculation of B. pseudomallei directly into the lung dramatically altered disease outcome, where we observed significant increases in both lung burden and septicemic spread not previously observed in intranasal inoculation studies. Our survival analysis of i.n. and IMIT-infected mice revealed that both routes of infection supported a disease process with very similar timing of inoculation to moribund endpoint; however, the major difference between the models was the difference in which host tissues supported the dominant site(s) of infection. IMIT also lowered the LD50 relative to the i.n. model, and provided an earlier development of pneumonia which progressed to an advanced systemic disease. Intranasal infection exhibits a clear bias to nasal cavity colonization, and conversely the IMIT model achieves systemic disease at moribund endpoints. This difference in infection site at moribund disease strongly suggests that the causation of moribund presentation is very different in these models, with IMIT providing systemic, general organ failure disease, while the moribund disease of the intranasal model is very directly related to the bacterial burden in the nasal cavity. Pathological analysis of the URT of mice infected by the i.n. route has revealed significant blockage by inflammatory cell debris in the nasal turbinates [27], thus the severe pathology/rhinitis of the intranasal model likely promotes moribund disease. Both the IMIT and i.n. models have moribund disease symptoms which include labored breathing of mice, and given that mice are obligate nasal breathers, we hypothesize that nasal cavity occlusion in the intranasal model drives moribund endpoints while the labored breathing of the IMIT model may reflect greater lung pathology, as the IMIT model supported >1 log more bacteria per lung than the i.n. model. Future studies will be required to investigate whether the aerosol model – which also would involve the nasal mucosa as a primary site of infection – is similarly is subject to preferential colonization of the URT over systemic spread.
The IMIT inoculation method we developed is distinct from other non-invasive intratracheal instillation methods, including those used previously for B. pseudomallei instillation [14]. IMIT inoculation is a two-step process in which mouse intubation is followed by instillation of bacteria via a long blunt needle, and the approach facilitates an intermediate confirmation of correct catheter placement into the trachea (rather than the esophagus), and is therefore not prone to user error associated with unintentional mis-inoculation of the GI tract [16]. IMIT inoculation also benefits from being a non-invasive approach which avoids overt deposition of bacteria into the blood stream which could occur as a result of surgical intratracheal inoculation.
This study incorporated use of albino C57BL/6J mice as a novel host model system in which to study respiratory melioidosis. A vast array of murine transgenic lines are available to the research community, the majority of which are available in the C57BL/6J background, which have been, and will continue to be important tools in melioidosis studies. C57BL/6 mice are commonly referred to as representing a chronic model of melioidosis, however both in the intranasal and IMIT infection studies we found that C57BL/6J mice develop an acute disease with a MTTD of 3–4 days. While C57BL/6J mice do appear to have a higher resistance to respiratory melioidosis with intranasal LD50 values 1–3 logs higher than their BALB/c counterparts [28], we conclude that C57BL/6J mice successfully model an acute respiratory disease presentation. We further made use of tyrosinase-negative mice which have albino coats and therefore offer greater sensitivity over black coated mice to detect bioluminescent bacteria, which is necessary to monitor the early stages of disease progression in vivo.
We hypothesized that the improved ability to study disease maturation of respiratory melioidosis in the absence of URT colonization might influence the role of virulence determinants in mediating B. pseudomallei pathogenesis. Given our prior interest in studying the role of capsular polysaccharide in mediating B. pseudomallei dissemination from the lung, we investigated the role of a capsule mutant using IMIT inoculation. Unlike our previous work which identified an attenuation of the Δwcb capsule mutant of 101.8–102.3 fold in intranasal models [13], [19], we found a non-significant attenuation of just 6.8 fold (100.8) in the IMIT model, suggesting that the capsular polysaccharide is not absolutely critical for the initial stages of lung colonization. From our growing understanding of the difference between i.n. and IMIT models, we conclude that the capsule mutant is attenuated in its ability to colonize the nasal mucosa as the contributor to its greater attenuation in the i.n. model, and conversely that capsular polysaccharide is not as critical for disease in the lung. More importantly, we further characterized whether the capsule mutant is required for dissemination beyond the lung. In our previous studies, we had found that there was no significant defect in dissemination of the capsular polysaccharide mutant when studied at the minimally lethal dose in the murine intranasal model [19]. This previous observation had not been anticipated given the previous demonstration that capsular polysaccharide is required to resist opsonization by host complement likely during dissemination through the blood stream [26], and further that capsule is a critical virulence determinant in systemic disease models of both the hamster and mouse with an attenuation of ∼105 fold [29], [30]. Importantly, our current studies provide a modified understanding of the role of capsular polysaccharide in mediating dissemination beyond the lung, as we now observe that a capsule mutant is defective in lung-specific dissemination both optical diagnostic imaging as well as tissue burden analysis. We retrospectively interpret our previous studies to suggest that capsular polysaccharide mutants are attenuated for colonization of the URT mucosa, and that a capsule mutant is competent to disseminate from the URT to the liver and spleen at wild type levels, possibly involving the NALT and lymphatic system, as has been proposed for B. pseudomallei spread by others [7]. Only through studying the capsule mutant in a lung-specific model system have we identified a dissemination defect for this mutant, consistent with a dominant role for capsular polysaccharide as a defense to innate immunity, thereby facilitating disseminated disease. Thus, the study of the role of virulence determinants in respiratory melioidosis may give different phenotypes dependent on whether disease is mediated by URT infection (i.n.) or systemic disease progression (IMIT).
Type 3 Secretion has been characterized as an important B. pseudomallei virulence determinant in hamster and murine systemic disease models as well as a murine intranasal model system [18],[25]. B. pseudomallei possesses three T3SS clusters in its genome [31], [32], [33], of which only cluster three was found to be important for mammalian virulence, with a calculated attenuation of 102.8 fold in a systemic hamster intraperitoneal model [18]. Given that our IMIT model revealed a reduced importance for the role of capsule in B. pseudomallei pulmonary pathogenesis, we investigated whether T3SS3 is an important B. pseudomallei virulence determinant in the lung, or whether it too is required preferentially for systemic infection rather than initial lung colonization. Interestingly, we found that a T3SS3 translocation defective mutant was attenuated 102.5 fold, similar to the 102.8 fold attenuation reported in a systemic model. Thus, unlike the capsule mutant which is critical for systemic, but not respiratory, disease, T3SS3 is required ubiquitously for both systemic and respiratory disease. It is understood that a critical phenotype associated with the T3SS3 locus is mediating the ability of B. pseudomallei to rapidly escape from the phagosome of professional phagocytes [34], [35]. Thus, T3SS3 mutants exhibit growth defects in intracellular niches associated with delayed vacuolar escape, suggesting that the decreased fitness which we have observed for the T3SS3 mutant in the lung is associated with reduced fitness in the intracellular environment. Our data suggests that B. pseudomallei inhabits intracellular niches, not only in the lung, but also in other tissues, which might explain why a similar degree of attenuation is observed for the T3SS3 mutant in both systemic and respiratory disease models. We are therefore interested to identify how specific effector proteins delivered by the T3SS3 apparatus participate in mediating vacuolar escape and increase the fitness of B. pseudomallei in the lung.
In summary, we have demonstrated that respiratory melioidosis in the murine model may be associated with severe upper respiratory inflammation which directly drives host morbidity. We have further demonstrated that simple approaches facilitating a lung-specific disease progression allow for abrogation of URT infection, and therefore allow mice to act as much better surrogates for human melioidosis, minimizing the role of URT-based morbidity and CNS involvement. This approach has profound impact both in translational studies as well as basic science investigations. In the case of the former, full disease progression in the mouse will allow an investigation of the efficacy of pre- and post-exposure prophylaxis to protect against an advanced septicemic disease state. With regards to basic science investigations, we have successfully used the IMIT model to meet prediction of the role of capsular polysaccharide in facilitating dissemination of B. pseudomallei from the lung, whereas our former intranasal model system did not allow us to draw these predicted conclusions. The IMIT model has also revealed a critical role for the T3SS3 in facilitating respiratory melioidosis.
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10.1371/journal.pntd.0005846 | Strong interferon-gamma mediated cellular immunity to scrub typhus demonstrated using a novel whole cell antigen ELISpot assay in rhesus macaques and humans | Scrub typhus is a febrile infection caused by the obligate intracellular bacterium Orientia tsutsugamushi, which causes significant morbidity and mortality across the Asia-Pacific region. The control of this vector-borne disease is challenging due to humans being dead-end hosts, vertical maintenance of the pathogen in the vector itself, and a potentially large rodent reservoir of unclear significance, coupled with a lack of accurate diagnostic tests. Development of an effective vaccine is highly desirable. This however requires better characterization of the natural immune response of this neglected but important disease. Here we implement a novel IFN-γ ELISpot assay as a tool for studying O. tsutsugamushi induced cellular immune responses in an experimental scrub typhus rhesus macaque model and human populations. Whole cell antigen for O. tsutsugamushi (OT-WCA) was prepared by heat inactivation of Karp-strain bacteria. Rhesus macaques were infected intradermally with O. tsutsugamushi. Freshly isolated peripheral blood mononuclear cells (PBMC) from infected (n = 10) and uninfected animals (n = 5) were stimulated with OT-WCA, and IFN-γ secreting cells quantitated by ELISpot assay at five time points over 28 days. PBMC were then assayed from people in a scrub typhus-endemic region of Thailand (n = 105) and responses compared to those from a partially exposed population in a non-endemic region (n = 14), and to a naïve population in UK (n = 12). Mean results at Day 0 prior to O. tsutsugamushi infection were 12 (95% CI 0–25) and 15 (2–27) spot-forming cells (SFC)/106 PBMC for infected and control macaques respectively. Strong O. tsutsugamushi-specific IFN-γ responses were seen post infection, with ELISpot responses 20-fold higher than baseline at Day 7 (mean 235, 95% CI 200–270 SFC/106 PBMC), 105-fold higher at Day 14 (mean 1261, 95% CI 1,097–1,425 SFC/106 PBMC), 125-fold higher at Day 21 (mean 1,498, 95% CI 1,496–1,500 SFC/106 PBMC) and 118-fold higher at Day 28 (mean 1,416, 95% CI 1,306–1,527 SFC/106 PBMC). No significant change was found in the control group at any time point compared to baseline. Humans from a scrub typhus endemic region of Thailand had mean responses of 189 (95% CI 88–290) SFC/106 PBMC compared to mean responses of 40 (95% CI 9–71) SFC/106 PBMC in people from a non-endemic region and 3 (95% CI 0–7) SFC/106 PBMC in naïve controls. In summary, this highly sensitive assay will enable field immunogenicity studies and further characterization of the host response to O. tsutsugamushi, and provides a link between human and animal models to accelerate vaccine development.
| Scrub typhus is a disease caused by bacteria that invade cells in our immune system and blood vessels. It is transmitted by mites and is treatable with antibiotics. Unfortunately diagnosis is difficult and requires techniques that are not easily accessible everywhere. Currently, there is no scrub typhus vaccine available. In order to improve diagnostics and vaccine development in future, we need to better understand our immune response against these bacteria. In this study, we developed a test where these bacteria were killed and prepared by a new purification method to stimulate the immune cells in our blood -not antibodies. We evaluated this test in hospitalized patients with scrub typhus disease and also in non-human primates to study the responses over time. The test proved to be very accurate and useful to study natural immune responses, and we found differences in responses in areas where scrub typhus is common, compared to areas where it is not common. This test will allow us to investigate the immune response to scrub typhus more in-depth in the future, and will support the development of better diagnostic tests and vaccines against scrub typhus.
| Scrub typhus is a zoonotic illness caused by the intracellular bacterium Orientia tsutsugamushi which is endemic mainly across the Asia-Pacific region [1]. The pathogen is transmitted by the bite of larval Trombiculid mites known as chiggers [2, 3]. Scrub typhus is a febrile illness with a wide spectrum of disease severity from mild febrile illness to potentially fatal illness influenced by O. tsutsugamushi strains and host immune status. The specific skin lesion, known as an eschar, has been reported in up to 68% of Thai patients with scrub typhus [4]. The disease is treatable by antibiotics such as doxycycline, tetracycline or chloramphenicol [5], although emergence of antibiotic resistant strains has been reported in northern Thailand [6]. If untreated, the mortality is around 6% [7]. Awareness of and research into scrub typhus has been limited by its non-specific symptoms and difficulty of diagnosis, and a vaccine is highly desirable [8, 9].
A greater understanding of the host immune response to O. tsutsugamushi is required for vaccine design. As an obligate intracellular pathogen, cellular immunity is likely to be necessary for host control of infection. Several studies have reported a role for type 1 cell mediated immunity and specifically IFN-γ production in response to O. tsutsugamushi for immune protection against infection. Increased levels of IFN-γ and other type 1 cytokines are seen in the blood of patients with scrub typhus compared to controls [10–13]. Adoptive transfer experiments of monocyte-depleted splenocytes [14] and antigen-specific IFN-γ T-cells in murine models [15] have supported an important role for cell mediated immunity in protection against death from scrub typhus. Replication of O. tsutsugamushi inside macrophages is impaired by extrinsic IFN-γ [16]. O. tsutsugamushi infected monocyte-derived dendritic cells induce production of IFN-γ from CD4+ T-cells [17]. Scrub typhus is universally fatal in CD8-deficient mice (compared to 50% fatality in wild type mice) [18], and CD8+ T-cells play a vital protective role in control of O. tsutsugamushi growth [19]. In humans, CD8+ T-cell proliferation was seen during the convalescent phase of scrub typhus in patients [20]. These studies suggest the importance of developing a reliable method of monitoring O. tsutsugamushi-specific IFN-γ responses during scrub typhus.
The enzyme-linked immunospot (ELISpot) assay is a very sensitive technique allowing quantification of antigen-specific T-cells at the single cell level from peripheral blood by detection of IFN-γ or other secreted cytokines [21, 22]. The ex-vivo IFN-γ ELISpot assay is the most widely used technique for monitoring T-cell-based immune responses against intracellular pathogens such as HIV [23], tuberculosis [24] and malaria [25]. There are several advantages of the ELISpot assay for use in clinical trials: it has high sensitivity, is relatively easy to perform, uses low number of cells in the assay, does not require expensive instrumentation, and has the potential for high throughput screening with numerous specific peptides, or to an entire pathogen proteome using overlapping peptides of varying lengths. The ELISpot is up to 200 times more sensitive for cytokine detection than ELISA [26, 27] and significantly more sensitive than flow-cytometric based techniques [28].
Non-human primates (NHP) represent a model for investigating immunity to scrub typhus and provide valuable information to develop potential candidate vaccines for future testing in the clinical setting. Infection of cynomolgus macaques (Macaca fascicularis) with O. tsutsugamushi causes infection and illness which closely resemble the course of scrub typhus in humans [29, 30]. Due to some limitations of using cynomolgus macaques, such as reagent and antibody availability, the rhesus macaque (Macaca mulatta) is likely to be more suitable for preclinical vaccine evaluation.
In this study, we developed a novel ex-vivo IFN-γ ELISpot assay using whole cell antigen of O. tsutsugamushi (OT-WCA) as an antigen to determine magnitude and frequency of cellular responses in peripheral blood mononuclear cells (PBMC) of rhesus macaques. Our results indicate that our ex-vivo IFN-γ ELISpot assay can be used to determine immune responses against O. tsutsugamushi with high sensitivity and potentially high specificity for evaluation of vaccine candidate efficacy against O. tsutsugamushi in rhesus macaques and human clinical trials.
All animal research was performed strictly under approved IACUC protocol by the Institutional Animal Care and Use Committee and Biosafety Review Committee at the Armed Forces Research Institute of Medical Sciences (AFRIMS) Bangkok, Thailand, an AAALAC International-accredited facility. The IACUC protocol numbers are: PN12-01 (approved 31st Jan 2012), and PN13-12 (approved 24th Jan 2014). The animal research was conducted in compliance with Thai laws, the Animal Welfare Act, and all applicable U.S. Department of Agriculture, Office of Laboratory Animal Welfare and U.S. Department of Defense guidelines. All animal research adhered to the Guide for the Care and Use of Laboratory Animals, NRC Publication (8th Edition) [31].
Animals were housed individually in standard squeeze-type stainless steel cages with a minimum floor space of 4.4 square feet equipped with standard enrichments and exposed to ambient environmental conditions inside an Animal Biosafety Level 3 (ABSL-3) containment laboratory. Monkeys were fed daily with commercially prepared old-world primate extruded feed and supplemented with fresh fruit or vegetable four times per week. Fresh chlorinated water (5–10 ppm) was provided ad libitum via automatic water valves. Cages were cleaned daily and sanitized biweekly. All procedures were performed under anesthesia using ketamine hydrochloride, and all efforts were made to minimize stress, improve housing conditions, and to provide enrichment opportunities. Animals were euthanized by ketamine hydrochloride injection followed by barbiturate in accordance with the Guidelines for the Euthanasia of Animals (2013 Edition of the American Veterinary Medical Association).
The O. tsutsugamushi Karp strain (New Guinea) used in this study was provided by the Naval Medical Research Center (NMRC), Silver Spring, MD, USA and is a well-characterized strain from a pre-existing collection of Orientia strains at the NMRC, previously used in related studies [30, 32].
Fifteen (9 male and 6 female) Indian-origin rhesus macaques (M. mulatta housed in AFRIMS (an AAALAC-accredited Program) were used in this study. Environmental conditions were maintained in accordance with the Guide for the Care and Use of Laboratory Animals 8th edition (2011) [31]. The animals ranged from 3 to 5 year of age and weighed between 4.1 and 5.9 kg at the start of the study. The animals were evaluated to ensure that they were negative for SIV, SRV, STLV-1 and herpes B virus and had no O. tsutsugamushi exposure from the experimental history. Additionally, their antibody titers to O. tsutsugamushi were confirmed to be negative prior to the start of the experiment. Aliquots of inoculum containing defined concentrations of O. tsutsugamushi Karp strain at a dose of either 107 or 107.8 muLD50 (cultured and prepared in yolk sacs of chicken eggs), were used for intradermal (ID) injections of a total of 10 macaques. The inoculum was prepared in collaboration with the Mahidol-Oxford Tropical Medicine Research Unit (MORU) and the Naval Medical Research Center (NMRC), Silver Spring, Maryland, USA. The dosages were administered in infected groups on day 0. The inoculum was suspended in 100 μl of Snyder’s buffer and was applied to the anterior medial aspect of the left thigh. A total of 5 macaques were used as control group. The control macaques received ID injection with homogenized un-infected inocula in Snyder’s buffer at the identical site on the anterior medial aspect of the left thigh. Blood samples for PBMC isolation were collected at 5 time points starting from Day 0 prior to ID inoculation and every week up to Day 28. Bacteremia was determined using a previously describes qPCR assay for O. tsutsugamushi-specific 47 kDa gene [33]. DNA was extracted from 200 μl whole blood from each macaque using DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The O. tsutsugamushi-specific 47 kDa htra gene real time PCR assay was used as previously described using a CFX96 Real Time PCR Detection System (Biorad, Hercules, CA “no template” negative controls were run with each reaction and plasmid DNA served for standard curves in serial dilution from 106 to 1 copies/μl of 47 kDa gene [34].
PBMC were separated from 12 ml heparinized blood samples by density centrifugation within 3 hours of blood draw. In brief, 3 ml of Ficoll-HyPaque singular (Pharmacia, Peapack, NJ) was preloaded in a 14 ml LeucoSep tube (Greiner Bio-One) by centrifugation for 1 min at 1,000 × g. The whole blood was added to the LeucoSep tube and centrifuged for 15 min at 800 × g at room temperature. The cell suspension was collected, and the cells were washed twice in complete RPMI medium [RPMI 1640 (Sigma-Aldrich, St. Louis, MO) containing 10% FBS (Invitrogen Corp., Carlsbad, CA), 2 mM L-glutamine, 50 U/ml gentamicin (Quality Biological Inc., Gaithersburg, MD), and 0.1 mM non-essential amino acids (Sigma-Aldrich, St. Louis, MO)] for 5 min at 640 × g and 7 min at 470 × g, respectively. After final washing, the pellet was resuspended in complete RPMI medium before counting.
The O. tsutsugamushi Karp strain was cultivated in L929 cells (mouse fibroblast cell line) cultured in RPMI1640 supplemented with 10% fetal bovine serum (FBS) and 2 mM L-glutamine. The stage of infection was determined by indirect immunofluorescence assay (IFA); when infected L929 cells approached 100% positive infection, the cells were harvested by centrifugation at 6,000 x g for 30 min at 4°C. The cells were pelleted and disrupted using glass beads (0.1 mm, Next Advance, Averill Park, NY), then homogenized with a bullet blender for 1 min. After centrifugation at 300 x g for 10 min to remove cell debris, the supernatant was collected and filtered through a 2.0 μm syringe filter. The supernatant was then centrifuged at 11,000 x g for 10 min to collect the bacterial pellet. After washing the pellet with PBS, the bacteria were resuspended in 50 μl PBS and heated at 80°C for 1 hour. The OT-WCA suspension was then aliquoted and stored at 4°C until used, with immunogenicity of the antigen confirmed up to five years of storage. The total protein concentration of OT-WCA protein was determined by BCA assay (BCA1 kit, Sigma-Aldrich, St. Louis, MO). All processes were performed in a biosafety level 3 laboratory. The protein concentration of the stock solution for the OT-WCA used in this study was 1.41 mg/ml. Phytohemagglutinin (PHA) was used at a final concentration of 5 μg/ml and complete RPMI as described above was added to positive control wells and negative wells, respectively.
The kinetics and magnitude of the cellular responses to whole O. tsutsugamushi were assessed by ex-vivo IFN-γ ELISpot following an 18 hour stimulation of PBMC with OT-WCA for each time point of the study. Fresh PBMC were used in all ELISpot assays, and separate ELISpot kits (Mabtech, AB, Sweden) for human (3420-2A) and monkey (3421M-2A) cells were used. Briefly, 96-well Multiscreen-I plates (Millipore, UK) were coated for 3 hours with 10 μg/ml GZ-4 anti-human IFN-γ (Mabtech, AB, Sweden) at room temperature. Fresh PBMC were added in duplicate wells at 2x105 PBMC in 50 μl per well and 50 μl of OT-WCA was added at the optimal concentration. For human studies, a T-cell epitope pool (Mabtech, AB, Sweden) at a final concentration of 1 μg/mL was used as control antigens. After 18 hours, secreted IFN-γ was detected by adding 1 μg/ml biotinylated mAb 7-B6-1-biotin for IFN-γ, which recognises an epitope completely conserved between human and macaques in the helical region of human IFN-γ, (Mabtech, AB, Sweden) for 3 hours and followed by 1 μg/ml streptavidin alkaline phosphatase (Mabtech, AB, Sweden). The plates were developed using the AP Conjugate Substrate Kit (Biorad, USA) according to the manufacturer’s instructions. ELISpot plates were scanned using a CTL ELISpot reader (Cellular Technology Limited, USA). Spots were then counted by Immunospot 3.1 software, using the manufacturer’s automated SmartCount™ settings. Results were expressed as IFN-γ spot-forming cells (SFC) per million PBMC. Background responses in unstimulated control wells were always less than 20 spots/106 PBMC, and were subtracted from those measured in OT-WCA stimulated wells.
The recruitment of human subjects for immunological studies has been described previously in a study of melioidosis [35]. PBMC were isolated from subjects in Ubon Ratchathani, (northeastern Thailand—an endemic area for scrub typhus), Bangkok, (central Thailand—a non-endemic for scrub typhus) and Oxford, UK (non-endemic for scrub typhus) participating in a study of melioidosis. Responses to O. tsutsugamushi were evaluated using the same ex vivo IFN-γ ELISpot assay, with PBMC from Ubon Ratchathani subjects known to known to be reactive to OT-WCA used in Oxford as positive controls.
Human anti-Orientia antibodies (IgM/IgG) were detected using IFA for scrub typhus, based on pooled whole-cell antigens from three strains of O. tsutsugamushi (Karp, Kato and Gilliam strains) as previously described [36]. IFN-γ ELISpot responses were compared for people with IFA IgG titers of ≥ 1:400 compared to those with titres < 1:400.
Statistical analyses were performed using GraphPad Prism Software v. 6. The results between the control group versus the infected group are expressed as means and were compared using the non-parametric Mann-Whitney U- test. Significant differences between timepoints within a group were determined with the non-parametric Wilcoxon t-test. The relationship between IFN-γ ELISpot responses and IFA IgG titers was evaluated using Spearman’s rank correlation test. Two-tailed P values < 0.05 were considered significant.
Frozen PBMC from rhesus macaques collected at Day 14 post inoculation (pi) with O. tsutsugamushi (BRI-02) and from a control uninfected macaque (BRI-06) were stimulated in duplicate with 50 μl of OT-WCA prepared at 3 different concentrations: 1.41 (1:50 dilution), 0.71 (1:100) and 0.14 (1:500) μg/well. PHA and ‘complete media’ were used as positive and negative controls respectively, (Fig 1 and Table 1). Strong IFN-γ ELISpot responses to OT-WCA were observed in infected macaques, whereas no responses to OT-WCA was observed in the uninfected macaque. Strong responses to PHA stimulation were found in positive control wells (Fig 1 and Table 1). Concentrations of OT-WCA above 1.41 μg/well were not tested because a blackout of the spot count was likely, and the optimized 0.14 μg/well concentration was selected for OT-WCA for the stimulation in further testing of experimental macaques.
The O. tsutsugamushi-specific cellular immune responses were measured with ex-vivo IFN-γ ELISpot assay from freshly isolated PBMC of rhesus macaques at five time points after O. tsutsugamushi infection (Day 0, 7, 14, 21 and 28) to study the kinetics and magnitude of the responses over time (Fig 2). Freshly isolated PBMC from each macaque were tested in duplicate with OT-WCA at 0.14 μg/well. Each well contained 2x105 PBMC, and SFCs were quantitated by the CTL ELISpot reader—therefore responses were multiplied by 5 to provide SFC / million PBMC. Strong responses were found from PHA stimulated wells in all macaques, and background responses assessed by media only were always less than 20 SFC/106 PBMC (Table 2). Specific responses to O. tsutsugamushi were calculated by subtraction of corresponding media only wells from OT-WCA. Wells with very high responses resulted in blackout of the spot count and are represented as 300 spots (1,500 SFC/106 PBMC), corresponding to the highest spot count that can be measured by CTL ELISpot reader in our experiment. Overall, O. tsutsugamushi-specific IFN-γ responses were observed from all infected macaques.
At Day 7, the response to OT-WCA from infected macaques (mean 235 SFC/106, 95% CI 200–270 SFC/106) was 20-fold higher than baseline level (Day 0; mean 12 SFC/106 and 95% CI 0–25 SFC/106). At Day 14 the specific IFN-γ responses rose >100-fold (mean 1,261 95% CI 1,097–1,425 SFC/106 PBMC), and were 125-fold higher at Day 21 (mean 1,498, 95% CI 1,496–1,500 SFC/106 PBMC) and 118-fold higher at Day 28 (mean 1,416, 95% CI 1,306–1,527 SFC/106 PBMC). No change in specific IFN-γ response to O. tsutsugamushi was found for uninfected control macaques. An overview of the adjusted spots count is shown in Table 2.
We explored the relationship in the macaque model between ex-vivo IFN-γ ELISpot of cellular immune responses to O. tsutsugamushi and antibody responses to O. tsutsugamushi as measured by an IgG and IgM IFA assay. We saw a correlation between the magnitude of the cellular response and the reciprocal titers of the IgG-based IFAs in the non-human primate model (r 0.79 = P < 0.001 by Spearman’s rank test) (Fig 3, panel A). A significant correlation was not seen for each individual time point. This may be because there are only ten data points per timepoint and this is insufficient to make a correlation. In addition, the relationship in the first 28 days is limited by the IgG rising more slowly than the Elispot response, the latter plateauing by Day 21 (Fig 3, panel A), whilst IgG responses to infections are generally believed to peak later at around 4 to 6 weeks [37].
We also investigated the relationship of cellular immune responses to O. tsutsugamushi and bacterial load in blood (expressed as AUC of bacteremia) at Day 14, which corresponds to the peak bacteremia phase in this model, and found an inverse correlation, with increased SFCs /106 PBMC relating to lower bacterial loads (Fig 3, panel B).
In order to explore the feasibility of using this assay in human populations, responses to OT-WCA were measured in patients participating in a study of immune responses to a different disease (melioidosis). Subjects living in the scrub typhus endemic region of Ubon Ratchathani, Northeast Thailand (n = 105), had a mean IFN-γ ELISpot response of 189 SFC / 106 PBMC (95% CI 88–290), compared to 40 SFC / 106 PBMC (95% CI 9–71) in subjects living in a non scrub typhus endemic area (Bangkok, Thailand, n = 14) some of whom may have grown up in or travelled to an endemic part of Thailand, and 3 SFC / 106 PBMC (95% CI 0–7) for subjects in Oxford, UK (n = 12) who had never encountered scrub typhus. 17/105 subjects (16%) in the endemic region had high responses greater than 200 SFC / 106 PBMC compared to none in the non-endemic and naïve regions (Fig 4, panel A). No differences between groups were seen for responses to a control panel of common T-cell epitopes for Epstein–Barr virus (EBV), cytomegalovirus (CMV), influenza etc (“T-cell control panel”, Fig 4, panel A). Provisional studies showed that ELISpot counts were greatly reduced in responders if cryopreserved PBMC rather than fresh PBMC were used, suggesting a requirement for fresh antigen presenting cells to optimally process whole bacteria. As for the macaque model, we saw a correlation between the magnitude of the cellular response and the reciprocal titers of the IgG-based IFAs in the non-human primate model (Spearman’s R = 0.57, P < 0.001 by Spearman’s rank test) (Fig 4, panel B).
When cellular immune responses to OT-WCA antigen measured by ex vivo IFN-γ ELISpot were compared between humans with either an IFA IgG titer of 1:400 or above (n = 22) or less than 1:400 (n = 84), significantly higher cellular immune responses were found in people with the higher IFA (Fig 4, panel C).
We have established a highly sensitive method for measuring the magnitude and kinetics of the adaptive cellular immune response to scrub typhus in rhesus macaque monkeys. This study builds on previous work demonstrating production of IFN-γ in the host defense against O. tsutsugamushi. The OT-WCA ELISpot assay was developed using PBMC from rhesus macaques (Macaca mulatta), the most commonly used NHP model for preclinical vaccine development. To evaluate antigen-specific cellular responses to Orientia after infection with O. tsutsugamushi strain Karp, freshly isolated PBMC from a total of ten O. tsutsugamushi infected rhesus macaques and five uninfected control macaques were tested with the novel ELISpot assay from Day 0 through Day 28. O. tsutsugamushi-specific IFN-γ responses were observed post infection from all infected macaques compared to uninfected macaques, with a 20-fold (mean 235, 95% CI 200–270 SFC/106 PBMC) increase at Day 7, a 100-fold increase at Day 14 and maintenance of high level responses to the end of the study (Day 28). The maximum measurable response was limited by the SmartCount™ software, which does not read any higher once a blackout of the well is obtained. No significant increases in cellular responses were found in the uninfected control group at any time point. Preliminary human studies in subjects from an endemic area, a non-endemic area where some of the population has previous exposure, and from non-exposed subjects gives support to the specificity of this assay for studying human populations, although the role of cross-reactivity to other rickettsial group pathogens merits further exploration.
The major reason for developing the IFN-γ ELISpot assay for O. tsutsugamushi is to allow immunogenicity monitoring in animal models, in scrub typhus exposed but healthy populations, in patient populations, and for future vaccine trials. IFN-γ was chosen as the read-out cytokine because of previous work demonstrating IFN-γ responses in scrub typhus patients [10–13] and mouse studies [14, 15]. The inverse relationship seen in this study for macaques 14 days post infection, where higher IFN- γ responses are associated with lower bacterial loads also lends support for the importance of the IFN-γ response in control of the bacteria, although other factors such as sicker animals having lower immune responses may be relevant. The importance of IFN-γ in response to intracellular pathogens has been reaffirmed recently by transcriptomic studies [38–40], and by a study demonstrating the link between BCG-specific T cells secreting IFN-γ and reduced risk of developing tuberculosis in South African infants [41]. However, other cytokine responses are important, for example IL-2 is involved in the development of memory responses and antibodies following vaccination against malaria [42], Hepatitis B [43] and tick-borne encephalitis [44]. Further studies by flow cytometry of multifunctional T-cell responses following scrub typhus infection are underway for humans and macaques.
For this initial characterization of the cellular response to Orientia, the OT-WCA was based on a single strain of high relevance in human disease (Karp strain).The antigen used for the IFA assay is based on pooled whole-cell antigens from three strains of O. tsutsugamushi (Karp, Kato and Gilliam) as this is the standard assay in the field. The conventionally used IFA slides based on the three reference strains have served over many years to document humoral responses against single strain Orientia infections, and results have been validated via gene sequencing methods [45, 46]. Ongoing work in the laboratory is exploring the immunogenicity of different strains and culture conditions.
A potential bias to the ELISpot assay is the potential for persistent presence of live O. tsutsugamushi bacteria in the PBMC of infected monkeys, driving the antigen specific response. However, we would expect to see responses in the media only in wells of the Day 7 and 14 macaque PBMC if residual bacteria were contributing to the measurable response. The cell culture media used contains penicillin and streptomycin to limit antimicrobial contamination in the laboratory, which may have a partial efficacy against the bacteria, but these antibiotics were used uniformly in all samples so should not introduce bias.
Previous vaccine development studies in cynomolgus macaques [29, 30] have demonstrated the presence of cellular immunity to two recombinant proteins derived from Karp stain O. tsutsugamushi (Kp r47b and Kp r56) using a 36-hour ELISpot assay. The magnitude of the IFN-γ responses to the whole bacteria reported in this manuscript was much higher than the levels to the individual proteins (range 125–800 SFC/106 PBMC), alongside a lack of responses in unexposed animals. This is likely to be associated with the new and modified approach to purify O. tsutsugamushi to produce the OT-WCA suspension as described above. This OT-WCA ELISpot assay therefore allows better demonstration of cellular immunity to the whole bacteria, and can be performed as a positive control alongside exploration of immunity to specific vaccine candidate proteins such as Kp r47b and Kp r56. The magnitude of cellular immune response seen to OT-WCA compared to reported responses to the 47 and 56 kDa proteins suggest that there are other immunodominant antigens in O. tsutsugamushi for discovery as vaccine candidates. Stimulation with this preparation of killed O. tsutsugamushi in scrub typhus naïve monkeys did not invoke measurable innate cellular responses by this assay, for example from macrophages and NK cells via pattern recognition receptor pathways. This may be due to inactivated whole cell antigen being suboptimal at this dose for induction of innate pathways, or involvement of other key cytokines in pattern recognition, such as TNF-α, IL-12 and IL-1β not measured by this assay. In addition, potential evasion of the host innate immune response by O. tsutsugamushi is of interest in understanding the pathogenesis of natural infection and will be the subject of ongoing studies.
Scrub typhus diagnostics is a major difficulty for both management of patients and for epidemiological studies, and the lack of a clear-cut “gold standard” reference means that statistical modeling has been required to evaluate novel alternative diagnostic tests [36]. The OT-WCA IFN-γ ELISpot assay uses the same technology platform as the T-SPOT interferon-gamma release assay (IGRA) developed for diagnosis of tuberculosis [47]. Due to the laboratory processing requirements of ELISpot assays using fresh cells we do not believe there will be a role for using the OT-WCA IFN-γ ELISpot assay for real-time diagnosis and patient management of scrub typhus in endemic areas. However, given the unexpectedly high sensitivity and potential high specificity observed in this study, the OT-WCA IFN-γ ELISpot assay could potentially be used as a reference standard in research studies evaluating novel diagnostics. However evaluation regarding cross-reactivity with other rickettsial group bacteria and/or cross-protection in heterologous re-infection/infection in scrub typhus are needed.
The optimization of OT-WCA preparation for antigen stimulation studies provides a way of exploring the host response to O. tsutsugamushi by flow cytometry, thus allowing detailed characterization of cell phenotype, infected cells, secretion of cytokines and pathways of response to the bacteria. Immune-phenotyping studies of infected cells in eschar biopsies from scrub typhus patients have demonstrated a cellular tropism of O. tsutsugamushi for antigen presenting cells in the skin [48]. Although the ELISpot assay is a highly sensitive method for measuring cellular responses [21, 22], this method is unable to identify the cell phenotype secreting the cytokine. Further studies including a small volume, whole blood stimulation assay are underway to define which cells contribute most to IFN-γ secretion, and to characterize other cytokines associated with O. tsutsugamushi infection. This study did not address the cross-reactivity of immunity to Karp strain compared to other bacterial strains, and cross-reactivity with other rickettsial group bacteria in the region. Further studies are addressing this complex issue.
In summary, we have successfully developed for the first time a novel ex vivo IFN-γ ELISpot assay to whole O. tsutsugamushi antigen. This assay will allow field immunogenicity studies, pave the way for more detailed flow cytometry studies of response to O. tsutsugamushi antigen and provide a link between human and animal models to enhance vaccine development.
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10.1371/journal.pntd.0005311 | Identification of BALB/c Immune Markers Correlated with a Partial Protection to Leishmania infantum after Vaccination with a Rationally Designed Multi-epitope Cysteine Protease A Peptide-Based Nanovaccine | Through their increased potential to be engaged and processed by dendritic cells (DCs), nanovaccines consisting of Poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) loaded with both antigenic moieties and adjuvants are attractive candidates for triggering specific defense mechanisms against intracellular pathogens. The aim of the present study was to evaluate the immunogenicity and prophylactic potential of a rationally designed multi-epitope peptide of Leishmania Cysteine Protease A (CPA160-189) co-encapsulated with Monophosphoryl lipid A (MPLA) in PLGA NPs against L. infantum in BALB/c mice and identify immune markers correlated with protective responses.
The DCs phenotypic and functional features exposed to soluble (CPA160-189, CPA160-189+MPLA) or encapsulated in PLGA NPs forms of peptide and adjuvant (PLGA-MPLA, PLGA-CPA160-189, PLGA-CPA160-189+MPLA) was firstly determined using BALB/c bone marrow-derived DCs. The most potent signatures of DCs maturation were obtained with the PLGA-CPA160-189+MPLA NPs. Subcutaneous administration of PLGA-CPA160-189+MPLA NPs in BALB/c mice induced specific anti-CPA160-189 cellular and humoral immune responses characterized by T cells producing high amounts of IL-2, IFN-γ and TNFα and IgG1/IgG2a antibodies. When these mice were challenged with 2x107 stationary phase L. infantum promastigotes, they displayed significant reduced hepatic (48%) and splenic (90%) parasite load at 1 month post-challenge. This protective phenotype was accompanied by a strong spleen lymphoproliferative response and high levels of IL-2, IFN-γ and TNFα versus low IL-4 and IL-10 secretion. Although, at 4 months post-challenge, the reduced parasite load was preserved in the liver (61%), an increase was detected in the spleen (30%), indicating a partial vaccine-induced protection.
This study provide a basis for the development of peptide-based nanovaccines against leishmaniasis, since it reveals that vaccination with well-defined Leishmania MHC-restricted epitopes extracted from various immunogenic proteins co-encapsulated with the proper adjuvant or/and phlebotomine fly saliva multi-epitope peptides into clinically compatible PLGA NPs could be a promising approach for the induction of a strong and sustainable protective immunity.
| Leishmaniases are a wide spectrum of parasite diseases caused by different species of the genus Leishmania. Among them, visceral leishmaniasis is the most severe form of the disease affecting millions of people worldwide. Currently chemotherapeutic agents are highly toxic and suffer from extensive drug resistance. However, the development of a safe and effective vaccine has to deal with the complications erased from the different immune responses of the human population. It is suggested that a solution to that problem is the design of candidate vaccines containing appropriate multi-epitope peptides for the induction of protective immunity. Furthermore, the selection of proper adjuvant and biocompatible delivery systems for the peptide(s) chosen should enhance their immunogenic potential. Subsequently, for this work, we used a synthetic peptide designed to contain overlapping epitopes obtained from the sequence of a known immunogenic Leishmania protein, Cysteine Protease A (CPA). The peptide was selected to be delivered along with MPLA adjuvant co-encapsulated in PLGA nanoparticles. The data presented in this study show the immunogenicity and the prophylactic potential of the proposed nanovaccine against L. infantum in the susceptible model of visceral leishmaniasis in BALB/c mice, further suggesting that rationally designed peptide-based nanovaccines are promising vaccine candidates against leishmaniasis.
| Leishmaniasis is an infectious diseases complex caused by protozoan parasites of the genus Leishmania. According to epidemiological data, visceral leishmaniasis (VL) is the second most common parasitic cause of death among tropical infections and is prevalent in 47 countries with about 200 million people at risk and an estimated annual incidence of approximately 500,000 cases [1,2]. Despite the fact that vaccination is considered to be the most promising and effective strategy for controlling leishmaniasis, to date no efficacious vaccine exists against human VL. First attempts at developing an anti-leishmanial vaccine were based on the injection of live virulent L. major parasites in healthy people, a process known as "leishmanization”. However, this process was discontinued due to safety and ethical reasons and replaced by first-generation vaccines composed by attenuated or inactivated pathogens or even pathogen subunits that in many cases showed inconsistent clinical outcomes [3,4,5].
Subsequently, many research efforts are focused on the development of second generation vaccines that are consisted of recombinant proteins or defined peptides. To date many different Leishmania antigens have been found to be potential vaccine candidates delivered by a plethora of immunizations regimens in animal models. However, these promising findings were overshadowed by mostly negative T-cell responses in humans [6,7]. During the last few years, remarkable advancements in immunoinformatics science have improved the selection of potential immunogenic epitopes from various pathogens. This coupled with in vitro immunogenicity testing of predicted peptides using exposed human blood samples may accelerate the development of candidate vaccines for leishmaniasis [7,8,9,10,11]. However, a major limiting factor for these poly-epitope peptide-based vaccines is their relatively low immunogenicity and their inability to trigger long-term immunity.
Previous studies proposed the encapsulation of whole proteins, soluble antigen or parasites in different nanoformulations in order to achieve a sustained antigen release for the development of strong and long-lasting T cell responses against leishmaniasis [12,13]. Among developed nanoparticles (NPs) Poly(D,L-lactic-co-glycolide) (PLGA) NPs are considered potent candidates for vaccine delivery systems due to their excellent safety profile, high encapsulation efficiency, tissue bio-distribution, controlled release pattern and their effectiveness to induce appropriate immune responses [14,15,16,17]. Moreover, the immunomodulatory properties of these particles can be significantly enhanced through the addition of adjuvants, such as Toll-like receptor (TLR) ligands [18]. A most common adjuvant used is Monophosphoryl lipid A (MPLA), a non-toxic derivative of the lipopolysaccharide (LPS) of Salmonella minnesota. MPLA is a well-tolerated adjuvant approved for human use which signals through TLR4 for the activation of T cell effector responses [19].
A vaccine against leishmaniasis is considered effective when it ensures a long-lasting cell-mediated immunity [20]. In VL, protective vaccination requires the activation of the innate arms of host defense consisting of macrophages and DCs resulting in a long-term activation of both CD4+ helper and CD8+ effector T cells [21]. For that reason, research efforts on vaccine development are focused on the identification of recombinant proteins or defined peptides, capable to induce appropriate immune responses [6,7]. Among them, Cysteine Protease A (CPA) is a conserved protein expressed not only in the sand fly promastigote stage, but more importantly, in the disease-causing mammalian amastigotes [22,23]. Furthermore, it has been shown to play an important role in the immunity against Leishmania, since it is recognized by sera obtained from either recovered or active cases of CL and VL, as well as by sera from asymptomatic or symptomatic dogs with leishmaniasis [24,25,26]. In a recent study, we designed a 30-mer multi-epitope peptide (CPA160-189) that contained 3 and 2 overlapping MHC class I and II-restricted epitopes, respectively, obtained from L. infantum CPA sequence by using in silico analysis. Furthermore, we showed that immunization of Leishmania-susceptible BALB/c mice with CPA160-189 in emulsion with Freund’s adjuvant elicited peptide-specific CD4+ Th1 and CD8+ effector T cell immune responses that are required for protection against VL [27].
The aim of the present study was to improve peptide’s immunogenicity by co-encapsulating it with MPLA adjuvant into PLGA NPs and evaluate its prophylactic potential against VL. For this purpose, analysis of the potentiating effects of this formulation in phenotypic and functional features of BALB/c bone marrow-derived DCs and its ability to induce strong T cell immunity against L. infantum was determined. Evidence presented from both in vitro and in vivo settings suggests that the development of a peptide-based nanovaccine consisting of a rationally designed multi-epitope peptide and a suitable adjuvant could be a promising tool to prevent VL.
Animal experiments were performed in strict accordance with the National Law 2013/56, which adheres to the European Directive 2010/63/EU for animal experiments and complied with the ARRIVE guidelines. The protocol was approved by the institutional Animal Bioethics Committee (Approval Number: 4455/10-07-2014). All efforts were made to minimize animal suffering. Serum samples from domestic dogs (Canis familiaris) were obtained from an already-existing biobank of our laboratory. Also, L. infantum used in the present study was obtained from the already-existing “Leishmania cryobank collection” of the Hellenic Pasteur Institute. All samples were coded and anonymized. No IRB approval was required for using the strain.
Studies were performed with female 6–8 weeks old BALB/c mice reared in the pathogen-free animal care facility at Hellenic Pasteur Institute. They were housed in a climatically controlled room receiving a diet of commercial food pellets and water ad libitum.
A strain of L. infantum (MHOM/GR/2001/GH8) originally isolated from a Greek patient suffering from VL [28] was cultured in vitro and was maintained infective through serial passage in BALB/c mice, as described elsewhere [29]. The promastigote form of the parasite was cultured at 26°C in RPMI-1640 medium (Biochrom AG, Berlin, Germany) supplemented with 2 mM L-glutamine, 10 mM HEPES, 24 mM NaHCO3, 100 U/ml penicillin, 10 μg/ml streptomycin and 10% (v/v) heat inactivated fetal bovine serum (FBS; Gibco, Paisley, UK).
For preparation of soluble Leishmania antigen (SLA), 1x109 late-log phase promastigotes were washed thrice in PBS and disrupted by five repeated freezing/thawing cycles (liquid N2/37°C) followed by 5 min incubation on ice. Partially lysed material was exposed for 30 sec in a sonicator (UP100H, Hielscher Ultrasonics GmbH, Teltow, Germany) and then centrifuged for 30 min at 8,000×g at 4°C. The supernatant containing SLA was collected and total protein concentration was determined spectrophotometrically using the MicroBCA Protein Assay Kit (Thermo Scientific, Rockford, IL, USA) at 570 nm. SLA was stored at -80°C in aliquots until use.
For the evaluation of CPA160-189 immunogenicity, peptide-specific IgG antibodies were detected in serum samples of naturally infected with L. infantum asymptomatic (n = 12) or symptomatic (n = 14) and healthy dogs (n = 6) [30]. For the detection of CPA160-189- or parasite-specific IgG antibodies, 96-well microtiter plates were coated with 5 μg/ml CPA160-189 or SLA, respectively, and incubated overnight at 4°C. After 3 washes with washing buffer (PBS with 0.05% Tween 20), plates were blocked with blocking buffer (2% BSA in PBS) for 2 h at 37°C and then 100 μl of dog sera were added at 1:400 dilution, and incubated for another 2 h at 37°C. HRP-conjugated anti-dog IgG were added and incubated for 1 h at 37°C. The enzyme-labeled complexes were detected by reaction with TMB substrate (Thermo Scientific) and the reaction was stopped by adding 2 M sulfuric acid. The absorbance was measured at 450 nm using an ELISA microplate spectrophotometer (MRX, Dynatech Laboratories, Guernsey, UK).
For NPs synthesis, PLGA 75:25 (Resomer RG752H, MW: 4–15 kDa), polyvinyl alcohol (PVA) (MW: 30–70 kDa, 87–90% hydrolyzed) and MPLA from Salmonella minnesota were purchased from Sigma-Aldrich (Vienna, Austria). CPΑ160–189 peptide obtained from the sequence of L. infantum CPA protein (GenBank Acc. No.: CAM67356) was synthesized by GeneCust (Labbx, Dudenange, Luxenmbourg) with purity ≥95%. All other reagents were of analytical grade and commercially available. PLGA NPs containing the peptide CPΑ160–189 and the adjuvant MPLA were prepared by the double emulsion method, as described previously [31]. Briefly, 2.9 ml of a PLGA chloroform solution (31 mg/ml) were mixed with 0.1 ml of an MPLA solution (10 mg/ml) in methanol:chloroform (1:4 v/v). A water-in-oil (w/o) emulsion was then formed by adding 0.3 ml of the peptide solution in PBS at a final concentration of 6.6 mg/ml into the PLGA/MPLA solution. The emulsification was performed in an ice bath with the aid of a microtip sonicator (Sonicator Sonics Vibra Cell VC-505, Sonics, Newtown, CT, USA) at 40% amplitude for 45 sec. Subsequently, the primary emulsion (w/o) was added into 12 ml of 1% (w/v) aqueous PVA solution. The mixture was then emulsified via sonication at 40% amplitude for 2 min. The resulting double (w/o/w) emulsion was stirred overnight to allow the evaporation of chloroform. The PLGA NPs were then purified by means of four successive centrifugation-redispersion cycles in sterilized water, at 13,860×g for 10 min at 4°C and were subsequently lyophilized (ScanVac Freezedryers CoolSafe 55–9, Scientific Laboratory Supplies, Yorkshire, UK). For the preparation of the PLGA-CPΑ160–189 NPs, the same volume of peptide solution as previously was added into 3 ml of a PLGA/chloroform solution at a final concentration of 30 mg/ml. Finally, to prepare blank PLGA NPs for controls, the CPΑ160–189 aqueous solution was replaced with 0.3 ml of PBS. Lyophilized PLGA NPs were stored at 4°C.
The surface morphology of the synthesized PLGA NPs was observed by scanning electron microscopy (SEM) (JEOL JSM 6300, Jeol, Peabody, MA, USA). Accordingly, the lyophilized NPs were first double coated with a gold layer under vacuum and then examined by SEM. The average particle diameter of the PLGA NPs was determined by photon correlation spectroscopy and their zeta potential by aqueous electrophoresis measurements (Malvern Nano ZS90, UK). The measurements were performed with aqueous dispersions of NPs prior to lyophilization.
The MicroBCA Protein assay kit (Thermo Scientific) was employed to determine CPΑ160–189 encapsulation (μg/mg) in the PLGA NPs. Accordingly, 2.5 mg of lyophilized PLGA NPs were dissolved in 0.25 ml DMSO for 1 h following a further dissolution in 1.25 ml of 0.05 M NaOH/0.5% SDS for 3 h at 25°C. Blank PLGA NPs were used as negative controls. The absorbance of the samples was measured at 562 nm using a microplate reader (EL808IU-PC, BioTek Instruments, Inc., Winooski VT, USA). Antigen encapsulation efficiency (EE) was calculated by the ratio of the antigen mass in the NPs over the total mass of antigen used. Also, the antigen loading was calculated by the ratio of the encapsulated mass of antigen over the total mass of PLGA NPs.
A Limulus Amebocyte Lysate (LAL) kit (Thermo Scientific) was used for the determination of the MPLA loading (μg/mg) in the PLGA NPs. Standard curve was established using different concentrations of aqueous MPLA solutions ranging from 0.01 to 10 ng/ml, which was found to be linear for the MPLA concentration range used, with a correlation coefficient of R2 = 0.9994. The encapsulation efficiency of MPLA was calculated by the ratio of the measured MPLA mass in the NPs over the total mass of MPLA used and the MPLA loading was calculated by the ratio of encapsulated MPLA mass over the total mass of the PLGA NPs.
For the determination of in vitro release of CPΑ160–189 and MPLA, PLGA NPs were dispersed in PBS at a final concentration of 1 mg/ml and were incubated at 37°C under constant shacking at 120.7×g. At predetermined time points (0, 1, 2, 4, 6, 8, 12, 24, 48 h, 1 and 2 weeks) 1 ml of the dispersion was centrifuged at 13,860×g for 10 min at 4°C. Then, the supernatants were collected and the amount of CPΑ160–189 and MPLA were determined using the MicroBCA and LAL kits, respectively.
DCs were generated from pluripotent bone marrow stem cells of BALB/c mice in the presence of rmGM-CSF, as reported previously [29]. On day 7, non-adherent and semi-adherent cells were collected and phenotypic analysis was performed by flow cytometry using antibodies against CD11c and CD8a surface markers. According to trypan blue exclusion, cell viability was >95% and the percentage of CD11c+CD8a- cells was >75%, as assayed by FACS analysis.
DCs maturation induced by PLGA NPs was determined by flow cytometry. All antibodies used were obtained from BD Biosciences (Erembodegem, Belgium). For this purpose, DCs were cultured into a 24-well plate at a density of 1x106 cells/ml/well in the presence of PLGA-MPLA, PLGA-CPA160-189 or PLGA-CPA160-189+MPLA NPs, or soluble CPΑ160–189 in the presence or not of soluble MPLA at various doses for 24 h at 37°C in a humidified CO2 incubator. DCs in medium alone and DCs stimulated with LPS (1 μg/ml) were used as negative and positive control, respectively. At the end of incubation, wells were washed to remove free PLGA NPs followed by a wash with FACS buffer (PBS– 3% (v/v) FBS). The cells were then labeled with PE-conjugated anti-mouse CD40, CD80, CD83, CD86, MHCI (1:100 dilution) or MHCII (1:200 dilution) mAbs for 30 min at 4°C. For intracellular staining, cells were subjected to brefeldin A (2.5 μg/ml) for the last 4 h of culture and then were fixed with 2% paraformaldehyde and stained with PE-conjugated IL-12p40 mAb (1:100 dilution) in permeabilization buffer (FACS buffer supplemented with 0.1% saponin). After staining, cells were washed with FACS buffer and subjected to flow cytometric analysis using a FACS Calibur system (Becton-Dickinson, San Jose, CA, USA). Data were analyzed using FlowJo software version 10.0 (Tree Star, Inc., Ashland, OR, USA).
DCs (1x106 cells/ml) that had been pulsed with medium, PLGA-CPA160-189 or PLGA-CPA160-189+MPLA NPs for 24 h were co-cultured with naive splenocytes of similar origin, at a responder/stimulator ratio of 5:1 in a 96-well round-bottom plate for 96 h. Splenocytes cultured in medium alone or in the presence of Con A (6 μg/ml; Sigma) served as negative or positive control of T cell proliferation, respectively. Proliferation was determined by addition of 0.5 μCi of [3H]-thymidine ([3H]-TdR; PerkinElmer, Boston, MA, USA) during the last 18 h of the culture period and subsequent measurement of [3H]-TdR incorporation on a microplate scintillation and luminescence counter (Microbeta Trilux, Wallac, Turcu, Finland). All assays were performed in triplicates. Stimulation index (S.I.) was calculated according to the following formula: S.I. = cpm measured in T cells in the presence of pulsed DCs / cpm measured in T cells cultured in medium alone (negative control).
IFN-γ, IL-4 and IL-10 mRNA expression by DCs-stimulated T cells was determined by real time quantitative PCR (qPCR). Specifically, complementary DNA (cDNA) was synthesized using RT2 HT First Strand Kit (Qiagen, Maryland, USA) from 1 μg of total RNA isolated from cells from each culture using the RNeasy mini kit (Qiagen) in GeneAmp PCR System 9700 (Applied Biosystems, NY, USA). Determination of IFN-γ, IL-4 and IL-10 mRNA levels was carried out by a SYBR green real time qPCR using a custom RT2 Profiler PCR Array (CAPM13028, Qiagen) in a Stratagene Mx3005P PCR System (Agilent Technologies, Santa Clara, CA, USA), according to manufacturer’s instructions. Mouse Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an endogenous control to avoid variations between the samples. Briefly, cDNA samples were subjected to an initial denaturation for 10 min at 95°C, followed by 40 cycles of denaturation at 95°C for 15 sec, and extension at 60°C for 30 sec, ending with incubation at 72°C for 5 min. Each reaction was carried out in triplicate. Non-pulsed DCs-stimulated T cells were used as calibrators and results were expressed as fold change by the ΔΔCt method. Briefly, the normalized to GAPDH gene expression (2-ΔΔCt) in cultures of interest were divided by the normalized to GAPDH gene expression (2-ΔΔCt) in the calibrators, where value of 1 is the gene expression baseline in non-pulsed DCs-stimulated T cell cultures. A non-template control without genetic material was included to eliminate nonspecific reactions.
Groups of BALB/c mice (n = 25/group) were vaccinated subcutaneously in the upper and dorsal region with (i) PLGA-MPLA NPs, (ii) PLGA-CPA160-189 NPs or (iii) PLGA-CPA160-189+MPLA NPs in a total volume of 100 μl sterile PBS, as described in Table 1. Taken into account the peptide and MPLA loadings, each mouse received 2 μg of peptide with or without 1 μg MPLA or 1 μg MPLA alone. Two booster doses followed at a 2 weeks interval. Mice receiving PBS alone served as control. Two weeks after each injection, mice (n = 5/group) were euthanized and sera samples and spleens were collected to analyze the immune response induced by vaccination. Two weeks after the final boost, the remaining vaccinated and non-vaccinated mice were challenged by injecting freshly transformed 2x107 stationary phase L. infantum promastigotes intravenously. Non-vaccinated non-infected mice served as negative control. The prophylactic efficacy of PLGA NPs formulations was assessed in spleen and liver at 1 and 4 months post challenge. The percentage of inhibition of parasite multiplication was calculated in comparison with the non-vaccinated control using the following formula: percentage of inhibition = (number of parasites from non-vaccinated infected control group–number of parasites from the vaccinated infected group / number of parasites from non-vaccinated infected control group) x 100.
In order to examine in vivo toxicity of the synthesized PLGA NPs, BALB/c mice (n = 5/group) were injected subcutaneously at the indicated doses in 100 μl PBS. BALB/c mice (n = 5/group) injected with PBS or LPS (1 μg) served as negative or positive control of inflammation, respectively. The viability and the behavior of mice were observed at predetermined intervals over a 3 days post injection period. Furthermore, for the determination of inflammatory mediators, blood was collected from mice (n = 5/group) 4 h post vaccination. Sera were analyzed for IL-1β, IL-6, TNFα and MCP-1 by magnetic bead multiplex array (Millipore, Billerica, MA, USA). Data were acquired on a Luminex 200 (Oosterhoot, The Netherlands) and analyzed using xPONENT software (Luminex).
The parasite burden was quantified by a limiting dilution assay as described previously [32]. The wells were examined for viable and motile promastigotes every 3 days, and the reciprocal of the highest dilution that was positive for parasites was considered to be the number of parasites per mg of tissue. The total parasite burden was calculated by reference to the whole organ weight.
Spleens were aseptically excised from mice of all experimental groups two weeks after each vaccination (Table 1) as well as 1 month post challenge, and single cell suspensions were prepared in complete RPMI-1640. Cells were cultured in triplicates in 96-well round bottom plate at a density of 2x105 cells/well at a final volume of 200 μl and stimulated with CPA160-189 (5 μg/ml) or SLA (12.5 μg/ml) for 96 h. Splenocytes cultured in medium alone or in the presence of Con A (6 μg/ml; Sigma-Aldrich, St. Louis, MO, USA) served as negative or positive control, respectively. Proliferation was determined by addition of 0.5 μCi of [3H]-thymidine ([3H]-TdR; PerkinElmer) during the last 18 h of the culture period.
In parallel, similar spleen cell cultures were performed for cytokine production 2 weeks after the end of vaccinations and 1 month post challenge. Specifically, spleen cells (1x106 cells/ml) from all groups of mice were plated in 24-well plates and incubated with CPA160-189 (5 μg/ml) or SLA (12.5 μg/ml) for 72 hours. At the end of incubation period, cell-free supernatants were harvested by centrifugation, aliquoted and stored at -70°C until assayed for specific cytokines. The levels of IL-2, IFN-γ, TNFα, IL-4, and IL-10 were measured by magnetic bead multiplex array (Millipore).
For intracellular analysis of IFN-γ-producing CD4+ and CD8+ T lymphocytes from all groups of mice at the end of vaccinations, flow cytometry analysis was carried out. In brief, two weeks after the end of vaccinations splenocytes were cultured as described above in the presence of 5 μg/ml of CPA160-189 or medium alone. At the last 4 h of incubation, cells were exposed to brefeldin A, washed in FACS buffer and stained with FITC-conjugated anti-CD4 or anti-CD8 mAbs (1:100 dilution; BD Biosciences) for 30 min. Then, cells were treated with permeabilization buffer and stained for 30 min on ice with PE-conjugated anti-IFN-γ mAb (1:100 dilution; BD Biosciences).
Sera were collected from mice of all experimental groups 2 weeks after each vaccination (Table 1) and production of IgG, IgG1 and IgG2a antibodies against CPA160-189 was determined by ELISA. In brief, 96-well microtiter plates were coated with 5 μg/ml of CPA160-189 and incubated overnight at 4°C. After 3 washes with washing buffer (PBS with 0.05% Tween-20), plates were coated with PBS– 2% (w/v) BSA for 2 h. Then, sera samples were added at a dilution of 1:100 and incubated for 90 min. After that, biotinylated anti-mouse IgG1 (1 μg/ml) and IgG2a (250 ng/ml) (both obtained from AbD Serotec, Oxford, UK), or HRP-conjugated anti-mouse IgG (1:5000) (Thermo Scientific) were added for 1 h at 37°C. When biotinylated Abs were used, streptavidin-HRP was added (1:5000) and incubated for another 1 h at 37°C. The enzyme-labeled complexes were detected by reaction with TMB substrate and the reaction was stopped by adding 2 M sulfuric acid. The absorbance was measured at 450 nm using an ELISA microplate spectrophotometer (MRX).
All results are expressed as mean±standard deviation (SD). GraphPad Prism version 5.0 software (San Diego, CA, USA) was used for statistical analysis. One-way ANOVA with multiple-comparisons, Tukey-Kramer post test or two-way ANOVA with Bonferroni post test were performed, when required, in order to assess statistical differences among experimental groups. A value of p<0.05 was considered significant for all analyses.
CPA is a highly immunoreactive molecule that is recognized by the sera obtained from either recovered or active cases of CL and VL, as well as by sera from asymptomatic or symptomatic dogs naturally infected with L. infantum. In order to determine whether CPA160-189 contains epitopes that are recognized by antibodies present in the serum of naturally infected dogs, an ELISA against CPA160-189 was performed. SLA of L. infantum was used as an internal control and a comparative antigen to CPA160-189. According to the results obtained, CPΑ160–189 peptide was recognized by highly reactive IgG antibodies present in both asymptomatic (OD values: 0.572±0.324, p< 0.05) and symptomatic (OD values: 0.674±0.322, p< 0.01) dog sera with a cut-off value of 0.173 (Fig 1), indicating the presence of antigenic epitopes of the synthetic peptide. As expected, healthy dogs recognized neither SLA nor CPA160-189 (Fig 1).
Peptide-based vaccines may benefit from particulate delivery systems and the presence of the right adjuvant. For that reason, CPA160-189 was chosen to be co-encapsulated with MPLA into PLGA NPs serving as antigen vehicle. According to scanning electron microscopy, the synthesized PLGA NPs were characterized by a well-defined spherical shape (Fig 2A and 2B). The average diameter of different type of synthesized PLGA NPs was in the range of 309.9±15.4 to 316.6±6.6 nm as determined by photon correlation spectroscopy (% intensity) (Fig 2C and 2D) with a z potential varying from -18.2±7.7 to -19.1±5.7 mV (Table 2), indicative of the presence of free -COOH groups on the NP surface. The mean encapsulation efficiency (EE) of CPΑ160–189 ranged from 77.88±0.47 to 83.8±5.6%, and the antigen loading was 17±0.7 μg and 18.2±2.1 μg of CPΑ160–189 per mg of PLGA-CPA160-189 and PLGA-CPA160-189+MPLA NPs, respectively (Table 2). MPLA encapsulation in PLGA-CPA160-189+MPLA NPs was 80.1±8.2% and in PLGA-MPLA NPs was 54.5±3.1% corresponding to MPLA loading of 8.7±2.7 μg and 6.1±0.3 μg per mg of PLGA NPs, respectively (Table 2). In vitro release experiments at pH 7.4 revealed an initial burst release of CPA160-189 in both NPs formulations in the first 1 hour of the study reaching 39.5% and 53.7% in PLGA-CPA160-189 NPs and PLGA-CPA160-189+MPLA NPs, respectively, followed by a gradual increase until 24 hours of incubation (PLGA-CPA160-189 NPs: 63.1±1.5% and PLGA-CPA160-189+MPLA NPs: 76.2±0.6%). After that time point, antigen release profile was characterized by a plateau. Finally, after 2 weeks the cumulative percent of released CPA160-189 reached 69.6±3.9% and 86.56±3.4% in PLGA-CPA160-189 NPs and PLGA-CPA160-189+MPLA NPs, respectively (Fig 2E). In contrast, the percent of MPLA released in the same conditions reached only 8% after 24 h of incubation and remained stable until the end of the study (9.9±2%; Fig 2F).
DCs play a central role in linking innate and adaptive immunity by successfully presenting antigens to T cells through MHC class I and/or II molecules. Moreover, MPLA, a synthetic analog of LPS, has the potential to activate DCs without the toxic effects of LPS, leading to activation of CD4+ Th1 and CD8+ T cell populations. In order to evaluate whether the uptake of the synthesized PLGA NPs could induce maturation of DCs, bone marrow-derived DCs were exposed to different PLGA NPs and the surface expression of CD40, CD86, CD80 and CD83 co-stimulatory and MHCI and MHCII molecules was determined by flow cytometry. For that reason, preliminary experiments were conducted in order to determine the optimum dose of PLGA NPs for efficient maturation of DCs assessed by CD40 and CD86 molecules expression. According to the results, PLGA-CPA160-189 NPs could not induce CD40 and CD86 co-stimulatory molecules expression, as assessed by low levels of MFI compared to DCs treated with medium alone (S1 Fig). In contrast, co-encapsulation of MPLA led to a dose-dependent upregulation of both maturation markers with the optimum dose of PLGA NPs ranging between 1 and 2 μg of CPA160-189 and 0.5 and 1 μg of MPLA, whereas at higher doses an advert effect on DCs maturation was observed (S1 Fig). For the purposes of the experiment it was selected the dose of PLGA NPs having co-encapsulated 2 μg of CPA160-189 and 1 μg of MPLA. As depicted in Fig 3, pulsing with PLGA-CPA160-189+MPLA NPs conferred significant increase not only in CD40 (92.5±1.8 vs 39.5±6.6, p<0.01) and CD86 (477.5±29.0 vs 188.1±27.1, p<0.001) co-stimulatory molecules expression but also in CD80 (339.5±47.38 vs 24.4±9.5, p<0.01), CD83 (74.1±18.53 vs 12.4±0.3, p<0.01), MHCI (660.5±218.5 vs 201.0±120.2, p<0.05) and MHCII molecules (3212.0±99.4 vs 1581.0±109.5, p<0.001) expression, as expressed by MFI values, compared to medium control (Fig 3A–3F). Interestingly, regarding CD83 expression both PLGA-MPLA NPs and PLGA-CPA160-189 NPs induced similar levels of increase compared to PLGA-CPA160-189+MPLA NPs in contrast to what happened to the other markers (PLGA-MPLA NPs: 81.2±14.99 vs PLGA-CPA160-189+MPLA NPs: 74.1±18.53; PLGA-CPA160-189 NPs: 85.9±9.76 vs PLGA-CPA160-189+MPLA NPs: 74.1±18.53) (Fig 3C). Also, it must be noted that the detected expression levels of all the above molecules, except CD86, were comparable to those detected in cells cultured in the presence of the positive control, LPS (CD40: 92.5±1.8 vs 129.0±26.87; CD80: 339.5±47.38 vs 186.5±118.1; CD83: 72.7±1.84 vs 74.1±18.53; CD86: 477.5±29.0 vs 635.5±63.0, p<0.05; MHCI: 660.5±218.5 vs 830.0±234.8; MHCII: 3212.0 ±99.4 vs 2542.0±242.0) (Fig 3A–3F). In contrast, the soluble mixture of peptide-adjuvant induced a minimal, not statistically significant increase in most of the maturation markers, except from CD80 and CD83, which was lower compared to that detected when DCs were pulsed with PLGA-CPA160-168+MPLA NPs (CD40: 70.8±5.2 vs 92.5±1.8, p<0.01; CD80: 26.1±3.5 vs 339.5±47.38, p<0.01; CD83: 13.80±2.12 vs 74.1±18.53, p>0.05; CD86: 358.5±21.9 vs 477.5±29.0, p>0.05; MHCI: 522.0±154.1 vs 660.5±218.5, p>0.05; MHCII: 2098±214 vs 3212±99.4, p<0.001) (Fig 3A–3F). Furthermore, pulsing with these PLGA NPs having co-encapsulated CPA160-189 and MPLA resulted to a significant increase of IL-12-producing DCs (7.7±3.1% vs 0.8±0.6%, p<0.05) compared to control (Fig 3G).
Subsequently, the capacity of these DCs to prime naive T cells by presenting CPA160-189 peptide was evaluated. To this end, DCs pulsed with the synthesized PLGA NPs were co-cultured with naive splenocytes of the same origin and their proliferation was assessed by 3[H]-thymidine incorporation. According to the results, DCs pulsed with PLGA-CPA160-189 NPs were capable of presenting the antigen since a 3.6-fold enhanced splenocyte proliferation was detected in comparison to splenocytes primed by DCs cultured in medium alone (SI: 22.4±5.5 vs 6.1±3.7, p>0.05) (Fig 4A). However, co-encapsulation of CPA160-189 with MPLA into PLGA NPs enhanced by 2-fold spleen cell proliferation compared to PLGA-CPA160-189 NPs (PLGA-CPA160-189+MPLA: 50.5±11.7 vs PLGA-CPA160-189: 22.4±5.5, p<0.05) (Fig 4A). In order to unveil whether DCs stimulated with the above PLGA NPs induced the differentiation and proliferation of peptide-specific Th1 or Th2 cell populations, analysis of mRNA expression for IFN-γ, IL-4 and IL-10 cytokines that are indicative of the respective T cell populations, was conducted. According to the results, priming of naive splenocytes with DCs that have been pulsed with the PLGA-CPA160-89+MPLA NPs stimulated significant upregulation of both IFN-γ and IL-10 transcripts by 1.5-fold (p<0.05) over those spleen cell cultures treated with immature DCs, whereas splenocytes that proliferated in the presence of PLGA-CPA160-189 NPs-pulsed DCs did not alter their IFN-γ and IL-10 expression compared to the control group (Fig 4B). On the contrary, IL-4 transcripts were not affected compared to the control group with both treatments (Fig 4B).
Taking together, these data indicated the requirement for the peptide and the adjuvant to be present in the same nanoparticulate formulation, since co-encapsulation of CPA160-189 and MPLA in PLGA NPs led to differentiation of IL-12-producing DCs and in vitro priming of CPA160-189-specific T cell populations.
Before processing to assessment of the synthesized PLGA NPs immunogenicity in vivo, their biocompatibility was investigated. All mice exhibited normal behavior throughout the study period. Moreover, induction of acute inflammation was not detected after the administration of all PLGA NPs, since IL-1β, IL-6, TNFα and MCP-1 production levels in the sera of vaccinated mice were detected at similar levels to the negative control group (Table 3) and were significantly lower to the positive control group injected with LPS.
The efficiency of the synthesized PLGA NPs to induce CPA160-189-specific immune responses in vivo was analyzed. For this purpose, naive Leishmania-susceptible BALB/c mice were subcutaneously injected with the indicated doses of PLGA NPs and boosted twice in two weeks intervals (Table 1). According to the results, a single vaccination with PLGA NPs carrying CPA160-189 having co-encapsulated or not MPLA, elicited comparable levels of CPA160-189-specific splenocyte proliferation in contrast to PLGA-MPLA NPs-vaccinated mice and PBS control group which remained negative (PLGA-CPA160-189 NPs: 7.4±2.8; PLGA-CPA160-189+MPLA NPs: 5.6 ±3.0) (Fig 5A). Two booster doses, however, induced further increase in spleen cell proliferation with S.I. values against CPA160-189 reaching 21.6±5.7 (p<0.001) for splenocytes obtained from PLGA-CPA160-189 NPs-vaccinated mice and 26±7.2 (p<0.001) for splenocytes from PLGA-CPA160-189+MPLA NPs-vaccinated mice (Fig 5A). Moreover, the cytokine levels secreted by the splenocytes isolated from mice two weeks after the second boost in response to CPA160-189 stimulation were measured (Fig 5B). According to results, splenocytes obtained from mice vaccinated with PLGA-CPA160-189+MPLA NPs showed significantly enhanced production of IL-2 (258.59±52.13 pg/ml vs n.d., p<0.001) and the pro-inflammatory cytokines IFN-γ (37.67±11.23 pg/ml vs 4.42±1.8 pg/ml, p<0.001) and TNFα (73.22±28.08 pg/ml vs 34.1±1, p<0.05) in comparison to PBS control group (Fig 5B). This result indicated the differentiation of CPA160-189-specific effector T cells. On the contrary, vaccination with PLGA-CPA160-189 NPs induced substantial levels of IL-2 (33.87±18.2 pg/ml, p<0.05), TNFα (63.72±23.48 pg/ml, p<0.05) and the anti-inflammatory cytokine IL-10 (18.94±5.2 pg/ml) and not IFN-γ (Fig 5B). Regarding IL-4, both vaccinated groups produced low amounts after CPA160-189 stimulation as compared to PBS control group (PLGA-CPA160-189 NPs: 6.99±3.2 pg/ml vs 1.85±0.05, p<0.05 and PLGA-CPA160-189+MPLA NPs: 3.8±1.96 vs 1.85±0.05, p<0.05) (Fig 5B). Detection of IFN-γ-producing T cell populations showed that vaccination with PLGA-CPA160-189+MPLA NPs induced the differentiation of CPA160-189-specific IFN-γ-producing CD4+ T cells (12.55±0.64%, p<0.05) followed by PLGA-CPA160-189-vaccinated mice (10.05±1.2%) compared to PLGA-MPLA NPs-vaccinated and PBS control mice (PLGA-MPLA: 9.31±0.62% and PBS: 9.08±0.25%) (Fig 5C). Moreover, mice vaccinated with PLGA-CPA160-189 NPs and PLGA-CPA160-189+MPLA NPs induced about a 2-fold higher numbers of CPA160-189-specific IFN-γ-producing CD8+ T cells compared to the control group (PLGA-CPA160-189 NPs: 1.46±0.23% vs PBS: 0.77±0.13%, p<0.05 and PLGA-CPA160-189+MPLA NPs: 1.62±0.11% vs PBS: 0.77±0.13%, p<0.05) and PLGA-MPLA NPs-vaccinated mice (PLGA-CPA160-189 NPs: 1.46±0.23% vs PLGA-MPLA NPs: 0.87±0.13% and PLGA-CPA160-189+MPLA NPs: 1.62±0.11% vs PLGA-MPLA NPs: 0.87±0.13%, p<0.05) (Fig 5C).
In vitro analysis of mice sera showed that PLGA-CPA160-189+MPLA NPs also generated secondary T-cell dependent sero-responses, since enhanced levels of peptide-specific IgG antibodies were detected after the second vaccination (1st boost: 6-fold increase, p<0.001) over control groups, followed by PLGA-CPA160-189 NPs-vaccinated mice (1st boost: 2.5-fold increase). However, at the end of vaccination both groups of vaccinated mice had comparable levels of CPA160-189-specific IgG antibodies (7-fold increase, p<0.001) (Fig 6A). Assessment of IgG1 and IgG2a antibodies showed that both isotypes were produced with a bias towards IgG1 (Fig 6B). Conclusively, the above results suggested that vaccination with PLGA-CPA160-189+MPLA NPs stimulated the activation and differentiation of CPA160-189-specific CD4+ Th1 and CD8+ T cell effector cells.
In order to evaluate whether the immune responses induced by vaccination were maintained during infection, the peptide-specific responses were assessed in both vaccinated and non-vaccinated mice infected with L. infantum one month post challenge. In conventional recall assays, splenocytes isolated from PLGA-CPA160-189 NPs and PLGA-CPA160-189+MPLA NPs-vaccinated mice showed comparable levels of significant peptide-specific proliferation (PLGA-CPA160-189 NPs: 4.1±0.8 vs PBS: 0.7±0.2, p<0.01; PLGA-CPA160-189+MPLA NPs: 5.1±1.6 vs PBS: 0.7±0.2, p<0.01). In contrast, spleen cells isolated from PLGA-MPLA NPs-vaccinated and non-vaccinated infected mice did not respond in the presence of CPA160-189 (Fig 7A). The observed immunosuppression in these mice groups extended also in parasite-specific immune responses, since only splenocytes obtained from both groups of PLGA-CPA160-189 NPs and PLGA-CPA160-189+MPLA NPs-vaccinated mice showed a 2-fold upregulation of lymphoproliferation in response to SLA compared to the non-vaccinated infected control group (PLGA-CPA160-189 NPs: 1.9±0.6 vs PBS: 1±0.4, PLGA-CPA160-189+MPLA NPs: 1.9±0.8 vs PBS: 1.0±0.4) (Fig 7A). Assessment of IL-2, IFN-γ, IL-4, IL-10 and TNFα production in the culture supernatant against CPA160-189 showed that the animals vaccinated with PLGA-CPA160-189+MPLA NPs produced enhanced levels of all cytokines after CPA160-189 treatment with an IFN-γ dominance (IL-2: 68.25±24.6 pg/ml, p<0.001; IFN-γ: 378.5±3.4 pg/ml, p<0.001; IL-4: 17.2±2.8 pg/ml, p<0.01; IL-10: 33.5±17.8 pg/ml, p<0.001; TNFα: 82.05±21.63 pg/ml, p<0.01) compared to the non-vaccinated infected control group which produced minimal amounts of IL-2 (23.11±9.29 pg/ml) and TNFα (53.88±11.47 pg/ml) cytokines in response to CPA160-189 stimulation. In contrast, PLGA-CPA160-189 NPs-vaccinated group was a low producer of IFN-γ (6.4±9.0 pg/ml) and produced mainly IL-2 (49.78±21.4 pg/ml, p<0.01) and TNFα (38.88±6.13 pg/ml) after stimulation with CPA160-189 (Fig 7B). Assessment of the parasite-specific immune responses showed that spleen cells from the mice that have been vaccinated with the PLGA-CPA160-189+MPLA NPs produced significantly higher amounts of IFN-γ compared to the PLGA-CPA160-189 NPs-vaccinated group (247.7±20.6 pg/ml vs 104.1±4.5 pg/ml, p<0.001) and the non-vaccinated infected control group (247.7±20.6 pg/ml vs 91.1±9.8 pg/ml, p<0.001), with minimal levels of IL-4 production (6.9±3.3 pg/ml vs 29.5±1.0 pg/ml, p<0.05) in response to SLA. In contrast, IL-2, IL-10 and TNFα levels were comparable to all groups tested (PLGA-CPA160-189+MPLA: IL-2: 24.4±14.3 pg/ml, IL-10: 59.7±21.9 pg/ml and TNFα: 51.3±12.9 pg/ml; PLGA-CPA160-189: IL-2: 27.2±10.3 pg/ml, IL-10: 55.6±21.1 pg/ml and TNFα: 44.5±3.3 pg/ml, PBS: IL-2: 26.1±12.7 pg/ml, IL-10: 38.9±14.9 pg/ml and TNFα: 61.8±15.7 pg/ml) (Fig 7C). Conclusively, PLGA-CPA160-189+MPLA NPs-vaccinated mice showed enhanced levels in CPA160-189- and parasite-specific IFN-γ production over IL-4 and IL-10 production resulting to high IFN-γ/IL-4 and IFN-γ/IL-10 ratios that indicated a predominance of Th1 immune responses. Moreover, the detection of CPA160-189-specific IL-2 and TNFα cytokines was indicative of the existence of effector T cell populations raised by vaccination with PLGA-CPA160-189+MPLA NPs. In contrast, splenocytes obtained from PLGA-CPA160-189 NPs- and PLGA-MPLA NPs-vaccinated and non-vaccinated infected mice groups were characterized by mixed parasite-specific Th1/Th2 immune responses and this was in accordance with the profile of VL characterized by such type of immune responses (Fig 7C).
In order to elucidate whether the PLGA NPs-induced CPA160-189-specific T cells detected could confer protection against Leishmania parasites, the parasite burden was determined in liver and spleen 1 month later by limiting dilution assay. According to the results, vaccination reduced hepatic parasite burden by 34.2% and 48.2% in PLGA-CPA160-189 NPs- and in PLGA-CPA160-189+MPLA NPs-vaccinated mice, compared to the PBS control group, indicating that vaccinations could promote the self-curing response seen in BALB/c livers (Fig 8A). Moreover, evaluation of splenic parasite burden in mice vaccinated with PLGA-CPA160-189 NPs showed a 32.2% reduction when compared with non-vaccinated mice, which was further enhanced when mice were vaccinated with PLGA-CPA160-189+MPLA NPs resulting in the significant reduction in parasite load of 90.2% (p<0.01) (Fig 8B). Evaluation of parasite burden 4 months post challenge showed that although PLGA-CPA160-189+MPLA NPs-vaccinated mice preserved the reduced parasite burden in liver (61.5% reduction), an increase in the splenic parasite burden was detected (30.6% reduction) indicating a partial vaccine-induced protection (Fig 9A). These results were well correlated with spleen and liver weights as compared with the PBS control group (Fig 9B). In all cases, no effect of the vaccination with PLGA-MPLA NPs over infection was detected, confirming that the protection seen in liver and spleen through PLGA NPs was CPA160-189-specific.
Despite the fact that many attempts to design an effective vaccine against leishmaniasis have been made, when these experimental vaccines reached the clinical trials phase they failed to initiate strong T cell responses [3,4,5]. Difficulties in the design of such a vaccine for induction of T cell mediated immunity are originated not only from the high diversity and variability of the parasite, but also the HLA polymorphisms of the human population, thus affecting the specificities of T cell responses in different individuals. Bioinformatic analysis of protein sequences offers a solution towards this problem by designing synthetic peptides consisting of selected conserved epitopes among different parasite strains that are recognized by a number of different HLA molecules [33,34,35]. Accordingly, in the present study, the selected antigen to be used in the experimental vaccine was a 30-mer peptide, namely CPA160-189, designed by our research group to contain promiscuous overlapping human and murine MHC class I and II-restricted epitopes. CPA160-189 peptide’s immunogenicity was confirmed when administered in emulsion with Freund’s adjuvant in BALB/c mice, since it induced peptide-specific Th1 and CD8+ T cell responses [27].
As DCs play a central role in priming and controlling T cell mediated immune responses, efficient delivery of antigens to this cell population is a critical issue in the design of vaccine formulations for generation of effective cellular immune responses. In this context, nanoparticle delivery systems hold promise for achieving the appropriate type of immune responses. In the present study, the vaccine was designed in order to target effectively DCs, by using PLGA NPs with a relatively small size (~310 nm) to facilitate their uptake by APCs and free drainage to the lymphatic organs. According to previous studies, NPs with size smaller than 10 μm were more efficiently phagocytosed by APCs, including DCs, and trafficked towards the lymph nodes in a DC-dependent manner, which is an important aspect of vaccine delivery [36]. Indeed, in a recently published research of our group, we confirmed that these PLGA NPs were efficiently taken up by DCs in vitro and when were injected subcutaneously in mice they were phagocytosed by APCs in lymphoid organs, such as DCs [37].
Further, in the present study, it was shown that MPLA adjuvant encapsulation in PLGA NPs loaded with CPA160-189 was pivotal for triggering DCs functional maturation, as expressed by increased surface expression of co-stimulatory, MHC class I and II molecules and IL-12 production. It is known that MPLA-TLR4 interaction on the surface of DCs induces their functional activation characterized by maturation and expression of pro-inflammatory cytokines, such as IL-12, which are crucial for the activation of Th1 immune responses [38,39,40]. Moreover, the detection of enhanced CD83 expression levels on the surface of DCs triggered with PLGA-CPA160-189+MPLA NPs indicated the efficiency of these DCs to stimulate peptide-specific CD4+ and CD8+ T cells activation. Respectively, results of this study showed that in vitro priming of naive spleen cells with DCs pulsed with PLGA-CPA160-189+MPLA NPs induced the differentiation and activation of CPA160-189-specific T cells characterized by upregulated IFN-γ transcripts, further confirming the effectiveness of this delivery system for supporting polarized Th1 and/or CD8+ T cell immune responses through antigen presentation in context of MHC class II and/or MHC class I molecules. In a recent study, Maji et al showed the importance of adding the MPL adjuvant to liposomal rgp63 which led to a significant enhancement of the antigen presentation by DCs through TAP-dependent MHC class I pathway resulting in more efficient antigen-specific CD8+ T cell responses [13]. However, it must be noted that in our study, significant levels of IL-10 transcripts were also detected suggesting the activation of CPA160-189-specific T regulatory cell subpopulations.
Evaluation of PLGA NPs effectiveness to target DCs in vivo has been tested indirectly by assessing the development of CPA160-189-specific clones after mice vaccination. Despite the fact that mice received an extremely low dose (2 μg) of peptide, PLGA NPs effectively induced T and B cell CPA160-189-specific clonal expansion as indicated by recall assays and IgG detection, underlining the efficacy of PLGA NPs as peptide delivery system. Similar results have been observed in other studies using PLGA NPs for the development of experimental vaccines against cancer or various infections, such as malaria, and their efficacy to induce the desirable immune responses was attributed to the encapsulation of the MPLA adjuvant [41,42]. Interestingly, in contrast to in vitro results, vaccination with PLGA-CPA160-189 NPs led to similar levels of CPA160-189-specific T cell clonal expansion compared to that detected after vaccination with PLGA NPs loaded with CPA160-189 and MPLA. According to flow cytometry results, these proliferating splenocytes obtained from PLGA-CPA160-189+MPLA NPs-vaccinated mice contained both IFN-γ-producing CD4+ and CD8+ T cells. The above results confirmed and further extended our previous work, showing CPA160-189 peptide’s immunogenicity by eliciting a mixed Th1/Th2 response followed by almost equal CPA160-189-specific IgG2a and IgG1 production, in parallel with CD8+ T cells activation [27]. Possibly, CD8+ T cell activation detected in the present study was catalyzed by the presence of even low amounts of IL-4, as it was shown in the cytokine assay of the present study. The important role of IL-4 in the generation of CD8+ T cell memory against leishmaniasis has been shown in previous studies [43,44]. More specifically, the protective effect of different antigens, such as HASPB1 and histone H1, against VL was attributed to an IL-4-mediated activation of CD8+ T cells [45,46]. Moreover, splenocytes obtained from PLGA-CPA160-189+MPLA NPs-vaccinated mice produced also significant amounts of IL-2 and TNFα which along with IFN-γ suggest the existence of vaccine-induced effector T cell populations. These T cell populations are considered significant for vaccine efficacy to induce long-term protection against various pathogens, among them Leishmania [47,48].
In the area of nanoparticles vaccination, previous studies have shown that liposomal delivery of cysteine proteases CPA, CPB and CPC in combinations or alone, soluble leishmanial antigen and recombinant gp63 in the presence of MPLA adjuvant induced short and long-term immunity against VL due to the presence of both antigen-specific CD4+ and CD8+ T cell responses [49,50,51]. Also, it has been shown that when PLGA NPs were used as antigen vehicle in anti-tumor vaccines, a potent activation of antigen-specific CD8+ T cells was detected [52,53,54,55]. Since Leishmania antigen-specific CD4+ and CD8+ T cells are essential for immunity against leishmaniasis [56,57], the protective effect of these populations detected in the present study was assessed by challenging vaccinated mice with a highly virulent strain of L. infantum promastigotes. The immune response elicited by PLGA-CPA160-189+MPLA NPs vaccination conferred significant reduction of parasite burden in spleen and liver by 90% and 40%, respectively. This decrease was positively correlated with the enhanced proliferation of splenocytes in response to CPA160-189 and SLA stimulation in comparison to control infected groups.
Determination of cytokine profile showed high levels of IL-2, IFN-γ and TNFα production contrary to the minimal levels of IL-4 and IL-10 leading to enhanced IFN-γ/IL-4 and IFN-γ/IL-10 ratios. It is well documented that whereas IL-4 does not play a decisive role in visceral infection establishment, IL-10 production by splenic cells correlates well with disease progression and pathology in human and experimental disease [58,59,60]. IL-10 has been shown to block Th1 activation and consequently cytotoxic response by down-regulating IFN-γ levels. Further, it decreases the ability of macrophages to destroy parasite by deactivating them. On the other hand, protective immunity against VL is dependent on IL-12-driven Th1 immune response and IFN-γ synergizes with TNFα resulting in the induction of parasite killing by macrophages [61]. However, the simultaneous existence of IL-2, IFN-γ and TNFα producers was followed by a partial protection, since determination of parasite load at 4 months post challenge showed that mice had limited vaccine efficacy to restrain uncontrolled parasite expansion in spleen with only 30% reduction, whereas a decrease in hepatic parasite burden was observed (61%). These results, in contrast to previous findings [47,48], indicated that CPA160-189-induced cell immune responses were not capable for the maintenance of long-term protective immunity against the parasite.
According to studies exploring CPA’s potential as an effective vaccine against leishmaniasis, CPA conferred significant protection in the experimental models of CL and VL, as wells as in the experimental model of canine leishmaniasis when was administered with other immunogenic proteins [62,63,64,65]. However, the fact that a single peptide of CPA (CPA160-189) co-encapsulated with MPLA in PLGA NPs, without the presence of peptides extracted from other immunogenic parasitic molecules, proved to confer significant protection further supporting the appropriateness of the current strategy for the peptide design. Furthermore, the proposed vaccine could be improved by encapsulating more than one synthetic peptides obtained from different Leishmania proteins in order to achieve more intense T-cell responses, since previous studies support that vaccines that address a broad range of specificities are capable of inducing polyclonal effector T cells promoting protection [64]. In addition, in the light of recent findings, anti-leishmanial vaccine efficacy could be improved by including vector-derived molecules in combination with parasitic molecules [66].
Taken together, the data of the present study could provide the basis for the development of peptide-based nanovaccines against leishmaniasis, since it reveals that vaccination with nanovaccines that contain rationally designed multi-epitope peptides covering areas that interact both with MHC class I and II molecules in combination with the appropriate adjuvant and biocompatible delivery system could be a promising approach for the induction of desirable protective immune responses.
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10.1371/journal.pgen.1008417 | European Roma groups show complex West Eurasian admixture footprints and a common South Asian genetic origin | The Roma population is the largest transnational ethnic minority in Europe, characterized by a linguistic, cultural and historical heterogeneity. Comparative linguistics and genetic studies have placed the origin of European Roma in the Northwest of India. After their migration across Persia, they entered into the Balkan Peninsula, from where they spread into Europe, arriving in the Iberian Peninsula in the 15th century. Their particular demographic history has genetic implications linked to rare and common diseases. However, the South Asian source of the proto-Roma remains still untargeted and the West Eurasian Roma component has not been yet deeply characterized. Here, in order to describe both the South Asian and West Eurasian ancestries, we analyze previously published genome-wide data of 152 European Roma and 34 new Iberian Roma samples at a fine-scale and haplotype-based level, with special focus on the Iberian Roma genetic substructure. Our results suggest that the putative origin of the proto-Roma involves a Punjabi group with low levels of West Eurasian ancestry. In addition, we have identified a complex West Eurasian component (around 65%) in the Roma, as a result of the admixture events occurred with non-proto-Roma populations between 1270–1580. Particularly, we have detected the Balkan genetic footprint in all European Roma, and the Baltic and Iberian components in the Northern and Western Roma groups, respectively. Finally, our results show genetic substructure within the Iberian Roma, with different levels of West Eurasian admixture, as a result of the complex historical events occurred in the Peninsula.
| Human demographic processes and admixture events leave traceable footprints in the genomes of the populations and they can modulate the genetic architecture of complex diseases. Here, we aim to study the Roma people, an admixed population with a particular demographic history recognized as the largest ethnic minority in Europe. Previous studies suggest that they originated in South Asia 1,500 years ago and followed a diaspora towards Europe with extensive admixture with non-Roma West Eurasian groups. However, the genetic components of the Roma have not been deeply characterized. Our study reveals a common South Asian origin of all European Roma, closely related to a Punjabi group from Northwestern India. Through fine-scale haplotype-based methods, we describe a complex West Eurasian genetic component in the Roma groups, identifying a common Balkan ancestry and country-specific admixture footprints consistent with the dispersion through Europe. Our findings provide new insights into the demographic history and recent admixture events that have shaped the genetic composition of European Roma groups and could enable a better genetic characterization of complex disease in this population.
| The diaspora of the Roma people, also known with the misnomer of Gypsies, is a not-well documented human movement, which is characterized by recent dispersals and multiple founder events. The Roma population is recognized as the largest transnational ethnic minority in Europe, with an estimated population of up to 10 million, although their exact number is difficult to estimate due to the lack of reliable census surveys. They consist of a heterogeneous and substructured mosaic of populations that differ linguistically, culturally, historically, and in their relation to nearby populations [1]. Their demographic history together with their endogamous social practices [1] have contributed to a particularly different spectrum of Mendelian disorders when compared with other neighboring European populations [2,3]. Historical records confirm the persecution and social marginalization that this population has suffered since their arrival to Europe [1].
Comparative linguistics has placed the origin of European Roma in India, particularly in the northwestern region, as Romani is closely related to Punjabi and Kashmiri languages [4,5]. However, the social organization and cultural dynamics in Indian populations lead to substructure in closely-related linguistic groups, as is reflected in the different proportions of Ancestral North Indian (ANI) and Ancestral South Indian (ASI) genetic components [6] shown in groups even living in the same geographic region, which prevents them to be considered as genetically homogeneous groups [7] and challenges the retrieval of the origins of Roma based solely on linguistic data. The Indian genetic component of the Roma population was first proposed after the identification of shared disease-causing mutations with Indian and Pakistani patients [8,9]. In addition, analyses of uniparental markers permitted to assign an Indian origin for some maternal and paternal lineages found in the Roma [10–12], namely those belonging to the M-haplogroups (M5, M18, M25, M35) in the mitochondrial DNA [13], and to H-M69 in the Y-chromosome [14]. Furthermore, genome-wide studies indicate that the European Roma originate from a reduced number of founders (proto-Roma) whose ancestral homeland was the current Punjab state of India [10,15,16].
According to previous historical and anthropological evidence, a subsequent migration from Northwest India through Persia and Armenia preceded the entrance in the Balkans, from where they spread across the entire Europe. During the 11th and 12th centuries, some Roma settled in the surroundings of the Ottoman Empire, in the Balkan Peninsula (Balkan Roma), other groups spread across the Danubian Principalities (present-day Romania, Moldova, and Hungary), where they were forced into slavery (Vlax Roma), while the Romungro group started a dispersion across the Austro-Hungarian Empire [2]. Finally, other small groups moved into North, Central, and Western Europe (Northwestern Roma), having arrived into Iberia in the early 15th century, as document a number of Iberian historical records mentioning the presence of Roma groups in Zaragoza and Barcelona in 1425 and 1447, respectively [17]. The Roma diaspora through the Middle East, Caucasus, and Europe was a very complex process during which the emerging pattern of genetic substructure was highly influenced by differential gene flow from different West Eurasian (European, Middle Eastern and Caucasian) non-Roma populations [15,18] and even within Roma groups [19]. Genome-wide data showed that the Roma genomes harbor around 80% of Western Eurasian ancestry, while the remaining ancestry is from South Asian sources [16]. However, this estimate of the West Eurasian component is not only derived from their recent (post-exodus) admixture with non-Roma Europeans, as prior to their arrival into Europe, Roma might already carried an Ancestral West Eurasian (AWE) component from South Asian sources [16], due to admixture events that occurred in South Asia around 1,900–4,200 years ago (ANI component) [20], thus before the proto-Roma people left South Asia.
However, previous genetic studies of the European Roma, despite the wealth of insights provided into their demographic history, show multiple limitations. First, South Asian populations have been primarily studied using the linguistic affiliation as criteria to classify individuals into groups, which often conflicts with genetic intra-group homogeneity. Second, the European Roma population has been approximated as a simple admixture between South Asian and European sources, without a detailed analysis of the West Eurasian component in Roma. In addition, most of the analyses relied in allele frequency-based methods, yet haplotype-based approaches provide a fine-scale characterization, and perform better than allele frequency analyses in populations that have been under strong genetic drift [21,22]. Finally, there are still few studies focused on the Iberian Roma population, which represents the westernmost expansion of the Roma diaspora in Eurasia. To overcome the mentioned limitations, the present study consists of a genome-wide analysis of the European Roma (including new samples from the Iberian Roma), with the following aims: (i) to shed light on the South Asian origin of the proto-Roma population; (ii) to assess the level of admixture of the Roma with other European populations as well as with Middle Easterners and North Africans; and, (iii) to characterize the patterns of genetic substructure among the Iberian Roma. Our analysis unravels at fine-scale the genetic components of European Roma groups, dissecting the original South Asian, ancestral West Eurasian, and recent European components.
The European Roma population was first assessed in a worldwide context (Dataset1, S1 Table, see Materials and Methods). A Principal Component Analysis (PCA) was performed with samples from Europe, Africa, Middle East, Caucasus, Central and South Asia. Roma samples fall between non-Roma European and South Asian populations (S1 Fig), in agreement with their demographic historical records [1] and previous genetic studies [15]. In addition, ADMIXTURE results further confirm PCA results, as at k = 3, European Roma show a combination of two cluster components (dark red and dark blue) mainly found in South Asian and West Eurasian samples. At k = 6 (lowest cross-validation error value), the Roma individuals displayed membership in a specific cluster and a yellow component mainly found in southwestern Eurasia, which reproduces previous results [15] (S2 Fig).
To further describe the Roma genetic substructure and to reveal fine-scale patterns, we used haplotype-based methods: ChromoPainter and fineSTRUCTURE. Most European Roma samples cluster together in a sister clade of MiddleEast-Caucasus and Europe super-group (S3 Fig). These Roma samples belong to ten different clusters correlated with geography, grouping together individuals from the same European regions (North, West, Central, and Balkans) (Fig 1A and 1B). As shown in the dendrogram (Fig 1A) and based on the Total Variance Distance (TVD) values, the most significantly differentiated Roma clusters are RomaIberia-2 and RomaMix-4 (p < 0.001) (S4 Fig, S2 Table). The non-Roma reference samples were classified in 51 genetic clusters from four different large super-groups (Europe, MiddleEast-Caucasus, Central-SouthAsia, and MiddleEast-Africa) (S3 Fig).
Admixture events that have shaped the genetic composition of the Roma population were inferred with GLOBETROTTER. For all European Roma clusters, “one-date” type of admixture event (single admixture date between two sources) was detected involving two sources: a West Eurasian-like major source and South Asian-like minor source, around 1270–1580 (S3 Table, Fig 2, Table 1). This interval of admixture dates overlaps with the period when the first historical records report the presence of Roma groups in each European country, although these records represent the lower limits for the actual first Roma settlements. In general, Roma from the surroundings of the Balkan Peninsula and Central Europe (RomaMix-1, RomaMix-2, RomaMix-3, RomaUkr) have earlier admixture dates (Table 1), which supports the dispersion into Europe via the Balkans [15].
Regarding the South Asian-like source, it contributes around 35% to the admixture and its most representative cluster is Punjabi-1, from Northwestern India, (Fig 2, S3 Table). Although Punjabis have a linguistically uniform identity [23], they are genetically heterogeneous. In fact, Punjabi samples do not cluster together, instead they are spread along PC2 (S1 Fig), as well as in the fineSTRUCTURE dendrogram (S3 Fig), with three different Punjabi clusters with increasing levels of ANI component (S5 Fig, S4 and S5A Tables). Thus, most of the South Asian ancestry of the Roma is mainly shared with the group of individuals from Punjab with less West Eurasian component (Punjabi-1, S3 Table).
The rest of South Asian surrogates identified in the minor source correspond to southeastern Dravidian-speaking populations (E-India, Irula clusters) (Fig 2, S3 Table), which also exhibit low levels of West Eurasian ancestry (S5 Fig, S5A Table).
Altogether, these findings suggest that the most likely proxy for the South Asian origin of the proto-Roma, is the ancestral source here described as a mixture of present-day South Asian groups with a low West Eurasian signature.
The West Eurasian-like source contributes around 65% to the admixture event. This component captures the recent West Eurasian admixture between the proto-Roma and West Eurasians during their diaspora from India to Europe, in other words, it does not include the AWE component present in South Asian populations (S1 Note, S6 Fig) estimated to be around 15% (S5B Table). This recent West Eurasian ancestry is lower in the Roma groups from the Balkan Peninsula and Central Europe (RomaMix-1 and RomaMix-2), around 60%, and it increases up to 80% (RomaIberia-2) as the distance from the Balkans increases (Fig 2, S3 Table).
The main contribution of this major source is from southeastern European clusters (Balkan-1 and Balkan-2), with this area being the historically reported gateway of the Roma groups into Europe [1]. The component from Middle East and Caucasian clusters was found to be moderate in the Roma groups. Besides these two components, additional distinct European ancestries are detected in the Northwestern Roma groups from the Baltic (Estonia-Lithuania) and Iberia (Spain-Portugal). Specifically, while RomaBalt cluster shows a northeastern European component (NE-Europe1 cluster), RomaIberia-1 and RomaIberia-2 contain a southwestern European component (SW-Europe1 and SW-Europe2) each. This result indicates that, in the Roma groups that migrated to Northern and Southwestern Europe, the Balkan component left a footprint still clearly detectable today, though having been highly reconfigured by admixture in the Baltic region and the Iberian Peninsula, respectively (Fig 2, S3 Table).
Regarding the Iberian Roma, the samples constitute two highly differentiated clusters (RomaIberia-1 and RomaIberia-2) not found elsewhere, which suggests a deep genetic substructure within the Roma settled in Iberia (Figs 1 and 2, S3 Table).
As mentioned above, the European Roma ancestry contains two main sources: the West Eurasian (European and MiddleEast-Caucasus) and the South Asian components. However, these ancestry proportions differ significantly when comparing the X chromosome to the autosomes: the South Asian ancestry is significantly higher in the X chromosome while the MiddleEast-Caucasus proportion is significantly higher in the autosomes (S6 Table, S7 Fig). These results point to a sex-biased admixture during the Roma diaspora, likely characterized by a higher influx of non-Roma males than females from the Middle East and Caucasus. The proportions of European ancestry contained in the autosomes and the X chromosome are similar, although RomaBalt, RomaIberia-1, RomaIberia-2 and RomaMix-4 show higher levels of European ancestry in the autosomes. These findings can also indicate different sex-biased gene flow processes in the European Roma groups, which might be the result of different social patterns among groups. Future studies with mtDNA and Y- chromosome data could add further insights into these results, as well as sex-specific fertility inheritance processes in the Roma population [24].
To investigate the effective population size (Ne) dynamics, we have estimated the Ne of each Roma group and the ancestry-specific Ne. On one hand, all Roma groups show a long uninterrupted Ne decrease followed by an increase of Ne (without reaching the levels of the NorthItaly cluster, which we used as a European reference) (S8 Fig). The change of the Ne trend is slightly correlated with the start of the admixture in each Roma group (S9 Fig), which might point to the gradual settlement of the Roma population in Europe. On the other hand, we inferred Ne through time for the three ancestral Roma source populations (European, MiddleEast-Caucasus and SouthAsian), focusing on their Ne before the admixture: 34 generations ago, as the more ancient lowest confidence interval (CI) inferred from GLOBETROTTER is found in RomaMix-2 at 1164 CE (S7A Table). The European Neg = 34 is 2.12 to 2.64 times higher than the South Asian Neg = 34, which is 1.27–1.43 times higher than the MiddleEast-Caucasus Neg = 34 (S7B Table). In contrast, the fold-change between the European and South Asian ancestry proportions is lower than 2 in all Roma groups (except RomaIberia-2 and RomaMix-4) and between South Asian and MiddleEast-Caucasus ancestry proportions is higher than 1.5 fold in all Roma groups (S7C Table). These differences between the ancestry proportions and the ancestry-specific Ne could be explained by the fact that a small South Asian proto-Roma group of founders had a continuous gene flow with different non-related groups from the MiddleEast and Caucasus and different non-Roma European populations, during their West Eurasian diaspora (see S4 Note).
Runs of homozygosity (ROH) were computed to assess the levels of inbreeding and the degree of genetic isolation in the Roma groups. In general, the mean ROH length of the Roma groups is significantly higher than the mean of the non-Roma reference Balkan-2 and Punjabi-1 clusters. For all ROH length categories, Roma groups present similar values than those of Kalash (S10 Fig, S8A Table), which is known to be a highly inbred population [25], possibly due to genetic isolation, although their isolation degree is in debate [26,27]. The average ROH lengths of the Roma maintain high values after a first significantly decrease between the first and the second categories (1–2 and 2–3 Mb, respectively) (S8B Table), which suggest that the inbreeding signals of Roma are the result of a continuous, although decreasing, level of isolation, from historical to recent times. Furthermore, the Roma groups with more West Eurasian ancestry (IberianRoma-2 and RomaMix-4) are the clusters with the lowest mean ROH length values across all categories (S10 Fig). Thus, these results additionally evidence a degree of heterogeneity within Roma from the Iberian Peninsula that need to be further investigated.
The demographic history of the Roma population is characterized by a series of bottlenecks and admixture events that have occurred since the proto-Roma left India, after their arrival to the Balkans and spread throughout Europe, and in the case of Iberian Roma, after their settlement in the Iberian Peninsula. The study of their genetic profile in a worldwide context places them between South Asians and Europeans, which confirms previous findings of admixture [10,15,16]. A fine-scale approach has allowed us to distinguish the recent West Eurasian component, which is the result of the admixture with non-Roma West Eurasian populations. Our estimates of this recent West Eurasian component, around 65%, are lower than the previously reported 80% [16], as it only includes the “post-exodus from India” admixture and not the “pre-exodus from India” AWE component (around 15% based on the f4 ratio estimates). This recent West Eurasian component was acquired between 1270–1580. Although GLOBETROTTER infers this admixture as a single pulse event (“one-date”), it would require large datasets to distinguish continuous from single pulse admixture [31].
Regarding the origin of the proto-Roma population, Northwestern India has been previously proposed as the putative source of their South Asian ancestry [4,5]. Although it is a geographically well-defined area, their populations are socially, linguistically, and genetically heterogeneous, with high levels of stratification and substructure: their lands comprise from tribe clans to upper-caste groups, and from Dravidian to Indo-European speaking groups [32]. Our analyses show that they are dispersed along the PC with different admixture proportions (S1–S3 and S5 Figs). Within the boundaries of Northwestern India, the Punjab region has been further placed as the ancestral homeland of the proto-Roma, through different approaches: identity by descend (IBD) sharing analyses [16], Approximate Bayesian Computation models [15], and mitochondrial M lineages [10] and tau haplotype [33] comparisons between Roma and South Asians. However, the linguistic identity that characterizes the Punjabi population is independent of their historical origin and social designation [23]. Punjab is a strategic region that has suffered repeated invasions from different sources [32], explaining why nowadays encompasses heterogeneous population with differential admixture and ancestral components. We have shown that the Punjabi samples are genetically heterogeneous, which mainly differ in the proportion of West Eurasian ancestry, further confirming previous results [7]. Our results add in the indication that the original genetic composition of the proto-Roma seems nearest to that of the Punjabi cluster from the less West Eurasian admixed group. Assuming that the individuals from this Punjabi cluster were already in Punjab when the rest of Punjabi clusters admixed with West Eurasians, socio-historical factors might have determined their differential admixture. In other words, this Punjabi cluster might derive from Punjabis who belonged to a lower caste group, since in agreement with previous studies, Indian lower caste groups are characterized by less West Eurasian admixture [6,7]. In addition, we have reported that Dravidian-speaking populations with high ASI ancestry (i.e. E-India and Irula clusters) are also involved in the South Asian source of the Roma individuals. These two sources of South Asian ancestry could solve the contradiction regarding the identification of uniparental Roma lineages with a Northwestern Indian origin [11] and the high Y-STR haplotype sharing among Roma and South Indian populations [34], as these findings could be explained by two overlapping scenarios. The first one, first mentioned by Turner [4], consists in considering a previous migration of nomadic groups into Northwestern India from Central India around 250 BCE and, after several centuries in Punjab with few external admixture, a single group of proto-Roma individuals left India. The second scenario refers to the fact that the genomes of present-day North Indians have more West Eurasian ancestry due to subsequent gene flow from West Eurasians after the proto-Roma left India [20], which explains the combination of populations with low West Eurasian ancestry identified in the South Asian Roma component. These two scenarios fit the idea that the Roma people descend from a single initial founder population [15].
After the exodus from India and during the diaspora through West Eurasia, the Roma population admixed with multiple non-Roma European, Middle Eastern and Caucasian groups. First, the European Roma ancestors arrived to Armenia through Persia [1]. Our results agree with a moderate Middle East and Caucasus gene flow during a rapid migration across this territory [15], specifically, we detect a higher rate of male gene flow, which could be related to the incorporation of Persian nomadic groups with the Roma [1]. Then, historical records suggest that, in Armenia, they followed the same route as the displaced Armenians towards Anatolia, due to the Mongol and Seljuq invasions (a Persian Muslim dynasty), from where they were pushed to the west until their entrance into Europe through the Thrace region in the Balkan Peninsula [35]. They settled in the Balkans for almost 200 years [35], where the Greek impact on the Romani language was much more extensive than the Persian [1]. Accordingly, we have identified the Balkan admixture footprint in the European Roma genomes with an ancestry gradient correlated with the distance to the Balkans: from 45% in Bulgarian, Greek, and Serbian Roma; to 25% in Lithuanian, Estonian, and Iberian Roma, which is further evidence that the dispersion into Europe took place via the Balkans [15]. After subsequent migrations and dispersions across Europe, Roma groups reached Northeastern Europe (e.g. Lithuania and Estonia) and Southwest Europe (e.g. Iberian Peninsula), at the beginning of the 16th and 15th centuries, respectively [1]. Particularly in these groups, we have identified the Baltic and Iberian components besides the common Balkan component.
In relation to the demographic dynamics, we have shown that the Ne reduction of the Roma groups ceased after the start of the admixture event, which points to the settlement of Roma in Europe and the beginning of more intense assimilation politics during the seventeenth century [1]. The Ne estimates (as discussed in S3A Note) might reflect Ne changes in the Roma groups due to a population expansion or the non-Roma West Eurasian admixture. In addition, the levels of inbreeding in the Roma population are higher than in non-Roma Europeans and similar to those of South Asian groups, which could be the result of endogamy practices and/or multiple founder events.
In the Iberian Peninsula, Roma groups were well-accepted at their arrival, but at the end of the fifteenth century, with the unification of Castile and Aragon crowns, the nomad Roma groups were forced to become sedentary and suffered continuous persecutions [1]. As we remark, the present-day Iberian Roma exhibit high levels of non-Roma European ancestry, with an admixture event estimated around 1250–1600. Although GLOBETROTTER did not infer two independent admixture events as might be expected in the Iberian Roma, two different European footprints are identified: the Balkan and the non-Roma Iberian components. The detection of a single signal of admixture could be explained by a rapid expansion from the Balkans to the Iberian Peninsula, with a short time gap between the two events, or due to continuous gene flow between non-Roma Europeans and Roma groups during their migration within Europe. In fact, if the time ranges between two events are close, the ability of GLOBETROTTER to distinguish between two admixture pulses from a single pulse decreases [31].
Besides between-country heterogeneity, the present study further identifies within-country Roma substructure in the Iberian Peninsula, partially correlated with geography: two clusters are restricted to the northwestern and central part of the peninsula (IberianRoma-1 and IberianRoma-2), another cluster mainly represents Roma samples from the south (IberianRoma-3) and the last one contains all the northeastern individuals (IberianRoma-4). These groups differ both in ancestry proportions and inbreeding levels, which can be the result of different demographic patterns, as the different laws concerning the Roma people in the Iberian Peninsula were neither homogeneous nor permanent [1]. As mentioned above, IberianRoma-4 is the most differentiated cluster. It exhibits more non-Roma Iberian ancestry, the inferred date of the admixture event is the most recent one (1532–1730), and it presents the lowest inbreeding levels. Altogether this can be explained by the extensive admixture with the non-Roma Iberian population. In fact, historical records confirm that both nomadic and sedentary Roma groups in the Principality of Catalonia were highly linked and interrelated with the non-Roma society [36]. In addition, their European ancestral source contains groups from North Italy and Northwestern Europe that are absent in the rest of Iberian Roma samples, which might point to either a posterior arrival to the Iberian Peninsula after admixing with these European populations or due to the constant movement of Roma groups between Southeastern France and Northeastern Spain [36]. The Iberian group representing the most southern location, IberianRoma-3, has a genetic particularity: it has around 1% of Northwest African ancestry, which probably corresponds to the North African admixture found in the southern and western parts of the Iberian Peninsula, during the Arab expansion (711–1248) [28,29]. The fact that the North African component is only found in IberianRoma-3 samples, who also show Balkan ancestry, contributes to reject the hypothesis of a Roma migration route to Iberia from North Africa [30]. IberianRoma-1 has more non-Roma Iberian component than IberianRoma-2, although these two clusters contain samples from the same region. These results highlight that, even within Roma groups who live in the same geographic region, distinct social dynamics (ie. itinerant vs sedentary lifestyles) caused the application of different laws that might have shaped their current genetic landscape. On the contrary, some geographical patterns have probably been diluted due to the continuous movement and admixture among Roma groups, especially after 1749 with the general imprisonment of Spanish Romani, who were captured and relocated, although the effects of this event were not uniform throughout the Roma community, enabling the identification of present-day geographical patterns within Iberia Roma [37].
The present study attempts to characterize the European Roma and describe their South Asian and West Eurasian components using fine-scale methods. On the one hand, we have targeted the putative South Asian ancestry of the Roma in a specific group of Punjabi and Southeastern Indian individuals, representing a small group of proto-Roma founders with low levels of the West Eurasian ancestry. Besides, our results show that the recent West Eurasian component (around 65% of the Roma genomes) was acquired between 1270–1580, during the Roma diaspora. Specifically, we have detected and characterized the Balkan genetic footprint in all European Roma groups and the Baltic and Iberian components in the Northern and Western Roma groups, respectively, likely due to a continuous non-Roma gene flow during their dispersal through Europe. On the other hand, we have found genetic substructure within the Iberian Roma, with different groups and different levels of non-Roma admixture, as a result of the complex historical events occurred in the Peninsula. Further studies are needed to fully understand the genetic substructure of the Roma population as well as to provide new insights into the migration routes undertaken by the European Roma shaping their current genetic landscape. The use of migration group data (Balkan, Romungro and Vlax group assignation) would add an additional layer of information in both genome-wide and complete uniparental markers analyses, as it has been suggested that Roma genetic diversity might be primarily structured by migration route [11,12].
Written informed consent was obtained from all the volunteers and the present project has the corresponding IRB approval (CEIC-Parc de Salut Mar 2016/6723/I).
A linkage disequilibrium pruning was performed for the analyses that require it using PLINK 1.9 [42] with standard parameters (window size of 50 SNPs, 5 SNPs shift at each step, and an r2 threshold of 0.5) in both Dataset1 and Dataset2, leaving 192,815 and 186,374 SNPs, respectively.
In order to examine the Roma population structure in a worldwide context, a PCA was performed with SmartPCA program implemented in EIGENSOFT 4.2 package [44], and 20 runs of ADMIXTURE [45] with different random seed tests were computed for different ancestral components (k = 2 to 8). We used pong [46] to identify and visualize modal ADMIXTURE results for each value of K. Both analyses were performed in Dataset1 and Dataset2 independently.
The phasing of the Dataset1 and Dataset2 autosomal data was performed, independently, with SHAPEIT [47], using the population-averaged genetic map from the HapMap phase II [48] and the 1000G dataset as a reference panel [38].
ChromoPainter [21], based on a Hidden Markov Model (HMM) algorithm, aims to reconstruct the chromosome of each target individual (“recipient”) as a mosaic of haplotypes from the reference individuals (“donors”). This procedure is known as chromosome painting and their results can be summarized in a coancestry matrix, which shows for each recipient the total counts and length in cM of haplotypes that share a most recent common ancestor with each donor [21]. Intuitively, this matrix shows the haplotypes shared between each recipient and each donor individual. First, in order to infer the switch rate and global mutation probability (n and m parameters), ChromoPainter v2 was run in chromosomes 1, 7, 14, and 20, for 10 iterations of the expectation-maximization (EM) algorithm, painting each recipient (all individuals in the dataset) using all the donors (the rest of individuals in the dataset). For Dataset1, the inferred n and m parameter values were 251.11459 and 0.00023, respectively. Then, ChromoPainter v2 was run again in all chromosomes fixing these parameters. The total counts and lengths coancestry matrices were obtained by adding the matrices of all chromosomes.
FineSTRUCTURE [21] is an algorithm that infers the clustering of the samples considering the information in the ChromoPainter coancestry matrix. Using this clustering, it is possible to group the samples into genetically homogeneous clusters. First, fineSTRUCTURE was run for 2 million Markov Chain Monte Carlo (MCMC) iterations, sampling values every 10,000 iterations after 1 million “burn-in” iterations [49]. Then, fineSTRUCTURE was run again to perform 100,000 additional hill-climbing moves from the MCMC sample with the highest posterior probability to get the final cluster membership in a dendrogram format. This procedure was repeated three times and after comparing the consistency of the three dendrograms, we classified the 952 individuals from Dataset1 into 63 clusters, where the European Roma branch contains ten Roma clusters. The rest of Roma samples outside this clade (e.g. Welsh Roma) cluster with other European non-Roma samples, due to high levels of non-Roma European ancestry as described previously [15], thus they were removed for further analyses.
In order to estimate the copying profiles (i.e. average proportion of ancestry attributed to each donor group), ChromoPainter v2 was run in a different mode than described above: haplotype sharing was inferred between groups rather than independent individuals [49]. For this analysis all the individuals were grouped in the genetic clusters established according to fineSTRUCTURE where the ten European Romani clusters were settled as recipients and the rest of clusters as donors. In addition, we calculated the TVD metric as described in [49], which measures the differences between a pair of clusters (A, B) with copying vectors a and b and it can be calculated as:
TVD(A,B)=0.5×∑i=1n(ai‐bi)
(1)
where n is the total number of donor groups. As suggested by Leslie S. et al [49], for each pair of clusters, individuals were randomly reassigned in one of the two clusters, and the new copying vectors a’ and b’, and the TVD values were recalculated for 1,000 permutations. P-values correspond to the proportion of permutations where TVD(A’,B’) > TVD(A,B) and reflect the strength of differences between the inferred pair of clusters. Corrected p-values were obtained after Bonferroni multiple test correction.
For Dataset2, the above procedures (ChromoPainter, fineSTRUCTURE, and TVD metric calculations) were also performed using the same approach, and the ChromoPainter switch rate and global mutation probability inferred using Dataset2 were 259.85269 and 0.00016, respectively. The fineSTRUCTURE dendrogram of Dataset2 was used to classify the 1,332 individuals into 88 clusters, where four of them belonged to Iberian Roma clusters. One Iberian Romani sample from Madrid (G32) was excluded, as it clustered with Iberian non-Roma samples, suggesting an extensive non-Roma ancestry.
We checked whether the ChromoPainter algorithm is able to correctly distinguish between the two sources of West Eurasian ancestry in the Roma population, in order to avoid misleading results when inferring the admixture sources: the AWE component (pre-exodus from India) as South Asian ancestry, and the recent West Eurasian admixture (post-exodus from India) as West Eurasian (see S1 Note, S5 and S6 Figs, S4 Table).
GLOBETROTTER [31] is a method designed to characterize and date admixture events between source populations (which are a composite of surrogate populations) that have shaped the genetic history of a target population. The dating estimation is based on the principle that the size of the haplotypes decreases over successive generations due to recombination. GLOBETROTTER algorithm uses the haplotype sharing results from ChromoPainter considering donor and recipients as groups of individuals. GLOBETROTTER was run for each of the ten Roma clusters in the European Roma branch from Dataset1 using ten painting samples per individual from ChromoPainter and the coancestry matrix of the genome-wide length of haplotype sharing. In order to identify the admixture events between source populations that have shaped the genetic history of European Roma, the surrogate populations included were all the European, Middle Eastern, Caucasian, and Asian clusters. The sample size of these clusters was normalized to a maximum of 21, which corresponds to the third quartile of all clusters sample sizes. First, in order to estimate p-values for evidence of admixture, GLOBETROTTER was run using the NULL procedure (standardize the coancestry curves by a “NULL” individual), with 100 bootstrap resamples. Then, GLOBETROTTER was run using the non-NULL inference to characterize the admixture events. These two GLOBETROTTER runs were checked for consistency. To estimate admixture date CIs, 100 bootstrap iterations were performed and a generation time of 25 years was considered.
The same procedure was used to infer admixture events that have shaped the genetic history of the Iberian Roma from Dataset2. Thus, the target populations were the four Iberian Roma clusters, and the surrogate populations were all the European, North African, Middle Eastern, Caucasian, and Asian clusters. Spatial distributions of the major source proportions in each Iberian Roma cluster were computed in R using the kriging model in the package fields [50].
When describing the admixture sources that have shaped the Roma today, we use the term “non-Roma populations” to facilitate the understanding, although the admixture events occurred with “non-proto-Roma” groups.
To further characterize the South Asian component of the Roma, we have estimated the proportion of WE ancestry in the South Asian clusters (ANI component) using f4 ratio estimation implemented in ADMIXTOOLS [51] as: α=f4(YRI,Basque;India,Onge)f4(YRI,Basque;Georgians,Onge) [20], computing standard error with a Block Jackknife with a block size of 5cM. For this analysis, we have included Onge samples from [52]. We have calculated the ANI proportion in the Roma groups from the relative contribution (inferred by GLOBETROTTER) of each South Asian cluster.
The X chromosome from Dataset1 was phased using the same parameters as the autosomes, as described previously [39]; and ChromoPainter v2 [21] was run with all European Roma samples as recipients and the non-Roma European, Middle East, Caucasus, and South Asian clusters as donors using only the X chromosome. Then, the ancestry profiles of the X chromosome were estimated for each individual in each Roma cluster by applying SOURCEFIND, a new Bayesian model-based approach [53], with 200,000 MCMC samples, sampling every 1,000 iterations. Once we obtained the estimated proportions of each donor cluster in the X chromosome of the Roma from the MCM sample with the highest posterior probability, we summed them to get the European, MiddleEast-Caucasus, and South Asian proportions that contribute to the Roma ancestry. The same procedure was applied to the autosomes. To test for sex-biased gene flow in the Roma samples, we obtained the ancestry differences per individual by subtracting the European, MiddleEast-Caucasus, and South Asian proportions between the autosomes and the X chromosome grouping all Roma individuals together. A Wilcoxon signed-rank test across individuals between the autosomes and the X chromosome was applied to obtain a p-value of the differences, with Bonferroni correction. In addition, we tested the European ancestry differences for each Roma cluster. To avoid possible biases due to different number of SNPs, we not only compared the whole set of autosomes against the X chromosome, but also each autosome separately against the X chromosome (see S2 Note, S7 Fig, S6 Table).
ROH analyses were performed to assess the inbreeding levels among the Roma groups. ROH segments were identified using PLINK 1.9 [42], considering ROH with at least 50 SNPs of length 500 kb and a maximum gap between a pair of consecutive SNPs of 100 kb, as these parameters account for locally low SNP density in SNP arrays [54]. For comparative purposes, Dataset1 analysis included two clusters with putative higher levels of inbreeding, from Europe (Basque) and from South Asia (Kalash); and two with low levels, from Europe (Balkan-2) and from South Asia (Punjabi-1). For Dataset2, we included Basque and Kalash clusters, and SW-Europe2 and Punjabi-1.
Changes in Ne through generations were estimated for the Roma groups from IBD segments. The Roma samples belong to an admixed population, and thus, in order to detect IBD segments, we applied RefinedIBD [55], a haplotype-based method, with default parameters; and merged the segments with gaps to avoid the underestimation of segment lengths [56]. Then, using these IBD segments and the HapMap GRCh37 genetic map [48], IBDNe [57] was run with default parameters to infer Ne estimates with 95% CIs at each generation, assuming 25 years per generation. Although these methods were first designed to deal with sequence data, this approach applied to genome-wide array data has a high confidence in recent periods (from present to around 50 generations ago) [57]. For Dataset1, the analysis was performed on the ten European Roma clusters and the reference cluster NorthItaly. For Dataset2, it was performed on the four Iberian Roma clusters and SW-Europe2 as reference. In addition, we checked whether the Ne estimations correlate with the admixture event detected with GLOBETROTTER in each Roma group, regarding both the proportion of West Eurasian source and the admixture dates (see S3A Note, S10 Fig).
Finally, we estimated the Ne of the ancestral Roma populations, following the same procedure as in Browning et al. [56], to compare the ancestry-specific Ne of the European, MiddleEast-Caucasian and South Asian sources prior to the admixture, grouping all Roma samples together (as we assume that the Roma groups split after the arrival to Europe). First, we performed a local ancestry inference (LAI) with RFMix v1.5.4 [58], using as sources the donor populations identified in the GLOBETROTTER analysis, grouped in three categories: Europe, MiddleEast-Caucasus and South Asia. Although Europe and MiddleEast-Caucasus ancestries are similar, Xue et al. [59] showed that RFMix is able to accurately infer local ancestry segments, using balanced reference panels with key features comparable to our study (e.g. SNP array data and admixture sources). After checking the correlation between the ancestry proportions of RFMix and GLOBETROTTER (see S3B Note, S19 Fig), we followed Browning et al. [56] pipeline: rephasing of the RFMix output, filtering of the IBD segments by ancestry and calculation of the ancestry-adjustment number of pairs of sampled haplotypes. Then, IBDNe [57] was run with default parameters to infer ancestry-specific Ne estimates with 95% CIs at each generation, assuming 25 years per generation. Finally, we calculated the fold-change of the Ne CIs between the three ancestral populations, one generation before the start of the admixture (i.e. lowest CI inferred from GLOBETROTTER) and compared it with the fold-change between the current ancestry proportions inferred with GLOBETROTTER.
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10.1371/journal.pgen.1007842 | RNA-on-X 1 and 2 in Drosophila melanogaster fulfill separate functions in dosage compensation | In Drosophila melanogaster, the male-specific lethal (MSL) complex plays a key role in dosage compensation by stimulating expression of male X-chromosome genes. It consists of MSL proteins and two long noncoding RNAs, roX1 and roX2, that are required for spreading of the complex on the chromosome and are redundant in the sense that loss of either does not affect male viability. However, despite rapid evolution, both roX species are present in diverse Drosophilidae species, raising doubts about their full functional redundancy. Thus, we have investigated consequences of deleting roX1 and/or roX2 to probe their specific roles and redundancies in D. melanogaster. We have created a new mutant allele of roX2 and show that roX1 and roX2 have partly separable functions in dosage compensation. In larvae, roX1 is the most abundant variant and the only variant present in the MSL complex when the complex is transmitted (physically associated with the X-chromosome) in mitosis. Loss of roX1 results in reduced expression of the genes on the X-chromosome, while loss of roX2 leads to MSL-independent upregulation of genes with male-biased testis-specific transcription. In roX1 roX2 mutant, gene expression is strongly reduced in a manner that is not related to proximity to high-affinity sites. Our results suggest that high tolerance of mis-expression of the X-chromosome has evolved. We propose that this may be a common property of sex-chromosomes, that dosage compensation is a stochastic process and its precision for each individual gene is regulated by the density of high-affinity sites in the locus.
| In humans and fruit flies, females and males have different sets of sex chromosomes. This causes gene dosage differences that must be compensated for by adjusting the expression of most genes located on the X-chromosome. Long non-coding RNAs are central in this compensation and in fruit flies this is mediated by two non-coding RNAs, roX1 and roX2 which together with five proteins form the male-specific lethal complex. The complex recognizes and upregulates gene transcription on the male X-chromosome. While non-coding RNAs are are engaged in numerous biological processes and critical for compensation their precise functions remain elusive. To understand the function of long non-coding RNAs we analysed the expression of all genes in roX1, roX2 and roX1 roX2 mutants to explore the roles of long non-coding RNAs. These mutants have different impacts on the genome-wide expression. Our results also suggest that the X-chromosome is highly tolerant to mis-expression and we speculate that this tolerance evolved in parallel with compensation mechanisms and may be a common property of sex-chromosomes. We propose that dosage compensation is a stochastic process that depends on the distribution of specific binding sites which will be selected for and optimized depending on the genes’ individual expression levels.
| In eukaryotic genomes several long non-coding RNAs (lncRNAs) are associated with chromatin and involved in gene expression regulation, but the mechanisms involved are largely unknown. In both mammals and fruit flies, they are required to specifically identify and mark X-chromosomes for dosage compensation, a mechanism that helps maintain balanced expression of the genome. The evolution of sex-chromosomes, for example the X and Y chromosome pairs found in mammals and flies, leads to between-gender differences in gene dosage. Although some genes located on the X-chromosome are expressed in a sex-specific mode, equal expression of most of the genes in males and females is required [1, 2]. Thus, gradual degeneration of the proto-Y chromosome causes an increasing requirement to equalize gene expression between a single X in males and two X-chromosomes in females. X-chromosome expression must also be balanced with expression of the two sets of autosomal chromosomes. Several fundamentally different mechanisms that solve the gene dosage problem and provide such balance have evolved [1–4]. In mammals, one of the pair of X-chromosomes in females is largely silenced through random X-chromosome inactivation, a mechanism that involves at least three lncRNAs [5, 6]. One, the long noncoding Xist RNA, plays a key role in marking one of the X-chromosomes and recruiting Polycomb repressive complex 2, thereby mediating its inactivation by histone H3 lysine 27 methylation [7].
In fruit flies, the gene dosage problem has been solved in an apparently opposite way, as X-chromosomal gene expression is increased by approximately a factor of two in males [2, 3]. This increase is mediated by a combination of general buffering effects that act on all monosomic regions [8–10] and the specific targeting and stimulation of the male X-chromosome by the male-specific lethal (MSL) complex. The MSL complex consists of at least five protein components (MSL1, MSL2, MSL3, MLE, and MOF) and two lncRNAs, roX1 and roX2 [3, 11, 12]. Although the mammalian and fly compensatory systems respectively inactivate and activate chromosomes in members of different sexes, both rely on lncRNA for correct targeting. Results of UV-mediated crosslinking analyses suggest that only one species of roX is present per MSL complex in Drosophila [13]. Furthermore, inclusion of a roX species is essential for maintaining correct targeting of the MSL complex to the X-chromosome [14]. Upregulation of the male X-chromosome is considered to be partly due to enrichment of histone 4 lysine 16 acetylation (H4K16ac), mediated by the acetyltransferase MOF. The increased expression of X-linked genes in male flies is generally accepted, but the mechanisms involved have not been elucidated. Proposed mechanisms, which are hotly debated [15–17], include increased transcriptional initiation [18, 19], increased elongation [20, 21] or an inverse dosage effect [22].
The roX1 and roX2 RNAs differ in sequence and size (3.7 kb versus 0.6 kb) but can still individually support assembly of a functional MSL complex. In an early study of roX1 and roX2, a short homologous stretch was detected [23], which subsequently led to the definition of conserved regions shared by the two RNAs named roX-boxes, located in their 3’ ends [24–26]. Confirmatory genetic studies have shown that expression of six tandem repeats of a 72-bp stem loop region from roX2 is sufficient for mediation of the MSL complex’s X-chromosome binding and initiation of H4-Lys16 acetylation in the absence of endogenous roX RNA [24].
The roX RNAs are not maternally deposited and transcription of roX1 is initiated in both male and female embryos at the beginning of the blastoderm stage [27]. Females subsequently lose roX1 expression and a few hours after roX1 is first detected roX2 appears, but only in males [28].
Despite differences in size, sequence and initial expression, the two roX RNAs are functionally redundant in the sense that mutations of either roX1 or roX2 alone do not affect male viability and they both co-localize with the MSL complex along the male X-chromosome [23, 27]. In contrast, double (roX1 roX2) mutations, which cause a systematic redistribution of the MSL complex, are lethal for most males [29–32]. It should be noted that in roX1 roX2 mutant the reduction in MSL complex abundance on the male X-chromosome is dramatic; more pronounced than the reductions observed in mle or mof mutants [14]. Nevertheless, some roX1 roX2 mutant males may survive, while mle, msl1, msl2, msl3 or mof loss-of-function mutations are completely male-lethal [29–31]. Whether other RNA species can fulfill the role of roX RNAs in these instances or the MSL complex can function without RNA species remains to be clarified. Furthermore, the degree of lethality in roX1 roX2 mutant is highly sensitive to several modifying factors, such as expression levels of MSL1 and MSL2 [33], expression of hairpin RNAs [34, 35], presence and parental source of the Y-chromosome [31], and a functional siRNA pathway [36]. The observations that roX1 roX2 mutations are not completely lethal and there are several modifying factors suggest an additional layer of redundancy in the role of lncRNAs in chromosome-specific targeting.
To further our understanding of the role of lncRNAs (particularly specific roles and redundancies of roX1 and roX2) in chromosome-specific regulation we here provide a comprehensive expression analysis of roX1, roX2 and roX1 roX2 mutants to explore the redundancy as well as the differences between the two lncRNA species. We show that roX1 and roX2 have partly separable functions in dosage compensation. In larvae, roX1 is the most abundant variant and the only variant present in the MSL complex when the complex is transmitted (physically associated with the X-chromosome) in mitosis. Loss of roX1 results in reduced expression of the genes on the X-chromosome, while loss of roX2 leads to MSL-independent upregulation of genes with male-biased testis-specific transcription. In roX1 roX2 mutant, gene expression is strongly reduced in a manner that is not related to proximity to high-affinity sites.
Initial evidence on localization of roX RNAs originates from immunostaining experiments on polytene chromosomes. Indeed, both roX1 and roX2 are expressed in salivary gland cells and co-localize on polytene chromosomes close to perfectly (Fig 1A). Overall, the intensities of roX1 and roX2 RNA in situ hybridization signals correlate closely, and the localization patterns along the X-chromosome are nearly identical, except at cytological band 10C, where the roX2 signal is notably stronger than the roX1 signal. As cytological band 10C is the location of the roX2 gene, this implies that roX2 is favored in MSL complexes targeting the roX2 region rather than roX1. At the onset of dosage compensation in the early male embryo, expression of roX is differentially regulated [27, 28]. A burst of roX1 transcription in the blastoderm stage is the initial step preceding assembly of the MSL complex. This occurs independently of roX2 expression, which does not begin until 2 h after the MSL complex is first detectable on the X-chromosome. In Schneider 2 cells, roX2 is expressed more strongly than roX1 and is detectable by FISH in 95% of them, while roX1 signals, although bright, are visible only in a small fraction of the cells [37]. We therefore asked whether roX1 and roX2 are expressed in different Schneider 2 cells. Simultaneous detection of both roX RNAs showed that the rare cells that express roX1 also express roX2 (Fig 1B). Therefore, in contrast to salivary glands and embryos, only a small fraction of S2 cells express both roX RNAs and all those expressing roX2 also express roX1.
To investigate roX localization and targeting in cells undergoing mitosis we subjected neuroblasts of male larvae and 5–6 h embryos to RNA in situ hybridization analysis. While both roX1 and roX2 were clearly visualized in the “X-territory” in most interphase cells, only roX1 signals were detected on the distal part of the metaphase X-chromosome (Fig 1C and 1D and S1A Fig). We also observed targeting of MLE to the distal part of the mitotic chromosome (S1A Fig), and such targeting by MSL2 and MSL3 has been previously shown [38, 39]. We conclude that expression and/or targeting of roX RNAs is differentially regulated depending on the cell type and cell cycle stage, and roX1 RNA is the dominant roX RNA bound to the X-chromosome as part of MSL complexes during mitosis.
The roX2 mutant allele Df(1)52, the most commonly used roX2 loss-of-function allele, carries a deletion spanning a gene-dense region, including roX2 [30]. Removal of this region is lethal, so it is compensated with a rescuing cosmid, frequently P{w+ 4Δ4.3}. Nevertheless, roX2 is not the only gene affected by the widely used combination Df(1)52 P{w+ 4Δ4.3}, and genes carrying it differ considerably in genetic background from roX1 and wild type flies. In a previous microarray analysis, potential background problems were solved by comparing roX1 roX2 mutant flies with roX2 flies as controls [40]. Here, to analyze differences in expression profiles of single (roX1 and roX2) mutants and double (roX1 roX2) roX mutants we decided to create a deletion mutant of roX2 without affecting adjacent genes. Such a mutant would permit analysis of single and double mutants using a roX1+ roX2+ strain as a control and facilitate various other genetic analyses. To create the desired mutant allele, we used the CRISPR-Cas9 technique to induce two double-strand breaks simultaneously in the roX2 locus and recovered four roX2 deletion mutant strains (Fig 2A and S2 Fig). All deletions in these mutants span the longest exon of roX2, including two conserved roX-boxes. As expected, all four mutant strains were viable and fertile. Further analysis was performed with the roX29-4 allele, hereafter designated as the roX2 mutant. This deletion does not uncover the intergenic regions flanking roX2 and therefore it is less likely to affect the flanking genes nod and CG11650. The breakpoints are located almost precisely at the sites of double-strand breaks, deleting the region from 7 bp upstream of the annotated transcription start site to 60 bp upstream of the annotated gene end. RNA in situ hybridization confirmed the absence of roX2 RNA in salivary glands (Fig 2B), while the roX1 signal intensity and binding pattern were apparently unchanged in the roX2 mutant. In larval brain of roX1 mutants the roX2 RNA was still observed in the X-territory of interphase cells, however it was not detected on the metaphase X-chromosome (S1B Fig). We recombined the newly made roX29-4 allele with the roX1ex6 mutant allele [30] to obtain the roX1ex6 roX29-4 double mutant flies, hereafter roX1 roX2 mutant. As observed with other mutant alleles, removal of both roX RNAs resulted in high male-specific lethality beginning at the third instar larvae stage and continuing through pupal development, although a small number of adult males hatched.
The next experiments were designed to investigate the specific roles (if any) of the roX RNA species in dosage compensation and assess potential additional functions in regulation of gene expression. For this, we sequenced (using an Illumina platform) polyadenylated RNA from wildtype, roX1 mutant, roX2 mutant and roX1 roX2 mutant 1st instar male larvae. This developmental stage was chosen to minimize indirect effects of dosage compensation failure in the roX1 roX2 mutant, as roX1ex6 roX29-4 1st instar larvae are healthier than those of later stages. The four genotypes compared are not isogenic, however, the outcrosses as described in Material and methods ensure that the entire autosomal complement is heterozygous in all genotypes and half of it will have identical origin. Still, we cannot fully exclude that remaining differences in genetic background could be a contributing factor to the observed changes in expression for some genes.
In wildtype larvae, roX1 RNA was approximately ten times more abundant than in roX2 mutant larvae (Fig 2C). Notably, we observed increases in abundance of both roX RNAs in response to absence of the other, but not establishment of wildtype roX levels, in the single mutants. More specifically, we recorded 89% reductions in roX RNA levels in the roX1 mutant, while removal of roX2 RNA (which normally constitutes only 7% of the total roX RNA complement) resulted in a 45% increase in roX1 RNA abundance on average. Therefore, the single mutants differ considerably in levels of roX RNA. Moreover, although viability and fitness are not affected in either of the single mutants, the efficiency of dosage compensation is significantly compromised in the roX1 mutant. The average log2 expression ratio of the X-chromosome in this mutant was -0.13, corresponding to an 8.6% reduction in average expression of X-chromosome genes relative to genes on the four major autosomes. In the roX2 mutant, the average expression ratio for X-chromosome genes was lower than that of autosomal genes, but density distributions for X and autosomal expression ratios were very similar (Fig 3A and 3B and S3 Fig). A Mann-Whitney U-test confirmed that the two populations cannot be differentiated in terms of these expression parameters, so global X-chromosome transcription is not significantly affected in the roX2 mutant. In conclusion, the roX2 mutant shows no lack of compensation and has roX levels comparable or even higher than wildtype. Thus, it is not clear whether the total amount of roX or the type of roX is responsible for the observed reduction in average expression of X-chromosome genes in the roX1 mutant. The results also implies that the observed increase in levels of roX1 RNA in the roX2 mutant (Fig 2C) does not lead to hyper-activation of the X-chromosome but is enough to maintain proper X-chromosome expression.
We and others have previously shown that in absence of roX RNAs, the MSL-complex become less abundant on the X-chromosome and relocated to heterochromatic regions including the 4th chromosome [14, 30, 37, 40]. In fact, the fourth chromosome is related to the X-chromosome and evolutionary studies have shown that the 4th chromosome was ancestrally an X-chromosome that reverted to an autosome [41, 42]. Importantly, upon analysis of the 4th chromosome we detected weak but significant downregulation of genes on the fourth chromosome as a specific consequence of roX2 deletion (Fig 3A), but not the previously reported downregulation of the fourth chromosome in the roX1 roX2 mutant flies [43].
As expected, strong downregulation of X-linked genes occurred in the roX1 roX2 mutant (Fig 3C). However, it was more severe (a 33% reduction relative to wildtype levels) than previously reported in microarray studies [40], and following RNAi depletion of MSL proteins [9, 43–45]. The distribution plot shows that the vast majority of genes were downregulated in the roX1 roX2 mutant and the entire distribution of X-chromosomal gene expression was shifted approximately -0.56 on log2 scale relative to the expression of genes on the four major autosomal arms.
The expression ratios of X-linked genes varied widely, especially in the roX1 roX2 mutant (Fig 3C). It has been proposed that MSL complexes are assembled at the sites of roX RNA transcription, then spread to the neighboring chromatin in cis direction, as well as diffusely, gradually binding to more distant loci. In addition, our in situ hybridization results indicate enrichment of roX2 RNA at cytological region 10C. We therefore tested if dosage compensation has a distinct spatial pattern along the X-chromosome. We observed some clustering of genes related to sensitivity to roX1 or roX2 RNAs, but it appeared to be randomly distributed spatially, except for a gradual decrease in expression of genes in the proximal X-chromosome region in the roX1 mutant, and the 10C region in the roX2 mutant (Fig 4A).
A number of studies have estimated that the MSL complex binds specifically to roughly 250 chromatin entry sites, high-affinity sites (HAS) or Pion-X sites. Since roX RNAs are important for the spreading of the MSL complex from these high-affinity sites we asked whether the extent of genes’ differential expression in roX mutants correlates with their distances from these sites. Dot plots of genes’ expression ratios against their distances from HAS or Pion-X sites showed weak trends, but were difficult to interpret due to high variation (S4 Fig). Thus, for more informative visualization we grouped the genes into bins with increasing distance from HAS (Fig 4B). In roX1 mutant, the average expression ratio was not significantly affected by the distance from HAS. This was also true for genes located within approximately 30 kb from HAS in roX2 and roX1 roX2 mutant. However, more remote genes had higher average expression ratios in roX2 and roX1 roX2 mutant, and thus are less suppressed in the double mutant and even upregulated in the roX2 mutant. On polytene chromosomes in the roX1 roX2 mutant we still observed MSL targeting on the X-chromosome, but only at HAS [14]. This might suggest that genes close to HAS would retain dosage compensation function also in the absence of roX RNAs. On the contrary, our results show that genes within approximately 30 kb from HAS are strongly and equally affected while genes more distal to HAS are less sensitive to the absence of roX and absence of bound MSL complex.
We next asked if the roX-dependent dosage compensation depends on the binding strength of the MSL complex, using publicly available chromatin immunoprecipitation data on MSL1, MOF and MSL3 [46] to correlate with our differential expression data (Fig 5A–5C and S5 Fig). All X-chromosome genes were ranked in order of increasing MSL complex enrichment and divided into five bins with equal numbers of genes. Thus, bin 1 included unbound and weakly bound genes, while bin 5 included genes highly enriched in MSL proteins. We found that genes in bins 1 and 2 responded more variably to removal of either or both roX RNAs, a pattern that is probably related to their low expression levels (Fig 5H). In the single roX mutants, expression ratios did not correlate with enrichment of MSL proteins (Fig 5A and 5B and S5A, S5B, S5D and S5E Fig), indicating that MSL complex-regulated genes uniformly respond to the absence of one roX RNA, regardless of the enrichment levels in wildtype flies. Strikingly, strong and significant upregulation of genes classified as non- or weakly MSL complex-binding was detected in the roX2 mutant, similarly to genes located far from HAS (Fig 5B and S5B and S5E Fig). In roX1 roX2 mutant, these weakly MSL complex-binding genes are still suppressed, but much less than strongly binding genes. Since the MSL complex is still enriched at HAS in the absence of roX it is surprising that dosage compensation by roX RNA-free MSL complexes has low efficiency even for genes with the highest MSL enrichment. The genes highly enriched in MSL1 and MSL3 (bin 5) were slightly less down-regulated, but this trend was not seen with MOF enrichment bins (S5F Fig).
Since genes with low MSL complex-binding levels are less suppressed than others in the roX1 roX2 mutant, and upregulated in the roX2 mutant, we asked whether dosage compensation in the absence of roX depends on genes’ expression level. For this, we divided the X-chromosome genes into 12 equally sized bins according to their expression levels. In accordance with observations regarding genes that weakly bind the MSL complex, we observed upregulation of weakly expressed genes in the roX2 mutant and less pronounced reduction in their expression in the roX1 roX2 mutant (Fig 5D–5F).
High-affinity sites are defined as those that retain incomplete MSL complexes in msl3, mle or mof mutants [45, 47–51], and it has been suggested that MSL complex-binding is directed by hierarchical affinities of target sites [49, 50]. In the roX1 roX2 mutant we observed more pronounced reductions in MSL complex abundance on the male X-chromosome than those reported in msl3, mle or mof mutants, but the remaining MSL targets in the roX1 roX2 mutant were highly reminiscent of those described in msl3, mle and mof mutants [14, 30]. We observed reduced expression of strongly MSL-binding genes in the roX1 roX2 mutant, which is intriguing as these genes are assumed to retain the MSL complex [14]. Thus, to test the suggestion, we explored correlations between the MSL binding bins and 263 high affinity sites defined by targeting in mle, mof or msl3 mutants, or following their depletion [45, 51, 52]. In parallel we analyzed the 208 peaks we previously identified in the absence of roX1 roX2 [14]. The previously defined 208 peaks in the roX1 roX2 mutant overlap 405 genes on the X-chromosome, 309 of which are among the 328 genes in bin 5 (Fig 5G). We conclude that the 208 MSL peaks defined in the roX1 roX2 mutant correspond more strongly with genes in the highest MSL binding class than the previously defined HAS do (Fig 5G). Intriguingly, expression of X chromosomal genes also correlates with MSL1 binding enrichment (Fig 5H), and thus overlap with HAS. This suggests that the distribution of MRE motifs and consequently MSL complex-binding is governed by gene expression in a manner that promotes adequate dosage compensation in males.
In higher eukaryotes replication timing is connected to the chromatin landscape and transcriptional control [53]. Generally, early replicating regions are associated with active transcription [54–56] whereas late replicating regions are associated with inactive regions and heterochromatin [57]. Genome-wide studies on cultured Drosophila cells have revealed dependency of male-specific early replication of the X-chromosome on the MSL complex [56, 58]. We therefore asked whether X-chromosomal or genome-wide sensitivity to a specific roX mutant condition correlate with replication timing. Using available data on replication timing from analyses of S2 and DmBG3 (male) and Kc167 (female) cells [58] we classified the genes as early or late replicating. Based on our RNA-seq data we then calculated expression ratios for genes grouped by their chromosome location (autosomal or X-chromosomal) and their replication timing as determined in the three cell types. Conceivably, early and late X chromosomal replication domains (determined from analyses of S2 and DmBG3 male cell cultures) are respectively associated with genes bound and unbound by the MSL complex, and thus are affected in similar manners by roX mutations (Fig 6 and S6 Fig). In female Kc167 cells the relation between sensitivity to roX and replication timing is generally similar to that observed in male cell cultures. However, in Kc167 cells the X-chromosome has a slightly different pattern of replication domains, which shifts the average expression ratio (Fig 6 and S6 Fig). In particular, the distribution of distinctively upregulated X chromosomal genes in the roX2 mutant only corresponds with the distribution of late-replication regions in male cells. Notably, in larval neuroblasts and embryonic cells (Fig 1C and 1D), we only detected roX1 RNA (no roX2 RNA) on mitotic X-chromosomes, suggesting that roX1-containing MSL complexes mediate dosage compensation in the G1 phase, when replication timing is established [59]. It is tempting to speculate that selective transmission of roX1-containing MSL complexes through mitosis enables the cells to quickly and efficiently establish the correct chromatin state and hence maintain correct replication timing.
Transcription upregulation of the X-chromosome in the roX2 mutant is associated with genes classified as having low expression levels, late replication and weak MSL complex-binding. We asked if this observed upregulation is caused by mis-targeting of MSL complexes associated with excess of roX1, i.e., if the upregulated genes are enriched in MSL complexes due to increases in roX1 levels and/or loss of roX2. To test this possibility, we assessed relative enrichments of MSL1 and H4K16ac on the upregulated genes by ChIP-qPCR analyses. In the roX2 mutant, none of the eight genes we tested became targeted by MSL1 or enriched in H4K16ac at a comparable level to known MSL target genes (S7 Fig). In contrast, enrichment levels were similar to those detected on the autosomal control genes RpS3 and RpL32. We therefore conclude that stimulation of weakly expressed X chromosomal genes in the roX2 mutant is not mediated by induced targeting of the MSL complex.
Further analysis of upregulated genes in the roX2 mutant showed that they included not only X chromosomal genes but also late-replicating autosomal genes. This, together with the absence of MSL complex-enrichment on these genes, indicates that the upregulation is a roX2-specific effect and at least partly separable from MSL complex-mediated gene regulation. Intriguingly, we discovered that these upregulated genes in the roX2 mutant strain include high proportions of genes (both X-chromosomal and autosomal) with male-biased testis-specific transcription (Fig 7). Whether roX2 has a specific role in transcriptional regulation of genes involved in spermatogenesis or the observed phenomenon is an indirect consequence of roX2 mutation is an intriguing question that warrants further investigation.
The dosage compensation machinery involving roX1 and roX2 RNAs provides a valuable model system for studying the evolution of lncRNA-genome interactions, chromosome-specific targeting and gene redundancy. LncRNAs differ from protein coding genes and are often less conserved at the level of primary sequence, as expected due to their lack of protein-coding restrictions. Like those encoding other lncRNAs, rapid evolution, i.e., low conservation of the primary sequences of roX genes has complicated comparative studies [24, 60]. Despite their differences in length and primary sequences, roX1 and roX2 have also been considered functionally redundant in Drosophila melanogaster. However, remarkably considering their rapid evolution and apparent redundancy, orthologs for both roX1 and roX2 have been found in all of 26 species within the Drosophila genus with available whole genome assemblies [60]. Models that explain evolutionarily stable redundancy have been proposed [61] suggesting that the presence of both roX1 and roX2 in these diverged species may be attributable to differences in targets, affinities and/or efficiency or additional functions.
On polytene chromosomes, binding patterns of roX1 and roX2 are more or less indistinguishable, except in region 10C where roX2 is almost exclusively present. In the roX2 mutant, genes located in the 10C bin are on average downregulated, but similar downregulation of genes in many other bins is observed, so the effect cannot be directly attributed to loss of roX2. In wildtype 1st instar larvae, levels of roX1 RNA are much higher than levels of roX2 RNA. Interestingly, in roX1 mutant larvae the absolute amount of roX2 RNA increases, but only to ~10% of wildtype levels of total roX RNA. This appears sufficient to avoid lethality, but still causes a significant decrease in X-chromosome expression. However, despite the huge difference in amounts, not only in number but even more considering the size of the two roX RNAs, the staining intensities of roX RNA on roX1 mutant and wildtype polytene chromosomes seem to be roughly equal. On mitotic chromosomes we only observed roX1 RNA in the MSL complexes bound to the distal X-chromosome and this binding is not redundant. This indicates that just after cell division roX1 RNA will be the dominating variant in assembled MSL complexes. Taken together, our results suggest that roX2 RNA has higher affinity than roX1 RNA for inclusion in MSL complexes. Moreover, varying amounts of the two species with different affinities at given cell cycle stages may support proper transmission, spreading of assembled MSL complexes and maintenance of appropriate levels of the complexes.
It should be noted that some male roX1 roX2 mutant escaped, so loss of roX is not completely male-lethal, unlike loss of mle, msl1, msl2, msl3 or mof [29–31, 62]. The complete male lethality in these mutants is attributed to reductions in dosage compensation that have been measured in several studies and observed not only in msl mutants but also following RNAi-mediated depletion of MSL proteins [9, 43–45]. Notably, the average reduction of X-chromosome expression, relative to wildtype levels, calculated in these cases has varied from ca. 20 to 30%; substantially less than the 35% reduction we observed in the roX1 roX2 mutant. Some of the reported differences may be due to use of different techniques and bioinformatics procedures (including use of different cut-offs for expression and developmental stages). However, the reasons why some males can survive the very dramatic imbalance observed in expression of a large portion of the genome are unclear. Furthermore, the reduction in expression of X-chromosome genes observed in the roX1 mutant is not accompanied by any reported phenotypic changes, indicating that D. melanogaster has high intrinsic ability to cope with significant imbalances in X-chromosome expression. We speculate that in parallel with a compensation mechanism that addresses dosage imbalances the fly has evolved a high degree of tolerance to mis-expression of the X-chromosome.
The 4th chromosome in D. melanogaster (the Muller F-element) is related to the X-chromosome. Evolutionary studies have shown that sex chromosomes do not always represent terminal stages in evolution—in fact, the 4th chromosome was ancestrally an X-chromosome that reverted to an autosome [41, 42]. Moreover, the fly shows high and unusual tolerance to dosage differences [63] and mis-expression [8, 64–66] of the 4th chromosome (although much smaller than the tolerance to those of the X-chromosome). These observations suggest that tolerance of mis-expression is a common outcome in the evolution of sex-chromosomes and this property has been retained with respect to the 4th chromosome, even after its reversion to an autosome. We propose that high tolerance of mis-expression in the absence of full functional dosage compensation may be selected for during evolution of sex-chromosomes. This is because gradual degeneration of the proto-Y chromosome will be accompanied by an increasing requirement to equalize gene expression between a single X- (in males) and two X-chromosomes (in females), but changes in genomic location of highly sensitive genes will be favored during periods of incomplete (or shifting) dosage compensation. On transcript level, responses to reductions in dosages of X-chromosome genes have been found to be similar to those of autosomal genes [67]. Thus, potential mechanisms for the higher tolerance are post-transcriptional compensatory mechanisms or selective alterations in gene composition (changes in genomic locations), similar to those proposed for the observed demasculinization of the Drosophila X-chromosome [68].
Prompted by the strong relationship between orchestration of the X- and 4th chromosomes by the MSL complex and POF system [2, 14, 69–71], respectively, we also measured effects of roX suppression on chromosome 4 expression in roX mutants. We observed weak but significant reduction of expression in the roX2 mutant, but the cause of this reduction remains elusive. In roX2 mutant we also observed transcriptional upregulation of X-chromosome genes classified as having low expression levels, late replication and weak MSL complex-binding. The loss of roX2 resulting in MSL complexes only including roX1 RNA might alter the spreading properties. We therefore hypothesized that the observed upregulation might be caused by mis-targeting of the MSL complex in the absence of roX2. However, our ChIP experiment revealed no enrichment of MSL complexes on these genes, and our results rather suggest that roX2 directly or indirectly restricts expression of these male-biased genes independently of its role in the MSL complex.
It is well known that roX RNAs are important for spreading of the MSL complex in regions between HAS [11, 14]. It is therefore surprising that loss of roX causes a relatively even reduction in expression of X-chromosomal genes and the decrease is not more dramatic with larger distances, as would be expected for reductions in spreading capacity. Indeed, observed reductions in expression were smaller for genes located far from HAS than for closer genes. A possible explanation is that expression of these genes is compensated by an MSL-independent mechanism. It has been previously shown that most genes on the X-chromosome are dosage-compensated [9, 72, 73], but a subset are not bound by the MSL complex and do not respond to its depletion [74]. Our results corroborate these findings since loss of roX RNA in the roX1 roX2 mutant had little effect on the expression of genes classified as having weak MSL complex binding, clearly indicating that at least one other mechanism is involved. The results further show that high-affinity sites, as defined by MSL-targets in the absence of roX1 and roX2, are highly correlated to genes with the highest MSL binding levels. Therefore, sites targeted in the absence of roX provide a more stringent definition of HAS, with stronger correlation to genes bound by high levels of MSL complex, than targets in the absence of mle, mof or msl3.
The increase in expression mediated by the MSL complex is considered a feed-forward mode of regulation, and appears to be more or less equal (ca. 35%) for all MSL-bound genes [9]. Evidently, highly expressed genes need a stronger increase in transcription than weakly-expressed genes. Our results suggest that dosage compensation is a stochastic process that depends on HAS distribution and is correlated with expression levels. Evolutionary analysis has shown that newly formed X-chromosomes acquire HAS, putatively via rewiring of the MSL complex by transposable elements and fine-tuning of its regulatory potential [75, 76]. Such a dynamic process may be required for constant adaptation of the system. Highly expressed genes tend to accumulate HAS in their introns and 3´UTRs, and thus bind relatively high amounts of MSL complex, thereby stimulating the required increase in expression. This also implies that the gene organization on X-chromosomes is under more constraints than autosomes.
This study presents, to our knowledge, the first high-throughput sequencing data and analysis of transcriptomes of roX1, roX2 and roX1 roX2 mutant flies. The results reveal that roX1 and roX2 fulfill separable functions in dosage compensation in D. melanogaster. The two RNA species differ in both transcription level and cell-cycle regulation.
In third instar larvae, roX1 is the more abundant variant and the variant that is included in MSL complexes transmitted physically associated with the X-chromosome in mitosis. Loss of roX1, but not loss of roX2, results in decreased expression of genes on the X-chromosome, albeit without apparent phenotypic consequences. Loss of both roX species leads to a dramatic reduction of X-chromosome expression, but not complete male lethality. Taken together, these findings suggest that high tolerance for mis-expression of X-chromosome genes has evolved. We speculate that it evolved in parallel with dosage compensation mechanisms and that it may be a common property of current and ancient sex-chromosomes.
The roX RNAs are important for spreading of the MSL-complex from HAS, but the reduction of X-chromosome expression in roX1 roX2 mutant is not affected by the need for spreading, i.e., distance from HAS. In addition, the genes targeted by the MSL complex in the roX1 roX2 mutant also show strongly reduced expression. Our results suggest that the function of the MSL complex which is still present at HAS is compromised in the roX1 roX2 mutant and that the dosage of distant genes is compensated by an alternative, unknown, mechanism. We propose that dosage compensation is a stochastic process that depends on HAS distribution. Creation and fine-tuning of binding sites is a dynamic process that is required for constant adaptation of the system. Highly expressed genes will accumulate and be selected for strong HAS (and thus bind more MSL complex) since they require high levels of bound MSL complex for the required increases in expression.
Flies were cultivated and crossed at 25°C in vials containing potato mash-yeast-agar. The roX1ex6 strain [77] was obtained from Victoria Meller (Wayne State University, Detroit). The new roX2 mutant alleles were generated by CRISPR/Cas9 genome editing using a previously outlined strategy [78]. Briefly, we constructed a transgenic fly strain expressing two gRNAs in the germline, which are designed to induce double-strand breaks 7 bp upstream of the roX2 transcription start site and 63 bp upstream of the annotated transcription termination. Males with the transgenic gRNA construct were crossed with y2 cho2 v1; attP40{nos-Cas9}/CyO females. The male progeny of this and subsequent two crosses were crossed individually to C(1)DX, y1 w1 f1 females. Strains with deletions spanning roX2 were identified by PCR-based screening followed by sequencing, using primers and gRNA oligos listed in S1 Table. Males carrying a roX29-4 deletion with the final genotype y1 cho2 v1 roX29-4 were crossed with y1 w1118 roX1ex6 females to obtain recombinant roX double mutant X-chromosome y1 w1118 roX1ex6 v1 roX29-4. This means that the crossover occurred between cho and v genes.
Previously described procedures were used in RNA-fluorescent in situ hybridization (FISH) analyses, and preparation of both salivary gland squashes [79] and larval brain squashes [80], following protocol 1.9, method 3, for the latter. Schneider’s line 2 cells were treated prior to hybridization as also previously described [37]. For embryo staining, y1 w1118 embryos were collected on apple juice-agar plates for 1 hour and incubated for 5–6 hours at 25°C. Squashes were prepared as follows: each embryo to be stained was manually dechorionated and transferred onto a cover slip. The vitelline membrane was pricked with a fine needle and a drop of 2% formaldehyde, 0.1% Triton X-100 in 1× PBS was added immediately. After 2 minutes, the solution was removed with a pipette and a drop of 50% acetic acid, 1% formaldehyde solution was added. After another 2 minutes incubation, a polylysine slide was placed over the cover slip. To spread the cells, the cover slip was gently pressed and then flash-frozen in liquid nitrogen. After removal of the coverslip the slide was immersed in 99% ethanol and stored at -20°C prior to hybridization. Antisense RNA probes for roX1 (GH10432) and roX2 (GH18991) were synthesized using SP6 RNA Polymerase (Roche) and DIG or Biotin RNA Labelling Mix (Roche), respectively. Primary antibodies were sheep anti-digoxigenin (0.4 mg/mL; Roche) and mouse anti-biotin (1:500, Jackson ImmunoResearch). The secondary antibodies were donkey anti-mouse labelled with Alexa-Fluor488 and donkey anti-sheep labelled with Alexa555 (Thermo Fisher Scientific).
To obtain 1st instar male larvae we collected 80–100 virgin females of the following genotypes: y1 w1118 (used as wild type), y1 w1118 roX1ex6 (roX1 mutant), y1 cho2 v1 roX29-4 (roX2 mutant), and y1 w1118 roX1ex6 v1 roX29-4/FM7i, P[w+mC ActGFP]JMR3 (roX1 roX2 mutant). The females were crossed with 50–80 FM7i, P[w+mC ActGFP]JMR3/Y males. Non-GFP 1st instar larvae were collected, 20 per sample. The collected larvae were flash-frozen in liquid nitrogen and stored at -80°C. Total RNA was extracted with 1 mL of Tri Reagent (Ambion) per sample, and libraries were prepared with a TruSeq RNA Sample Prep Kit v2 (Illumina) according to the manufacturer’s instructions. In total, three wildtype, roX2 mutant and roX1 roX2 mutant biological replicates were prepared and four roX1 mutant replicates. The samples were sequenced using a HiSeq2500 instrument at SciLife lab (Uppsala) and 125 bp long paired-end reads were obtained, and mapped to Drosophila melanogaster genome version 6.09 using STAR v2.5.1b with default settings. Read counts were obtained with HTseq version 0.6.1 using htseq count with default settings. The samples used for the analysis had 29.3–56.2 M reads with STAR mapping quality values of 22.9–52.1 and mean mapping coverage of 201–497. After removing genes with low read counts, means of the total expression of the four major autosome arms were centered to zero. Genes were annotated using the dmelanogaster_gene ensembl dataset from BioMart, Dm release 6.17.
Fold-differences in expression of genes among the investigated genotypes were calculated using the DESeq2 software package. Genes for which less than 20 reads were obtained from as a sum of all samples were excluded from the analysis. Of the 1000 most variable genes, 856 genes with an adjusted p-value for at least 2-fold differential expression between the wildtype and each of the three roX mutants exceeding 0.01 were also excluded from the analysis. In addition, the white gene and its upstream neighbors (CG3588, CG14416, CG14417, CG14418 and CG14419) were excluded from the analysis due to strain background dissimilarities among strains in this genomic region. In total, 2356, 2659, 2571, 3164, 105, 10750 and 2042 genes on chromosomes 2L, 2R, 3L, 3R, 4, all autosomes except chromosome 4, and X, respectively, were included. For each of these genes, the average differential expression between replicates was log2-transformed and mean-centred, by subtracting the mean log2 fold change in expression of genes on the major autosomes (2L, 2R, 3L, 3R) from the value for each individual gene (S2 Table). Thus, the observed differences are relative and based on the assumption that overall expression of the four major autosomal arms is constant under all relevant conditions.
The coordinates of PionX sites used in the analysis have been previously published [81], and the HAS coordinates on the X-chromosome were extracted from available data [45, 51], compiled and kindly provided by Philip and Stenberg [74]. The HAS coordinates were converted from release 5 to release 6 of the Drosophila genome using the flybase.org online conversion tool. The distances to the closest PionX and HAS sites were calculated for each gene on the X-chromosome, then genes were ranked in order of increasing distances to these sites and split into 10 bins with equal numbers of genes (S2 Table).
Binding values of MSL1, MSL3 and MOF in S2 cells were calculated and kindly provided by Philip and Stenberg [74] using the E-MEXP-1508 chromatin immunoprecipitation dataset [46] (S2 Table). Only X chromosomal genes with binding values for all three proteins were included in the analysis (1640 genes). Genes were ranked by increasing binding value and split into five equal bins. Genes located within MSL1 binding sites in the roX1 roX2 mutant were determined using previously obtained ChIP data [14]. The percentage overlap between genes and the previously defined top 1.5% of peaks was calculated using the annotate function of BEDTools. A gene was considered to be within a MSL1 binding peak if any of its transcripts had at least 1% overlap.
Bed files with data on early and late replicating domains in S2, Kc167 and DmBG3 cell lines were kindly provided by David MacAlpine [58]. The coordinates were converted from Drosophila genome release 5 to release 6 using flybase.org’s online coordinate converter. The annotate tool from BEDTools [82] was used to calculate the overlap between genes and replication domains. Genes were classified as early or late replicating in a given cell line if the entire transcript was within an early or late replicating domain.
Two replicates of formaldehyde cross-linked chromatin from third instar larvae of each strain were prepared according to a previously published protocol [83], then subjected to immunoprecipitation analysis with polyclonal rabbit anti-MSL1 antibodies, rabbit anti-H4K16ac antibodies (Millipore) or rabbit serum (mock negative control). Quantitative PCR was performed using SybrFast qPCR Master Mix (Kapa Biosystems), PCR primers listed in S1 Table, and a CFX Connect Real Time System (Bio-Rad laboratories).
Since the distribution of expression ratios varied between mutants we defined upregulated genes as those with a log2 fold change above the third quartile of the autosomal set (combined set of autosomes excluding chromosome 4). This resulted in thresholds for transcription up-regulation of 0.2, 0.068 and 0.307 for roX1, roX2 and roX1 rox2 mutant, respectively. The expression in testis data were extracted from the FlyAtlas2 database [84] (S2 Table). Gene with testis-enrichment values above 4 were classified as testis-biased.
All calculations were performed using R [85] and plots were generated using the ggplot2 R package [86].
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10.1371/journal.pgen.1003257 | The [PSI+] Prion Exists as a Dynamic Cloud of Variants | [PSI+] is an amyloid-based prion of Sup35p, a subunit of the translation termination factor. Prion “strains” or “variants” are amyloids with different conformations of a single protein sequence, conferring different phenotypes, but each relatively faithfully propagated. Wild Saccharomyces cerevisiae isolates have SUP35 alleles that fall into three groups, called reference, Δ19, and E9, with limited transmissibility of [PSI+] between cells expressing these different polymorphs. Here we show that prion transmission pattern between different Sup35 polymorphs is prion variant-dependent. Passage of one prion variant from one Sup35 polymorph to another need not change the prion variant. Surprisingly, simple mitotic growth of a [PSI+] strain results in a spectrum of variant transmission properties among the progeny clones. Even cells that have grown for >150 generations continue to vary in transmission properties, suggesting that simple variant segregation is insufficient to explain the results. Rather, there appears to be continuous generation of a cloud of prion variants, with one or another becoming stochastically dominant, only to be succeeded by a different mixture. We find that among the rare wild isolates containing [PSI+], all indistinguishably “weak” [PSI+], are several different variants based on their transmission efficiencies to other Sup35 alleles. Most show some limitation of transmission, indicating that the evolved wild Sup35 alleles are effective in limiting the spread of [PSI+]. Notably, a “strong [PSI+]” can have any of several different transmission efficiency patterns, showing that “strong” versus “weak” is insufficient to indicate prion variant uniformity.
| The [PSI+] prion (infectious protein) of yeast is a self-propagating amyloid (filamentous protein polymer) of the Sup35 protein, a subunit of the translation termination factor. A single protein can form many biologically distinct prions, called prion variants. Wild yeast strains have three groups of Sup35 sequences (polymorphs), which partially block transmission of the [PSI+] prion from cell to cell. We find that [PSI+] variants (including the rare [PSI+] from wild yeasts) show different transmission patterns from one Sup35 sequence to another. Moreover, we find segregation of different prion variants on mitotic growth and evidence for generation of new variants with growth under non-selective conditions. This data supports the “prion cloud” model, that prions are not uniform structures but have an array of related self-propagating amyloid structures.
| Prions in yeast are a new form of gene, composed of proteins instead of nucleic acids [1]. As such, their inheritance, mutation and segregation are not expected to follow the same rules as the majority DNA/RNA genes. The [PSI+] prion was first recognized as a non-chromosomal genetic element enhancing the read-thru of the premature termination codon in ade2-1 [2]. Its unusual genetic properties led to its identification as a prion of Sup35p [1], a subunit of the translation termination factor [3], [4], specifically an amyloid form (β-sheet-rich filamentous polymer of protein subunits) of the normally soluble Sup35p [5]–[9]. In the amyloid form, the protein is largely inactive, resulting in increased read-through of termination codons. Yeast prions are important models for mammalian prion diseases, and for amyloid diseases in general.
Sup35p consists of C, an essential C-terminal domain (residues 254–685), responsible for the translation termination function [3], [4], [10]; N, an N-terminal domain necessary for prion propagation (residues 1–123) [10] that normally functions in the general mRNA turnover process [11]–[15] and functionally interacts with Sla1p [16]; and M (residues 124–253), a middle charged region that is also implicated in prion propagation [17]–[20]. In the infectious amyloid form, the N domain, and probably part of the M domain, is in an in-register parallel β-sheet form, with folds in the sheet along the long axis of the filament [21], [22].
Prions can often be transmitted between species, as was first recognized by infectivity of sheep scrapie brain extracts for goats [23]. However, cross-species transmission is inefficient (or completely blocked) as a result of sequence differences between the donor and recipient prion proteins [24]. This phenomenon is called the species barrier, and has also been observed in yeast prions [19], [25]–[31]. Wild isolates of S. cerevisiae also show considerable sequence variation in Sup35p sequence [20], [32], and these sequence differences produce barriers to transmission of [PSI+] [20], presumably evolved to protect cells from the detrimental, even lethal, effects of this prion [33], [34].
A single prion protein can propagate any of a number of prion variants (called ‘prion strains’ in mammals), with biological differences due to different self-propagating conformations of the amyloid [9], [35], [36]. Although there is evidence for conformational differences between prion variants, the nature of those differences is not yet known. In yeast, prion variants differ in intensity of the prion phenotype, stability of prion propagation, interactions with other prions, response of the prion to overproduction or deficiency of various chaperones, and ability to cross species barriers [30], [31], [37]–[41]. Different variants arise during prion generation as a result of some stochastic events occurring in the initial formation of the prion amyloid. Generally, prion variant properties are rather stable, even during propagation in a species different from that in which the prion arose (e.g. [42]).
In a previous report, we demonstrated transmission barriers between Sup35 alleles from wild strains of S. cerevisiae, an ‘intraspecies barrier’. These intraspecies barriers are of particular interest since they must operate in nature, when S. cerevisiae strains mate among themselves. Interspecies matings are less efficient than intraspecies matings (e.g., [43]), and diploids formed produce almost no viable meiotic spores [44], [45]. In most cases, the intraspecies barriers were incomplete, with occasional transmission between strains with different Sup35 sequences. Were the prions transmitted the same variant as the original, or were they prion ‘mutants’, heritably changed in their properties? Under selective conditions, prion variant properties may change, a phenomenon first demonstrated in mice [46] and also known in yeast [30], [47]. Selection in the presence of a different prion protein sequence, or a drug interacting with amyloid could induce a new prion by inaccurate cross-seeding, and reflect generation of a new prion, rather than propagation of one of several sub-variants already present. Here, we examined variation in prion properties under non-selective conditions, finding evidence for the existence of a ‘cloud’ of variants with stochastic fluctuation.
Wild SUP35 alleles fall into three groups: the ‘reference’ sequence is essentially that of laboratory strains; Δ19 has a 19 residue (66–84) deletion in the prion domain; E9 is representative of a group with N109S and several polymorphisms in the M domain [20]. Three independent prion variants of the E9 Sup35p (E9A, E9F, E9G) were selected in strain 4828 (Table S1). We tested the transmission of these variants by cytoduction to strain 4830 expressing E9 itself, Δ19 or reference Sup35. None of these variants were transmitted well into the strain containing the Δ19 Sup35 polymorph. However, two variants (A, G) propagated very poorly with reference Sup35 sequence, while the other variant (F) was able to efficiently transmit the prion to the reference sequence (Table 1, p<10−10). This indicates that intraspecies transmission barriers are variant-specific.
Two of the few E9G→Δ19 cytoductants that were [PSI+] (Table 1) were tested for transmission to strains with different Sup35 polymorphs (Table 2). Each had lost the transmission specificity and were now able to transmit the prion more efficiently into all sequences (Table 2, p<10−10), unlike two [PSI+] isolates initially selected in cells expressing Δ19, which propagated poorly to E9 or reference [20] (p values between 10−10 and .002). This again shows the prion variant specificity of transmission barriers. Note that these two E9G→Δ19 cytoductants differ in that [PSI+E9G]Δ19A was white (a strong [PSI+]) while [PSI+E9G]Δ19B was pink (a weak [PSI+]). This indicates that either the original E9G was a mixture of two prions or that new prion variants were selected by the difficulty of transmission into Δ19.
An E9G→ref cytoductant from Table 1, similarly analyzed, showed ready propagation into reference (100%, p<10−10) and the original E9 sequence from which the prion originated (69%), but only poor transmission to the Δ19 sequence (Table 2). This result differs from a [PSI+ref]ref (originating and propagating in the ref sequence) which propagates poorly into E9 (19%, p<10−8) [20], again showing prion variant dependence of prion transmission. As expected the E9G prion transmitted to another yeast strain with the E9 Sup35 had similar propagation characteristics to the original [PSI+E9G] (compare Table 1 and Table 2).
The [PSI+ref]ref in strain 779-6A was transmitted to cells with the other Sup35p polymorphs and, as expected, transmission was limited (Table 3). When [PSI+] cytoductants were examined for stability on extensive further mitotic growth, we found that the [PSI+ref]ref cytoductants were fully stable, while the [PSI+ref]Δ19 were significantly less stable and [PSI+ref]E9 cytoductants even less so. Nonetheless, stability was sufficient that [PSI+ref]Δ19→Δ19 and [PSI+ref]E9→E9 cytoductions showed >90% transmission (Table 3).
The variant-dependence of transmissibility was again evident in cytoduction of [PSI+ref]ref in strain 779-6A [48] to cells with the other Sup35p polymorphs (Table 3). This variant originated in the reference sequence, but when transferred to Sup35Δ19, is then transferred well to either the reference or the Δ19 Sup35s, but very poorly to E9 (Table 3). In contrast, either of two E9-originating prions in a Δ19 host ([PSI+E9G]Δ19), transfer well to all polymorphs (Table 2, p<10−10). The [PSI+ref]E9 transfers well to both reference and E9 sequences (Table 3), like [PSI+E9F], but unlike two other prions originating in E9 (Table 1, p<10−10). As expected, the prion originating in E9 and transmitted to E9, or that originating in the reference sequence and transmitted to the reference sequence, each maintain their original properties.
Having transferred [PSI+ref] to each of the Sup35 polymorphs, we transferred them back to the original host (cured of [PSI+]) and re-examined their transmission properties to see if they had changed as a result of their experience (Table 4). The original [PSI+ref] transmitted poorly to either Δ19 or E9 hosts, but the ‘experienced’ prions all transmitted better to E9 than the original, indicating selection of a ‘mutant’ prion (Table 4, p<.002, 10−6, 10−10). Moreover, the prion that passed through Δ19 could transmit 91% to another Δ19 (Table 3), but when passed back to the reference sequence, only transmitted 20% to Δ19 (Table 4). Similarly, the prion passed through E9, and able to transmit to another E9 host at 92% (Table 3), once passed back to the reference host could only transmit 46% to E9 (Table 4).
These results indicate that the predominant variant has changed. But is this change due to mistemplating as the prion passes from Sup35 molecules with one sequence to those with a different sequence, or is there an ensemble of variants present within the population that can be selected based on the specific selection pressure, to be visible with a specific transmission phenotype?
If the population contains an array of prion variants from which one or another can be selected, one might expect these to segregate during mitotic growth, much as differently marked plasmids sharing the same replicon or mitochondrial genomes will segregate mitotically, even without exposure to a selective condition. In contrast, if the changes in prion variant are due solely to mistemplating when a prion crosses a transmission barrier to a different sequence, then the transmission pattern should not change substantially even after extensive propagation in the original strain. We designed this experiment to separate the mitotic segregation phase, in which there was no change of Sup35p polymorph, from the transmission phase, in which the test of prion variant is then made by cytoduction to the three Sup35 polymorphs.
We subcloned single colonies of the 779-6A [PSI+ref] yeast strain (reference Sup35p) without selection on ½ YPD plates for at least 75 generations. Table 5 illustrates our surprising result, that many subclones had transmission profiles considerably different from the parent strain 779-6A. This indicates that there is an ensemble of variants or a prion cloud that has different transmission profiles. We have classified these variants as being type A if they transmit well into reference sequence but poorly into Δ19 and E9 sequences. Type B transmits well into reference and E9 sequences, but poorly into the Δ19 sequence. Type C transmits well into Δ19 and reference sequence, but poorly into the E9 sequence and type D transmits well into all sequences. From this subcloning we now had yeast strains that were carrying prion variants of type B (Y1), type C (Y2) and of type D (Y5). These strains repeatedly display these propagation patterns even after many months in frozen stocks. We then wanted to determine if we had now isolated single variants within the original ensemble so each of three clones, of transmission types B, C and D, were subcloned an additional 75 generations on ½ YPD plates with ten clones of each tested as before. To our surprise these extensively grown subclones of each of the three types still produced clones with an ensemble of prion variants (Table 6). Even the Y1 strain, which did not initially propagate into the Δ19 sequence, produced subclones with a variety of transmission profiles.
To determine if the appearance of different predominant variants was due to some unrecognized selective pressure on these strains while propagating on ½ YPD plates, the subcloning was performed in liquid YPD media maintaining the culture in exponential growth phase throughout. Once cell density reached 0.3 absorbance units at 600 nm the cultures were diluted, transferring only 1000 cells to a fresh culture, a process continued for at least 84 generations. Even under exponential growth phase (Table S2), an array of transmission profiles was observed similar to that in Table 6.
The presence of changed transmission patterns in a majority of the clones without any selection having been applied made it clear that the changes were not due to a chromosomal mutation. Nonetheless, we tested for such a chromosomal change by curing [PSI+] from Y5 by growth on guanidine, and cytoducing cytoplasm from Y1, Y2 or Y5 into strain 4830 and then 8 cytoductants from each were cytoduced into a rho° derivative of the cured Y5 (Table S3). These cytoductants were then cytoduced into recipients each carrying one of the three SUP35 polymorphs. In each case the transmission pattern followed that of the original Y1, Y2, or Y5 donor of cytoplasm, rather than the Y5 pattern of the recipient (Table S3), confirming that the change was due to a new variant of [PSI+] and not a chromosomal change. The frequency with which the transmission pattern changed without selection or protein over expression is orders of magnitude higher than for the generation of any new prion, and the fact that the change is one of changing the specificity of transmission to different Sup35p polymorphs proves that it is indeed a change of [PSI+], and not the generation of some other prion.
To further test the presence of an ensemble of prion variants, one subclone of Y1, which had the same profile as the parent, not being able to transmit into the Δ19 sequence, was subcloned for an additional 75 generations. As shown in Table S4, subclones were obtained with various profiles some with very good transmission into the Δ19 sequence containing strain. These results indicate that a single variant had not been selected and that an ensemble or cloud of prion variants must exist with a dynamic propagation pattern under non-selective conditions. Each isolate has a specific transmission pattern, even after frozen storage for many months (Table S5). We infer that during growth, events must allow for a stochastic shift of the ensemble to allow for isolation of variants with specific reproducible transmission patterns.
[PSI+] is rare in wild strains [33], but was found in 9 of 690 wild isolates [49], each expressing the reference Sup35 (ref. [49] and Amy Kelly, personal communication). How do these wild [PSI+] variants respond to the intraspecies barriers we previously reported [20]? We used both reference sequence and E9 sequence Sup35 fused to GFP and could see dots in the reported wild [PSI+] strains 5672, UCD#885, UCD#978 and UCD#2534, though infrequently, but not in strains UCD#521, 587, 779, 824, 939 (Figure S1). To test these strains genetically for nonsense suppression, we crossed the wild strains with strain 4972 (Table S1), carrying the [PSI+]-suppressible ade1-14 marker, and tested dissected tetrads to determine if ade1-14 is suppressed. We found that for seven of the wild strains, ade1-14 was weakly suppressed in the segregants, and this suppression could be cured by growth in the presence of guanidine, which is known to cure the [PSI+] prion. We could not obtain tetrads from diploids formed with strain UCD# 978 and strain 5672 gave poor spore germination.
The transmission of the wild [PSI+] isolates into cells expressing the Sup35 polymorphs in strains 4828 and 4830 by cytoduction is shown in Table 7. The wild [PSI+] strains transmit well into the reference sequence, but most showed poor transmission to one or both of the Δ19 or E9 sequences (Table 7). All four transmission patterns were observed (Table 7), but all of the isolates were ‘weak’ [PSI+] (Figure 1B). Thus, each of the strains tested transmitted [PSI+] even though several did not show dots with Sup35NM-GFP. Of course, their presumed independent origin means that these wild isolates are not derived from one prion cloud.
Variants of [PSI+] may be weak or strong in phenotype, stable or unstable in propagation, and have various responses to deficiency or over expression of chaperones or other cellular components, have different patterns of ability to cross species barriers, and, as shown here, to cross intraspecies transmission barriers. To what extent these various parameters are correlated is largely unknown. We tested the several prion variants derived from the [PSI+] in strain 779-6A with different transmission patterns for their ‘strong’ vs ‘weak’ character (Figure 1A). We note that, with identical chromosomal genotype, they are indistinguishable in the ‘strength’ parameter in spite of having substantially different transmission properties. As noted above, the wild [PSI+] variants are indistinguishably ‘weak’, but have different transmission patterns to the Sup35 polymorphs.
Yeast prion variants are distinguishable based on intensity of the prion phenotype, stability or instability of prion propagation, sensitivity of prion stability to overproduction or deficiency of several chaperones and other cellular components and ability to overcome barriers to transmission between species [30], [31], [37]–[41] – or even within species, the last documented here for transmission across the barriers found in wild strains of S. cerevisiae. Yeast prion amyloids are all folded parallel in-register β-sheet structures [21], [50], [51], but within this architectural restraint, different prion variant structures are proposed to vary in the extent of the β-sheet structure (how much of the N and M domains are in β-sheet), the locations of the folds in the sheets and the association of protofilaments to form fibers.
We find that separation of prion variants based on sensitivity to intra-species barriers cuts across separation based on ‘strong’ vs ‘weak’ assessment of strength of prion phenotype. The four transmission variant types derived from the [PSI+] in strain 779-6A were all strong [PSI+], like the parent prion. Interestingly, the prions in wild strains were all weak [PSI+], presumed to arise independently and thus not part of the same ‘prion cloud’, but fell into the same four transmission variant types. Likewise, two similarly ‘weak’ [PSI+] variants showed different transmission across a barrier set up by deletions in the prion domain [52]. These results show that prion variant uniformity is not demonstrated by showing uniformity of a single property (for example, colony color). It is unlikely that the variation in transmission barriers observed are due to a prion other than [PSI+] because the sequences of Sup35p are involved, and no yeast prion is known to arise at a frequency high enough to explain our results.
After crossing an intraspecies barrier, we find that the [PSI+ref] examined is unstable in its new host, emphasizing the effectiveness of these barriers. We also find that the rare [PSI+] prions found in wild strains are, in most cases, sensitive to the intraspecies barriers, suggesting that these barriers have evolved to protect yeast from the detrimental effects of this prion.
The [PSI+] in strain 779-6A, with the reference Sup35p sequence, showed a reproducible strong preference for the reference sequence, transferring only very inefficiently to the Δ19 or E9 Sup35 backgrounds. However, simple mitotic growth of this strain resulted in the mitotic segregation of at least four variants distinguished by their abilities to cross intraspecies barriers. These variants were stable and reproducible with limited expansion of the corresponding clones, but following many generations of growth, each of those tested gave rise again to the same four general classes of subclones. Prion mutation is well documented in mammals and in yeast under selective conditions [30], [46], [47], [53], and Weissmann's group has suggested that prions resistant to a drug can arise during prion propagation in tissue culture cells in the absence of the drug [54], [57]. We observe changes in the predominant prion variant under non-selective conditions in vivo. Selection only happens during the test, when cytoplasm is passed by cytoduction from the subclones to be tested to the recipient expressing one of the three Sup35p polymorphs. A new prion variant, recently described by Sharma and Liebman [55], may represent a phenomenon similar to that described here. Certain induced [PSI+] clones continually gave off subclones that were a mixture of strong and weak variants, what the authors called “unspecified [PSI+]”.
Although multiple de novo prion generation events in forming amyloid in vitro result in multiple prion variants on transfection into yeast, even a [PIN+] cell generates [PSI+] clones too rarely to explain our results as de novo prion generation. Rather, mis-templating must be the mechanism of generation of variant diversity that we are observing. Our results imply that there must be a finite rate of amyloid mis-templating that is not due to a mismatch of two prion protein sequences. In spite of extensive purification by mitotic growth and subcloning, we were unable to obtain a prion variant that was completely stable in its transmission pattern to polymorphs. These results are consistent with the ‘prion cloud’ hypothesis [56], [57], in which it is supposed that even a prion variant purified by end-point titration consists of a major variant as well as an array of minor variants. This production of new prion variants during non-selective growth is analogous to the generation of RNA virus mutants during viral replication (reviewed in ref. [58]), in which a cloud of sequence variants accumulate because of the error-prone nature of RNA-dependent RNA polymerases.
The segregation of a mixed prion population could be considered analogous to the segregation of differently marked plasmids with the same replicon. The latter situation has been carefully examined by Novick and Hoppenstadt [59], who find that the fraction of cells remaining with a mixture of plasmids is H = H0 [(N−1)(2N+1)/(2N−1)(N+1)]n , where H0 is the starting fraction of mixed cells, N is the copy number of the plasmid, and n is the number of generations [59]. Random replication of plasmids and equal partition at mitosis is assumed. One result of this treatment is that after N generations, H≈0.36 H0.
The copy number in the case of yeast prions might be taken as the ‘seed number’ determined by the methods developed by Cox et al. [60], found to be ∼20–120 for the strains examined. The assumption of equipartition is probably not accurate here, since yeast daughter cells are smaller than mother cells [60]. Moreover, the sticky nature of amyloids might suggest that progeny filaments might stick to parent filaments exaggerating this effect. We have propagated our [PSI+] strains for a number of generations comparable to the presumed copy number, so segregation of different prions is not surprising.
However, we find that even when we have apparently purified a variant, further non-selective growth and subcloning leads to further appearance of the full range of variants among the progeny (Figure 2). This indicates that we are not only observing segregation, but also the (repeated) generation of variants during growth. While varying with respect to transmission, they remain ‘strong’ variants, suggesting that the structural differences responsible for this transmission barrier differ from those involved in the strong vs. weak differences. King has shown that residues 1–61 are sufficient to propagate strong vs weak prion strains [8], [61], but the sequence differences among the Sup35 polymorphs are outside this area, and transmission variants may thus largely differ in the region C-terminal to the 1–61 area, perhaps a region with more variable structure. Other studies have indicated effects of this region on propagation of some prion variants [52], and β-sheet structure of Sup35NM amyloid extends throughout N and even into M [21], [22].
We refer to the standard laboratory yeast sequence [62]–[64] as the ‘reference sequence’. Two common sequence polymorphs found within the wild population were used. The first, with deletion of 19 amino acids from residues 66 to 84 and the G162D change, is referred to as Δ19, and the other includes N109S, G162D, D169E, P186A, T206K, H225D and is denoted E9 [20]. A prion originating with the Sup35p sequence of strain E9, for example, but being propagated in a strain expressing only the reference sequence will be designated [PSI+E9]ref, in analogy with similar nomenclature for [URE3] [31]. Cytoductants (see below) generated with strain A as donor and strain B as recipient are denoted A→B. They have the nuclear genotype of strain B and the cytoplasmic genotype of both A and B. In an abuse of language, we often use “[PSI+E9]ref was transferred to Sup35 Δ19” to mean “[PSI+E9]ref was transferred to cells expressing Sup35 Δ19”.
Sup35p is a subunit of the translation termination complex, and the incorporation of a large proportion of Sup35p into the prion amyloid filaments makes it inactive, resulting in increased read-through of termination codons. This is measured by read-through of ade2-1, with an ochre termination codon in the middle of the ADE2 gene. In addition to ade2-1, strains carry the SUQ5 weak suppressor mutation, which leaves cells Ade- unless the [PSI+] prion is also present [2].
The strains used are listed in Table S1. Plasmids used containing reference, Δ19 or E9 sequences were generated as described [20]. All yeast media and plates contained 20 µM copper sulfate unless noted. Rich and minimal media (YPAD and SD) are as described [65]. Only nutrients required by the strains used in a given experiment were added to minimal plates.
Cytoplasm may be transferred from one strain to another utilizing the kar1-1 mutation [66], defective for nuclear fusion. Cells fuse, but the nuclei do not fuse, and nuclei separate at the next cell division. However, cytoplasmic mixing has occurred, and so a genetic element (prion or mitochondrial DNA) present in one strain (identified by its nuclear genotype) will be transferred to the other. We use transfer of mitochondrial DNA as a marker of cytoplasmic transfer, and score prion transfer. Reference, Δ19 or E9 sequence plasmids were transformed into both laboratory strains 4828 and 4830, loss of p1215 (URA3 SUP35C) was selected by growth on 5-fluoroorotic acid media and Ade- transformants were made rho° by growth on YPAD containing 1 mg/ml ethidium bromide. Donor and recipient strains at high density were mixed in water at a ratio of about 5∶1, and the mixture was spotted onto a YPAD plate. After 18 hours at room temperature, the mating mix was streaked for single colonies on media selective against growth of the donor strain. Clones are shown to be cytoductants by their growth on glycerol and failure to grow on media selective for diploids. As further tests of a sample confirm, Ade+ cytoductants are judged to have received and propagated [PSI+].
[PSI+] Strain 779-6A [48] was streaked to single colonies on ½ YPD media and twelve colonies were selected, named Y1-Y12. These isolates were streaked to single colonies three additional times, each time selecting just one colony for further propagation. From the third plate a single colony was selected and expanded on ½ YPD, and cells from this plate were used for cytoduction. From dilution tests there are approximately 2×107 cells per colony, indicating a total of at least 75 generations of growth of clones Y1-Y12 before cytoduction. Additional subclones were handled in the same manner with only ten colonies selected from the initial ½ YPD plate. In experiments to rule out selection during stationary growth phase, subclones of Y1 and Y2 were grown in a 125 ml Erlenmyer flask containing 25 ml of liquid YPD medium. When A600 reached 0.3, the culture was diluted, transferring 1000 cells of each to a fresh flask. These subclones were propagated in exponential phase for 84 generations and were then streaked for single colonies on ½ YPD plates. After one day of growth on ½ YPD, 10 subclones were selected for each of Y1 and Y2, expanded and tested for transmission via cytoduction.
Strains reported to be [PSI+] [49] were obtained from the UC Davis Department of Viticulture and Enology culture collection. The cultures were first tested to determine if dots were visible using either reference sequence Sup35NM-GFP pDB65 or E9 sequence Sup35NM-GFP pDB81 [20]. Images were obtained with a Nikon Eclipse TE2000-U spinning disc confocal microscope with 100× NA 1.4 Nikon oil lens with 1.5× magnifier and captured with a Hamamatsu EM-CCD ImagEM digital camera with IPLab version 4.08. Wild strains were sporulated and spores were crossed on rich medium with strain 4972 selecting G418-resistant prototrophs. The diploids formed were again sporulated and tetrads were dissected for each wild strain except for strain 978, whose diploid with 4972 would not sporulate. Ade positive segregants were tested for guanidine curing using two successive streaks on YPAD with 5 mM guanidine. MATα strains were cytoduced into strain 4830 carrying pRS316 (URA3) for selection. Lys2 mutants of MATa strains were selected on plates with DL-α-aminoadipic acid as a nitrogen source [67]. Selected strains were retested for Ade positive growth and curing and cytoduced into strain 4828.
The cytoduction data follows the binomial distribution, because each data point expresses two alternative results, transmission of [PSI+] or failure of its transmission. However, because of the large number of observations, the results should be approximately normally distributed. We want to calculate the probability that two sets of data are due to chance. Let p1 and p2 be the observed proportions of transmission in cytoductions 1 and 2, and ni the number of cytoductants tested in each experiment. Let p = (p1n1+p2n2)/(n1+n2) be the average of the proportion of transmission in the two experiments. The estimated standard error of the difference between the two proportions isThe null hypothesis is that cytoductions 1 and 2 are samples from the same population with transmission efficiency p and standard error S. Then the expected proportions are expected to be the same and their difference is expected to be zero. [(p1−p2)−0]/S = z = the number of standard deviations that the observed difference in proportions differs from the expected difference (0). The frequency of “z” being greater or equal to the observed value (assuming the null hypothesis) is obtained from a table of the normal distribution. The calculated “p values” are shown in the tables and at appropriate points in the text.
Cytoductants examined have been treated as independent since the chance that they represent sister cells is close to zero. This is because cytoductant mixtures were incubated at 20C where the cells divide slowly and because only about 30 cells were examined from several million in the zygote mixture on each plate.
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10.1371/journal.pcbi.1002222 | Coupled Information Diffusion–Pest Dynamics Models Predict Delayed Benefits of Farmer Cooperation in Pest Management Programs | Worldwide, the theory and practice of agricultural extension system have been dominated for almost half a century by Rogers' “diffusion of innovation theory”. In particular, the success of integrated pest management (IPM) extension programs depends on the effectiveness of IPM information diffusion from trained farmers to other farmers, an important assumption which underpins funding from development organizations. Here we developed an innovative approach through an agent-based model (ABM) combining social (diffusion theory) and biological (pest population dynamics) models to study the role of cooperation among small-scale farmers to share IPM information for controlling an invasive pest. The model was implemented with field data, including learning processes and control efficiency, from large scale surveys in the Ecuadorian Andes. Our results predict that although cooperation had short-term costs for individual farmers, it paid in the long run as it decreased pest infestation at the community scale. However, the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time, giving rise to natural lags in IPM diffusion and applications. We further showed that if individuals learn from others about the benefits of early prevention of new pests, then educational effort may have a sustainable long-run impact. Consistent with models of information diffusion theory, our results demonstrate how an integrated approach combining ecological and social systems would help better predict the success of IPM programs. This approach has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations.
| Food security of millions of people in the third world has faced a growing number of challenges in recent years including risks associated with emergent agricultural pests. Worldwide, the promotion of integrated pest management practices has been heavily promoted through participative methodologies relying on farmer cooperation to share pest control information. Recent studies have put into doubt the efficiency of such methodologies evoking our poor knowledge of farmers' perceptions, behavioral heterogeneity, and complex interaction with pest dynamics. While pest management programs have a larger place than ever on the international policy agenda, the debate concerning their efficiency at large scales has remained unresolved. Here, we developed an innovative modeling approach coupling pest control information diffusion and pest population dynamics to study the role of cooperation among farmers to share the information. We found that the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time, giving rise to natural lags in pest control diffusion and applications. However, our model also predicts that if individuals learn from others about the benefits of early prevention of invasive pests, then a temporary educational effort may have a sustainable long-run impact.
| In view of the growing number of challenges related to controlling agricultural pests, the promotion of Integrated Pest Management practices (IPM; a range of methods used for responsible pest control) has a larger place than ever on the international policy agenda [1], [2]. The participation of local communities and other stakeholders in such management processes has long been advocated as an essential step to achieve sustainable development [3]. Over the past decades, extension science has developed many types of participatory approaches towards farmers [4] to promote knowledge of agro-ecological concepts, apply IPM practices, reduce the use of pesticides and improve crop yields [5]. As budget and manpower constraints do generally not allow for direct interaction with every member of the target population, the strategy of most participative IPM programs is to train a limited number of farmers in the community who commit themselves to share the information they learn with other farmers [6]. Following Rogers' “diffusion of innovation theory” [7], the success of extension practices depends on the effectiveness of cooperation among farmers which determines IPM information diffusion from trained farmers (graduate farmers) to other farmers (exposed farmers).
Funding from international development organizations often relies on the important, but poorly studied, assumption that farmers cooperate with their peers, neighbors, or friends [8]. Increasing our understanding of farmers' cooperation theory and practice is a timely issue as field-level interactions among small-scale farmers are increasingly limited in a world of intense social reorganizations associated with land distribution, privatization of ownership, and market-oriented society [9].
A collective action problem that requires farmers to cooperate in information diffusion is exemplified by invasive pest control in fragmented agro-ecosystems [10]. If neighbors of graduate farmers do not adopt IPM measures, then the invasive pests from their fields can re-infest the graduate farmers' fields even if they apply IPM principles [11]. Moreover, in the case of emergent invasive species, farmers cannot rely on preexisting local knowledge, which makes them even more dependent on externally based experience. In farmer communities, IPM for invasive species is therefore characterized by a conflict of interest between individual and group benefit leading to cooperation dilemma [12], [13]. On the one hand, cooperation by graduate farmers to share IPM information is expected, in the end, to benefit the whole community of farmers (including themselves) by an area-wide suppression of the pest. On the other hand, under the assumption that graduate farmers want to prioritize control in their fields instead of training other farmers, theory predicts that individuals might have little incentive to cooperate and will not contribute to the public good [12]. Both types of behaviors have been classically observed in a wide array of agricultural situations [1]. In the specific case of IPM, farmers' decisions about whether to disseminate or not pest control practices will be closely dependent on pest infestation levels in their own field [1]. This means that farmers' dilemma to train others or not will be tightly linked to pest dynamics at the landscape level, itself depending on landscape characteristics, pest ecology and control behaviors of other famers. Exploring the relative merits of helping others vs. self interest in IPM information diffusion therefore requires the coupling of ecological and sociological models, an approach which has, to our knowledge, never been performed in the context of IPM.
The objective of our study was to develop a methodological framework to explore the relevance of participative IPM extension programs for pest control. We carried out these investigations in the context of an IPM program launched to help small scale farmers facing the arrival of an invasive insect pest, the potato tuber moth (Tecia solanivora Povolny) in the Ecuadorian Andes [14]. This region was highly relevant for our study as there is a long history of social reciprocity in the Andes that extends to pre-Incan times and has been one of the keystones for why farmers have been able to successfully farm for centuries in such harsh conditions [15]. We then built an agent-based model (ABM, [16], [17]) merging a spatially explicit pest population dynamic model through a cellular automaton (CA) with a field-based multi-agent system describing farmer features and behaviors (Fig. 1A). The global output of our ABM was determined from pest–landscape interactions, pest-farmer interactions, and inter-farmer interactions. To mimic real-world patterns of farmer behaviors as closely as possible, our ABM was implemented with field data, including learning processes and control efficiency, from large scale surveys from c.a. 300 farmer households in the Ecuadorian Andes. In our model, the agricultural landscape was modeled as a lattice composed of cells that represented various land plots of groups of farmers (hereafter named agents) within the same community (in total, 6 neighbor agents in the same community representing about 220 people, Fig. 1B). Pest dynamics was driven by the intrinsic population growth, migration, and pest control practiced by agents depending on their IPM knowledge. Under our IPM program, one agent was trained to control pest infestation in his fields. In return, this graduate agent was required to diffuse the IPM information to other agents so that they can increase their IPM knowledge and implement efficient practices. Agent decision to diffuse the information to others mainly depended on pest infestation level in his fields but also on social and economic factors included in the diffusion process of IPM information among farmers. Therefore, pest control at the community level was modeled as emerging from IPM information acquired by one graduate agent and spreading through exposed agents (see Text S1).
We believe that the relevance of our study stands in two main points. First, recent works on collective actions of IPM diffusion have reported that because behaviors and perceptions towards new information and technology can vary widely among farmers, farmers' behavioral heterogeneity is a key issue to understand and predict the success of pest control information diffusion throughout the community, and therefore the success of the IPM program at a large scale [14], [18]. In this context, ABMs may reveal ideal tools to better understand and predict the sustainable development of farmers' control practices [19]–[21] as they allow simulating the actions and interactions of autonomous agents (either individual or collective entities such as organizations or groups of farmers) with a view to assessing their effects on the system as a whole. Using ABM therefore allows integrating behavioral complexity of farmers and performing theoretical experiments (e.g., varying the level of farmer cooperation) which could not be performed in the real world (for time, ethical or financial reasons). Although ABM have increasingly been applied to physical, biological, medical, social, and economic problems [22], [23], [16] it has been, to our knowledge, completely disregarded by IPM theory and practice. Second, our study proposes an innovative computational framework merging recent advances in contagion-like model of knowledge diffusion through human populations [24], [25] and coupled land management models with spatially explicit species spread models (see papers presented at LandMod 2010 or Global Land Project 2010). Such a framework combining two approaches which developed in relative independence likely has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations.
The field survey revealed that, at the beginning of our program, a majority of farmers (87%) had a low IPM knowledge (score ranging between 0 and 2) regarding potato moth control (Fig. 2A). Our data further showed that although this knowledge could be greatly increased through training (graduate farmers reached an IPM knowledge of 4.39±0.61), those skills were not easily diffused to exposed farmers by informal training sessions (Fig. 2B). After having graduate farmers shared information with exposed farmers the mean knowledge score of the 64 surveyed exposed farmers increased only slightly when compared to control, from 0.96±0.80 to 1.65±0.53 (Student t-test, t = −1.717, P = 0.111). Interestingly, although moth control gradually increased with increasing IPM knowledge scores (linear model fit, R2 = 0.51, P<0.001), there were a few cases in which farmers with relatively high IPM knowledge had also poorly efficient pest control in their fields, probably due to contamination from neighboring fields (Fig. 2C).
Once the ABM was set up with these real-world data, we explored on a 20-year time scale the influence of the level of cooperation among agents (i.e. how often graduate agents did share their information with others) on pest infestation levels. Our model predicted that knowledge acquisition by exposed agents would follow a logistic regression through time (R2 = 0.50±0.11, P<0.05, Fig. 3A). Our simulations further predicted that both IPM knowledge diffusion and spillover after training would significantly decrease moth infestation by 60 to 70% from their initial levels (Fig. 3B). Time dedicated by graduate agents to train exposed agents instead of controlling pest had the short term consequence of increasing pest infestation in his own land (interviews with farmers revealed that training others would demand time and compromise of coordination with consequences in terms of pest control in their own field.). However, as exposed agents were being trained, graduate agents were less solicited thereby being able to dedicate more time to pest control. Importantly, the patterns of IPM information diffusion among agents predicted by our ABM was consistent with the Bass model (F-test, P<0.001, Fig. 4), a model traditionally used in diffusion of innovations [24]. The ability of our ABM to reproduce Bass model predictions therefore provided a validation of the correctness of information adoption patterns among agents, mainly through internal (“word-of-mouth”) influences.
Results of our simulation of the effect of farmer's cooperation level on pest control showed that within the first 6–7 years, pest infestation levels in both graduate and exposed agents' lands remained higher than those expected in the lands of a non-cooperating agent, whatever the cooperation levels. After 6–7 years, cooperating graduate agents had lower pest infestation level than non-cooperating ones, and therefore received the benefit of cooperating. Finally, for high levels of cooperation among agents (>0.5), our model predicted that after 6–7 years, pest infestation levels at the scale of the entire community (i.e. in all lands of agents) would be lower than levels expected in the fields of a non-cooperating graduate agent. The benefit of cooperation had therefore scaled up at the level of the whole community of agents (Fig. 5).
Since the emergence of the concept of knowledge based economy [26], the analysis of information diffusion has become a key issue to organization research [27]. Our results showed that the slow IPM learning process measured in Andean farmer communities placed restrictions on the amount of information that could be diffused within the community over time, giving rise to natural lags in IPM applications. This reinforces the view that IPM outcome at the community level will be achieved on a relatively long-term scale for the farmer, a feature which may be common to many agriculture programs. In an influential study that spawned an enormous diffusion of literature in rural sociology, [28], estimated that it took 14 years before hybrid seed corn was completely adopted in two Iowa communities. Rogers [7] also reported slow adoption in crop protection management in the Colombian Andes and Berger [21] showed that behavioral heterogeneity among Chilean farmers, delayed for almost 10 years the use of new irrigation methods. In our study, the six year delay in benefits of cooperation was mainly due to the limited spread of IPM information from graduate to exposed farmers which itself may have been a consequence of high IPM knowledge heterogeneity among farmers. Information is indeed expected to flow less smoothly in a heterogeneous population, particularly when the performance of new practices is sensitive to imperfectly transmitted information [29].
Our simulations also showed that there were short-term costs for the diffusion of IPM information resulting from our assumption that farmers cannot control pests in their own fields when they share IMP information with other farmers. Indeed, “lack of time” is a common motive invoked by farmers when they are questioned why they do not share IPM practices they learned with neighboring farmers [30]. As farmers often believe that there is a trade-off between diffusing and practicing IPM information, we think that an important outcome of our study was to show that, even if such a trade-off is included in the model, cooperating farmers would still benefit from IPM information diffusion in the long run. It is also likely that, in some cases, farmers may practice and diffuse new information simultaneously [1]. Cooperating farmers would then not suffer from short-term costs, potentially increasing their cooperation will, thereby speeding up information transfer throughout the community.
Obviously, our modeling approach made a series of simplifications which may be important to consider. For example, farmers usually tend to make high contributions initially but over time contributions dwindle to low levels. Many people are conditional cooperators, who in principle are willing to cooperate if others do so as well, but get frustrated if others do not pull their weight [31]. In agricultural systems personal networks, where trusted people (prestigious individuals, people of authority or holding otherwise vested power and influence) often play a key role in decision making, are difficult to integrate into models due to their dynamic, multi-directional, and non-symmetric nature [32]. Moreover the spread of behaviors may arise from the spread of social norms or from other psychosocial processes, such as various types of innate mimicry [33]. A recent study has shown that cooperative behaviors can cascade in human social networks even when people interact with strangers or when reciprocity is not possible; people simply mimic the behavior they observe, and this mimicking can cause behaviors to spread from person to person to person [34]. In this case, the rate of diffusion is largely dependent upon the knowledge (i.e., relative advantage, compatibility within the social setting, observability, and simplicity). Finally, another limitation may arise from the use of a behavioral reciprocity model. Theoretically, the adoption of IPM cooperative behavior among farmers could be favored as the reciprocated benefit outweighed the immediate cost [27]. However, in practice, the delay between the cost of a cooperative act and the benefit of reciprocated cooperation (from 7 to 20 years for graduate agents in our study) would introduce a number of cognitive challenges. For example, temporal discounting (for example devaluing of future rewards in the case of shift in crop type produced), often results in a preference for smaller, immediate rewards over larger, delayed rewards [35]. Variation in human discounting and cooperation validate the view that a preference for immediate rewards may inhibit reciprocity [35].
Despite these limits the ability of our model to capture real-world patterns of pest control (Fig. S5 in Text S1) and information diffusion (Fig. 4) indicates that our findings may yield important insights for IPM science and policies. First, IPM programs worldwide are confronting the reality of increasingly subdivided habitats managed as smaller areas, reducing the likelihood that pest population will be controlled, thereby requiring higher levels of cooperation among farmers [10]. We showed that when farmers make control decisions based on lower levels of damages occurring on their own land, they can increase information spread and the speed with which the whole community can control pest populations. Second, our study stresses the need to develop a comprehensive and empirically-based framework for linking the social and ecological disciplines across space and time [19]. In our model, predictions of the coupled dynamic of pests and farmer behavior show the evidence that farmer to farmer training can help the broader community control pest infestation in the long term. Third, as institutions increasingly seek to help communities sustainably providing local public goods themselves rather than depend on external assistance, the idea that development projects should aim at financial sustainability through local cooperative actions has had tremendous influence on funders. Our study shows that sustainable approaches to providing local public goods concerning invasive pest control would be possible despite a challenging delay between the cost of a communal act and the benefit of reciprocated cooperation. However, if individuals learn from others about the benefits of early prevention of invasive pests (i.e. cooperation takes from low levels of pest populations), then a temporary educational effort may have a sustainable long-run impact.
We addressed the issue of the importance of farmer cooperation in invasive pest management in the socio-agricultural system of the Ecuadorian highlands where potatoes (Solanum tuberosum L), are a major staple [36]. In 1996 a new pest, T. solanivora, invaded the country attacking potato tubers in the field and in storage and becoming one of the most damaging crop pests in the region [37]. Under the climatic conditions of the Ecuadorian highlands (sierra) potatoes are grown at any time of the year between elevations of 2400 m and 3800 m elevation [38]. The agricultural landscape of the highlands is made up of a mosaic of small potato fields (<1 ha) at various stages of maturation in which potato moths are active all year round. IPM programs have been implemented for about 10 years by the INIAP (Ecuador's National Institute for Agronomy Research) and the CIP (International Potato Center), through the Farmer Field School methodology [39]. In the North Andean region, collaborative work in the form of “mingas” and “Aynis” is necessary among small groups of farmers in order to realize hard tasks like sowing or harvesting. These labor force exchanges, despite of being very hierarchical, share common practices [40]–[42].
We built a representation of socio-agronomical landscapes of the central Andes at an altitude of 3000 m, which corresponds to the zone where most farmers cultivate potato. This landscape comprised three key elements: the socio-agricultural landscape, the potato moth population, and the groups of farmers (Fig. 1B). First, characteristics of the socio-agricultural landscape were set up using data from published field surveys: 1) the median community size in the study area was about 150 people [14] which roughly corresponded to 6 household units (i.e. a group of fields cultivated by one group of farmers). 2) The size of elemental cells was set up to 500 m×500 m in order to accurately model pest dispersion among cells with regards to insect's flight capability [43]. 3) Seasonal variability in climatic features (both temperature and rainfall) for each cell was obtained using the Worldclim data set [44].
Second, potato moth dynamics were simulated through a cellular automaton (CA) recently developed by our team [43]. Briefly, the CA is spatially explicit, stage-structured, and based on biological and ecological rules derived from field and laboratory data for T. solanivora's physiological responses to climate (temperature and rainfall). Main processes include moth survival (climate dependent), dispersal to neighbor cells through diffusion processes (density dependent), and reproduction (climate dependent) (see Fig. S1 in Text S1). In each time step (equivalent to one moth generation, about 2 months) the infestation grows and spread over household units. A Mathematical presentation of the underlying principles of the pest model, along with general results identifying the important simulation details and their consequences, are given in [45].
Third, to transfer the pest model into an ABM we populated the agricultural landscape with artificial agents acting individually upon pest dynamics (see Fig. 1A and Appendix for a complete description of the model structure). Briefly, each agent represented a group of farmers and was set with a behavioral model that guided his or her decisions. Potato moth control at the community level was modeled as emerging from IPM information spreading through agents that composed the community. The ability to learn IPM recommendations was considered as an adaptive trait that indirectly contributed to agent's fitness by improving their capability of controlling pest populations (and therefore assuring their crop production). Agents with different IPM knowledge interacted directly with each other to exchange information (agents with less information learned from other agents). We used a reciprocity model for cooperation in which agents paid a short term cost of cooperation for the future benefit of a community member's reciprocated cooperation [35]. Agents indeed perform multiple roles which constrict the amount of time and energy they may allot to any single activity. They perceived and controlled pest infestation levels in their field depending on their IPM knowledge (see below and Protocol S1, S2).
To explore the profitability of our IPM program as a function of the coupled dynamics of agent behaviors (and learning spillover) and pest population, we needed three pieces of field information: 1) the initial IPM knowledge of each agent in the community, 2) the relationship between IPM knowledge and pest control, and 3) the efficiency of IPM information diffusion between graduate and exposed agents (including a wide range of social factors influencing innovation diffusion). We acquired these data through a farm-level empirical survey from nationally representative samples of farmers in rural Highland Ecuador. Our database was obtained through a three-year household survey conducted in 2006–2008 in four provinces of the Ecuadorian highlands (Bolivar, Tungurahua, Cotopaxi, and Chimborazo) using standard household survey techniques [46]. Survey zones had not been covered by any educational program regarding potato moth management. In total, 293 potato grower families from about 100 different communities were interviewed, gathering data on IPM knowledge in communities and pest control. The efficiency of IPM learning and dissemination processes was assessed through farmer field schools as described in details by [30]. Briefly in each target community, we first performed a baseline study of IPM knowledge for as many community members as possible. Farmers interested in IPM extension were then trained through FFS procedures during eight one-day sessions over the duration of potato crop cycle (about 4 months). Each graduate farmer committed himself in training at least five other farmers. Informal discussion with trained framers revealed that the amount of time they dedicated in training other farmers varied greatly, between several hours to several days. Exposed farmers were then interviewed to measure their IPM knowledge and the efficiency of the IPM information diffusion process.
In each community, the IPM knowledge of agents were set up according to the frequency distribution presented in Fig. 2A (one agent with a score of 0, two with a score of 1, two with a score of 2, and one with a score of 3). We then increased the knowledge of the agent with a score of 3 to a score of 5 as if it had participated in a FFS (see Fig. 2B). This agent became the graduate agent of the community. According to FFS recommendations, this agent (in the case he or she was eager to cooperate) shared his information with exposed agents of his community (defined as an agent with a lower IPM knowledge). Once other exposed agents achieved, in turn, a higher IPM knowledge, they could also share their information with neighbor agents. An agent could share information with only one agent with a lower IPM knowledge (during this time the farmer could not control pest in his fields). When not sharing their information each agent was able to control pest in his field with an efficiency which depended on their IPM knowledge (following Fig. 2C). Again, the pest level in each cell was driven by both intrinsic population growth and diffusion from neighbor cells (see above).
Once the ABM was set up and sensitivity analysis performed (Fig. S2–S4 in Text S1), we further explored how agents' level of cooperation (i.e. how available agents were to share their information with others) would influence the benefits of our IPM program at both individual farmer and community levels. Because decision of poor farmers to cooperate for crop protection is likely to be driven by self-interest rather than altruism [14], [15], we assumed that farmers would be more prone to cooperate in IPM information diffusion when they perceive that a pest represents a danger for themselves. In our model, varying levels of cooperation were obtained by changing the pest infestation level that triggered a control action by agents (see Text S1). Each simulation was repeated 100 times over 120 time steps (i.e. about 20 years) and pest infestation levels were given for exposed agents, graduate agents, and the whole farmer community.
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10.1371/journal.ppat.1006313 | Predicting HIV-1 transmission and antibody neutralization efficacy in vivo from stoichiometric parameters | The potential of broadly neutralizing antibodies targeting the HIV-1 envelope trimer to prevent HIV-1 transmission has opened new avenues for therapies and vaccines. However, their implementation remains challenging and would profit from a deepened mechanistic understanding of HIV-antibody interactions and the mucosal transmission process. In this study we experimentally determined stoichiometric parameters of the HIV-1 trimer-antibody interaction, confirming that binding of one antibody is sufficient for trimer neutralization. This defines numerical requirements for HIV-1 virion neutralization and thereby enables mathematical modelling of in vitro and in vivo antibody neutralization efficacy. The model we developed accurately predicts antibody efficacy in animal passive immunization studies and provides estimates for protective mucosal antibody concentrations. Furthermore, we derive estimates of the probability for a single virion to start host infection and the risks of male-to-female HIV-1 transmission per sexual intercourse. Our work thereby delivers comprehensive quantitative insights into both the molecular principles governing HIV-antibody interactions and the initial steps of mucosal HIV-1 transmission. These insights, alongside the underlying, adaptable modelling framework presented here, will be valuable for supporting in silico pre-trial planning and post-hoc evaluation of HIV-1 vaccination or antibody treatment trials.
| Successful solicitation of the potential of neutralizing antibodies for HIV-1 prevention will require a deepened understanding of HIV-1 transmission and antibody neutralization. In this study, we experimentally determined molecular parameters of the HIV-1-antibody interaction, and subsequently used this knowledge to devise a mathematical model of HIV-1 infection and antibody neutralization in vivo. First, we experimentally confirmed that binding of one antibody per HIV-1 envelope trimer is sufficient for trimer neutralization. This finding, in combination with the number of trimers per HIV-1 virion, the number of trimers required for virus entry, and the affinity of antibody-trimer binding, enabled precise modelling of HIV-1 antibody neutralization. We employed our model for a post-hoc analysis of non-human primate infection studies, thereby obtaining estimates of HIV-1 neutralization in vivo and the probability for a single HIV-1 virion to initiate host infection. We further modelled HIV-1 infection and antibody neutralization during male-to-female transmission in the human host, which delivered estimates for the likelihood of HIV-1 transmission per sexual act and predictions of protective mucosal antibody concentrations. The quantitative insights into HIV-1 infection and antibody neutralization derived here, spanning from the molecular to the systemic level, contribute to a refined understanding of HIV-1 transmission and may prove useful for pre-study planning or post-hoc analyses of HIV-1 clinical trials and vaccine studies.
| Recent years have seen tremendous success in the isolation and characterization of broadly neutralizing antibodies (bnAbs) from selected HIV-1 infected patients. By binding to the HIV-1 envelope glycoprotein trimer (Env), bnAbs succeed to neutralize a majority of circulating HIV-1 strains. It is assumed that the elicitation of antibodies will constitute a crucial component of a successful HIV-1 vaccination strategy, and known bnAbs are intensely explored as templates for HIV-1 vaccine development [1–5]. Indeed, it has been conclusively demonstrated in animal models that passive immunization with bnAbs can protect against virus challenge, delay viral rebound and transiently lower viremia [6–19]. Furthermore, passive immunization in human patients demonstrated an impact of bnAbs on established HIV-1 infection [20–22], underscoring the potential relevance of bnAbs to prevent or treat HIV-1 infection.
However, despite this wealth of information on the protective effects of bnAbs in vivo, key parameters of the HIV-1 nAb interaction and HIV-1 host-to-host transmission remain ill-defined. This concerns both fundamental molecular aspects of Env trimer-nAb binding and systemic factors of mucosal HIV-1 transmission. Importantly, comprehensive knowledge of the molecular and systemic parameters governing HIV-1 transmission and nAb neutralization would empower in silico modelling of nAb activity and be instrumental to guide vaccine development or nAb treatment trials [23, 24]. We thus propose that precise numerical quantification of the parameters that steer nAb efficacy and in vivo HIV-1 transmission is needed. Moving towards this aim, we report here on a combined experimental-mathematical analysis providing comprehensive quantitative insight into mucosal HIV-1 transmission and nAb neutralization (Fig 1).
Starting at the molecular level, the first question we addressed regards the number of nAbs required to neutralize each HIV-1 Env trimer (the stoichiometry of neutralization, N). This number, in combination with the mean number of trimers per HIV-1 virion (η¯) and the number of trimers required for virus entry (T, Fig 1A) defines the number of nAbs needed to neutralize single HIV-1 virions or entire virion populations [25–31]. While such stoichiometric definitions may appear academic given the well-known potential of nAbs to neutralize HIV-1, we show here that these parameters are indeed crucial for an in-depth understanding of HIV-1 nAb neutralization and enable mathematical predictions of nAb neutralization potency.
Moving to the systemic level, we next addressed uncertainties in our understanding of mucosal HIV-1 transmission. Non-human primate studies revealed the interplay between nAb potency, in vivo nAb concentrations, and the resulting susceptibility or protection against virus infection [6–19]. However, a detailed systemic understanding of the mucosal infection process and the factors resulting in nAb protection from infection, ideally down to the single-virion level, are missing. Utilizing our stoichiometric model framework we performed a post-hoc analysis of selected animal studies, and obtained precise quantitative insight into mucosal nAb neutralization and the probability for single infectious HIV-1 virions to establish a systemic host infection.
Lastly, significant uncertainty is associated with the process and the probabilities of mucosal HIV-1 transmission in the human host via sexual contact. Per-exposure risk estimates of HIV-1 transmission vary widely, and uncertainty prevails regarding the concentration of nAbs in genital mucosal tissues that would provide protection from infection. This is not surprising, given the difficulties in estimating these parameters directly in the human population [32, 33]. Thus, in a final step we build on all previously determined parameters to model human HIV-1 penile-vaginal transmission. This analysis yielded predictions of HIV-1 male-to–female per intercourse transmission probabilities that match empirical data, and provided estimates of mucosal nAb concentrations expected to provide protection from HIV-1 infection.
We devised an experimental and mathematical framework to analyze interactions between HIV-1 virions and nAbs both on a molecular level and during HIV-1 transmission in vivo. Specifically, we (i) investigated the stoichiometric parameters of nAb binding to Env trimers on virions leading to neutralization, (ii) modelled nAb neutralization and HIV-1 infectivity both in vitro and in macaque passive nAb immunization virus challenge studies, and (iii) modelled HIV-1 transmission risk and protective effects of nAbs in the human host during penile-vaginal sexual contact. Our model framework relies on a defined set of parameters, retrieved from the literature or determined in this study (Fig 1A and 1B and S1 Table). Within our analytical framework, we conceptualize HIV-nAb interactions as follows:
In Fig 1C, we depict the sequence of experimental and mathematical approaches in this study, highlighting the parameters required for each successive modelling step.
The topic of HIV-1 neutralization stoichiometry has previously been studied experimentally and computationally, with N = 1 being the most common conclusion [26, 29, 34]. However, ambiguity in these estimates prevailed, predominantly due to previously poorly defined parameters of HIV-1 entry stoichiometry, T, and mean virion trimer number, η¯, which are essential for the analysis of N [27–29]. Here, we utilized recently determined values of T and η¯ [28] to conclusively estimate N across different HIV-1 strains and bnAbs identified in recent years, for which estimates of N are currently lacking. As precise information on N across nAbs is indispensable for an in-depth numerical understanding of HIV-1 nAb neutralization (Fig 1A), we set out to estimate N for a range of nAbs and HIV-1 strains.
To estimate N, we measured nAb neutralization of HIV-1 pseudoviruses carrying mixed trimers of nAb-sensitive and resistant Envs and analyzed the data with mathematical models (Fig 2A). Relative virus infectivities (RI) under saturating nAb concentrations were set in relation to the fraction of resistant Env (fR), shown for nAb 2F5 with Envs JR-FL wt (2F5 sensitive) and mutant JR-FL D664N (2F5 resistant) (Fig 2B, S1 Fig, S2 Table). Taking the JR-FL entry stoichiometry (T = 2) and mean trimer number per virion (η¯=11.8) into account (S3 Table), our model predicts different RI profiles for different N (Fig 2C). Mathematical analysis of the experimental data indicated a neutralization stoichiometry of N = 1 for JR-FL by nAb 2F5 (Fig 2C). We confirmed the robustness of the N = 1 estimate against variation in virus entry stoichiometry (T) and mean virion trimer number (η¯) by sensitivity analyses (Fig 2D). To investigate the observed deviations of the model fit from the experimental data (Fig 2C), we performed a goodness-of-fit analysis (Fig 2E). This analysis indicated that lower values of T and/or η¯ could result in better curve fits. While we expect T to be constant for each viral strain, fluctuations in η¯ from experiment to experiment are conceivable and provide a potential explanation for the deviation between experimental data and model predictions. In addition, mean virion trimer numbers of a given virus preparation may decrease over time as spontaneous Env shedding can occur resulting in non-functional trimers [36].
To further test variation in T and η¯ on predictions of N, we assessed N for nAb 2F5 against Env variants of HIV-1 strains JR-FL and NL4-3 that differ in T or η¯ (S2A–S2F Fig, S3 Table) [28, 37]. We indeed observed divergent RI profiles upon variation in T or η¯ as predicted by our model. However, for all Env variants tested we retrieved estimates of N = 1.
To explore if nAb avidity influences estimation of N, we compared nAb 2F5 IgG and Fab fragment. Both yielded overlapping RI profiles for strains JR-FL and NL4-3 and identical estimates of N = 1 (S2G–S2I Fig), confirming that estimation of N is independent of nAb valency.
Having validated our approach to estimate N (Fig 2), we sought to obtain a comprehensive analysis of N for various HIV-1 nAbs including VRC01, NIH45-46, PGV04, b12, PGT121, PGT128, PGT135, 2G12, PG9, PGT145, and 2F5 (S4 Table). We therefore generated a panel of Env mutants in five divergent HIV-1 strains with single or combined nAb resistance mutations (S1 Fig). Several of these Env mutants showed a significant reduction in virus infectivity (S2 Table). This is critical to note, as matched virus entry parameters, notably T and η¯, are a prerequisite for the analysis of N (S2 Fig). Indeed, we observed that strong infectivity differences between nAb-resistant and sensitive Envs in mixed trimer assays result in substantial deviations of the RI profiles that prohibit determination of N (S3 Fig). We thus restricted our analysis to mixed trimer assays with nAb sensitive-resistant Env pairs of comparable infectivity (≤ 2-fold infectivity difference). In a direct comparison of eight different nAbs across five Env mixed trimer combinations, we thereby obtained a consistent estimate of N = 1 irrespective of nAb epitope specificity, potency or HIV-1 strain (Fig 3A–3E, S4 Fig). The estimate of N = 1 was confirmed by bootstrap analyses (S5 Fig) and goodness-of-fit plots across all analyzed Env-nAb pairings (S6 Fig). As shown before (Fig 2E), the goodness-of-fit would in many cases be improved for lower values of T and/or η¯, possibly indicating slight deviations in these parameters within the experimental setup.
In the above analysis we employed a hard threshold model in which, according to our analyses, binding of one nAb to a trimer results in complete loss of function. Soft threshold models, which allow for partial loss of trimer functionality upon nAb binding, were introduced in earlier studies by us and others [26, 29]. To investigate how a soft threshold model would fit to our current data set, we re-analyzed our data accordingly [26]. Here, we allow the probability of a virion to infect a cell to scale with the number of functional trimers (a soft threshold for virus entry) and the functionality of a trimer to decrease by successive nAb binding (a soft threshold for neutralization). Our analysis revealed that the functionality loss of a trimer upon nAb binding to one subunit is dominant, that is, a soft threshold model is not supported (S7 Fig). This finding thereby underscores the assumption of a hard threshold for the stoichiometry of HIV-1 nAb neutralization.
To validate that N = 1 across all nAbs tested here, we tested a set of nAbs targeting different epitopes against Env variants with multiple nAb escape mutations, allowing parallel analysis of diverse nAbs on the same set of mixed trimer virus stocks (Fig 3F and 3G, S8 Fig, S2 Table). Two Env combinations (Fig 3F and 3G) were infectivity matched and allowed mathematical analysis of N, yielding N = 1 for all nAbs tested. To derive an estimate of N in the non-infectivity matched setups we included nAb 2F5 throughout, as we previously established that it neutralizes with N = 1 (Fig 2). Furthermore, nAb PG9, which is known to bind with only one nAb per trimer [38, 39], was included in two of these setups (S8 Fig). As 2F5 and PG9 yielded highly similar curves compared to the other nAbs, we conclude that our analysis univocally inferred N = 1 for all probed nAbs.
Regarding this consistent N = 1 estimate across all nAbs tested, we asked whether non-antibody Env inhibitors would show a similar neutralization behaviour. To test this, we generated Env mutants resistant against the peptide T-20, a clinically used HIV-1 entry inhibitor targeting the Env gp41 subunit [40]. Using mixed trimer assays, we found that T-20 neutralizes with an N = 1 stoichiometry (S9 Fig). This indicates that, regardless of inhibitor type, interference with one Env trimer subunit is sufficient for HIV-1 trimer neutralization.
Antibody responses in the majority of HIV-1 infections are largely ineffective in neutralizing the virus within the patient, and bear only limited neutralization potency and breadth against other HIV-1 strains. In contrast, the bnAbs available to date were isolated from rare HIV-1+ patients with high HIV-1 neutralization potency and breadth [41–43]. In our stoichiometry analysis, these bnAbs uniformly showed N = 1 (Fig 3A–3G). This raises the question if weakly neutralizing Abs as most commonly elicited during HIV-1 infection require a higher N, and whether N = 1 is a distinguishing feature of bnAbs. To test this, we determined N of the polyclonal antibody mix in HIV-1+ patient plasma. We first tested plasma from an individual with typical HIV-1 neutralization escape [44] and corresponding plasma neutralization-resistant and sensitive Env variants (ZA110 wt and ZA110-V1V21.7, respectively; S2 Table). Mixed trimer assays yielded N = 1 for the plasma Abs of this individual (Fig 3H). We further tested plasma from three chronically HIV-1 infected individuals (Pat117, Pat118, Pat122) showing only weak HIV-1 neutralization activity. We tested these plasma on mixed trimer stocks of Envs ZA110 wt (resistant) and ZA110 ΔV1V2 (sensitive) and further included weakly neutralizing Abs b6, 17b and 48d in this analysis (Fig 3I). Comparison of the RI profiles indicated identical N for all probed plasma and nAbs. Additionally, plasma Pat122 neutralized HIV-1 strain JR-FL in an infectivity-matched setup with N = 1, as did several nAbs (Fig 3J).
Thus, irrespective of nAb potency, breadth or epitope specificity, neutralization of HIV-1 trimers requires only a single Env subunit to be bound by antibody (Fig 3K). This estimation of N defines numerical requirements for HIV-1 antibody neutralization [27, 29–31, 45], which we employed in subsequent modelling steps to assess HIV-1 virion population neutralization in vitro and in vivo.
We previously established a mathematical framework that predicts the number of nAbs required to neutralize a given HIV-1 virion population based on the stoichiometry parameters N, T and η¯ [27]. The conclusive estimation of N (Figs 2 and 3), together with previously determined parameters T and η¯ [28], now enabled us to use our framework for quantitative predictions of nAb neutralization. We extended the framework by including the affinity of nAb binding to Env trimers (represented by the binding constant, KD) [35, 46] to predict the fraction of neutralized HIV-1 virions for given nAb concentrations. In essence, this allowed us to simulate HIV-1 nAb neutralization curves in silico (Fig 4A).
For this analysis we utilized nAb binding constants, KD, recently reported for the HIV-1 strain BG505 SOSIP trimer [49]. We employed these KD values together with T and η¯ of BG505 (S3 Table) and N = 1 to model nAb neutralization of an HIV-1 BG505 virion population (Fig 4B–4D). As expected, we found that nAbs with high Env binding affinity (low KD) are predicted to require lower concentrations to achieve virion population neutralization (Fig 4B). The required nAb concentrations increase slightly with fewer trimers needed for HIV-1 entry (low T) (Fig 4C) and higher virion trimer content (high η¯) (Fig 4D). The latter two trends can be rationalized as follows: in both cases (a small T or a high η¯), more nAbs will be needed to neutralize a given virion population, resulting in higher predicted nAb concentrations.
The relation between nAb trimer binding and HIV-1 virion population neutralization is influenced by all parameters included in our model (Fig 4A). Especially the influence of T and η¯ on nAb neutralization predictions should not be underestimated. This is highlighted in Fig 4E, depicting estimated values of nAb concentrations required for 50% virus population neutralization (IC50) in dependence on T and η¯.
In addition to predicting nAb neutralization curves and IC50 values, we determined nAb concentrations required to achieve sterilizing neutralization of HIV-1 virion populations. Due to unproductive nAb binding to Env trimers (Fig 4H) [27], the fraction of Env subunits required to be bound by nAb for sterilizing neutralization of a virion population increases with virion population size (Fig 4F). Likewise, the predicted nAb concentrations required for sterilizing neutralization of virion populations increase with virion population size (S10A–S10C Fig). As shown in Fig 4B–4D for nAb neutralization curves, these nAb concentrations are influenced by nAb KD, T and η¯ (S10A–S10C Fig). Of note, only nAb KD has a direct linear relationship with sterilizing nAb concentrations, while the influence of T and η¯ follows non-linear relations (S10D–S10F Fig).
We noted that our predicted neutralization curves show steeper slopes than commonly observed in HIV-1 neutralization assays (S11A Fig). We thus asked which parameters of our model could explain this deviation, and found that assuming broader virion trimer number distributions (i.e., higher variance in trimer numbers between virions) results in less steep predicted neutralization curves (S12 Fig). Of note, we also observed that our predicted curves are in closer agreement to in vitro neutralization data obtained with replication-competent virus and PBMC target cells (S11B Fig). While a detailed analysis of this relation and the underlying parameters is beyond the scope of this manuscript, this observation may be taken as indication that our model predicts neutralization curves better for replication-competent virus than for pseudovirus preparations.
To test the predictive power of our in silico approach, we compared experimentally derived IC50 values of HIV-1 strain BG505 and nAbs VRC01, PGV04, PGT121, PGT123, PGT145 and 2G12, originating from in vitro experiments by three independent laboratories [47–49], with nAb IC50 values estimated by our model (Fig 4G, S11 Fig, S5 Table). Our predicted IC50 values for nAbs VRC01 and PGT121 were in close agreement with the measured values. For nAbs PGT123 and 2G12, we observed wide variations in experimentally derived IC50 values; interestingly, the IC50 values predicted by our model lay in between these values. For nAbs PGV04 and PGT145, our estimated IC50s are slightly lower and higher than experimentally determined IC50s, respectively, though not far off (see below for a detailed discussion of the PGT145 IC50 estimation). This good agreement between experimental and predicted nAb IC50s highlights that the parameters included in our model (N, T, η¯, nAb KD and nAb concentration) capture relevant steps of HIV-1 virion neutralization. Of note, our model performs significantly better than a simpler model based on nAb KD alone (S13 Fig).
We also derived predictions of nAb neutralization for scenarios of N = 1 but assuming that exclusively one nAb can bind one of the three epitopes displayed on each trimer (a binding behavior described for nAbs PG9, PG16 or PGT145 [38, 39, 49]). We compared these predictions to our standard model where we assume that all three trimer subunits can potentially be bound, although only one Env subunit needs to be bound to achieve neutralization. In this case, binding of the second and third nAb represents unproductive binding of nAbs, since the trimer is already neutralized by binding of the first nAb (Fig 3K). Importantly, we assume that also for PG9-like nAbs, initially the same number of epitopes is present as for “typical” nAbs (i.e., three per trimer), with the difference that binding of PG9-like nAbs shows negative cooperativity: after binding of the first antibody the remaining two epitopes are inaccessible. In our analysis, we assume two nAbs with the same KD for their protomeric epitopes, but either a three-nAb-per-trimer or one-nAb-per-trimer binding behavior (Fig 4H). According to our predictions, binding of only one nAb per trimer results in a 2-fold decreased IC50, thereby demonstrating the effect of unproductive nAb binding (Fig 4H). Intriguingly, the PGT145 IC50 predictions using this model are closer to experimental PGT145 IC50 values than the predictions obtained with the standard model (Fig 4G). This indicates that nAbs with a one-nAb-per-trimer binding behavior should have a slight neutralization advantage, especially under conditions of low nAb concentrations.
We next predicted nAb neutralization efficacy and host infection probability in vivo by re-assessing data from four studies of rhesus macaque vaginal virus challenge following passive immunization with nAbs b12 [8], 2G12 [7], PGT121 [10] and PGT126 [16]. Our approach consists of two parts: first, we utilized the nAb neutralization prediction model developed above (Fig 4) to estimate how many virions of the challenge dose are neutralized in vivo in dependence on the nAb immunization regime. Secondly, we connected this number of non-neutralized virions to the number of animals protected or infected for a given immunization and challenge regime. Ultimately, this allows us to derive the probability for a single infectious virion to establish a host infection (Fig 5A).
This modelling of HIV-1 neutralization in vivo following vaginal challenge requires data on vaginal nAb concentrations and nAb KD as well as the virus-specific parameters of inoculum size, η¯, T and N. Importantly, we also require the probability of virions to penetrate mucosal epithelial layers to come in contact with target cells (ppen); this parameter was recently estimated elegantly by Carias et al. [50]. The four selected macaque studies reported the majority of nAb and virus-specific parameters (S6 and S7 Tables), allowing us to employ our modelling approach in a post-hoc analysis. Essential data of the four analyzed studies are listed below:
To derive nAb KD data for the challenge virus Env P3 used in all four studies, we utilized the known IC50 for 2G12, b12, PGT121 and PGT126 against P3 and inferred the corresponding KD values by linear regression (S6 and S7 Tables). We further assumed that virus strain P3 has T = 2 and η¯=20 as previously determined for the closely related strain P3N (S3 Table). These data enabled us to predict neutralization curves for the four nAbs against SHIV-P3 (S14 Fig). We then superimposed these nAb neutralization curves with the estimated nAb concentrations in the vaginal mucosa at the time of challenge. In this analysis, we allowed both nAb KD and mucosal nAb concentrations to vary 2-fold around the estimated values to account for potential inaccuracies in the extrapolation procedures. This analysis yielded windows for the extent of SHIV-P3 inoculum neutralization by the four nAbs in vivo (S14A Fig, colored parts of the neutralization curves). We predicted almost complete SHIV-P3 inoculum neutralization for the highest PGT121 and PGT126 immunization regimes, while the b12 and 2G12 immunizations and the low doses of PGT121 and PGT126 immunizations yielded intermediate SHIV-P3 neutralization levels, mirroring the protective effects seen in the respective challenge studies [7, 8, 10, 16].
Importantly, our model also allowed us to determine the probability that a single infectious virion starts a host infection. To this end, we first calculated the fraction of SHIV-P3 virions that remained potentially infectious in the four macaque challenge studies, i.e. virions with at least T non-neutralized trimers (S14B Fig). Multiplying these fractions of non-neutralized virions with the total number of virions in the respective challenge inocula provided an estimate of the number of virions that remained potentially able to infect each animal (Fig 5B).
To investigate how this number of non-neutralized, infectious virions translates into systemic host infection we utilized a further parameter. During vaginal challenge not all virions of the inoculum will penetrate the vaginal epithelial layers to come in contact with mucosal CD4+ target cells; the probability of epithelial penetration is given by ppen. We assumed that only 0.235% of virions will penetrate the genital tract epithelium as experimentally estimated [50]. Based on this, we derived the number of infectious virions in the four challenge studies that can potentially contact mucosal target cells and set these number in relation to the observed infection outcomes (Fig 5A). This delivered the probability for a single infectious virion to establish a systemic host infection, denoted ψ (Fig 5C). Intriguingly, we obtained closely matching ψ-estimates for the four independent macaque studies, with an average value of ψ^=1.65×10−5.
Next, we asked whether a similar analysis would be possible using in vitro nAb neutralization data (i.e., nAb IC50 and Hill parameter). For this analysis we utilized in vitro neutralization data of SHIV-P3 with nAbs PGT121, PGT126 and b12 (S11B Fig) and analyzed the respective macaque challenge studies. We obtained a closely matching estimate of ψ, i.e. ψ^=2.95×10−5 (S15 Fig) as with our mechanistic population neutralization model (Fig 5C). Based on these two complementary analyses, we conclude that only one in ~30.000 to 60.000 infectious virions that have penetrated the vaginal epithelial layers will succeed in systemically infecting the host. This estimate thereby provides a quantitative evaluation of the bottlenecks encountered by HIV-1 during transmission in the genital mucosa.
We next used the estimate of HIV-1 virion host infection probability (ψ^=1.65×10−5) to predict male to female HIV-1 transmission risk per sex act and nAb protection efficacy. In this analysis, we define the per-act HIV-1 inoculum as the number of HIV-1 virions per ejaculate; this number is given by per-ejaculate semen volume and semen viral load. We then assumed that only a fraction of virions in the inoculum will penetrate the vaginal epithelial layers, defined by ppen [50]. We next determined the number of virions that are potentially infectious, i.e. have at least T trimers, and multiplied the fraction of penetrating infectious virions with the probability for each infectious virion to initiate host infection, ψ. Thus, we obtained the probability of host infection in dependence on HIV-1 inoculum size. In addition, we modelled protective effects of nAbs present in the vaginal mucosa; here, the extent of HIV-1 inoculum neutralization (and hence the magnitude of protection) depends on mucosal nAb concentration and nAb binding affinity (KD) for the Env trimer.
In a first step, we calculated infection probabilities of women during a single penile-vaginal intercourse in dependence on HIV-1 inoculum size, in absence of nAbs. We performed this analysis for HIV-1 virions with the entry characteristics of the transmitted-founder strain BG505 (η¯=9.5 trimers per virion, T = 2 entry stoichiometry; S3 Table). The resulting relation between HIV-1 inoculum size and per-act female host infection probabilities is shown in Fig 6A.
This relation needs to be interpreted with regard to empirical data of human HIV-1 semen viral loads and per-act transmission risk. In chronic HIV-1 infection, semen viral load typically ranges between ≤ 200 and 20.000 HIV-1 RNA genome copies per mL seminal plasma (i.e., ≤ 100 to 10.000 virions / mL, assuming two viral genomic RNA copies per virion). During acute infection, semen viral load typically ranges between 20.000 and 200.000 RNA copies / mL (10.000 to 100.000 virions / mL) [33]. In rare cases, semen viral loads of several million RNA copies / mL (>1.000.000 virions / mL) were reported [51]. Assuming an average per-ejaculate semen volume of 3 mL [51, 52], this results in typical HIV-1 inoculum sizes of ≤ 300 to 30.000 virions during chronic HIV-1 infection, 30.000 to 300.000 virions during acute infection, and > 3.000.000 virions in rare cases. The corresponding per-act female infection probabilities predicted by our model are ≤ 0.001 to 0.11% during chronic infection (one in ≥ 87.000 to one in 876 sexual contacts), 0.11 to 1.14% during acute infection (one in 876 to one in 88 sexual contacts), and maximum values exceeding 11% (one in 9 sexual contacts) (Fig 6A).
How do these estimates compare to empirical data on female per-act infection probabilities? A frequently stated number for male-to-female penile-vaginal transmission risk is 1 in 1000 sexual contacts (0.1%). However, as previously discussed [32, 53, 54], this likely represents a lower bound of per-act infection risk and may be dangerously misleading. Our model predicts that an HIV-1 inoculum of ~26.000 virions (i.e., a semen viral load of ~17.000 RNA copies per mL, assuming 3 mL semen volume) would result in a female per-act infection risk of 0.1%. This semen viral load lies within the range typically observed during chronic HIV-1 infection. In contrast, several studies reported per-act penile-vaginal female infection risks between 0.5 and 10%, presumably linked to acute or late-stage HIV-1 infection of the male partner and correspondingly higher semen viral loads ([32, 53, 54] and references therein). Indeed, our model predicts HIV-1 inoculums of 130.000 to 2.770.000 virions (semen viral loads of 87.000 to 1.850.000 HIV-1 RNA copies / mL) to result in per-act infection risks of 0.5 to 10%. These values represent the range of semen viral loads typically observed during acute HIV-1 infection and rare cases of exceedingly high semen viral load. Thus, our model appears to precisely capture and recapitulate the interplay between HIV-1 inoculum size and infection probability during HIV-1 transmission in the female genital tract suggested by empirical studies.
Having thus obtained good indications that our model and the decisive parameter of ψ reliably mirror empirical data of human mucosal HIV-1 transmission, we next aimed to model the protective effects of nAbs present in the vaginal mucosa. First, we assumed an HIV-1 inoculum of 100.000 virions and modelled neutralization by four nAbs with a broad range of KD (Fig 6B). Not surprisingly, this showed that with increasing nAb KD (decreased Env trimer binding affinity), higher mucosal nAb concentrations are required to provide protection from infection. Specifically, we found that nAbs with a high Env binding affinity, such as PGT121, would afford protection from infection well below mucosal nAb concentrations of 0.1 μg/mL. This suggests that nAbs with high Env binding affinity could exert protective effects in vivo at relatively low doses.
We next looked more closely at the protective effects of a nAb with intermediate Env affinity and reduced potency, reasoning that such nAbs may be more readily inducible by vaccination. We chose nAb b12 as an example and modelled its protective effects in the vaginal mucosa against various HIV-1 inoculum sizes (Fig 6C). This analysis showed that such medium-potent nAbs may provide considerable protective effects starting in the range of 1 μg/mL mucosal nAb concentration.
In summary, the analyses shown in Fig 6 suggest that our model has the potential to retrieve information relevant to human mucosal HIV-1 transmission, offering opportunities for further model extension and application in HIV-1 vaccine research (Fig 7).
Broadly neutralizing antibodies are considered a crucial component of many vaccines or infectious disease therapeutics. For HIV-1, defining the best lead antibodies has proven complex and affords intensive in vitro and in vivo efficacy testing in both animal models and humans. Thus, in silico modelling approaches that support such trials would be of enormous value; however, establishment of such models depends on detailed mechanistic knowledge of HIV-1 transmission and nAb neutralization processes. Here, we provide a step towards this by presenting an experimental and mathematical analysis of HIV-1 nAb neutralization spanning from the molecular to the organismal level, providing highly relevant quantitative insights into the initial steps of mucosal HIV-1 infection and its inhibition by nAbs.
We first assessed the molecular interplay between nAbs and the HIV-1 Env trimer and confirmed that nAb occupancy of one Env subunit is sufficient for trimer neutralization (N = 1). It is important to note that N refers to the number of subunits within one trimer required to be nAb-bound for loss of functionality. This definition does not exclude the possibility that one nAb binds two adjacent trimers on the virion surface, which would result in lower nAb concentrations required for virion population neutralization.
N = 1 proved true for all nAbs tested here, irrespective of epitope specificity or nAb breadth and potency, including weakly neutralizing polycloncal IgG in HIV-1+ patient plasma (Figs 2 and 3). This indicates that the intricate machinery of the HIV-1 Env trimer, required to mediate binding to and fusion with the target cell membrane, is dependent on full functionality of all three trimer subunits. By confirming that HIV-1 neutralization follows an N = 1 stoichiometry, we defined a decisive numerical requirement for HIV-1 virion neutralization by nAbs. Indeed, in combination with the mean number of trimers per virion (η¯) and the number of trimers required for entry (T), N defines the threshold number of antibodies required to neutralize a single HIV-1 virion or entire virion populations [27].
Alongside a range of additional parameters that were only recently determined (Fig 1), the estimation of N enabled us to use a mathematical model to analyze the interplay between nAbs, HIV-1 virion populations and the animal or human host during HIV-1 infection. We demonstrate the power of this model through several analyses. First, we predicted IC50 values of various nAbs and found that the predicted values closely matched empirical IC50 values (Fig 4). Next, we analyzed published data of macaque passive antibody immunization, vaginal virus challenge studies (Fig 5). We utilized the data presented in these studies to recapitulate virus inoculum neutralization by nAbs in vivo and to estimate the probability that a single infectious virion starts a productive host infection (ψ^=1.65×10−5).
In a final set of analyses, we applied the estimate of ψ to model HIV-1 infection in the female genital tract and its neutralization by nAbs (Fig 6). We found that the per-act likelihood of female HIV-1 infection is clearly driven by the size of the virus inoculum, and we retrieved per-act virus transmission probabilities in good agreement with empirical estimates [32, 33, 51, 53]. Furthermore, we provide estimates for mucosal nAb concentrations required to provide protection from infection, indicating that nAb concentrations in the low μg/ml range may provide protection from mucosal HIV-1 transmission. Similar concentrations of HIV-1-specific IgG are readily detectable in the vaginal mucosa of women with chronic HIV-1 infection, raising the possibility that such vaginal IgG concentrations may be achievable by vaccination [55].
The analyses of female infection risk shown in Fig 6 represent the synthesis of all previous analyses (Figs 2 to 5), incorporating all modelling parameters (Figs 1C and 7). It will remain difficult if not impossible to precisely determine per-act HIV-1 inoculum sizes and infection outcomes in a human study population as well as the effect of nAbs thereon. The data we obtained here suggests that our mathematical framework has the potential to retrieve some of this much needed information by in silico modelling of in vivo HIV-1 infection and nAb neutralization. Given this potential relevance, in the following we discuss these estimates and the underlying model assumptions in more detail.
First, our model framework was built on data from macaque vaginal challenge studies. While host differences certainly exist, basic principles of mucosal HIV transmission in macaques and humans are similar [56, 57]. Penetration of mucosal barriers has been shown in both cases to be a rapid but inefficient process resulting in focal infection of few mucosal CD4+ cells, with productive host infection frequently ensuing from a single transmitted-founder virus [50, 58–60]. The probabilities we derive here for virion infectivity in macaque vaginal mucosal transmission should thus provide valuable insight into human infection processes.
Secondly, we would like to point out the importance of patient-to-patient variation, especially for the relation between HIV-1 inoculum size and host infection probability (Fig 6A). Of note, the per-patient and per-act HIV-1 inoculum size may differ widely based on variation in semen viral load and semen volume, which may both range over several orders of magnitude [33, 51, 52, 61, 62]. Our analyses support the hypothesis that HIV-1 transmission probability from an infected male partner with semen viral loads as typically observed in the chronic stage of HIV-1 infection is relatively low (≤ 0.1%), and that the HIV-1 pandemic may be primarily driven by transmissions occurring through high semen viral loads during acute or late-stage infections [33, 63].
Third, we focused our analysis on male to female HIV-1 infection, as it represents the most frequent pathway of human HIV-1 transmission [64]. However, our model is not per se limited to the setting of penile-vaginal transmission and can be adapted to capture rectal, mother-to-child or intravenous HIV-1 transmission. For example, many recent non-human primate studies tested passive nAb immunization followed by rectal virus challenge [11–13, 16, 18, 19]. The data presented in these studies could be leveraged by our model framework, as demonstrated here for vaginal challenge, to estimate ψ for rectal infection and subsequently test hypotheses for rectal HIV-1 transmission risk.
Fourth, mucosal nAb levels are challenging to measure and were not available in all macaque challenge experiments analysed here. Thus, we extrapolated mucosal nAb concentrations based on blood plasma nAb levels, using the ratio between plasma and mucosal nAb concentrations from studies where both parameters were experimentally determined. While this provided valuable estimates, precise measurement of mucosal nAb levels would be ideal for future studies building on our model framework.
Overall, we would like to note that our model should be viewed as a starting point to further investigate in vivo HIV-1 infection and nAb neutralization processes, as it focuses solely on virus-antibody interactions leaving additional factors, such as the mucosal milieu and nAb effector functions, not accounted for. The model can and should be fine-tuned by incorporation of additional parameters once they become known (Fig 7). Mucosal HIV-1 infection is incompletely understood and bottlenecks in transmission that may specifically select viral variants have not been specified. It remains debated whether HIV-1 transmitted-founder strains show distinct properties or whether transmission is purely stochastic [65–68]; our approach may help to shed light on this important question. Additionally, a range of factors are considered to influence mucosal HIV-1 transmission including epithelial micro-trauma, local inflammation, presence of other sexually transmitted infections, mucosal target cell availability, and innate immune defences [24, 69–71]. Reliably parametrizing these conditions will remain challenging but could add valuable information in forthcoming studies utilizing our model framework. Furthermore, our model currently does not include selected nAb features that may impact on neutralization efficacy, including the effect of neutralization plateaus [13, 49, 72], the contribution of Fc-mediated mechanisms [73–75], the effect of non-neutralizing Abs [76–79] and the role of IgA in HIV-1 inhibition [80–82]. Most importantly with respect to neutralization efficacy, information on nAb half-life and tissue distribution is only starting to emerge [83, 84]. In combination, these factors likely contribute substantially to inter-patient variation in susceptibility to HIV-1 infection, and it will be highly interesting to incorporate them in future model extensions.
By estimating the probability that an infectious HIV-1 virion establishes an infection (ψ), and by being able to predict the effect of mucosal nAbs on HIV-1 infection risk, our study occupies a sweet spot between HIV-1 mathematical epidemiology and virus dynamics studies. Modelling studies of the epidemiological spread of HIV-1 [85–87], on the one hand, are typically not accounting for the transmitted virus dose, but rather assume a fixed probability of transmission between donor and recipient upon encounter. While the probability of transmission is often stratified by cofactors (such as sex or age of donor or recipient, or set-point viral load in the donor) it lacks the detailed mechanistic underpinning that our approach provides. Virus dynamics studies of HIV-1, on the other hand, are mostly concerned with the virus dynamics once the infection has been established, often focusing on changes brought about by treatment [88, 89]. In these studies, the anatomy of the host is usually not considered in detail. The necessity of integrating the within-host dynamics into the epidemiological modelling of any pathogen has been theoretically conceived [90, 91]. In the context of HIV-1, however, such so-called nested or embedded approaches have so far been used only in theoretical studies on the evolutionary dynamics of HIV-1 [92–94]. In a few studies, the probability of establishment of an infection along with its potential modulators, such as microbicides, T cells, exposure history, or latency, has been investigated for HIV-1 and SIV [95–101] as well as for other pathogens [102]. However, these studies did not provide the bottom-up empirical link between the establishment of an infection and mucosal antibody levels that is central to our approach. A notable exception is the study by McKinley et al. [103] who presented a model for the early virus dynamics and the effect of antibodies. In contrast to our model, however, they predict neutralization success purely on the basis of the binding kinetics (KD) of antibodies to the HIV-1 Env trimer. In summary, our study thus provides a comprehensive set of essential and empirically-derived parameters for modelling efforts that aim to combine the within and between host dynamics of HIV-1 infection.
In conclusion, our combined experimental-mathematical approach delivers precise estimates of virion-antibody interaction stoichiometry, single-virion mucosal transmission probability, male to female per-act infection risk and in vivo nAb neutralization efficacy. These data represent novel quantitative insight into both the molecular details of HIV-1 antibody neutralization and the systemic level of mucosal HIV-1 infection. Our findings suggest that the model framework introduced here incorporates essential parameters that capture decisive steps of early HIV-1 infection and nAb neutralization, and thus provides means to predict and analyse the effects of nAbs on blocking mucosal virus transmission in vivo. Furthermore, our framework offers vast options for model extensions to investigate additional parameters or entirely different infection scenarios (Fig 7). Thus, our work represents a versatile, generalizable modelling tool to enhance our fundamental mechanistic knowledge of virus-antibody interactions and viral mucosal transmission, and can serve as stepping stone for planning and post-hoc evaluation of HIV-1 antibody-based treatment and vaccine trials.
Plasma samples from chronically infected individuals (ZA110, Pat117, Pat118, Pat122) were obtained from biobank samples previously collected during two approved clinical trials, the Swiss treatment interruption trial [104–108] and the Zurich Primary HIV-infection (ZPHI) study (ClinicalTrials.gov identifier NCT00537966) [109]. Written informed consent was obtained from all individuals in the respective studies according to the guidelines of the ethics committee of the canton Zurich.
293-T cells (obtained from the American Type Culture Collection) and TZM-bl cells [110] (obtained from the NIH AIDS Reagent Program) were cultured as described [111]. The origin of HIV Env plasmids is listed in S3 Table. Env point mutations were generated by site-directed mutagenesis (Agilent QuikChange II XL kit). All Env mutants were confirmed by sequencing. V1V2-deleted Envs were previously described [44]. The Luciferase reporter HIV-1 pseudotyping vector pNLLuc-AM was previously described [111].
MAbs (see S4 Table) were kindly provided by: PG9, PGT121, PGT128, PGT135, PGT145, b12 and b6 by Dr. Dennis Burton, The Scripps Research Institute, La Jolla, USA. 2F5 and 2G12 by Dr. Dietmar Katinger, Polymun Scientific, Vienna, Austria. 17b and 48D by Dr. James Robinson, Tulane University, New Orleans, USA. 447-52D was purchased from Polymun Scientific. Expression plasmids for VRC01 and PGV04 were provided by Dr. John Mascola, National Institutes of Allergy and Infectious Diseases, Bethesda, USA. Expression plasmids for NIH45.46 and 1.79 were provided by Dr. Michel Nussenzweig, The Rockefeller University, New York, USA. T-20 was purchased from Roche Pharmaceuticals.
To produce HIV-1 pseudovirus stocks expressing mixed trimers with varying ratios of nAb-sensitive to nAb-resistant Env, 293-T cells in 6-well plates (250.000 cells per well in 2 ml complete DMEM, seeded 24 h pre-transfection) were transfected with 3 μg pNLLuc-AM and 1 μg Env expression plasmids, using polyethyleneimine (PEI) as transfection reagent. The ratio of sensitive to resistant Env was varied to yield combinations with 100, 90, 70, 50, 30, 10 and 0% of resistant Env. After overnight incubation the transfection medium was replaced with 2.5 ml fresh complete DMEM and virus-containing supernatants were harvested 48 h post transfection. To determine virus infectivity, serial dilutions of virus stocks were added to TZM-bl cells in 96-well plates (10.000 cells per well) in DMEM supplemented with 10 μg/ml DEAE-Dextran. TZM-bl infection was quantified 48 h post-infection by measuring activity of the firefly luciferase reporter (in arbitrary relative light units, RLU). The neutralization activity of mAbs and patient plasma against the mixed trimer virus stocks was evaluated on TZM-bl cells as described [111]. Sufficiently high starting concentrations of inhibitors were chosen to yield clear neutralization plateaus, and data were fitted in GraphPad Prism version 7.0 using the sigmoidal dose-response (variable slope) function. In cases where no clear neutralization plateaus were obtained (less than two consecutive data points giving the same level of neutralization), the position of the expected plateau was provided by the curve fit. Subsequently, the relative infectivity (RI) of each virus stock under saturating inhibitor concentrations was calculated. The resulting RI values were plotted over the fraction of resistant Env (fR) of each virus stock and the data were analyzed with mathematical models.
293-T cells were transfected with Env and Rev plasmids and processed for flow cytometry as described [37]. Env on the cell surface was detected with biotinylated mAb 2G12 (5 μg/ml) and Streptavidin-APC (BioLegend, San Diego, USA; 1:400 dilution) or Abs 1.79 and PG9 (5 μg/ml) and Cy5-conjugated F(ab′)2 goat anti–human IgG (Jackson ImmunoResearch, West Grove, USA; 1:500 dilution) followed by cell analysis on a CyAN ADP flow cytometer (Beckman Coulter, Brea, USA).
To tackle the question how high nAb concentrations must be to neutralize a given virion population, we first study the number of nAbs required to perform this task. We start with a virus population with nv virions. Each virion has a random trimer number Si that follows a discretized Beta distribution with mean η¯ and variance v=49/14×η¯ (see above). We let nAbs bind to these virions until all virions are neutralized. Neutralization of virion i is achieved when at least (Si − T + 1) trimers are bound to at least N nAbs. This simulation is repeated nr = 1000 times and the mean of the nAb numbers to reach neutralization is calculated. We introduced this procedure in Magnus and Regoes, 2011 [27].
To transition from nAb numbers required for virion neutralization to nAb concentrations, we model the binding of a nAb, Ab, to an envelope protein, E, with a chemical binding equation [35]:
E+Ab⇌kdkaEAb
where kd is the off-rate constant and ka the on-rate constant. Assuming a first order reaction, the quotient of the product of the reactant concentrations divided by the product concentration follows:
KD≔kdka= c(E)c(Ab)c(EAb)
The fraction of bound envelope proteins, fb, when the equilibrium is reached can then be calculated by
fb= c(EAb)c(E)+c(EAb)=(c(E)c(EAb)+1)−1= (KDc(Ab)+1)−1
This equation can be transformed to
c(Ab)= KDfb1−fb
With this equation it is possible to determine the nAb concentrations needed for sterilizing neutralization by determining the fraction of bound envelope proteins with the simulation tool described above.
We start with calculating the percentage of neutralized virions when nAb nAbs are bound to nv virions. Each virion has a discretized Beta distributed trimer number, Si, with mean η¯ and variance v=49/14×η¯. We then distribute the nAb nAbs to the virions such that each envelope protein has the same probability of being bound. Thus Ai nAbs bind to virion i.
The probability that a virion with s trimers is neutralized when a nAbs bind to the complete virion can be calculated as follows:
P(neut|S=s,A=a)=0 if a<(s−T+1)N; P(neut|S=s,A=a)=1 if a≥3(s−T)+(N−1)T+1
and
P(neut|S=s,A=a)=∑(y1, y2,y3)∈εa,sP(Y1=y1, Y2=y2, Y3=y3)
where P(Y1 = y1, Y2 = y2, Y3 = y3) is the probability that yj trimers are bound to j nAbs and ℇa, s is the set of all combinations of a nAbs to s trimers such that at least (s − T + 1) trimers are bound to at least N nAbs. For N = 1 this set is
εa,s=((y1,y2,y3)∈ℕ03|y1=m,y2=3ζ−a−2m,y3=a+m−2ζ with 0≤m≤min(s,a) and s−T+1≤ζ≤s)
The fraction of neutralized virions, fnv, can thus be calculated by:
fnv=1nv∑i=1nvP(neut|Si=si,Ai=ai)
To calculate the mean fraction of neutralized virions, the above described procedure is performed nr = 1000 times.
Several nAbs have been shown to bind with only one antibody per trimer, including nAbs PG9, PG16 and PGT145 [38, 47, 49]. To account for this binding behavior, we extended the above described model. We still assume that each trimer has three epitopes. However, as soon as one nAb binds to a trimer, no additional nAbs can bind. The fraction of bound epitopes is then fb, epitopes = 1/3fb, trimers and a virion with s trimers is neutralized when (s − T + 1) trimers are bound by one nAb.
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10.1371/journal.pcbi.1001082 | Loss of Genetic Redundancy in Reductive Genome Evolution | Biological systems evolved to be functionally robust in uncertain environments, but also highly adaptable. Such robustness is partly achieved by genetic redundancy, where the failure of a specific component through mutation or environmental challenge can be compensated by duplicate components capable of performing, to a limited extent, the same function. Highly variable environments require very robust systems. Conversely, predictable environments should not place a high selective value on robustness. Here we test this hypothesis by investigating the evolutionary dynamics of genetic redundancy in extremely reduced genomes, found mostly in intracellular parasites and endosymbionts. By combining data analysis with simulations of genome evolution we show that in the extensive gene loss suffered by reduced genomes there is a selective drive to keep the diversity of protein families while sacrificing paralogy. We show that this is not a by-product of the known drivers of genome reduction and that there is very limited convergence to a common core of families, indicating that the repertoire of protein families in reduced genomes is the result of historical contingency and niche-specific adaptations. We propose that our observations reflect a loss of genetic redundancy due to a decreased selection for robustness in a predictable environment.
| Bacteria have found many niches in which to live, and one of them is inside eukaryotic cells. These intracellular bacteria include endosymbionts like Buchnera aphidicola, which provides its host, an aphid, with essential amino acids, as well as many pathogenic bacteria such as Mycobacterium leprae and Rickettsia prowazekii, the causative agents of leprosy and typhus, respectively. Even though they all evolved their intracellular lifestyle independently, all these bacteria lost a large number of genes as they adapted to their hosts, presumably because the rich environment where they found themselves no longer required such functions. For example, biosynthetic genes are frequently lost. It has been a matter of debate what decides whether a gene can be lost in evolution, and intracellular bacteria have been used as model systems to study these processes. In our study, we propose that when adopting an intracellular lifestyle, these bacteria extensively lost duplicated genes. We propose that this represents loss of copy redundancy that is possible because the host cell represents a predictable environment in which there is little pressure for the bacteria to retain these backups. In simplistic terms, if the road is always smooth, you are probably OK without a spare tire.
| Living organisms evolved to be functional in frequently harsh and variable environments, buffering internal molecular noise, genetic variation and unpredictable environmental fluctuations. Such ability is termed robustness [1]. One common source of robustness is genetic redundancy, in which one or more genes can perform the same function [2]. The exact contribution of genetic redundancy to the robustness of biological systems has, however, been a subject of considerable debate. On the one hand, it is hard to understand how full redundancy can be evolutionarily stable. After duplication the two copies will have identical functions and the loss of one by the accumulation of mutations is buffered by the other, having no fitness cost [3].
On the other hand, there is strong evidence for functional redundancy by duplicates. The deletion of singleton genes, i.e., those without copies, is frequently lethal [4]. In contrast, deletion of genes with paralogues has frequently little fitness cost [4], even though the deletion of pairs of paralogues has frequently high fitness costs [5], suggesting that their compound function is essential, and arguing for functional redundancy of the paralogues. The capacity for functional compensation correlates with sequence divergence, with closer paralogues more likely to provide it [4], which argues for gene duplication providing functional redundancy. This redundancy can in fact be maintained over large periods of time, as two independent studies of functionally redundant duplicates showed recently [6], [7]. A theoretical analysis of the metabolic network of S. cerevisiae estimated that the dispensability of up to 28% of metabolic enzymes can be attributed to the existence of a compensating paralogue [8]. Recent work has suggested that the cost of maintenance of complete redundancy can be, to some extent, offset by partial functional redundancy [9]. Furthermore, incomplete and presumed functionally distinct duplicates may also provide additional backup [10].
The discussion on the role of genetic redundancy in robustness is also centered on the conditions for the emergence of robustness. A series of theoretical studies have resulted in the prediction that high robustness can only evolve in the presence of frequent perturbations (reviewed in [11]). Little attention has been given to the conditions necessary for the loss of robustness. Based on the above, we would anticipate that predictable environments should not place a high selective value on robustness. The intracellular environment is relatively invariant over time. Organisms that occupy this ecological niche are not subjected to repeated nor frequent perturbations, and represent a good system to study adaptation to such predictable environments. The rapid increase in the number of sequenced intracellular endosymbionts and parasites provides an ideal system to study the evolution of genetic redundancy, and for an empirical study on the importance of external perturbation in the emergence of robustness.
Intracellular lifestyles have been frequently and independently adopted by bacteria and eukaryotes, in the context of endosymbiosis or parasitic relationships [12]–[16]. Obligate intracellular parasites and endosymbionts have committed to an intracellular lifestyle, only capable to replicate inside a host eukaryotic cell [14]. They include organisms like Buchnera aphidicola, a bacterial endosymbiont of aphids and also parasitic, pathogenic bacteria, such as Mycobacterium leprae and Rickettsia prowazekii, the causative agents of leprosy and typhus, respectively. The adaptation to the intracellular niche is invariably accompanied by extensive gene loss [12]–[14]. Reduction in gene repertoires is believed to be associated with adaptation to a new lifestyle where many molecules can be obtained from the host [17]. Since the host provides metabolites, many loci in the endosymbiont/parasite would become redundant and previously deleterious mutations would become de facto neutral, due to relaxed selection. Examples are the loss of biosynthetic pathways in many endosymbionts (e.g. [15]). A second driver of gene loss is the drastic reduction in effective population sizes [18]–[20], associated with high mutation rates [21]. Furthermore, inheritance modes of intracellular bacteria imply that only few individuals are transmitted across generations and/or hosts, generating repeated population bottlenecks [15]. Even “important” genes involved in DNA repair, transcriptional regulation and replication have been lost in Buchnera, suggesting that drift plays an important role in genome reduction [22]–[25]. Extreme reductive genome evolution is also observed in obligate parasitic bacteria like the Mycoplasmas, which are often described as the simplest self-replicating organisms [26]. These organisms are obligate parasites of vertebrates, living under an invariant environment within the hosts. We consider these organisms, together with obligate intracellular parasites and endosymbionts, as “Reduced genomes” living under predictable environments.
Here we study the dynamics of gene loss in Reduced genomes, investigating which genes can be lost, and find a previously undescribed driver for gene loss. By combining data analyses with evolutionary simulations we find empirical evidence for a selective drive to maintain diversity of protein families at the expense of family size, with the emergence of many genes without any paralogues. We propose that the latter represents a loss of genetic redundancy due to a decreased selection for robustness in a predictable environment.
Protein families represent groups of proteins that share a common evolutionary history [27]. Within protein families there is conservation of structure and biochemical function across large evolutionary distances [28]. The number of protein families can be construed as the degree of information coded in a genome – the more distinct families exist, the more information. Early analysis of a small number of completely sequenced genomes suggested that larger genomes have more protein families than smaller ones [29]–[31], and there is, in fact, a linear relationship between the number of genes and number of protein families [29]. Larger genomes also tend to have larger protein families [30], [31]. Furthermore, intracellular parasites and endosymbionts that have the smallest genomes known, also have the smallest gene families [31]. With the accumulation of completely sequenced genomes of bacterial parasites and endosymbionts we can now address whether these reduced genomes living under nearly constant environments display the same use of protein families. We chose to define protein families based on structural domain architectures [32], which provides a higher sensitivity than other sequence-based methods [33] and allows us to capture distant evolutionary relationships. Members of each family should be traceable to a common ancestor by duplication and speciation [27], [34]. Note that in bacteria, Lateral Gene Transfer is frequent and generates copies of genes (xenologues) that are indistinguishable from copies resulting from duplication (paralogues) [35]–[37]. For the purpose of this analysis, their specific origin is not relevant and we use the term paralogue loosely to include both.
We studied 69 bacteria that have undergone extensive reductive genome evolution that we label “Reduced”, consisting of the obligate parasitic mycoplasmas and obligate intracellular parasites and endosymbionts, and 308 Free living bacteria, which we label “FL”. In our analysis these two classes are mutually exclusive and their genome size distribution significantly different (Figure 1A). Species name are provided in tables S3 and S4 in Text S1. As expected we observed a strong positive correlation between the number of genes and families (Spearman's rank correlation ρ = 0.97). We noted however that there were two statistically distinct trends in FL and Reduced organisms (Figure 1B). Reduced genomes have more families than would be expected if they were part of the FL. The same trend is observed when we consider individual protein domains instead of protein families (Figure S1 in Text S1). Because the number of genes and families in the two populations are very different and hence difficult to compare, we tested the potential difference between the two populations of organisms by estimating the elasticity of each population, a measure that captures the responsiveness of a function to parameters in a relative scale. The elasticity of Families in Reduced genomes is two times higher when compared to FL. In other words, adding one gene is 50% more likely to drive a number of families increase in Reduced than in FL. Technically, a 1% change in the number of genes will determine a variation of 0.73% in the number of families, compared to a variation of 0.48% in FL genomes. Thus FL genomes are more robust to gene number variation than are Reduced genomes.
Smaller genomes, such as those found in intracellular parasites and endosymbionts, were previously shown to have smaller families [30], [31]. Our results reveal that Reduced genomes had smaller families than could be expected if they followed the same trend as the FL genomes, in particular, they had a significantly higher number of singletons, i.e. families of size one (Figure 1C - note that family size has been subjected to a high pass filter - see methods for details). These results hold when these comparisons are made only for organisms within the same order, which suggests that phylogenetic distance is not an important bias in this result (Figures S2, S3, S4 and S5 in Text S1). A simple averaging of the fraction of singletons in both populations illustrates this trend well - 22% of the families in FL are singletons, but this number rises to 48% in Reduced genomes (p<2.2×10−16; Mann-Whitney U test, Figure 2C). Another way to look at the same problem is to compute the number of genes in paralogous families [31] (Figure S6 in Text S1). As before, phylogenetic distance does not bias this result (Figure S7 in Text S1). Note that although gene loss is the dominant force accounting for the difference in size between Reduced and Free Living families, there is also gene duplication in Free living organisms, and what we measure is the compound signal of both FL duplication and Reduced loss.
Taken together these observations indicate that the reduced genomes are not a random sample of the FL genomes. They have relatively more distinct protein families than FL genomes but less elements per family, which suggests a selective drive to keep diversity of protein families at the expense of redundancy. This is the hypothesis we will test here.
In order to claim that protein diversity or redundancy are selectively lost and/or retained we first need to determine whether this is not the outcome of a neutral process, or that it is not the byproduct of a selective drive on some other character. To address these points we modeled gene loss under a variety of scenarios. We considered two scenarios modeling neutral gene loss and two capturing functional selection. The details of the simulation are described in the methods section, and summarized in Figure 2A. In short, we randomly sample the FL genomes and then simulate gene loss up to a final genome size according to predefined scenarios, where the key variable between scenarios is the probability of losing each gene. We run the simulations 10,000 times for each scenario, creating populations of simulated reduced genomes that we then compare with the Reduced set.
We first simulated two independent scenarios of neutral gene loss. In the first scenario (S1) genes to be lost are randomly sampled and have a constant probability of loss that corresponds to the average difference in number of genes in the genome between FL and Reduced genomes. A second, more sophisticated scenario accounts for the fact that longer genes may receive more mutations, which we simulate in scenario S2 by tying the probability of gene loss to its size. Neutral loss would result in significantly lower protein family diversity than observed in the Reduced genomes (Figure 2B). For example, a Reduced genome with 1000 genes would have 510 families, whereas simulated genomes under scenarios S1 and S2 would have 449 and 398 families respectively. Moreover, neutral loss would result in significantly fewer singletons than we observe in Reduced genomes, i.e. higher genetic redundancy (<39%, compared to 48% in Reduced - Figure 2C). These results hold even when we we perform the simulations within the same bacterial order, which indicates that our results are robust to the large phylogenetic distances considered (Figure S8 in Text S1).
From this we conclude that neutral gene loss alone cannot account for the observed diversity of protein families, nor for the reduced genetic redundancy. Rejection of a neutral scenario is suggestive of selection but does not allow us to determine what is being selected. In other words, we cannot state that there is selection for protein family diversity or against redundancy as it is altogether plausible that there is selection on some completely unrelated character and what we observe is the byproduct of that selective drive. The genes preferentially conserved could be enriched in specific protein families, thus biasing our results. We now consider the major factors that can constrain gene conservation, and by extension its loss.
We now investigate the possibility that there is preferential retention of a subset of genes on some functional grounds that incidentally result in retention of protein diversity. We first consider that Essential genes may define such set of genes that are preferentially retained, where essential genes are defined by having a lethal gene deletion phenotype. Essential genes in bacteria are preferentially retained in evolution [38], [39]. In eukaryotes essential genes have a lower probability of being lost in the context of lineage-specific gene loss [40]. We observed, as expected, that essential genes in E. coli are preferentially conserved in bacterial parasites and endosymbionts (Figure S9 in Text S1). Note that these genes can still be lost, as is well illustrated by experimental evolution studies of genome reduction in Salmonella enterica where essential genes were in fact lost [41]. In scenario S3 we thus preferentially keep protein families that have essential genes in E. coli, i.e. we consider essentiality a property of the family [42].
Although genes are lost in all categories, some functional classes are preferentially lost and others preferentially retained [13], [15], [43]. We calculated the functional class distributions in both populations, and observed several statistical significant differences, for example a preferential retention of genes annotated to the functional class Translation (Figure S10, Table S1 and S2 in Text S1). In scenario S4 we preferentially retain protein families annotated to the most abundant functional classes in the Reduced genomes.
Proteins do not work in isolation but they establish interactions and form pathways, and this could constraint the probability of gene loss. We consider participation in metabolic pathways as these can be inferred from sequence alone with reasonable confidence, and physiological coupling in pathways was shown to be a constraint in reductive genome evolution, i.e. coupled reactions are more likely to be lost together [44]. We simulate gene loss in a scenario where once a member of a pathway is lost, the probability of losing other members of the pathway increase three-fold (S5). Protein-protein interactions may also play a role in gene retention, however we lack the data to address these interactions, and it is unclear at which evolutionary distances it is safe to transfer protein-protein interactions. Furthermore, there is conflicting evidence regarding the role they can play in gene loss. Ochmann and co-workers found that poorly connected proteins are more likely to be lost in the evolution of γ-proteobacteria [45], while Tamanes and co-workers found that in the reductive evolution of Buchnera aphidicola APS, gene loss did not correlate with the absolute number of links of a protein in the protein interaction network (some hubs were more likely to be preserved than others), nor did they observe any drive to keep functional modules intact [46].
We consider a final scenario where gene positioning can determine the likelihood of a gene being lost, as larger deletions could simultaneously delete more than one gene. This has been in fact proposed to be frequent for example in the evolution of B. aphidicola [47] and of Burkholderia mallei [48], even though other studies suggests that loss of individual genes may also be frequent [49]. Note that the organization of bacterial chromosomes in operons makes this scenario also pertinent to understand functional constraints to gene loss, as genes that are part of the same operon likely code to proteins that are functionally associated, as part of the same pathway, complex or directly interacting with each other, and gene order is frequently conserved [50]. We thus modeled a final scenario (S6) where once a gene is lost, adjacent genes become twice as likely to be lost.
Comparison of these selective loss scenarios with the Reduced genomes indicates that selection based on predicted essentiality, functional classes, co-participation on predicted metabolic pathways or adjacency in the genome cannot account for the increased protein family diversity observed, which is substantially higher than observed in the simulations. Using the same example as above, simulated genomes with 1000 genes would have S3 = 464, S4 = 453, S5 = 386 and S6 = 431 families, compared to the 510 families in Reduced genomes. Furthermore, none of these simulations can produce singleton numbers as high as observed in reduced genomes (S3 = 43%, S4 = 39%, S5 = 34%, S6 = 37% compared to 48% in Reduced - Figure 2C. Thus, although all the factors we tested can constraint gene loss, our simulations indicate that they cannot account for the protein family diversity nor the reduction in genetic redundancy we observe in Reduced genomes.
Genome reduction happened multiple independent times in the course of evolution, but it is plausible that there is convergence to a particular small set of genes necessary for parasitic or endosymbiotic life. Such convergence to a minimal gene set could represent a constraint to gene loss accounting for some of the protein family diversity we observe in the Reduced genomes. Previous attempts to define minimal gene sets compatible with cellular life using orthology, revealed a small number of genes [26], [51]. This lead to the proposal that non-orthologous gene displacement, where the same function is performed by unrelated or very distantly related, non-orthologous proteins [52], was far more important than previously anticipated [26]. In fact a comparison of the shared homologous protein coding genes between endosymbionts and the parasite Mycoplasma genitalium revealed a small set of 175 homologous groups that could represent the minimal core for cellular life [24].
Using a sensitive protein family detection method we now ask whether we can detect the convergence to a common set of protein families in the genomes we analyzed here. This could represent a minimal core of families necessary for parasitic and/or endosymbiotic life. We found that only a small proportion (8%) of the families observed in Reduced genomes is present in more than 90% of the organisms (118 protein families in 1433). Similarly, only 4% of the FL families are present in more than 90% of these organisms (293 out of 7405 - Figure 3). The 118 families common to Reduced organisms are a subset of the families common to FL organisms. This suggests that the common core of families necessary for parasitic or endosymbiotic life is a subset of those necessary for free life. Note that only 43% of the protein families retained in most Reduced genome are essential in E. coli (51/118), which further strengthens the idea that each ecological niche requires distinct sets of proteins families. Note that this small number is not due to the existence of two distinct life styles in the Reduced group, as when we break this group into parasites and endosymbionts, we observe a only marginal increase in the number of families that are present in more than 90% of the organisms (132 in parasites and 162 families in endosymbionts). In contrast, we find that most families are present in less than 10% of the organisms. In both populations the majority of the protein families falls into this group, but these “unique” families are more common in FL (84%) than in Reduced genomes (52%). From this we can extrapolate that although niche- and taxon-specific adaptations dominate Reduced genomes, they are comparably less important than in FL organisms.
Thus, convergence to a common core of protein families does not appear to be a major force shaping the protein family diversity in reductive genome evolution.
Our results so far are compatible with a scenario where there is a selective drive to retain a minimal set of families compatible with life in the specific niche occupied by the organism, and that this includes a small core of families common to all reduced genomes, as well as retention of specific functions. This results in the measured increase in protein family diversity in Reduced genomes. However, none of the neutral and selective scenarios we modeled or analyzed above can account for the marked reduction in protein family size, in particular the increase in the number of singletons in Reduced genomes. We hypothesize that this observation may be explained by loss of genetic redundancy, i.e. when more than one gene can perform the same function in a free living organism (larger families), those copies will be lost in the course of reductive genome evolution up to a point where only a single gene per function is retained (singletons). There are abundant anecdotal evidence that supports this hypothesis. For example, most Bacteria have two peptide chain release factor proteins with partial overlap in codon specificity (PrfA: UAG,UAA; PrfB: UGA,UAA). Legionella Pneumophila, a pathogenic γ-proteobacteria that is a facultative parasite, has even a third member of this family (lpg0167); in contrast the related intracellular parasite Coxiella burnetii, the causative agent of Q fever, retained only PrfB.
This scenario requires then that larger families are more likely to lose genes than smaller ones. We tested this hypothesis and found that the probability of gene loss in families present in most organisms is positively correlated with family size (Spearman's rank correlation ρ = 0.74) (Figure 4). This relationship is best approximated by an inverse function (r2 = 0.55), which suggests that the probability of gene loss is essentially random for larger families, but as families become smaller it decreases sharply, with small families having very small probabilities of gene loss. The probability of a gene being lost thus depends on the number of paralogues it has. In neutral scenarios such positive correlation is absent (ρS1 = 0.04, ρS2 = −0.32), and is also absent in the scenario where we retain specific functional classes (ρS4 = 0.07), members of the same pathway (ρS5 = 0.06) or adjacent genes (ρS6 = 0.06). In scenario S3 we observed a correlation between family size and probability of gene loss (ρS3 = 0.74), but inspection of the data in Figure 4 suggests that this is an artifact resulting from hardwiring two distinct levels of Probability of loss in the simulation.
Are the genes being lost those that were functionally redundant with their paralogues? Anecdotal evidence suggest that this is the case. There are for example at least seven Cof-like phosphatases in E.coli (Cof, YidA, YbhA, YigL, YbiV, YbjI, YedP), with substantial overlap in their substrate specificities . In contrast, the endosymbiont Candidatus Blochmannia pennsylvanicus has a single gene assignable to this family (YigL), which is predicted to maintain 4 out of the 5 substrates that the different E.coli enzymes are known to process [53], [54]. Is this a general case? To answer this question we struggle with the absence of extensive functional information for most of the organisms studied here, the varying phylogenetic distances between these organisms and difficulty of large-scale mapping of orthologues in paralogous families.
We first seek to address the issue of functional redundancy in a way that does not require such mappings nor functional information, by focusing on the most similar pairs of paralogues [4], [10]. The rational of our experiment is the following: if the most similar pairs of paralogues in a protein family are the ones that are more likely redundant, then one member of the pair will be preferentially lost in Reduced genomes, resulting in a decrease in the similarity between the pairs of paralogues in the family. Thus, we computed the sequence similarity between the pairs of closest paralogues for each family and within each genome (Figure 5A). We observed that the pairs of closest paralogues in the families in Reduced genomes are significantly less similar than those in the FL genomes (Figure 5B). We detected a reduction in the similarity of the closest paralogues in nearly 90% of the protein families (Figure 5F). Note that this is not an artifact of the increased sequence divergence in Reduced genomes, as we control for this - in fact, the overall sequence similarity within Reduced families is higher than in the same FL families (not shown). Furthermore, those families that did not reduce in size do not display this reduction in similarity (Figure S11 in Text S1). Additionally, the difference in family size could bias this analysis, but when we control for it we show that the reduction in similarity still holds (Figure S12 in Text S1). This analysis is also potentially biased by phylogenetic distance between organisms compared and different sizes of the universes being compared. However, when we consider specific pairs of phylogenetically close FL and Reduced organisms, i.e. one-to-one comparisons, we find the same trend (Figures 5C, D, E, G, H, I). Thus, reductive genome evolution results in the increasing of the distance between the closest paralogues, which we interpret as evidence that there is preferential loss of one of the pair of closest paralogues.
One example of this scenario is the protein family that includes in E. coli the two redundant transketolases TktA and TktB (E.C. 2.2.1.1) [55], [56], as well as the functionally distinct Dsx (1-deoxyxylulose-5-phosphate synthase, E.C. 2.2.1.7). TktA and TktB are 99% identical, but only 29% identical to Dsx. In the closely related B. aphidicola, only one transketolase was retained (Tkt), together with the Dsx ortholog - they are ∼13% identical.
Finally, we used predicted enzymatic functions to further investigate the loss of functional redundancy. We considered enzyme function predicted in KEGG [57], and described by E.C. numbers. This is a hierarchical classification of enzyme function, that describes enzyme function and substrate specificity. Two proteins that have the same E.C. number have the same function. In Figure 6A we show that when comparing phylogenetically close Reduced and FL genomes, the former have less enzymes that map to the same predicted E.C. number, which is consistent with the notion that in reductive genome evolution there is a drive to retain a single copy of each function. This is not simply a consequence of genome reduction, as when we simulate gene loss under a neutral scenario (S1 in Figure 2), using a closely related Free Living genome as a starting point of the simulation, we always obtain artificially reduced genomes with more proteins per E.C. number, i.e. more redundant, than observed in the Reduced genomes (Figure 6B).
Our results show that organisms that suffered extensive genome reduction in response to adaptations to predictable environments maintain a higher than expected protein family and protein domain diversity, and concomitantly lost genetic redundancy.
The excess diversity at the protein family and protein domain level that characterizes the reduced genomes cannot be accounted by a neutral scenario nor does it appear to be the by-product of selection on other characters. These families observed in Reduced genomes differ from organism to organism, and only 8% of these are present in more than 90% of the organisms, suggesting that the protein family repertoires of the Reduced genomes are the product of historical contingency as well as the specific adaptive value they represent in the ecological niche occupied by each organism. Historical contingency was also observed to play an important part in theoretical studies of reductive genome evolution of metabolic pathways [44]. Interestingly, less than half of the protein families defined by essential genes in E. coli are kept in Reduced genomes, which clearly illustrates how different environments demand different sets of solutions, in this case protein families.
Our results suggest that while protein family diversity is preserved in genome reduction, genetic redundancy is lost. Bacterial genomes are widely reported to have smaller protein families than eukaryotes [29]–[31], relying less on genetic redundancy as a means of robustness. In fact, recently Freilich and co-workers showed that enzymes in prokaryotes are less functionally redundant than in eukaryotes [58]. Our results however suggest that free living bacteria still rely on genetic redundancy as a source of robustness. Reduced genomes have twice the number of singletons as FL, i.e. twice the number of genes that do not have copy backup. This is a lower bound for an estimate of the decrease in genetic redundancy in reductive genome evolution. We are not considering, for example, partial or domain redundancy [10], additional redundancy that may also be sacrificed in reductive genome evolution. One such example is that most members of the order Enterobacteriales, which includes E. coli, have the chaperone DnaJ as well as two proteins that share specific domains with it, CbpA and DjlA. These have been shown to be functionally redundant with DnaJ [59]. The intracellular endosymbionts Buchnera aphidicola APS and Candidatus Blochmannia floridanus, members of the same order, still have DnaJ but lost CbpA and DjlA.
It is important to note however that genetic redundancy is but one source of robustness. There is anecdotal evidence suggesting that reductive genome evolution may sacrifice other types of robustness that do not involve copy redundancy, complete or incomplete. For example, loss of network redundancy, i.e. alternative pathways in the synthesis of acetylCoA (two pathways in E. coli), was reported in the reductive evolution of B. aphidicola (one pathway) [44]. In another example, Cyanobacteria have an oscillator coded by three unrelated genes (KaiA, KaiB, KaiC), capable of maintaining cell cycle rhythms independently of external light-dark cycles. Members of the marine genus Prochlorococcus, although free living, have undergone extensive genome reduction [60], and have lost KaiA. As a consequence, the oscillator became less robust to external light cycles [61]. One promising avenue of research is then to understand to what extent other sources of robustness are affected in the reductive genome evolution.
An abundant body of theoretical work predicts that variable, unpredictable environments select for, or promote the emergence of robustness (reviewed in [11]). Abundant anecdotal examples support this prediction. For example, Sanchez-Perez and co-workers [62] proposed recently that after duplication, paralogues may retain the initial function but specialize to work under different environmental conditions. These ‘ecoparalogues’ which could still effectively compensate for each other, i.e. are functionally redundant, would support a link between environmental unpredictability and the emergence of robustness. They were able to find examples of proteins that are predicted to perform the same function but have different isoelectric points, and hence are predicted to operate at different ranges of salinity. Thus protein specialization under varying environments may provide the drive for the emergence of genetic redundancy. We now invert this reasoning and show that the transition to a predictable environment removes that drive, resulting in the selective loss of genetic redundancy and hence, robustness. To the best of our knowledge our results provide the first systematic description of the loss of robustness by genetic redundancy in the evolution of cellular organisms.
Redundancy is common in higher organisms that experience low mutation rates and small population sizes, and low in organisms that have high mutation rates and large population sizes [63]. Since commitment to an intracellular lifestyle is typically associated to a radical reduction in the effective population size [18]–[20] and high mutation rates [21], it would be reasonable to expect that there would be a concomitant increase in redundancy [63], [64]. This is however the opposite of what we observe – obligate parasites and endosymbionts that suffered a decrease in population size and increase in mutation rate experiencing a decrease in (genetic) redundancy. We thus provide empirical support to the notion that the predictability of the environment is of paramount importance in the evolution of redundancy. Supporting our conclusion is the observation that modularity, a characteristic of biological systems that has been linked to robustness [2], has also been shown to vary with environmental predictability, with more modular networks being found in more unpredictable environments [65]. Note that genome reduction may not be a pre-requisite for loss of genetic redundancy, as even organisms like the marine bacteria of the genus Pirellula, inhabiting a predictable environment, have a remarkably small number of paralogues, while retaining very large genomes [31]
Finally, many of the Reduced organisms that we studied here are causative agents of human diseases such as Lyme disease, leprosy, typhus, tularemia, pneumonia, among others. The realization that they all share a lack of robustness due to the loss of redundancy suggests new avenues for the identification of drugable targets. Instead of aiming to identify genes or pathways that are specific to the pathogenic organism, we can aim to target fragile parasite pathways in the context of robust host functions.
The complete list of species used in this study is given as supplementary material (Tables S3 and S4 in text S1). It consists of 308 free living bacteria and 69 reduced genomes. Reduced genomes include obligate intracellular parasites (34 organisms) and endosymbionts (15 organisms), obtained from [15], [16], [66]. It further included parasitic bacteria like Mycoplasma sp., which while not being intracellular are obligate parasites displaying signs of extreme genome reduction [51] (20 organisms). Essential genes in E. coli were obtained from [67] and from the PEC database (www.shigen.nig.ac.jp/ecoli/pec/). Functional class assignments were obtained from the COGs database [68], [69]. Analysis involving COGS included only genomes with more than 50% COG coverage: 176 free living and 54 reduced genomes. Functional classification with E.C numbers was obtained from KEGG [57].
We used domain architecture as defined in the Superfamily database [32] to identify protein families. Two proteins are considered part of the same family if they display the same N- to C-terminal domain architecture, ignoring gaps as described in [70]. Domain assignments were based on Superfamily release 1.69 [71]. Sequence similarity was computed using BLAST [72] at a cutoff of E≤0.01 and orthologues were identified as reciprocal best hits [73].
Considering a power law function y = γxα, the elasticity of y in relation to x is a constant: (dy/dx)(x/y) = α. The elasticity can be estimated using the linearization ln(y) = β1+β2ln(x), where β1 = ln(γ) and β2 = α. The filtered average family size is computed as (F/N)•(n/N)2, where n is the number of organisms where it appears and N the total number of organisms.
We simulated gene loss scenarios the following way. We randomly picked one free-living genome from the set of 309 as the start point. Then we used a log-normal distribution approximated to the Reduced genome size distributions to randomly generate an end point of the simulation, i.e. the final size of the artificially reduced genome. We then randomly picked genes from the start genome to be “lost”, until we reached the final size. The probability of gene loss was adjusted in six alternative ways. In S1 it was totally random and represents also the background of all other scenarios. In S2 the probability of loss is made to depend linearly on the number of protein domains, i.e. a protein with two domains was twice as likely to be lost as a protein with a single domain. In S3 we consider essentiality a property of the family [42]. We made the probability of loss depend on the protein family distribution of known essential genes in E. coli. Protein families rich in essential genes (>50%) had a 2 fold decrease in the probability of loss, and protein families with less than 50% had just the random background probability of loss. In S4 we adjusted the probability of loss to the functional class distributions in the reduced genomes. Functional classes that are more frequent in Reduced genomes (Figure S5 in Text S1) had its probability of loss reduced to half (functional classes F,J,L,O and U), and those functional classes that are less frequent in reduced genomes had double the probability of loss (E,K,P,Q,R,S and T). In scenario S5 we used KEGG pathway assignment to predict pathway participation and considered that once a gene was lost, members of the same pathway were three times more likely to be lost afterwards. Finally, bacterial genomes are frequently organized in operons, which results in functionally related proteins being coded by genes in close proximity on the chromosome. We considered this in scenario S6 where once a gene is lost, the probability of its adjacent genes being lost afterwards increases twofold.
We estimate the probability of losing proteins in a given family Ploss as the ratio between the total number of elements lost in the family over the size of that family in FL. Ploss(FFL) = (FFL−FReduced)/FFL. FFL and FReduced are the total number of elements of the family in each class of genomes; for this analysis we only considered families that appear in 90% or more organisms in both classes.
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10.1371/journal.ppat.1006823 | Importin α1 is required for nuclear import of herpes simplex virus proteins and capsid assembly in fibroblasts and neurons | Herpesviruses are large DNA viruses which depend on many nuclear functions, and therefore on host transport factors to ensure specific nuclear import of viral and host components. While some import cargoes bind directly to certain transport factors, most recruit importin β1 via importin α. We identified importin α1 in a small targeted siRNA screen to be important for herpes simplex virus (HSV-1) gene expression. Production of infectious virions was delayed in the absence of importin α1, but not in cells lacking importin α3 or importin α4. While nuclear targeting of the incoming capsids, of the HSV-1 transcription activator VP16, and of the viral genomes were not affected, the nuclear import of the HSV-1 proteins ICP4 and ICP0, required for efficient viral transcription, and of ICP8 and pUL42, necessary for DNA replication, were reduced. Furthermore, quantitative electron microscopy showed that fibroblasts lacking importin α1 contained overall fewer nuclear capsids, but an increased proportion of mature nuclear capsids indicating that capsid formation and capsid egress into the cytoplasm were impaired. In neurons, importin α1 was also not required for nuclear targeting of incoming capsids, but for nuclear import of ICP4 and for the formation of nuclear capsid assembly compartments. Our data suggest that importin α1 is specifically required for the nuclear localization of several important HSV1 proteins, capsid assembly, and capsid egress into the cytoplasm, and may become rate limiting in situ upon infection at low multiplicity or in terminally differentiated cells such as neurons.
| Nuclear pore complexes are highly selective gateways that penetrate the nuclear envelope for bidirectional trafficking between the cytoplasm and the nucleoplasm. Viral and host cargoes have to engage specific transport factors to achieve active nuclear import and export. Like many human and animal DNA viruses, herpesviruses are critically dependent on many functions of the host cell nucleus. Alphaherpesviruses such as herpes simplex virus (HSV) cause many diseases upon productive infection in epithelial cells, fibroblasts and neurons. Here, we asked which nuclear transport factors of the host cells help HSV-1 to translocate viral components into the nucleus for viral gene expression, nuclear capsid assembly, capsid egress into the cytoplasm, and production of infectious virions. Our data show that HSV-1 requires the nuclear import factor importin α1 for efficient replication and virus assembly in fibroblasts and in mature neurons. To our knowledge this is the first time that a specific importin α isoform is shown to be required for herpesvirus infection. Our study fosters our understanding on how the different but highly homologous importin α isoforms could fulfill specific functions in vivo which are only understood for a very limited number of host and viral cargos.
| Herpesviruses such as herpes simplex virus (HSV), human cytomegalovirus or Epstein-Barr virus cause human diseases ranging from minor ailments to life threatening acute infections, blindness or cancers, particularly in immunocompromised patients. They are complex DNA viruses that depend on many nuclear functions; e.g. triggering the release of the viral genomes from incoming capsids, nuclear import of viral genomes, viral gene expression, genome replication, assembly of progeny capsids, genome packaging into capsids and nuclear capsid egress. Despite these multiple interactions, little is known about the host transport factors that herpesviruses rely on for import through the nuclear pore complexes (NPCs) during infection.
NPCs are the gateways for bidirectional trafficking between cytoplasm and nucleoplasm. The GTPase Ran controls the activity of transport factors to achieve active nuclear import and export of host and viral cargoes. While some import cargoes bind directly to a member of the importin β superfamily, the majority requires one of the importin α isoforms as an adaptor to interact with importin β1. All importin α isoforms share an N-terminal auto-inhibitory importin β1 binding domain followed by a helical core domain of 10 stacked armadillo repeats (ARM), and a small C-terminal acidic cluster; the 7 human importin α isoforms have an amino acid sequence conservation of 42% ([1–4]; reviewed by [5,6]). Classical mono-partite nuclear localization signals (NLSs) utilize a major binding site on ARM 2 to 4, and bipartite NLSs in addition to ARM 2 to 4 a minor binding site on ARM 6 to 8 [7,8]. Furthermore, the C-terminal acidic domain and ARM 9 and 10 contain a third binding site for non-canonical binding motifs [3,9–11]. Different importin α isoforms bind to similar, if not identical NLSs in vitro, and their recognition mechanisms are structurally conserved from yeast to human; yet, the affinities to specific importin α isoforms can vary considerably, and they display striking differences in cargo recognition in vivo ([2,8,12–14]; reviewed in [5,6]). Importin α links its cargo to importin β1, which in turn binds to NPC proteins to import such ternary complexes into the nucleoplasm, where they disassemble upon interaction with RanGTP (reviewed in [7,15–17]. The nuclear import of several herpesvirus proteins has been shown in transient expression experiments to occur via binding of their NLS to importin α and thus indirectly to importin β1. However, few studies have investigated the specificity of importin α usage in vitro, let alone in vivo in the context of a viral infection.
Among the herpesviruses, interactions of host nuclear transport factors with viral proteins have been investigated at most for herpes simplex virus type 1 (HSV-1), an alphaherpesvirus that productively replicates in epithelial cells, fibroblasts and neurons. After viral fusion with a host membrane, the incoming capsids utilize dynein for microtubule-mediated transport to the nucleus [18–22]. Capsids covered by inner tegument proteins can bind to the NPCs on nuclei isolated from rat liver or reconstituted from Xenopus laevis egg extracts [23,24]. Incoming capsids lacking the large inner tegument protein pUL36 are not targeted to nuclei, and antibodies directed against pUL36 reduce nuclear targeting [25–27]. O’Hare and collaborators have characterized a conserved N-terminal NLS in pUL36 that is essential for targeting incoming capsids to the nucleus and for genome release [28,29]. A likely scenario is that this NLS interacts with host nuclear transport factors to mediate capsid docking to the NPCs. Furthermore, importin β, the RanGTP/GDP cycle and capsid-NPC interactions are required to trigger genome uncoating from capsids; however, a function for importin α could not be uncovered in these in vitro assays [23].
HSV-1 promotors in general contain regulatory sequences common with host genes, and are sequentially regulated with immediate-early, early and late gene expression kinetics unless the incoming genomes are repressed and silenced by facultative heterochromatin (reviewed in [30–33]). The tegument viral protein VP16 dissociates from incoming capsids and complexes with the host cell factor HCF-1 and the POU homeodomain protein Oct-1 to keep immediate-early HSV1 promotors de-repressed for transcription (reviewed in [34]). VP16 does not seem to contain an own NLS but piggy-backs onto HCF-1 in the cytosol for co-import into the nucleus; VP16 is not imported into the nucleoplasm, when the NLS in HCF-1 has been mutated [35]. In the nucleoplasm, VP16/HCF binds to Oct-1 that is already associated with HSV-1 promotors [36]. The NLS of Oct-4 interacts with importin α1, Oct-6 with importin α5, while the one of Oct-1 has not been characterized [11,37,38]. In addition to binding sites for VP16, immediate-early HSV-1 promotors also include response elements for the host transcription factors SP1 and GABP [39].
HSV-1 early and late promotors also contain SP1 transcription factor binding sites, and the transcription of viral genes increases after DNA replication due to the increased template number [32,40,41]. The major transactivator ICP4 (infected cell protein 4), the regulators ICP22 and ICP27, and the E3-ubiquitin ligase ICP0 are immediate-early nuclear HSV-1 proteins important for early and late transcription. While their NLSs have been mapped, their nuclear transport factors are not known [42–45]. ICP4 is required for maximal expression from early and late promotors; it recruits the host RNA polymerase II and other host factors, ICP22 and ICP27, and stabilizes the pre-initiation complex [46]. ICP27 is required for efficient viral transcription and translation of some early and early-late genes and perhaps all true late genes. It needs to shuttle between the cytosol and the nucleoplasm to enhance the nuclear export of intron-lacking viral mRNAs and thus their expression (reviewed in [47]). ICP0 also increases the expression of early and late genes; particularly at a low MOI and in vivo (reviewed in [48]).
The formation of the nuclear HSV-1 DNA replication compartments results in host chromatin marginalization towards the nuclear rim, and requires seven HSV-1 proteins synthesized with early kinetics. These are the origin-binding protein pUL9, the ssDNA binding protein ICP8 (pUL29), the heterotrimeric helicase-primase complex (pUL5, pUL8, pUL52), and the DNA polymerase with the catalytic subunit pUL30 and its processivity factor pUL42 (reviewed in [49,50]). An NLS of pUL9 has been mapped to its amino acid residues 793 to 804 [51], and the nuclear localization of ICP8 is mediated by its 28 C-terminal amino acid residues [52]. In contrast, the subunits of the primase/helicase complex remain cytosolic when translated in isolation; but their assembly is sufficient to generate an NLS for nuclear import in the case of HSV-1, Epstein-Barr virus, and Kaposi sarcoma herpesvirus [53–55]. The NLSs of the DNA polymerase subunits have been well characterized for HSV-1, the human cytomegalovirus, Epstein-Barr virus and Kaposi sarcoma herpesvirus (reviewed in [56]. Capsid assembly and packaging of the viral genomes also occur in the nucleoplasm, but the major capsid protein VP5, the capsid protein VP23, and the small capsid protein VP26 are not capable of nuclear import on their own [57]. VP5 requires the capsid scaffolding protein VP22a for localization to the cell nucleus [58], and a non-classical NLS of the triplex capsid protein VP19c is responsible for the nuclear import of the other triplex protein VP23 [57,59,60]. Furthermore, the NLSs of pUL15 and pUL33, of the terminase that catalyzes genome packaging into preassembled capsids, have been characterized in detail [61].
Thus although some few direct interactions between host transport factors and viral nuclear proteins have been elucidated, host transport factors required for specific steps in the herpesvirus life cycle have not been identified yet. Considering that herpesviruses rely on so many nuclear functions, we conducted an RNAi screen to identify nuclear transport factors that are relevant for efficient HSV-1 gene expression. Of the 17 host factors that we had targeted, importin β1, importin α1, importin α6, and transportin 1 were required for efficient HSV-1 gene expression while importin 11, importin 8, transportin 3 and importin 9 seemed to repress HSV-1. Our experiments with fibroblasts from knock-out mice or transduced with lentiviral vectors encoding for shRNAs to perturb the expression of specific importin α isoforms showed that efficient nuclear import of the HSV-1 immediate-early proteins ICP4 and ICP0, and the early proteins ICP8 and DNA polymerase required importin α1 and importin α3 but was restricted by importin α4. Furthermore, the assembly of nuclear capsids, capsid egress into the cytoplasm and formation of infectious virions were reduced in the absence of importin α1, while nuclear targeting of incoming capsids, nuclear import of VP16 and of incoming genomes were not impaired. Similarly, when the expression of importin α1 had been silenced in neurons, nuclear targeting of incoming capsids from the somal plasma membrane or the axonal compartment were also not impaired, but the nuclear import of ICP4, HSV-1 gene expression, and the formation of nuclear capsid compartments was prevented. Our data indicate that the nuclear import of several important HSV-1 proteins and thus efficient HSV-1 infection depend specifically on importin α1 in fibroblasts, and even more so in neurons.
To identify nuclear transport factors required for HSV-1 replication, we transfected HeLa cells with specific siRNAs and infected them at 72 hpt (hour post transfection) with the reporter strain HSV1(17+)Lox-pMCMVGFP which expresses GFP under the control of a murine cytomegalovirus promoter. At 12 hours post infection (hpi), the HSV1-mediated GFP expression (Fig 1A), and the cell density based on DNA staining were measured in a plate reader (Fig 1B). The GFP signals upon transfection of a scrambled siRNA were normalized to 100% and the background signals of a mock-infected control to 0%. An siRNA directed against the GFP transcripts or treatment with nocodazole served as controls, and reduced HSV1-mediated GFP expression by more than 75%, as expected [62,63]. Nocodazole depolymerizes microtubules that are required for efficient transport of incoming capsids to the nuclear pores, and thus for viral gene expression in epithelial cells [18,20,64,65]. Franceschini et al. (2014) have developed an algorithm to subtract some off-target effects of siRNAs with promiscuous seed regions [66]. We applied their criteria to our data which resulted in re-calculating the effect of 4 siRNAs on HSV-1 gene expression (c.f. S1 Table, GFPcorr). Silencing the expression of some nuclear transport factors reduced cell density, particularly in the case of importin β1 (KPNB1), which is involved in many physiological processes [67,68]. We therefore determined the ratios of the GFPcorr signals over the DNA signals, and ranked the nuclear transport factors according to a decreasing inhibition of HSV1-mediated GFP expression per cell upon siRNA treatment (Fig 1C; S1 Table).
Individual siRNAs targeting importin β1 (gene KPNB1), importin α1 (KPNA2), importin α6 (KPNA5), or transportin 1 (TNPO1) decreased HSV-1 mediated GFP/DNA expression on average by more than 30%, whereas most siRNAs directed against importin α7 (KPNA6), importin 4 (IPO4), importin α3 (KPNA4), importin 7 (IPO7), importin α4 (KPNA3), importin α5 (KPNA1), transportin 2 (TPNO2), or Ran binding protein 5 (RANBP5) on average had little effect. In contrast, HSV1-mediated GFP expression was markedly increased by some siRNAs aiming at importin 13 (IPO13), importin 11 (IPO11), importin 8 (IPO8), transportin 3 (TPNO3), or importin 9 (IPO9). These data suggested that HSV1-mediated GFP expression in human HeLa cells particularly depended on importin ß1, importin α1, importin α6, and transportin 1, but might have been restricted by the activities of importin 13, importin 11, importin 9, transportin 3, and importin 9. The nuclear transport factors that were required for efficient HSV-1 mediated GFP expression might contribute to (i) the release of the incoming HSV-1 genomes into the nucleoplasm, (ii) the nuclear import of host transcription factors operating on the MCMV promoter, such as NF-ΚB, AP-1, and SP-1, or (iii) the nuclear import of host or viral factors required for HSV-1 DNA replication, since the amount of the GFP reporter protein depends on the copy number of HSV-1 genomes in the nucleus.
Since we had already shown that importin β1 promotes targeting of incoming HSV-1 capsids to NPCs and viral genome uncoating [23], we focused on the next potential hit, importin α1 (KPNA2). Promiscuous siRNA seed regions might result in off-target effects [66], and importin α isoforms are highly homologous; we therefore decided to use murine embryonic fibroblasts (MEFs) derived from specific importin α knock-out mice for functional experiments. Like others, we use the numbering of the human proteins also for their closest murine homologs: importin α1 (hImp α1, gene KPNA2; mImp α2, kpna2) and importin α8 (KPNA7; kpna7) for members of the RCH-family, importin α3 (hImp α3, KPNA4; mImp α4, Kpna4) and importin α4 (hImp α4, KPNA3; mImp α3, Kpna3) for the QIP family, and importin α5 (KPNA1; Kpna1), importin α6 (KPNA5; no murine homolog), and importin α7 (KPNA6; Kpna6) for the SRP family [5,69–72]. Mouse embryonic fibroblasts (MEFs) derived from importin α1 (MEF-Impα1-/-), importin α3 (MEF-Impα3-/-), or importin α4 (MEF-Impα4-/-) [73] knock-out mice lacked the respective importin α proteins while the levels of other importins had not been reduced (S1A Fig). These data validate the specificity of the polyclonal anti-peptide antibodies and the respective MEF lines used in this study.
The first step of the HSV-1 life cycle suggested to recruit an importin α via an NLS is docking of incoming capsids at the NPCs [23–25,28]. We therefore infected MEFs with HSV1(17+)Lox-CheVP26 in the presence of cycloheximide to prevent synthesis of progeny HSV-1 proteins, and analyzed the subcellular localization of incoming capsids by confocal fluorescence microscopy. In this HSV-1 reporter strain, the small capsid protein VP26 has been tagged with monomeric Cherry (CheVP26; [74–76]). At 4 hpi, many HSV-1 capsids, detected by CheVP26 (Fig 2Aii; red in Fig 2Aiv) and/or by antibody labeling (Fig 2Aiii, green in Fig 2Aiv), had accumulated at the nuclear rims (Fig 2Ai and blue line Fig 2Aii, 2Aiii and 2Aiv) of MEFwt (Fig 2A). As in epithelial cells [18,20], nocodazole treatment reduced nuclear targeting in MEFwt, and instead the capsids were dispersed throughout the entire cytoplasm (Fig 2B). In contrast, incoming capsids accumulated at the nuclear rims of MEF-Impα1-/- (Fig 2C), MEF-Impα3-/- (Fig 2D) or MEF-Impα4-/- (Fig 2E). Thus, HSV-1 internalization into cells and nuclear targeting of incoming capsids were not impaired in MEF-Impα1-/-, MEF-Impα3-/-, or MEF-Impα4-/-.
As efficient HSV-1 gene expression depends on genome uncoating from the capsids and release into the nucleoplasm, we examined the nuclear import of incoming HSV-1 genomes. MEFs were inoculated with HSV1(17+) at a high MOI in the presence of cycloheximide, denatured at 3 hpi with an ethanol/acetic acid mixture, and hybridized with a Cy3-labeled DNA probe specific for HSV-1. The cytoplasm and the nuclei of the MEFwt contained many spots of hybridized HSV-1 genomes and mRNAs (S2Aiii Fig). In contrast, there were no signals for HSV-1 in mock-treated cells (S2Biii Fig). The amount of nuclear HSV-1 nucleic acids appeared similar to MEFwt in MEF-Impα1-/- (S2Ciii Fig), MEF-Impα3-/- (S2Diii Fig), and MEF-Impα4-/- (S2Eiii Fig).
Efficient HSV-1 gene expression also depends on nuclear VP16, and we therefore investigated its subcellular localization upon inoculation in the presence of cycloheximide. At 4 hpi, HSV1-VP16 had accumulated to a similar extent in the nuclei of MEFwt (S2Fi Fig), MEFwt treated with nocodazole (S2Gi Fig), MEF-Impα1-/- (S2Hi Fig), MEF-Impα3-/- (S2Ii Fig), and MEF-Impα4-/- (S2Ji Fig). In MEFwt inoculated with the mutant HSV1(17+)Lox-ΔgB [77], VP16 had not reached the nucleoplasm as expected, but been retained in virions, located either at the plasma membrane or within endosomes (S2Ki Fig). Glycoprotein B (gB) is essential for HSV-1 cell entry as it catalyzes the fusion of viral with host membranes [78,79]. Consistent with an unimpaired nuclear targeting of incoming capsids, of genomes, and of VP16, we furthermore did not detect any major reorganization of the microtubule network or the distribution of NPC proteins among the different MEF lines (S3 Fig). Taken together, HSV-1 internalization, nuclear targeting of incoming capsids, nuclear import of HSV-1 genomes, and nuclear import of VP16 occurred with similar efficiencies in MEFwt, MEF-Impα1-/-, MEF-Impα3-/- and MEF-Impα4-/-.
To determine whether importin α1 is required for viral protein expression, MEFwt, MEF-Impα1-/-, MEF-Impα3-/-, or MEF-Impα4-/- were infected with HSV1(17+)Lox and analyzed at 6 hpi by immunoblot. For calibration, we compared the lanes of the knock-out cell lines to lanes in which 25%, 50% or 100% of a comparably infected MEFwt lysate had been loaded (S4 Fig; WT, 25, 50, 100). By 6 hpi, MEFwt and the 3 knock-out lines expressed the immediate-early protein ICP4, the early protein ICP8, and the late tegument proteins VP16 and VP22 (S4A Fig). In contrast, when MEFwt had been inoculated in the presence of nocodazole these proteins were barely detected. A quantitation showed that the expression of ICP4, ICP8 and the late tegument protein VP22 were moderately reduced in the absence of importin α1, but increased in cells lacking importin α4 (S4B Fig). These data indicate that neither importin α1, importin α3, or importin α4 were obligatory but that importin α1 facilitated efficient HSV-1 protein expression while importin α4 restricted it to a certain extent.
We next determined the impact of different importin α isoforms on the subcellular localization of several HSV-1 proteins required for early gene expression and for DNA replication. MEFwt, MEF-Impα1-/-, MEF-Impα3-/-, or MEF-Impα4-/- were infected with HSV1(17+)Lox-CheVP26, labeled for various HSV-1 proteins, stained for DNA, and analyzed by confocal fluorescence microscopy. By 4 hpi, ICP4 was detected in most nuclei of MEFwt although its amount varied considerably among individual cells (Fig 3Ai). After infection of MEFwt in the presence of nocodazole, ICP4 was not detected (Fig 3Bi), whereas in MEF-Impα1-/- (Fig 3Ci) and in MEF-Impα3-/- (Fig 3Di) there was some nuclear ICP4, although considerably less than in MEFwt or MEF-Impα4-/- (Fig 3Ei). A quantification of more than 150 cells for each condition showed that the control nocodazole treatment prevented nuclear localization of ICP4, and that there was significantly less nuclear ICP4 in MEF-Impα1-/- and in MEF-Impα3-/-, but more in MEF-Impα4-/- when compared to MEFwt (Fig 3F). Similar results were obtained for ICP0 (S5 Fig). Infection in the presence of nocodazole had also prevented ICP0 expression (S5B Fig), and there was less nuclear ICP0 in MEF-Impα1-/- (S5Ci Fig) and in MEF-Impα3-/- (S5Di Fig), but not in MEF-Impα4-/- (S5Ei Fig) when compared to MEFwt (S5Ai Fig). The quantification confirmed that the nuclear localization of ICP0 depended on both importin α1 and importin α3, but not on importin α4 (Fig 3G).
Seven HSV-1 early proteins including ICP8 and pUL42 catalyze nuclear viral DNA replication. By 6 hpi, ICP8 was detected in most nuclei of MEFwt although its amount varied also among cells. ICP8 was diffusively distributed over the entire nucleoplasm, but clearly enriched in certain nuclear regions (S5Fi Fig) which are the sites of HSV-1 DNA replication (reviewed in [49,50]). Infection of MEFwt in the presence of nocodazole did not reveal any ICP8 (S5Gi Fig), whereas in MEF-Impα1-/- (S5Hi Fig) and MEF-Impα3-/- (S5Ii Fig), there was some nuclear ICP8, although considerably less than in MEFwt (S5Fi Fig) or MEF-Impα4-/- (S5Ji Fig). Similarly, the amount of nuclear pUL42 was lowered in MEF-Impα1-/- (S5Mi Fig) and in MEF-Impα3-/- (S5Ni Fig) when compared to MEF-Impα4-/- (S5Oi Fig) or MEFwt (S5Ki Fig), and there was very little nuclear pUL42 if the MEFwt had been inoculated in the presence of nocodazole (S5Li Fig). The quantification of these images showed that the nuclear localization of ICP8 was reduced in the absence of importin α1 to a similar level as treatment with nocodazole, and also reduced in the absence of importin α3, but increased without importin α4 when compared to MEFwt (Fig 3H). Similarly, the nuclear localization of pUL42 was also dependent on importin α1 and on importin α3 but not on importin α4 (Fig 3I).
While the MEF cell lines derived from knock-out animals unequivocally did not express the targeted importin α isoform, they may have compensated its absence during passage in cell culture by increased or decreased expression of other isoforms or related transport factors. As an additional approach, we therefore validated lentiviral vectors expressing shRNAs to silence the expression of importin α1, importin α3, or importin α4 without impairing the expression of other importin α isoforms (S1B Fig). We then infected MEFwt transduced with specific shRNAs or a scrambled shRNA with HSV-1 using the same conditions as for the MEF knock-out lines. The nuclear localization of ICP4 (S6A–S6E Fig, Fig 3J), ICP0 (Fig 3K), ICP8 (S6F–S6J Fig, Fig 3L), and pUL42 (Fig 3M) was significantly reduced upon silencing the expression of importin α1 or importin α3. In contrast, silencing importin α4 expression did not affect the nuclear targeting of ICP4, ICP0 or ICP8, but increased the nuclear amounts of pUL42.
In summary, targeting importin α4 with shRNA did not affect the nuclear amounts of three HSV-1 proteins but lead to an increase of nuclear pUL42. Similarly, the nuclear amount of ICP0 and pU42 was similar in MEF-Impα4-/- as in MEFwt, but increased for ICP4 and ICP8. In contrast, importin α1 and importin α3 were required for efficient nuclear localization of the immediate-early expressed proteins ICP4 and ICP0 and the early expressed proteins ICP8 and pUL42.
As infection progressed to later phases of the viral life cycle, MEFwt, MEF-Impα1-/-, MEF-Impα3-/-, or MEF-Impα4-/- infected with HSV1(17+)Lox-CheVP26 were analyzed for nuclear capsid compartments. By 8 hpi, the nuclei of MEFwt (Fig 4Ai), MEF-Impα1-/- (Fig 4Ci), MEF-Impα3-/- (Fig 4Di), and MEF-Impα4-/- (Fig 4Ei) contained prominent amounts of nuclear capsid proteins but no nuclear capsid proteins were detected upon infection in the presence of nocodazole (Fig 4Bi). A quantitation showed that the amount of nuclear capsid protein was similar in MEFwt, MEF-Impα1-/-, and MEF-Impα3-/-, and even increased in MEF-Impα4-/- (Fig 4F). A similar experiment with MEFwt transduced with specific or scrambled shRNAs indicated a moderate reduction in the amount of nuclear capsid protein upon silencing importin α1 expression but no changes in the absence of importin α3 or α4 (Fig 4G).
However nuclear import of capsid proteins does not necessarily indicate proper nuclear capsid assembly. Consistent with an impairment of nuclear events during infection, the production of cell-associated infectious HSV-1 particles was reduced by one log for MEF-Impα1-/-, and delayed for MEF-Impα3-/- (Fig 4H). Accordingly, the release of extracellular infectious virions was also delayed and reduced from MEF-Impα1-/-, and delayed from MEF-Impα3-/- when compared to MEFwt (Fig 4I).
To obtain further insights into capsid and virion assembly, we infected MEFwt (Fig 5A) or MEF-Impα1-/- (Fig 5B) with HSV(17+)Lox for 12 h, fixed them, and processed them for analysis by conventional electron microscopy. In both cell types, all known assembly intermediates had been formed: nuclear A, B and C capsids (Fig 5Ai and 5Bi), primary enveloped virions between the inner and the outer nuclear envelope (white star in Fig 5Ai), cytosolic capsids (white arrowhead in Fig 5Aii and 5Bii), capsids in the process of secondary envelopment (black arrowhead in Fig 5Aiii), intracellular vesicles harboring apparently intact virions (black star in Fig 5Aii, 5Aiii, 5Aiv and 5Biii), and extracellular virions attached to the plasma membrane (arrow in Fig 5Aiv and 5Biv). To quantify the amounts of these different assembly intermediates, we systematically evaluated entire cross sections of 10 randomly imaged cells for each cell line (Table 1).
The amount of intracellular capsids per sampled area was reduced in MEF-Impα1-/- when compared to MEFwt. However, although there were fewer nuclear capsids the proportion of nuclear C capsids was increased. In contrast, while there were also fewer cytoplasmic capsids, the relative proportions of the different cytoplasmic capsids, such as cytosolic capsids, capsids in the process of being wrapped by cytoplasmic membranes, and enveloped capsids within transport vesicles was rather similar.
Taken together these observations indicate that importin α1 is required for efficient nuclear capsid assembly and efficient capsid egress. However, those capsids that are translocated into the cytosol seem to associate with cytoplasmic membranes and to become enveloped to a similar extent to form infectious virions that are released from the infected cells also in the absence of importin α1.
Since importin α isoforms exhibit unique expression profiles in neurons [80], we also investigated the role of importin α in post-mitotic primary neurons derived from the dorsal root ganglia (DRG) of adult mice. We have shown previously that such neurons are susceptible to productive HSV-1 infection [81–83]. We cultured DRG cells for 1 day, transduced them for 7 days with lentiviral vectors expressing an shRNA targeting importin α1, importin α3, importin α4, or expressing a scrambled shRNA, and infected them then with HSV1(17+)Lox-GFP. Immunoblotting showed that the expression of the respective importin α isoforms as well of the late tegument protein VP22 was clearly reduced in the DRG cultures when compared to the loading control p150Glued, a subunit of the dynein cofactor dynactin (Fig 6A).
We then used confocal fluorescence microscopy to limit our analysis to neurons identified by their typical morphology, their DNA staining pattern (Fig 6Bi–6Fi), and expression of the neuronal β-tubulin-III ([83]; see also Fig 7 below). Neurons expressing scrambled shRNA were well infected as indicated by a prominent HSV-1 mediated expression of GFP (Fig 6Bii). In contrast, there was no GFP detected upon infection in the presence of nocodazole (Fig 6Cii), silencing importin α1 (Fig 6Dii), or silencing importin α3 (Fig 6Eii), while silencing importin α4 did not impair GFP expression (Fig 6Fii). Quantification showed that the levels of nuclear GFP were very heterogeneous among individual neurons and as strongly inhibited in the absence of importin α1 or importin α3 as in the presence of nocodazole (Fig 6G).
We focused the subsequent experiments on the role of neuronal importin α1, since silencing importin α3 often induced changes of the chromatin architecture (arrow in Fig 6Ei). Neurons transduced for shRNA expression were inoculated with HSV-1 in the presence of cycloheximide, fixed at 2.5 hpi, labeled with antibodies against capsids, stained for DNA, and analyzed by confocal fluorescence microscopy. Incoming HSV-1 capsids were as efficiently targeted to the nuclei (Fig 7Ai and 7Ci) of neurons expressing a scrambled shRNA (Fig 7Aii) as after silencing importin α1 (Fig 7Cii). In contrast, nocodazole treatment reduced the number of incoming capsids reaching the neuronal nuclei (Fig 7Bii). Since importin α can contribute to retrograde axonal transport of some cargos [84–87], we also cultured DRG neurons in microfluidic chambers to selectively inoculate the neurons via the axons and not via the plasma membrane of the cell bodies for 4 h. However, nuclear targeting of HSV-1 capsids that in this experimental set-up was strictly dependent on axonal transport was as efficient in neurons expressing a scrambled shRNA (Fig 7Dii) as in neurons silenced for importin α1 expression (Fig 7Eii).
To further assess later stages of the HSV-1 life cycle, we infected neurons with HSV1(17+)Lox-GFP for 4 h, and labeled them for DNA, ICP4 and capsids. Neurons expressing the scrambled shRNA were well infected as indicated by nuclear targeting of ICP4 (Fig 8Aii), expression of the reporter GFP (Fig 8Aiii), and nuclear and cytoplasmic progeny capsids (Fig 8Aiv). In contrast, there was little expression of ICP4 (Fig 8Cii) or of GFP (Fig 8Ciii), and only incoming capsids were detected at the nuclear rims (Fig 8Civ) after importin α1 expression had been silenced. When the neurons had been infected in the presence of nocodazole, the incoming capsids were rather distributed over the cytoplasm than at the nuclear rims (Fig 8B). A quantitation of these signals in more than 50 neurons revealed that silencing importin α1 had reduced ICP4 (Fig 8D) and GFP (Fig 8E) expression and also the formation of nuclear capsid assembly compartments (Fig 8F) almost as efficiently as the nocodazole treatment. These experiments show that in primary neurons nuclear ICP4 expression, HSV1-mediated GFP expression, VP22 expression, and the formation of nuclear capsid assembly compartments depended on importin α1.
The herpesvirus life cycle depends on many nuclear functions, and we therefore tested the relevance of nuclear transport factors during infection. Our RNAi screen targeting 17 host transport factors demonstrated that importin β1, importin α1, importin α6, and transportin 1 were important for efficient HSV1-mediated GFP reporter expression in HeLa cells. A reduction in HSV-1 gene expression upon silencing importin β might have been expected as an NLS in the capsid associated tegument protein pUL36 and importin β are required to dock incoming HSV-1 capsids to the NPCs, and to inject their genomes into the nucleoplasm [23,25–29]. Subsequent experiments showed that importin α1 and importin α3 were required for efficient nuclear import of crucial HSV-1 proteins and infection of fibroblasts (c.f. S2 Table for a summary). Furthermore, silencing importin α1 expression in neurons abolished the formation of nuclear replication and capsid assembly compartments. While the lack of importin α1 or importin α3 delayed but did not prevent replication in fibroblasts, HSV-1 infection was dependent on importin α1 in differentiated neurons. Our data suggest that in neurons, HSV-1 infection requires specifically importin α1 and importin α3, whereas in dividing cell lines the lack of these importin α isoforms could be partially compensated, possibly by another importin α isoform. In view of their high sequence conservation ([1–4]; reviewed in [5]), our study revealed a remarkable specificity for distinct importin α isoforms required during HSV-1 infection.
While we focused here on importin α, future studies have to address the role of the other nuclear transport factors which were potential hits of our RNAi screen. Transportin 1 is another nuclear import factor that interacts with proline-tyrosine NLSs, such as e.g. those in hnRNP1 [88], which differ from the NLSs of the importin αs. Interestingly, we identified importin 9, 8, 11, 13, and transportin 3 as potential HSV-1 restriction factors. Importin 9 mediates the nuclear import of actin that is required for maximal host transcriptional activity [89], but apparently restricted HSV1-mediated GFP expression. Similarly, transportin 3 mediates nuclear import of splicing factors and has been implicated in HIV replication [90], but also seemed to restrict HSV-1. A depletion of importin 8 interferes with miRNA-guided gene silencing and RNA metabolism [91]. Importin 11 mediates nuclear import of E2 ubiquitin-conjugating enzymes [92,93], and importin 13 the bidirectional nuclear transport of the E2 SUMO-conjugating enzyme Ubc9 that catalyzes post-translational modifications important for intrinsic antiviral resistance [94,95]. Considering the diverse regulatory functions of transcription, miRNA, sumoylation and ubiquitination, it will be a challenge to dissect potential specific contributions of importin 9, transportin 3, importin 8, importin 11, and importin 13 to HSV-1 replication.
The production of infectious cell-associated and extracellular virions was delayed and nuclear targeting of ICP4, ICP0, ICP8 and the DNA polymerase subunit pUL42 impaired in the MEF cells lacking importin α1 or importin α3. In contrast, nuclear targeting of incoming capsids as well as nuclear import of VP16 and the HSV-1 genomes seemed not to be affected. Although we could not test this directly since we lack sufficiently powerful antibodies, we suppose that HCF-1 had co-imported VP16 into the nucleus, and together with other nuclear host transcription factors such as Oct-1, SP1 and GABP initiated immediate-early transcription. The nuclear functions of HCF-1 are essential for cell viability, as regulatory processes controlled by this critical transcription factor do not operate properly, when HCF-1 is sequestered experimentally to the cytosol [96]. Consistent with this assumption, we detected similar expression levels of the immediate-early protein ICP4 by immunoblot in the different MEF lines. However, the nuclear import of ICP4 and another immediate-early protein ICP0 was severely impaired without importin α1 or importin α3. Based on the coordinated interdependent and temporally regulated HSV-1 expression program reported in other systems [30,31,33], we expected that reducing the nuclear amounts of ICP4 and ICP0 would delay subsequent steps of the HSV-1 life cycle. Yet, expression of the early and late proteins ICP8, VP16, and VP22 was not or only moderately reduced in MEFs lacking importin α1 or importin α3, and even increased in the absence of importin α4.
Although HSV-1 gene expression seemed rather unperturbed, the nuclear import of the ssDNA binding protein ICP8 and the DNA polymerase processivity factor pUL42 were reduced in the absence of importin α1 or importin α3. The two DNA polymerase subunits pUL30 and pUL42 of HSV-1 rely on several mechanisms for nuclear import, and can be imported individually or as a holoenzyme (reviewed in [56]). HSV1-pUL30 comprises a non-canonical and a classical bipartite NLS, and binds to importin α5, but other importin α isoforms have not been tested [97–99]. A bipartite NLS in HSV1-pUL42 has been shown to bind to importin α7 and to some extent to importin α1 but actually not to importin α3 [100]; nevertheless its nuclear import was reduced in the absence of importin α1 or importin α3. pUL30 and pUL42 with mutated NLSs are still efficiently imported and targeted to the DNA replication compartments when co-expressed with the wild-type version of the other, but the holoenzyme is retained in the cytosol when the NLSs on both subunits are mutated [100]. Thus, it is possible that the lowered amounts of nuclear ICP8 were sufficient to sustain some DNA replication by a nuclear pUL30 despite reduced amounts of its accessory factor pUL42. Importin α1 was a hit in our targeted RNAi screen for HSV1-mediated GFP expression; possibly because the nuclear HSV1 DNA replication had been reduced. Furthermore, the nuclear import of one of the host factors NF-ΚB, CREB/ATF, AP-1, or SP1 that bind to the major immediate-early promotor of murine cytomegalovirus controlling GFP expression in our reporter virus might have been impaired [101,102].
Although immediate-early, early and late HSV-1 proteins had been synthesized, the electron microscopy analysis shows that the assembly of nuclear capsids, and thus the overall amount of capsids was significantly reduced in the absence of importin α1. Furthermore, the targeting of the HSV-1 pUL31/pUL34 nuclear export complex to the inner nuclear membrane (reviewed in [103,104]) might have been impaired, leading to the reduced nuclear egress of progeny capsids, and the reduced amount of cytoplasmic capsids. Consistent with an overall reduced nuclear targeting of important HSV-1 proteins, a reduced formation of nuclear capsids, and a reduction in nuclear egress, the production of infectious HSV-1 virions was delayed but not prevented in MEF-Impα1-/-, and to some extent also in MEF-Impα3-/-. The specific requirement for importin α3 over importin α4 is remarkable, considering that their amino acid sequences are to 86% identical and to 92% conserved, and considering that importin α4 might even restrict certain steps of the HSV-1 replication cycle. It may nevertheless be possible that when one importin α is missing, the HSV-1 proteins could utilize another importin α homolog.
In the differentiated, post-mitotic neurons, HSV-1 infection depended even more on importin α1 and importin α3. When importin α1 expression had been reduced by RNAi, the amounts of ICP4, HSV-1 mediated GFP, VP22, as well as the formation of nuclear capsid assembly compartments were reduced, while nuclear targeting of incoming capsids was not inhibited irrespective of an inoculation via the somal plasma membrane or the axons. The distribution of importin α isoforms is highly regulated in different cell types and during development (reviewed in [3,5,6]). During neuronal differentiation, expression changes from being initially high in importin α1 and low in importin α3 and importin α5 to low in importin α1 and high in importin α3 and importin α5 [105]. The importin α repertoire of post-mitotic neurons might be more limited than that of MEFs, and therefore silencing the expression of importin α1 or importin α3 had a stronger impact on HSV-1 infection in neurons.
Having available the novel knock-out mice [73,106], MEF lines lacking specific importin α isoforms [2,12,73], and shRNA lentiviral vectors targeting specific importin α isoforms without influencing the expression of other importin α isoforms, we could validate antibodies specific for particular importin α isoforms or subfamilies. While importin α1 has been considered the general nuclear transport factor for cargoes with a classical NLS [2], we and others could generate knock-out mice for specific importin αs suggesting that their host functions could be compensated at least to some extent [73,106]. Our study contributes to elucidating the mode of importin α isoform specificity in vivo that is so far only understood for a limited number of cargoes (reviewed in [5]). Furthermore, not all binding reactions of a substrate to an importin α result in nuclear import of this substrate; for example, Oct-6 can bind to multiple importin-α isoforms, but while binding to importin α1 causes retention in the cytoplasm, binding to importin α5 results in nuclear import [11].
It will be interesting to determine, whether other alphaherpesviruses, betaherpesviruses, and gammaherpesviruses depend on the same importin α isoforms for viral protein import into the nucleus, capsid assembly, and capsid egress to the cytoplasm. Since the early years of the nuclear transport field, the interaction of viral proteins with import factors has been studied, and in several proteins of the herpesviruses and also other viruses replicating in the nucleus, NLS motifs recruiting specific import factors have been identified (for review see [5,56]). Interestingly, the polymerase subunit PB2 of avian influenza A virus strains, an RNA virus replicating in the nucleus, preferentially binds to importin α3, while mammalian adapted strains prefer importin α7, and this switch might be a virulence factor in avian-mammalian host adaptation [107]. Other viruses actually do not utilize but disarm specific importin α isoforms. The structural protein VP24 of Ebola virus and the polymerase of hepatitis B virus block the nuclear import of STAT1, and thus interferon signaling by competitive binding to importin α5 [108–110].
Although the exact intracellular concentration of different nuclear transport factors is hard to measure in situ, it will be interesting to determine to which extent the specific importin isoforms are expressed in epithelial cells, fibroblasts, neurons, and immune cells that are targeted by HSV-1 and other herpesviruses. In future work, it may be possible to reduce expression of all isoforms of one importin α subfamily in cell lines or in primary cells derived from tissues of these knock-out mice in order to reveal potentially redundant virus-host interactions. Further binding studies using recombinant HSV-1 proteins and limiting and competing amounts of different importins will dissect whether herpesvirus proteins comprise additional binding determinants that provide preferential specificity for importin α1 and importin α3 in addition to the already known NLSs. Finally, herpesviruses may also utilize NLSs of tegument proteins, e.g. the one in the N-terminal part of pUL36, or in capsid proteins exposed on the surface of the incoming capsids to recruit specific importin α isoforms and importin β for capsid targeting to the nuclear pores for genome release into the nucleoplasm.
All cell lines were maintained as adherent cultures in a humidified incubator at 37°C and 5% CO2 and passaged twice per week. BHK 21 cells (ATCC CCL-10) and Vero-D6.1 expressing HSV1-gB (Helena Browne, University of Cambridge, personal communication; [78]) were maintained in minimum essential medium (MEM; Cytogen, Wetzlar, Germany) supplemented with 10% (v/v) FCS (PAA Laboratories GmbH, Cölbe, Germany; Life Technologies Gibco) and Vero cells (ATCC CCL-81) in MEM supplemented with 7.5% FCS. HeLaCNX cells [62], human embryonic kidney cells (HEK293T, ATCC CRL-11268; [111]) and mouse embryonic fibroblasts (MEFs) derived from wild type (MEFwt), MEF-Impα1-/- from importin α1-/-, MEF-Impα3-/- from importin α3-/-, and MEF-Impα4-/- from importin α4-/- [73] C57Bl/6 mice were cultured in Dulbecco’s modified Eagle’s medium (DMEM)-GlutaMAX-I (Life Technologies Gibco, Darmstadt, Germany) supplemented with 10% (v/v) FCS.
Cells from DRG of adult C57Bl/6JHanZtm mice were cultured using established protocols [83,112–114]. The mice strain C57Bl/6JHanZtm (not genetically modified) were bred and maintained without any perturbation. On the day of the experiment, they were taken up from the animal facility, within 3 hours sedated with CO2-inhalation prior to killing by cervical dislocation without any prior experimental perturbation, and the DRG from the cervical, thoracic and lumbar levels of 3 to 4 mice were dissected afterwards. Those DRG were pooled in 1x HBSS-complete buffer (Hank’s balanced salt solution, pH 7.4 with 5 mM HEPES and 10 mM D-Glucose), incubated with 20 mg/mL papain (Sigma-Aldrich; in 0.4 mg/mL L-Cysteine, 0.5 mM EDTA, 1.5 mM CaCl2xH2O, pH 7.4) for 20 min at 37°C, with 10 mg/mL collagenase IV (Invitrogen) and 12 mg/mL dispase II (Sigma-Aldrich) for another 20 min at 37°C, and then triturated using Pasteur pipettes with narrowed ends. The cells were sedimented through 20% (v/v) Percoll (Sigma-Aldrich) cushions in CO2-independent medium (Life Technologies Gibco, Carlsbad, CA, USA) containing 10 mM D-glucose, 5 mM HEPES, 10% FCS, 100 U/mL penicillin and 0.1 mg/mL streptomycin, suspended in Ham’s F-12 nutrient mix medium with 10% FCS, 50 ng/mL 2.5S nerve growth factor (Promega Corporation, Fitchburg, WI, US), 100 U/mL penicillin and 0.1 mg/mL streptomycin, and seeded onto cover slips of 20 mm diameter in 24-well plates or into microfluidic devices (SND 450, Xona Microfluidics, LLC, Temecula, CA, USA) attached to 24 x 32 mm cover slips. The cover slips had been pre-coated with 0.01% (w/v) poly-L-lysine (Sigma-Aldrich) and 7 ng/μl murine laminin (Invitrogen). The cells were cultured at 37°C and 5% CO2 in a humidified incubator, and the media were replaced twice a week. The mitosis inhibitor 1-β-D-arabinofuranosylcytosine (Sigma-Aldrich) was added at 1 to 2 div to a final concentration of 2 μM to suppress proliferation of dividing, non-neuronal cells, but removed at 4 div prior to HSV-1 infection.
We used HSV1(17+)Lox, HSV1(17+)Lox-pMCMVGFP, or HSV1-GFP for short, which expresses soluble GFP under the control of the major immediate-early promoter of murine cytomegalovirus [62], HSV1(17+)Lox-CheVP26, in which monomeric Cherry has been fused to the N-terminus of VP26 [76], HSV1(17+)Lox-CheVP26-UL37GFP [76], and HSV1(17+)Lox-ΔgB lacking the UL27 gene that encodes the essential glycoprotein gB [77]. Virus titers were assessed by plaque assays [115], or for HSV1(17+)Lox-ΔgB estimated by comparing an immunoblot analysis of extracellular viral particles to HSV1(17+)Lox-pMCMVGFP expressing gB and used in parallel. For infection experiments, extracellular virus sedimented from the medium of infected BHK 21 cells was used [18,115].
The stocks of the different HSV-1 strains used for infection as well as the MEF-associated virus and the virus released from infected MEFs were titrated on Vero cells. At 4, 8, 12, 16 and 20 hpi, the supernatants of infected MEF were collected and cleared by low-speed sedimentation, and the cells were scraped into 1 mL/well MNT buffer (30 mM MES, 100 mM KCl, 20 mM Tris, pH 7.4) and subjected to 3 cycles of freeze-thawing. Vero cells were cultured to just confluency in 6-well dishes, and incubated for 1 h at room temperature on a rocking platform with 10-fold serial dilutions of the different virus suspensions in CO2-independent medium (Life Technologies Gibco) with 0.1% [w/v] cell culture grade bovine serum albumin (PAA Laboratories GmbH). The inoculum was removed and 2 mL/well growth medium containing 20 μg/mL pooled human IgG (Sigma-Aldrich) was added. The cells were incubated for 3 d, fixed in absolute methanol, and stained with 0.1% [w/v] crystal violet and 2% [v/v] ethanol in H2O.
To stain DNA, we used 4’,6-diamidino-2-phenylindole (DAPI; Roth) or TO-PRO-3-iodide (Life Technologies) at final concentrations of 50 μg/mL or 1 to 2 μM, respectively. We used rabbit polyclonal antibodies (pAbs) raised against human importin α1 (#70160, Abcam), human importin α3 (Enno 31; Pineda Antikörper Service, Berlin, Germany), human importin α4 (Enno 32; Pineda Antikörper Service), human importin α5/α6/α7 (MDC 220; [2]), HSV1-VP16 (#631209, BD Biosciences), HSV-1 tegumented capsids (Remus, bleed V; [23]), or nuclear HSV-1 capsids. To generate a polyclonal serum directed against HSV-1 capsids (SY4563, anti-capsid), rabbits were immunized with purified nuclear capsids (Kaneka Eurogentec S.A., Seraing, Belgium). Mouse monoclonal antibodies (mAb) were directed against α-tubulin (DM1A, Sigma-Aldrich), nuclear pore complexes (mAb 414, Abcam), actin (mAb 1501, Millipore), β-III-tubulin (mAb 5564, Millipore), p150Glued (#610474, BD Biosciences), HSV1-ICP0 (mAb 11060, sc-53070, Santa Cruz Biotechnology), HSV1-ICP4 (mAb 10F1, ab6514, Abcam), HSV1-ICP8 (mAb 11E2, ab20194, Abcam), or HSV1-pUL42 (ab19311, Abcam). Secondary antibodies for immunoblotting were conjugated to fluorescent infrared dyes (anti-rabbit IgG-IRDye 800CW, anti-mouse IgG-IRDye 680RD, LI-COR Biosciences), and for immunofluorescence microscopy to Cy3 (goat-anti-rabbit IgG; Dianova), Cy5 (goat anti-mouse IgG; Dianova), Alexa Fluor488 (A488; goat anti-rabbit IgG; goat-anti-mouse IgG, Invitrogen) or fluorescein isothiocyanate (FITC; goat anti-rabbit IgG; Dianova). All secondary antibodies were highly pre-adsorbed to eliminate cross-reactivity to other species than the intended one.
To silence importin α1, importin α3, or importin α4 by short hairpin RNAs (shRNAs; Sigma Mission library; S3 Table) or to express a non-mammalian shRNA control (SHC002, Sigma Mission library), we used lentiviral transduction. HEK 293T cells were transfected with 5 μg pRSVRev, 2 μg pMD2.g (Addgene Inc., Cambridge, MA, USA, Cat. No. 12259), 10 μg pCDNA3.GP.CCCC, and 10 μg transfer plasmid per 10 cm dish as described previously ([116]; plasmids provided by Axel Schambach). The supernatants were harvested at 36 and 48 h, and sedimented in a SW32.Ti rotor at 24,000 rpm for 90 min at 4°C (Beckman Coulter). The re-suspended lentiviral particles were snap frozen in liquid N2 and stored in single-use aliquots at -80°C. Cell culture supernatants and concentrated lentiviral stocks were titrated using a p24 ELISA [117]. MEFwt were transduced with lentiviral particles at 4 to 12 μg/mL p24 and at 1 dpt, selection with puromycin at 2.5 μg/mL was started. DRG cells were transferred after 1 day in vitro to neuronal growth media containing lentiviral particles at 4 to 12 μg/mL p24 but no AraC. After 2 dpt, the media were replaced by F12-complete with 2 μM AraC and 5 μg/mL puromycin to select for transduced cells.
Small interfering RNAs (siRNAs) against human transport factors as well as scr siRNAs were from QIAGEN (c.f. S1 Table; Hilden, Germany) and the GFP silencer siRNAs from Ambion (AM4626; Darmstadt, Germany). 3,500 to 4,000 HeLaCNX cells per well of 96-well plates were reverse transfected with 50 nM of siRNA using Lipofectamine 2000 (Invitrogen, Life Technologies). After 3 days, cells were left untreated or pre-treated with 50 μM nocodazole for 1 h and infected with 4 x 106 PFU/mL of HSV1(17+)Lox-pMCMVGFP for 12 h in the absence or presence of nocodazole. Cells were fixed with 3.4% paraformaldehyde (PFA), permeabilized with 0.1% Triton-X-100 and stained with DAPI. DAPI and GFP fluorescence were measured using a fluorescence plate reader (BioTek Synergy 2, Bad Friedrichshall, Germany) and the GFP background signal of the mock infected cells was subtracted. To allow comparison of different experiments, the median values of cells transfected with scr siRNAs of each experiment were set as 100% and GFP/well and DAPI/well values were calculated. To reduce the impact of potential off-target effects introduced by miRNAs binding the siRNA seed region, the results were corrected using a dataset of seed region phenotypes [66]. The seed regions of siRNAs classified by Franceschini et al. (2014) to result in off-target effects were compiled, and the mean of significantly altered seed region phenotypes were determined using a threshold of p< = 0.05 after Bonferroni correction [118]. Franceschini et al. (2014) propose an additive model with the seed phenotype contributing with a factor of 0.6 to the overall gene expression results. This adjusted seed phenotype was subtracted from the gene expression results, and the medians of GFP/well, GFPcorr/well or DAPI/well respectively were determined (c.f. S1 Table, GFP, GFPcorr, DAPI). To normalize for potential effects of RNAi on cell density, GFPcorr/DAPI ratios were determined for each well, and the median from the single values was calculated (c.f. S1 Table, GFPcorr/DAPI).
For immunofluorescence microscopy, immunoblot analysis and viral growth curves, MEFs were seeded onto coverslips in 24-well plates at densities of 1 x 105 cells/well or into 6-well dishes at 2.5 x 105 cells/well, and on the next day pre-cooled and inoculated with HSV-1 in CO2-independent medium with 0.1% (w/v) cell culture grade bovine serum albumin (BSA; PAA Laboratories GmbH). MEFs were inoculated for 1 h on ice for nuclear targeting assays, for 0.5 h on ice for measuring nuclear import of viral genomes and VP16, and for 2 h at RT for measuring viral gene expression by immunoblot and measuring nuclear import of viral proteins by confocal fluorescence microscopy. DRG cells were inoculated for 0.5 h at RT. After washing off the unbound virions, the cells were shifted to growth medium at 37°C for the indicated times. We used 5 x 107 pfu/mL (MOI of 100) of HSV1(17+)Lox-CheVP26 to analyze nuclear targeting of incoming HSV-1 capsids, 1 x 108 pfu/mL (MOI 200) of HSV1(17+)Lox-pMCMVGFP or of HSV1(17+)Lox–ΔgB to study the subcellular localization of incoming VP16, 1 x 108 pfu/mL (MOI of 200) of HSV1(17+)Lox-CheVP26-UL37GFP to examine the nuclear import of incoming viral genomes, 0.5 to 1.25 x 106 pfu/mL (MOI 2 to 5) of HSV1(17+)Lox-CheVP26 to examine the synthesis of structural HSV-1 proteins by immunoblot, 0.5 to 1.25 x 106 pfu/mL (MOI 2 to 5) of HSV1(17+)Lox-CheVP26 to determine the subcellular localization of the immediate-early proteins ICP4 and ICP0, the early proteins ICP8, and the late structural protein CheVP26. For the virus growth curves, the different MEF lines were infected with 1.3 x 106 pfu/mL (MOI 5) of HSV1(17+)Lox at a reduced level of 1% [v/v] FCS. Primary cells derived from the DRGs were infected with 2.5 x 107 pfu/mL for nuclear capsid targeting, with 5 x 106 pfu/mL for gene expression upon infection from the somal plasma membrane, or with 1.3 x 108 pfu/mL for nuclear capsid targeting upon infection from the axonal compartment in microfluidic chambers. In those experiments analyzing the subcellular localization of incoming HSV-1 capsids, incoming VP16 or incoming viral genomes, 0.5 mM cycloheximide (Sigma-Aldrich) was added to prevent synthesis of new viral proteins [18]. When nocodazole (25 or 50 μM for MEFs, 10 μM for neurons; Sigma-Aldrich) was used to depolymerize microtubules, cells were pretreated for 1 h at 37°C, and the drug was present during all further steps.
Cells were lysed in hot sample buffer (1% [w/v] SDS, 50 mM Tris-HCl, pH 6.8, 1% [v/v] β-mercaptoethanol, 5% [v/v] glycerol bromophenol blue) containing a protease inhibitor cocktail (cOmplete Roche, #11873580001), and the DNA was sheared using 20-gauge needles. The lysates were loaded onto linear 5 to 12% gradient or 10% SDS gels, and proteins were transferred to nitrocellulose membranes. Membranes were incubated with a blocking solution of 5% [w/v] low-fat milk in PBS followed by incubation with primary antibodies in blocking solution, washed with PBS containing 0.1% [w/v] Tween-20 and 0.5% milk, incubated with secondary antibodies in blocking solution, washed and scanned (Odyssey Infrared Imaging System, LI-COR Biosciences, NE, USA). The band areas and mean intensities were measured using a rectangular selection tool to calculate the integrated intensity (ImageJ version 1.50e, NIH, USA). The background was subtracted, the integrated intensities were normalized to untreated MEFwt, and the ratios of the respective viral protein to actin used as loading control were calculated.
Infected cells were either simultaneously fixed and permeabilized with PHEMO-fix (68 mM PIPES, 25 mM HEPES, 15 mM EGTA, 3 mM MgCl2, 10% [v/v] DMSO, 3.7% [w/v] PFA, 0.05% [v/v] glutaraldehyde, 0.5% [v/v] Triton X-100, pH 6.9) for 10 min at 37°C and washed twice with PHEMO buffer (68 mM PIPES, 25 mM HEPES, 15 mM EGTA, 3 mM MgCl2, 10% [v/v] DMSO, pH 6.9), or fixed with 3% [w/v] PFA in PBS for 20 min at room temperature as described before [18,19]. Fixed cells were treated with 50 mM NH4Cl/PBS for 10 min, and permeabilized with 0.1% Triton X-100/PBS for 5 min in the case of PFA fixation. The HSV1-Fc receptor [119] and other unspecific antibody binding were blocked with 0.5% (w/v) BSA and 10% (v/v) serum from HSV1-negative volunteers. After the immunolabelling, the samples were embedded in Mowiol containing 10% [w/v] 1,4-diazabicyclo[2.2.2]octane, and imaged with plan-apochromatic 63x oil-immersion objectives with a numerical aperture of 1.4 with a confocal fluorescence microscope (LSM 510 Meta; Carl Zeiss Microscopy, Jena, Germany; TCS SP6, LEICA Microsystems, Wetzlar, Germay). Contrast and brightness were adjusted identically across each set of images (Adobe Photoshop version 6.0 or version CS4). Figures were assembled using Adobe Illustrator CC (version 20.1.0).
To quantify the nuclear accumulation of ICP4, ICP0, ICP8, pUL42, GFP or capsid proteins, we developed a pipeline using the CellProfiler software ([120]; http://cellprofiler.org/; BI-2013-070, version 2.1.1, NIH, USA) that segmented the nuclei based on DAPI fluorescence and size, and then determined the mean fluorescence intensity of the labeling for the above mentioned proteins. To measure the number of capsids at the nuclear rim of neurons, nuclear corridors around the outer rim of the segmented nuclei were defined by both expanding and shrinking the nuclear area by several pixels. Then the number of capsids localized within that area was counted. Thresholds for the recognition of the capsid signal were based on the typical signal intensity and size of capsids and considering the background intensity of the anti-capsid antibody in uninfected neurons. For each protein, the average grey values per nuclei were calculated to compile box and whisker plots. The p values were determined with a Kruskal-Wallis test followed by Dunn’s multiple comparison testing (software Prism, version 6; Graphpad, San Diego, CA, USA).
To analyze the subcellular distribution of incoming HSV-1 genomes, cells were infected as described above and fixed with a mixture of 95% ethanol and 5% acetic acid, and processed for fluorescent in situ hybridization (FISH). HSV-1 probe synthesis and hybridization were performed as described previously [121,122] using a HSV1(17+)Lox-ΔUL36 genome cloned into a bacterial artificial chromosome [123] to generate a Cy3-labelled DNA probe. For detection of incoming HSV-1 genomes, the DNA probe was used at 20 μg per coverslip, and the samples were analyzed by confocal fluorescence microscopy.
MEFwt or MEF-Impα1-/- seeded on glass cover slips were infected with HSV1(17+)Lox with an MOI of 10 pfu/cell at 2.5 x 106 pfu/mL. The cells were fixed at 12 hpi with 2% glutaraldehyde and 2.5% formaldehyde in cacodylate buffer [130 mM (CH3)2AsO2H, pH 7.4, 2 mM CaCl2, 10 mM MgCl2] for 1 h at room temperature. Cells were contrasted with 1% (w/v) OsO4 in cacodylate buffer (165 mM (CH3)2AsO2H, pH 7.4, 1.5% (w/v) K3[Fe(CH)6]) followed by 0.5% (w/v) uranyl acetate in 50% (v/v) ethanol overnight. The cells were embedded in Epon plasticServa, Heidelberg, Germany) and 50 nm ultrathin sections were cut parallel to the substrate. Images were acquired with a Morgani transmission electron microscope (FEI, Eindhoven, The Netherlands) at 80 kV. Viral structures were counted and sectioned nuclear and cytoplasmic areas were measured using Fiji software (fiji.sc).
Human sera of exclusively adult, healthy, HSV-1 seronegative volunteers were obtained after written informed consent by the blood donors. Permission was granted by the Institution Review Board (Hannover Medical School; Approval Number 893). According to the German Animal Welfare Law §4, killing of animals needs no approval, if the removal of organs serves scientific purposes, and if the mice had not undergone experimental treatment before. The animal care and sacrifices were performed in strict accordance with the German regulations of the Society for Laboratory Animal Science (GV-SOLAS), the European Health Law of the Federation of Laboratory Animal Science Association (FELASA) and the German Animal Welfare Law. This study here does not contain animal experiments that require pre-approval, and the total number of killed mice was reported at the end of each year to the animal welfare deputy of Hannover Medical School. This information was registered annually as the number of animals killed according to §4 of the German Animal Welfare Law and the number of killed mice was registered with the animal welfare application number 2012/20 at the local state authority (LAVES; Niedersächsisches Landesamt fuer Verbraucherschutz und Lebensmittelsicherheit, Oldenburg, Germany).
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10.1371/journal.ppat.1002098 | A Role for the Chemokine RANTES in Regulating CD8 T Cell Responses during Chronic Viral Infection | RANTES (CCL5) is a chemokine expressed by many hematopoietic and non-hematopoietic cell types that plays an important role in homing and migration of effector and memory T cells during acute infections. The RANTES receptor, CCR5, is a major target of anti-HIV drugs based on blocking viral entry. However, defects in RANTES or RANTES receptors including CCR5 can compromise immunity to acute infections in animal models and lead to more severe disease in humans infected with west Nile virus (WNV). In contrast, the role of the RANTES pathway in regulating T cell responses and immunity during chronic infection remains unclear. In this study, we demonstrate a crucial role for RANTES in the control of systemic chronic LCMV infection. In RANTES−/− mice, virus-specific CD8 T cells had poor cytokine production. These RANTES−/− CD8 T cells also expressed higher amounts of inhibitory receptors consistent with more severe exhaustion. Moreover, the cytotoxic ability of CD8 T cells from RANTES−/− mice was reduced. Consequently, viral load was higher in the absence of RANTES. The dysfunction of T cells in the absence of RANTES was as severe as CD8 T cell responses generated in the absence of CD4 T cell help. Our results demonstrate an important role for RANTES in sustaining CD8 T cell responses during a systemic chronic viral infection.
| Chemokines are small proteins that attract cells and play complex roles in coordinating immune responses. RANTES is one such chemokine that attracts many different cell types. The receptor for RANTES, CCR5, is also a coreceptor for HIV and drugs blocking the RANTES∶CCR5 pathway are in clinical use to treat HIV-infected individuals. Despite the importance of CCR5 during HIV infection, the role of RANTES during other chronic infections remains poorly defined. In this study, we found that the absence of RANTES limited the ability of mice to control chronic LCMV infection resulting in higher viral loads and more severe T cell exhaustion. Our data suggest that the impact of blocking the RANTES∶CCR5 pathway on the ability to control other chronic infections should be given careful consideration when treating HIV-infected individuals.
| During many chronic infections, virus spreads rapidly from the site of initial infection to distal tissues. T cells, on the other hand, must first become activated in the LNs and spleen and then gain the ability to migrate to infected organs. Chemokines play a key role in orchestrating all stages of this T cell response from recruitment of naïve T cells to inflamed lymphoid tissue, migration of T cells within lymphoid organs, movement of activated T cells from lymphoid tissues to effector sites, and the movement of effector T cells within non-lymphoid tissues [1]. While chemokine receptor-ligand pairs such as CCR7-CCL19/21 and CXCR5-CXCL13 are important for migration of T cells into and within lymphoid tissues, others such as CCR4-CCL17/22 and CCR10-CCL27/28 are important for T cell migration into peripheral tissues [2].
One chemokine that has been shown to play a role in immune responses to viral infections is the beta chemokine RANTES (regulated on activation normal T cell expressed and secreted). While RANTES was originally considered a T cell-specific chemokine, it is now known to be expressed by a number of other cell types including epithelial cells and platelets and acts as a potent chemoattractant for many cell types such as monocytes, NK cells [3], memory T cells [4], eosinophils [5] and DCs [6]. A receptor for RANTES, CCR5, is a G protein coupled receptor that, in addition to being the major receptor for RANTES, can also bind MIP1α (CCL3) and MIP1β (CCL4). While the importance of these and many other chemokine∶chemokine receptor pathways has been examined following acute infection or immunization, the role of specific chemokines in regulating T cell responses to chronic viral infections is less clearly defined.
One role for chemokines in regulating T cell responses is the regulation of spatial organization and cellular interactions within lymphoid tissues. For the initiation of an immune response, rare antigen-specific lymphocytes must come into contact with peptide-presenting APCs. Castellino et al showed that antigen-specific interactions of CD4 T cells with antigen-bearing DCs leads to the local production of MIP1α and MIP1β that then recruits naïve CD8 T cells to the same peptide-presenting DC activated by the CD4 T cell [7]. Thus, these chemokines can contribute to the provision of CD4 T cell help for optimal CD8 T cell priming. While Castellino et al found only a modest effect of RANTES neutralization in their protein immunization system, the relative importance of MIP-1α, MIP-1β and RANTES during infection is unknown. Given the overlap in the function of MIP-1α, MIP-1β and RANTES, these studies suggest a potential role for RANTES early in T cell responses to infection possibly via CD4 help. The importance of CD4 T cell help has long been appreciated for a number of chronic viral infections including LCMV, HCV and HIV [8], [9], [10]. When CD4 T cells are transiently depleted at the time of infection with LCMV clone 13, the mice become viremic for life in contrast to untreated mice that control viremia in 2–3 months [8]. Moreover, the CD8 T cells in the CD4 depleted mice are more severely exhausted [11]. Thus, chemokines play important roles during immune responses including aiding in the organization of tissues and in regulating cell-cell interactions.
RANTES regulates protective immunity to viral infections. For example, lymphocytes and epithelial cells produce RANTES in response to infection with respiratory syncytial virus [12] or influenza virus [13], [14], [15], [16], [17]. During respiratory infections, the RANTES∶CCR5 pathway has been shown to be important for DC migration to the dLN [18], survival of alveolar macrophages [19] and the accelerated recruitment of effector and memory T cells to the lung after challenge [20]. Evidence that chemokines can also regulate acute systemic infections arose from the infection of mice lacking CCR5 with west nile virus (WNV), which resulted in markedly higher viral titers in the central nervous system [21]. Humans with the CCR5-Δ32 genotype (a 32-base pair deletion in the CCR5 open reading frame of the CCR5 gene) also have a risk for more aggressive disease following WNV infection [22]. Thus, the RANTES∶CCR5 pathway can influence immune responses in multiple ways during acute viral infections.
In addition to the role of the RANTES∶CCR5 pathway in coordinating spatial interactions during immune responses, CCR5 is a co-receptor for HIV [23], [24]. Humans with the CCR5-Δ32 genotype have slower progression with HIV infection [25] and therapeutic strategies targeting RANTES and CCR5 are being used for treatment against HIV infection [26]. For example, the CCR5 inhibitor maraviroc, in combination with other antiretroviral agents, is indicated for patients with CCR5-tropic strains of HIV. While the benefit of maraviroc in patients with CCR5-tropic strains of HIV is clear (maraviroc can reduce viral loads), how the therapeutic targeting of the CCR5 pathway affects immune responses to other pathogens is unclear.
The role of the RANTES∶CCR5 pathway in respiratory infections, WNV infection and HIV infection suggests that the function of this pathway could be important during other viral infections and that the effect of RANTES during HIV infection might be complex. For example, the CCR5-Δ32 is beneficial during HIV infection because of a direct impediment to viral entry, however, this same mutation is detrimental during WNV infection [27] as well as tick-borne encephalitis [28]. Subjects with the CCR5-Δ32 mutation also have reduced DTH responses [25]. In contrast to acute infections with WNV, influenza virus and Sendai virus, little information exists on how RANTES impacts the T cell function or control of chronic viral infection where viral entry is not affected by CCR5 or RANTES. Thus, we used the mouse model of acute or chronic LCMV infection to investigate the role of RANTES in sustaining CD8 T cell responses during chronic infection. RANTES expression is upregulated during acute LCMV infection [29], [30] but very little is known about the expression or role of RANTES during chronic LCMV infection. Here we demonstrate that RANTES is upregulated to a much higher degree during chronic LCMV infection compared to acute LCMV infection. Unlike acute LCMV infection where RANTES deficiency had little impact on T cell responses or viral control, the absence of RANTES during chronic LCMV infection led to more severe CD8 T cell exhaustion including compromised cytokine production, higher inhibitory receptor expression and reduced cytotoxicity. The loss of IFNγ production coincided with a decrease in Tbet expression similar to levels seen in CD4-depleted mice during chronic LCMV infection. This increase in T cell dysfunction in the absence of RANTES corresponded to a substantially reduced ability to control chronic infection compared to WT mice but was not due to an intrinsic requirement of CD8 T cells to produce or respond to RANTES directly. These results suggest that manipulation of the RANTES pathway may hinder immune responses to, and thus control of, chronic infection with some pathogens.
C57BL/6 and Ly5.1 mice were purchased from the National Cancer Institute (NCI). CCR5−/− mice were purchased from Jackson laboratories (Bar Harbor, Maine). RANTES−/− mice were a gift from Michael Holtzman, Washington University St Louis and bred in-house at AALAC-approved animal care facility at the Wistar Institute, Philadelphia, PA. P14 mice were maintained at the Wistar Institute and crossed to the RANTES−/− mice.
For primary infections, mice were infected with either LCMV Armstrong (2×105 pfu) i.p. or LCMV clone 13 (2×106 pfu) i.v. For re-infections, mice were infected intranasally (i.n.) with recombinant influenza virus expressing the LCMV GP33 epitope (x31-GP33, 1.6×105 TCID50). Prior to i.n. infection, mice were anaesthetized by intraperitoneal injection of ketamine hydrochloride and xylazine (Phoenix Scientific) in 0.2 ml of PBS. Recombinant influenza strains were obtained from Dr. Richard J. Webby and were propagated in specific-pathogen-free eggs and stored at −80°C before use.
For adoptive transfer experiments, single-cell suspensions of CD8 T cells were equalized for the number of antigen-specific CD8 T cells and adoptively transferred by i.v. injection into the tail vein. CD8 T cells were purified (>90% purity) from whole lymphocytes using magnetic beads (CD8+ T cell isolation kit, MACS beads; Miltenyi Biotec) and the CD8 T cells stained with tetramer and the numbers of LCMV-specific CD8 T cells normalized before being transferred i.v. For the P14 experiments, LNs were isolated from P14 WT or P14 RANTES−/− mice. The number of P14 cells was equalized and a total of 1,000 P14 cells were transferred into C57BL/6 mice at a 50∶50 ratio. Mice were infected the following day with LCMV clone 13.
Ly5.1 mice from NCI were irradiated with 950 RADS. The following day, bone-marrow cells from Ly5.1 WT mice and Ly5.2 RANTES−/− mice or Ly.2 CCR5−/− mice were depleted of T, B and NK cells with MACs magnetic beads and adoptively transferred i.v. at a 1∶1 ratio. A total of 1–5×106 BM cells were transferred per mouse. Mice were fed antibiotics for 2 weeks following irradiation and allowed to reconstitute for eight weeks before use.
Mice were euthanized and the hepatic vein cut. The liver was perfused by injecting PBS into the left heart ventricle. Livers were incubated in 0.25 mg/ml collagenase D (Roche Diagnostics) and 1 U/ml DNase I (Roche Diagnostics) at 37°C for 30 min. Digested livers were homogenized using a cell strainer, applied to a 44/56% Percoll gradient, centrifuged at 850 g for 20 mins at 4°C and the lymphocyte population was harvested from the interface. Red blood cells were lysed using ACK lysing buffer (Quality Biological) before cells were washed and counted. Spleens were homogenized using a cell strainer. Red blood cells were lysed using ACK lysing buffer and the cells washed and counted.
Lymphocytes isolated from different tissues were stained using standard techniques and analyzed by flow cytometry. Virus-specific CD4 and CD8 T cells were analyzed at the peak of the response (LCMV day 8) and in the memory/chronic phase (>day 30). Virus-specific T cells were quantified in tissues using MHC-I and MHC-II tetramer staining. MHC class I peptide tetramers were made and used as described [31]. MHC-II tetramer was obtained from the NIH Tetramer Core Facility (Emory University, Atlanta, GA). For examination of cytokine production, 1×106 splenocytes were cultured in the absence or presence of the indicated peptide (0.2 µg/ml for CD8 peptides and 2 µg/ml for GP66-77) and brefeldin A for 5 h at 37°C. Intracellular cytokine staining was carried out using the BD cytofix/cytoperm kit followed by antibodies for IFNγ, TNFα, IL-2 and MIP-1α. Samples were collected using the LSR II flow cytometer (Becton Dickinson). For CD107a staining, the antibody was added during the stimulation as described [32].
Activated CD8 and CD4 T cells were sorted using a FACSAria (BD Biosciences). Cells were stimulated with PMA/ionomycin for five hours and the supernatant used for ELISAs. The RANTES ELISA was purchased from Peprotech (Rocky Hill, NJ) and carried out according to the manufacturer's instructions.
DbGP33-specific CD8 T cells and IAbGP66-specific CD4 T cells were sorted on a FACSAria (BD Biosciences). RNA extraction was performed with Trizol (Invitrogen). cDNA was generated using the High Capacity cDNA Archive Kit (Applied Biosystems). Relative quantification real-time PCR was performed on an ABI Prism 7000 with primers purchased from Applied Biosystems. HPRT was used as an endogenous control. Results are expressed relative to naïve cells.
C57Bl/6 mice were infected with LCMV Armstrong or clone 13 and bled at day 8 and day 32 p.i. Serum samples were sent to Glaxo Smithkline for examination of RANTES protein by the luminex assay.
Protocol was similar to [33]. Ly5.1+ splenocytes were labeled with carboxyfluorescein diacetate succinimidyl ester (CFSE); half with 100 nM CFSE and half with 1.25 µM CFSE. The CFSE-labeled cells were then pulsed with 2 µg/ml of GP33-44 or OVA257-264 peptide, respectively, for 90 mins at 37°C and then rinsed three times in RPMI with 10% fetal calf serum. The peptide pulsed targets were incubated with magnetic bead purified Ly5.2+ CD8+ T cells from spleens of WT or RANTES−/− mice with a 2∶1 effector∶target ratio for 18 h. Cells were washed and stained with Ly5.1 and Live/Dead fixable red dead cell stain kit from Invitrogen (Carlsbad, CA). The killing efficiency was determined as previously described [33].
Data were analyzed using a two-tailed Student's t-test and a p value of ≤0.05 was considered significant.
All animal experiments were performed in accordance to NIH guidelines, the Animal Welfare Act, and US federal law. The experiments were approved by the Wistar Institutes Institutional Animal Care and Use (IACUC) committee, animal welfare assurance number A3432-01. The Wistar Animal Care and Use Program is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC).
Infection of mice with the Armstrong strain of LCMV results in an acute infection that is cleared within 8–10 days. CD8 T cells are important for the control of acute LCMV infection and competent CD4 T cell help is required for optimal memory CD8 T cells to develop [34], [35], [36]. We infected both WT and RANTES−/− mice with LCMV Armstrong to determine whether RANTES played a role in regulating T cell responses to this infection. WT and RANTES−/− mice were equally capable of clearing infection with LCMV Armstrong (data not shown). LCMV-specific CD8 T cells expanded similarly in the blood and resulted in comparable absolute numbers of antiviral memory CD4 and CD8 T cells (figure 1A and B). Moreover, the expression of CD62L and CD127 on virus-specific memory T cells on day 52 p.i. was similar in the presence or absence of RANTES (figure 1C) suggesting that the pattern of memory T cell differentiation was unchanged in the absence of this chemokine. Virus-specific memory CD4 and CD8 T cells from RANTES−/− mice were also able to co-produce multiple cytokines equally well (figure 1D, E and F) again showing that there was little, if any, influence of RANTES deficiency on the pattern of differentiation of anti-viral CD4 and CD8 T cell responses during acute LCMV infection.
Given the role of the beta chemokines in regulating ‘helped’ CD8 T cell memory [7], we tested whether the memory CD8 T cells formed during acute LCMV infection could generate an anamnestic response, a key feature of optimal memory CD8 T cells. WT or RANTES−/− mice were infected with LCMV Armstrong to generate GP33-specific memory CD8 T cells. CD8 T cells were isolated from WT and RANTES−/− mice on day 52 p.i and equal numbers of DbGP33-specific CD8 T cells were adoptively transferred to congenically marked WT recipient mice. These recipient mice were then infected intranasally with influenza virus expressing the LCMV GP33 epitope (figure 2A). The ability of donor WT or RANTES−/− memory GP33-specific CD8 T cells to expand upon rechallenge was assessed on day 10 p.i. Both WT and RANTES−/− GP33-specific CD8 T cells expanded vigorously and to a similar degree (figure 2C). Moreover, the RANTES−/− memory cells formed secondary effector CD8 T cells that were phenotypically and functionally similar to WT secondary effectors (figure 2D–F). Thus, memory CD8 T cells generated in the absence of RANTES were fully functional, responded efficiently to local infection rechallenge and showed evidence of having received CD4 T cell help during priming.
Memory CD8 T cells generated in response to LCMV Armstrong are able to protect from infection with the more virulent strain LCMV clone 13. To determine whether memory CD8 T cells generated in the absence of RANTES were able to protect from LCMV clone 13 infection, we adoptively transferred equal numbers of either WT or RANTES−/− memory CD8 T cells into naïve WT or RANTES−/− mice and then challenged with LCMV clone 13. As a control, a cohort of WT mice did not receive any cells. After 9 days, the mice were sacrificed and the viral loads examined (figure 3a). The mice that did not receive any cells had high viral titers in the serum and kidneys (figure 3b). In contrast, both WT and RANTES−/− mice that received either WT or RANTES−/− memory CD8 T cells were protected against chronic infection. Thus, RANTES was not required for memory CD8 T cells to protect from LCMV clone 13 infection.
Infection of naïve adult mice with LCMV clone 13 results in a chronic infection with viremia lasting 2–3 months. In contrast to LCMV Armstrong infection, during clone 13 infection the virus-specific CD8 T cells lose the ability to perform effector functions efficiently. This “exhaustion” is hierarchical and progressive with virus-specific CD8 T cells gradually losing the ability to produce IL-2, proliferate robustly, kill efficiently, make TNFα and, in severe exhaustion, produce IFNγ [31]. These exhausted CD8 T cells also express inhibitory receptors such as PD-1, LAG-3, 2B4 and CD160 [37], [38]. These receptors are actively involved in restraining CD8 T cell function during chronic infection and blockade of these pathways can reinvigorate antiviral T cell responses [32], [38].
To begin to address the role of RANTES during chronic infection we first measured RANTES protein in serum. During LCMV clone 13 infection, RANTES levels are increased in the serum at day 8 and day 32 p.i. compared to naïve mice and mice infected with LCMV Armstrong (figure 4A). RANTES expression was also examined at day 6 p.i., when virus was still present in both sets of mice. Both LCMV Armstrong and LCMV clone 13 induced RANTES expression early p.i. (figure 4B and [39] [30]) but high amounts of circulating RANTES were sustained only during LCMV clone 13 infection. Both LCMV-specific CD8 T cells and CD4 T cells upregulated RANTES mRNA expression, with a high amount of RANTES mRNA maintained in LCMV-specific CD8 T cells past day 30 following LCMV Armstrong or clone 13 infection (figure 4C). Given that RANTES transcription can continue in the absence of protein production [40] and that RANTES protein can be stored in granules in the absence of secretion [41], we also measured secreted RANTES protein. CD8+CD44hi T cells and CD4+CD44hi T cells were sorted from mice infected eight days previously with LCMV Armstrong or LCMV clone 13 and RANTES secretion measured after 5 hours of stimulation with PMA/ionomycin. CD8 T cells from LCMV Armstrong- or clone 13-infected mice secreted high levels of RANTES protein following stimulation with PMA/ionomycin (figure 4D). CD4 T cells also secreted RANTES, though the amounts were lower compared to CD8 T cells (figure 4D). Thus, while the high amounts of circulating RANTES found in mice with chronic LCMV infection could come from many cell types, T cells clearly have the potential to contribute to this circulating chemokine production particularly in the presence of persisting antigen. Expression of the main receptor for RANTES, CCR5, is also upregulated on LCMV-specific CD4 and CD8 T cells during both acute and chronic LCMV infection suggesting that not only do T cells produce RANTES upon infection but they also have an increased ability to bind RANTES (figure 4E).
Given the high circulating amounts of RANTES during LCMV clone 13 infection we next investigated whether RANTES had any role during chronic infection. WT and RANTES−/− mice were infected with LCMV clone 13 and T cell responses examined eight days later. While the total number of DbGP33 and DbGP276 tetramer positive CD8 T cells as well as IAbGP66 tetramer specific CD4 T cells were similar in WT and RANTES−/− mice, the total number of GP33- and GP276-specific CD8 T cells producing IFNγ was significantly reduced in RANTES−/− mice (figure 5A and B). This difference in functionality was only observed in virus-specific CD8 T cells but not CD4 T cells as GP66-specific CD4 T cells from WT and RANTES−/− mice had similar cytokine co-production profiles (figure 5B, C and D). Thus, CD8 T cell responses (but not CD4 responses) are functionally compromised at day 8 p.i. in the absence of RANTES during LCMV clone 13 infection.
To determine whether the absence of RANTES led to a change in the development of T cell exhaustion, we examined later timepoints during clone 13 infection. In contrast to day 8 p.i., at day 30 p.i. the number of virus-specific CD8 T cells in the RANTES−/− mice determined by tetramer staining was significantly reduced compared to WT mice (figure 6A). The reduced LCMV-specific CD8 T cell responses in the spleen were unlikely to be due to enhanced migration to peripheral tissues since the LCMV-specific CD8 T cell response was not increased in the liver (figure 6E and F). Even though both WT and RANTES−/− CD8 T cells were highly dysfunctional at this time, exhaustion was substantially more severe in the absence of RANTES (figure 6B and 6C). Indeed, LCMV GP33- and GP276-specific CD8 T cells were significantly less polyfunctional (i.e. more exhausted) in RANTES−/− compared to WT mice (figure 6B) suggesting that the absence of RANTES led to more severe exhaustion of virus-specific CD8 T cells. Similar to day 8 p.i., the LCMV-specific CD4 T cell response was unaffected by the absence of RANTES in terms of numbers of tetramer-specific CD4 T cells and production of IFNγ (figure 6G). A second hallmark of T cell exhaustion is elevated expression of inhibitory receptors. RANTES−/− virus-specific CD8 T cells had higher expression of PD1, LAG3 and 2B4 indicating that by multiple parameters virus-specific CD8 T cells are more exhausted in the absence of RANTES (figure 6D).
The cytotoxic ability of CD8 T cells is critical during chronic infections. While granzyme B levels were slightly higher in LCMV-specific CD8 T cells from RANTES−/− mice (figure 7A), the ability of these cells to kill was lower than WT T cells from chronically infected mice (figure 7B). Degranulation, as measured by surface CD107a staining, was also slightly lower in LCMV-specific CD8 T cells from RANTES−/− mice suggesting that granule contents might not be released as effectively by CD8 T cells from RANTES−/− mice leading to an accumulation of granzyme B intracellularly (figure 7C).
Given the reduced cytokine production and cytotoxicity in CD8 T cells from mice lacking RANTES, we examined whether these T cell defects had an impact on viral control. At day 8 p.i., viral titers in RANTES−/− mice were similar to WT mice in multiple tissues and sera (figure 8A). However, by day 30 p.i., RANTES−/− mice had higher viral load, consistent with a reduced and more dysfunctional CD8 T cell response (figure 8A). Moreover, when RANTES−/− mice were examined 3–4 months p.i., some of the RANTES−/− mice still had high levels of virus in the liver and were still viremic (figure 8A and B) while WT mice had controlled virus from the serum. These results demonstrate that the absence of RANTES compromises the ability to control viral replication, in some cases leading to a long-term failure to efficiently contain persisting infection.
Given the reduction in CD8 T cell responses and greater exhaustion in RANTES−/− mice we made mixed bone-marrow chimeras to determine whether the role of RANTES was T cell intrinsic or whether supplying RANTES in trans could prevent more severe CD8 T cell dysfunction. Congenically marked Ly5.1 mice were lethally irradiated and reconstituted with 50% Ly5.1 BM and 50% Ly5.2 RANTES−/− BM (figure 9A). These mice were infected with LCMV clone 13 and examined thirty days later. LCMV-specific CD8 T cell responses were similar for WT versus RANTES−/− CD8 T cells at this time point (figure 9B). Moreover, in a situation where ∼ half of the cells were able to produce RANTES, the RANTES−/− CD8 T cells were as functional as WT T cells in terms of the percentage of IFNγ-producers able to make TNFα and the MFI of IFNγ (right) (figure 9C). Finally, RANTES−/− T cells in the mixed chimeras had similar expression of the inhibitory receptors 2B4, PD-1 and LAG-3 as WT T cells (Figure 9D, E). We also used a non-bone marrow chimera TCR transgenic adoptive transfer system to determine whether the need for RANTES was T cell intrinsic. P14 mice bearing a T cell receptor specific for the DbGP33 epitope from LCMV were crossed to RANTES−/− mice. Equal numbers of WT and RANTES−/− P14 CD8 T cells were co-transferred into WT mice before infection with LCMV clone 13 and the CD8 T cell responses examined. Again, LCMV-specific CD8 T cells did not need to make RANTES themselves since the expression of PD-1 and the ability to make IFNγ and TNFα was similar between WT and RANTES−/− P14 cells in the same chronically infected mice (figure S1). Thus, the critical role of RANTES in sustaining T cell responses during chronic LCMV infection was not cell intrinsic. In other words, the defects in T cell responses to chronic viral infections observed in the complete absence of RANTES could be corrected by providing RANTES signals in trans.
While intrinsic RANTES production was not required by the CD8 T cells, it remained possible that the CD8 T cells need to bind RANTES themselves. To test this idea we generated mixed bone marrow chimeras using Ly5.1 WT and Ly5.2 CCR5−/− BM (figure 10A). Upon reconstitution, mice were infected with LCMV clone 13 and CD8 T cell responses examined. This chimera system confirmed that WT LCMV-specific CD8 T cells expressed CCR5 during chronic LCMV infection (figure 10B). A similar response was observed for WT and CCR5−/− CD8 T cells in this setting as measured by the frequency of DbGP33 positive CD8 T cells (figure 10C). Expression of PD-1 and production of IFNγ was also similar for WT and CCR5−/− LCMV-specific CD8 T cells (figure 10D, E and F). Thus, it appears that the major impact of RANTES during chronic LCMV infection could be on a non-CD8 T cell and that more severe CD8 T cell exhaustion was a symptom rather than a cause of poor control of infection.
The transient depletion of CD4 T cells at the time of infection with LCMV clone 13 results in life-long viremia and high viral titers throughout the mouse [8]. This deficiency coincides with more severe exhaustion of the CD8 T cell response demonstrated by further diminished cytokine production [31], [42]. Given the reduced cytokine potential of RANTES−/− mice, we examined how this dysfunction compared to CD4-depleted WT mice and whether CD4 T cell depletion of RANTES−/− mice could further increase the severity of exhaustion. When we compared CD8 T cell cytokine production, we found that the reduced IFNγ production in RANTES−/− mice was similar to that of WT mice depleted of CD4 T cells. Moreover, the depletion of CD4 T cells in RANTES−/− mice did not further decrease cytokine production (figure 11A). CD4-depletion of WT and RANTES−/− mice ablated any difference in cytokine potential of CD8 T cells (figure 11D) and resulted in similarly high viral titers in WT and RANTES−/− mice (figure 11E). This observation suggested that RANTES plays a role in mitigating the severity of exhaustion and that either RANTES−/− CD4 T cells provide little benefit to the CD8 T cell response in RANTES−/− mice or the higher viral load in RANTES−/− mice drives more severe CD8 T cell exhaustion despite the CD4 T cells.
Transcription factors have recently been demonstrated to play a key role in regulating CD8 T cell exhaustion during clone 13 infection. We have recently found that Tbet is downregulated in exhausted CD8+ T cells during chronic LCMV infection and this downregulation is accentuated in the absence of CD4 help (Kao et al. submitted) (figure 11F). This loss of Tbet results in more severe T cell exhaustion during chronic viral infection. We therefore next examined whether Tbet expression was impacted by the loss of RANTES. In chronically infected RANTES−/− mice Tbet expression was substantially lower than in WT mice (figure 11F). In fact, the loss of RANTES alone reduced Tbet expression in virus-specific CD8 T cells to levels seen in CD4 depleted WT mice. To determine whether this loss of Tbet was also seen earlier during clone 13 infection, we examined Tbet expression at day 8 p.i. Tbet expression was already slightly reduced by day 8 p.i. (this reduction reached significance with the DbGP276-specific CD8 T cells but only a trend in the DbGP33-specific CD8 T cells) (figure 11G). Reduced Tbet expression was consistent with the reduction in IFNγ production observed at this early time p.i.
The role of chemokines in regulating immune responses during chronic viral infections is poorly understood. Here we investigated the importance of RANTES in response to a chronic infection where CCR5 is not a viral co-receptor. RANTES was more highly expressed during chronic LCMV infection compared to acute infection. While the absence of RANTES did not impact T cell responses following acute LCMV infection, a different scenario emerged during chronic LCMV infection. During chronic infection, CD8 T cells become exhausted and their dysfunction was characterized by a loss of cytokine production, reduced cytotoxicity and increased inhibitory receptor expression, all of which can hinder the ability to control the infection [31], [32], [43], [44]. In the absence of RANTES, CD8 T cell exhaustion was more severe with reduced virus-specific CD8 T cell numbers, cytokine production and higher expression of inhibitory receptors. The cytotoxic potential of virus-specific CD8 T cells responding to clone 13 infection in RANTES−/− mice was also reduced compared to WT controls. Consistent with the more severe exhaustion of the CD8 T cell response, mice lacking RANTES also had higher viral loads. Thus, the absence of RANTES resulted in the dysfunction of virus-specific CD8 T cells and poor viral control suggesting that RANTES has an important role in regulating and/or sustaining optimal immune responses during chronic viral infection.
There are a number of ways in which the absence of RANTES could result in the higher viral titers and reduced CD8 T cell function during clone 13 infection. First, slightly higher viral loads at the beginning of the response could lead to more severe CD8 T cell exhaustion. One possible mechanism for RANTES affecting viral load is via one of the main cell types infected by LCMV, macrophages. Macrophages play a key role in the immune defense against LCMV. Marginal zone macrophages and metallophilic macrophages may act as filters, controlling the spread of LCMV [45]. The increased tropism of LCMV clone 13 for macrophages and DCs is thought to result in the ability of the virus to persist [46]. The absence of RANTES could impact macrophage function or survival. For example, RANTES is essential to prevent apoptosis of macrophages infected with Sendai virus [19]. Thus, it will be important to investigate the role of RANTES in regulating DC and macrophage differentiation during persisting infections. A second possibility is that RANTES regulates the homing dynamics of the T cells, preventing T cell migration to the peripheral tissues or microenvironments and therefore limiting the ability of these cells to control the infection. However, during chronic LCMV infection the LCMV-specific CD8 T cells were found in spleen, blood and liver showing that the virus-specific T cells could still migrate to peripheral tissues at least at the level of the whole tissue. This observation does not rule out potential differences in movement within tissue, however, and a more detailed analysis of the migration dynamics of exhausted CD8 T cells in the absence of RANTES could be important. Third, CD4 T cell help could be reduced/absent in mice lacking RANTES. At least with LCMV Armstrong infection, CD4 T cell help appears to be intact as CD8 T cell memory cells are fully functional upon secondary challenge. Moreover, LCMV-specific CD4 T cell expansion and cytokine production in RANTES−/− mice were similar to WT mice in response to both LCMV Armstrong and LCMV clone 13. While the phenotype of the CD8 T cells in RANTES−/− mice was similar to CD4-depleted mice, the viral titers in mice lacking CD4 T cells was much higher suggesting that the CD4-depleted phenotype is more severe. Given that CD4 T cells also produce RANTES, it is possible that CD4 T cells are an important source of RANTES during LCMV clone 13 infection but that remains to be determined. A fourth possibility is that RANTES directly affects T cell activation/differentiation leading to reduced effector functions and that loss of RANTES directly results in functional defects in T cells leading to higher viral loads. While RANTES has been shown to act as a costimulator of T cells [47], [48], CCR5−/− T cells responded similarly to WT CD8 T cells in a competitive environment suggesting that the importance of RANTES during LCMV clone 13 infection was not due to direct costimulation of CD8 T cells, though other receptors capable of binding RANTES could have a role.
CD8 T cell activation/differentiation is clearly negatively impacted by the absence of RANTES since IFNγ production by CD8 T cells was reduced even at day 8 p.i. in RANTES−/− mice and this reduced IFNγ production was even more dramatic at the chronic stage of disease. Given that IFNγ has been shown to regulate the ability to clear LCMV infection [44], [49], [50], this initial decrease in IFNγ at the early stage of infection could result in a reduced ability to control viral replication, leading to further CD8 T cell exhaustion. Our data supports a role for RANTES in allowing the efficient activation and differentiation of CD8 T cells that are required to help control clone 13 infection. Interestingly, RANTES was not required for memory CD8 T cells to clear clone 13 infection. This observation, along with the similar T cell response to acute LCMV infection supports a role for RANTES during a sustained infection and further supports the model that minor defects early in the response to a rapidly disseminating infection are magnified as the infection persists leading to more severe T cell dysfunction and pathogen persistence.
Transcription factors that regulate effector functions of CD8 T cells during LCMV infection include Tbet and eomesodermin [51], [52]. Tbet expression was reduced in the absence of RANTES during LCMV clone 13 infection. How the absence of RANTES regulates the expression of Tbet, however, is currently unclear. These findings do suggest that the CD8 T cells responding to clone 13 in RANTES−/− mice have differential expression of transcription factors compared to those from WT mice and perhaps these differences in transcription factor regulation impact their effector functions. Determining whether this effect can be directly attributed to RANTES or is a byproduct of higher viral load requires further investigation.
Interestingly, while CD8 T cell numbers and function were clearly reduced in the absence of RANTES, the CD4 T cells were not as sensitive to the loss of RANTES. CD4 T cells were unaffected in terms of numbers and the ability to produce IFNγ. Thus, the absence of RANTES had differential effects on CD4 versus CD8 T cells. These observations are somewhat surprising given that both CD4 and CD8 T cells produce RANTES and express the main receptor, CCR5. Further, this observation suggests that at least some aspects of CD4 and CD8 T cell exhaustion are regulated differently during chronic LCMV infection. Perhaps the differential effects of RANTES on CD4 versus CD8 T cells could be due to differences in expression of the other receptors for RANTES.
The CCR5-Δ32 mutation is found at a high frequency in European populations and is thought to have arisen through selective pressure during Yersinia pestis or variola major infection [53]. While absence of CCR5 can clearly be protective against HIV, CCR5 plays a role in protecting against WNV and tick-borne encephalitis. CCR5 may also play a protective role in the response against yellow fever virus; viscerotropic disease following yellow fever virus (YFV) vaccination in one subject was associated with the CCR5-Δ32 polymorphism as well as an additional mutation in the RANTES promoter [54]. The dichotomy of protection versus susceptibility of various infections and the use of CCR5 inhibitors suggests the need for more research on subjects with the CCR5-Δ32 mutation in terms of susceptibility to infection with different pathogens.
Understanding the role of RANTES during chronic infection is highly relevant due to the interest in CCR5 inhibitors for the treatment of HIV. CCR5 inhibitors prevent the entry of the R5-tropic stains of HIV virus into the cell [26]. While CCR5 inhibitors can be of tremendous benefit to those infected with the CCR5-tropic stain of HIV, our data suggests that blocking the RANTES pathway could negatively influence ongoing immune responses to other persisting infections. Many patients infected with HIV are also co-infected with other pathogens and the effect of the RANTES∶CCR5 pathway on these co-infections is not well understood. As many as 30% of HIV-infected patients in western Europe and the USA are coinfected with hepatitis C virus (HCV) and complications from HCV coinfection have emerged as a significant cause of morbidity and mortality [55], [56], [57]. Given the role of RANTES in regulating responses to the flaviviruses WNV and YFV, and that serum levels of CC-chemokines are increased in patients infected with chronic hepatitis [58], it will be interesting to determine whether RANTES also plays a role in regulating T cell responses to another member of the flavivirus family HCV.
Our data suggest that therapeutic interventions targeting the RANTES pathway could have negative effects on the ability to control some chronic infections and indicates the need for further research into any link between the CCR5-Δ32 mutation and persistent infections. These observations also suggest that blocking the RANTES∶CCR5 receptor pathway could alter the development and or quality of antiviral immune responses to chronic viral infection and, therefore, CCR5 inhibitors that block only HIV binding but not the RANTES∶CCR5 pathway may be more ideal.
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10.1371/journal.pgen.1007369 | mTOR signaling regulates central and peripheral circadian clock function | The circadian clock coordinates physiology and metabolism. mTOR (mammalian/mechanistic target of rapamycin) is a major intracellular sensor that integrates nutrient and energy status to regulate protein synthesis, metabolism, and cell growth. Previous studies have identified a key role for mTOR in regulating photic entrainment and synchrony of the central circadian clock in the suprachiasmatic nucleus (SCN). Given that mTOR activities exhibit robust circadian oscillations in a variety of tissues and cells including the SCN, here we continued to investigate the role of mTOR in orchestrating autonomous clock functions in central and peripheral circadian oscillators. Using a combination of genetic and pharmacological approaches we show that mTOR regulates intrinsic clock properties including period and amplitude. In peripheral clock models of hepatocytes and adipocytes, mTOR inhibition lengthens period and dampens amplitude, whereas mTOR activation shortens period and augments amplitude. Constitutive activation of mTOR in Tsc2–/–fibroblasts elevates levels of core clock proteins, including CRY1, BMAL1 and CLOCK. Serum stimulation induces CRY1 upregulation in fibroblasts in an mTOR-dependent but Bmal1- and Period-independent manner. Consistent with results from cellular clock models, mTOR perturbation also regulates period and amplitude in the ex vivo SCN and liver clocks. Further, mTOR heterozygous mice show lengthened circadian period of locomotor activity in both constant darkness and constant light. Together, these results support a significant role for mTOR in circadian timekeeping and in linking metabolic states to circadian clock functions.
| The circadian clock coordinates daily physiology and metabolism in animals. There has been considerable interest in identifying mechanisms that link metabolic signals to circadian time keeping. mTOR (mammalian/mechanistic target of rapamycin) is a major intracellular sensor that integrates nutrient and energy status to fundamental cellular processes. Previous studies have identified a key role for mTOR in regulating photic entrainment and synchrony of the central circadian clock in the suprachiasmatic nucleus (SCN). Given that mTOR activities exhibit robust circadian rhythms in a variety of cells and tissues including the SCN, here we investigated the role of mTOR in orchestrating autonomous functions in central and peripheral circadian clocks. Using a combination of genetic and pharmacological approaches we show that mTOR inhibition slows down the circadian clock and dampens clock oscillations, whereas mTOR activation accelerates the clock and enhances clock oscillations in cells, tissues as well as in mice. Together, these results support a significant role for mTOR in linking metabolic states to circadian time keeping.
| The circadian clock regulates the sleep/wake cycle and all associated cellular, metabolic and physiological processes in animals. Disruption of the circadian system is associated with a variety of disease states, including sleep disorders, metabolic syndromes, and cardiovascular diseases[1–3]. In mammals, the central clock is located in the hypothalamic suprachiasmatic nucleus (SCN). The SCN receives photic input from the intrinsically photosensitive retinal ganglion cells and relays the light/dark information to extra-SCN brain regions and peripheral tissues via neural and endocrine signals. In this manner, the SCN synchronizes a myriad of peripheral oscillators into a coherent time-keeping system[4]. At the molecular level, the circadian clock is based on a transcriptional negative feedback loop, in which the bHLH-PAS domain containing transcriptional activators BMAL1 and CLOCK form a heterodimeric complex to activate E-box cis element-mediated transcription of Period (Per1, 2, 3) and Cryptochrome (Cry1, 2) genes. PER and CRY proteins are the repressor components that, upon translocation to the nucleus, suppress the transcriptional activity of BMAL1 and CLOCK[4,5]. This core loop regulates and intertwines with two interlocking loops mediated by the RRE and D-box elements. These molecular mechanisms underlie rhythmic expression of thousands of genes and consequently various cellular functions and processes[6,7].
While the genetic basis of circadian behavior has been well established, additional clock components exist[8] and new players have emerged in recent years[9–11]. Although there have been major advances in our understanding of circadian outputs and tissue/cell type-specific physiological functions, especially at the genome-wide level, the converse regulation is less understood, i.e. how the circadian clock is integrated with and regulated by the many processes that are under its own control. In an effort to identify additional clock components and modifiers, we carried out a genome-wide RNAi screen in a human U2OS cellular clock model and identified hundreds of genes whose knockdown impacted cellular clock function[12]. Pathway analysis revealed that these modifiers were members of many cellular pathways and functions, among which the insulin signaling pathway is the most overrepresented. Downregulation of multiple components of the insulin pathway resulted in period changes, e.g. PI3K (PIK3R5, long period) and mTOR (FRAP1, long period). Conversely, PI3K and mTOR are among the many pathway components that are regulated at the transcriptional level by the circadian clock[7,13]. These results highlight the functional interaction between insulin signaling and the circadian clock. Recent studies have uncovered several more examples of this interplay, especially with metabolism[14,15]. In the current study, we show how the mTOR pathway, a key metabolic regulator, modulates the circadian clock function.
The mammalian/mechanistic target of rapamycin (mTOR), also known as FK506-binding protein 12-rapamycin-associated protein 1 (FRAP1), is a highly conserved nutrient-activated Ser/Thr protein kinase that functions to sense and integrate with cellular metabolism and growth[16–19]. The upstream signals include growth factors such as insulin and insulin-like growth factor-1 (IGF-1), energy status such as ATP levels, and nutrient availability (e.g. leucine and arginine levels)[16–19]. In response to these extracellular and intracellular cues, mTOR activity is regulated to control a wide range of fundamental cellular processes including protein synthesis, mitochondria metabolism and autophagy. mTOR activation involves the tuberous sclerosis complexes (TSC) and Ras homolog enriched in brain (Rheb), in which Rheb acts downstream of TSC2 and upstream of mTOR. Biochemically, TSC2 is a GTPase-activating protein (GAP) and acts as a negative regulator of Rheb. Rheb is a small GTPase and binds to the kinase domain of mTOR to stimulate the phosphorylation and activation of mTOR in a GTP-dependent manner. In the signaling cascade, TSC2 serves as a convergence point for upstream signaling inputs to mTOR complexes 1 (mTORC1), in which phosphorylation and subsequent inactivation of TSC2 leads to activation of Rheb and subsequently activation of mTOR. mTOR interacts with other factors and serves as a core component of mTORC1 and mTORC2. In particular, ribosomal protein S6 kinases (S6Ks) and eIF4E-binding proteins (4E-BPs) represent the best-characterized downstream targets of mTORC1 whereby subsequent activation of ribosomal protein S6 and translation initiation factor eIF4E leads to up-regulation of protein synthesis.
In our previous studies, we uncovered a critical role for mTOR signaling in entrainment and synchronization of the SCN circadian clock[20,21,22]. First, light pulses at night activate the mTORC1/S6K pathway in the SCN, which causes phase shifts in behavioral rhythms and facilitates photic entrainment[20]. Second, the mTOR/4E-BP1 pathway controls mRNA translation of vasoactive intestinal polypeptide (VIP), a major neuropeptide responsible for neuronal synchronization in the SCN[21,22]. Further, genetic alteration of mTOR and its downstream signaling components impacted circadian rhythms in Drosophila[23,24] and in mice[21,22]. Interestingly, mTOR activities exhibit circadian rhythms in a variety of tissues and cells outside the central clock, including cardiac and skeletal muscles, adipocytes, retinal photoreceptors and renal carcinoma cells[25–29]. Inspired by these findings, in the present study, we employed genetic and pharmacological approaches to systematically examine the effect of mTOR perturbation on the autonomous properties of the circadian clock, including period length and rhythm amplitude, in central and peripheral circadian oscillators. We found that mTOR activation accelerates the speed and enhances the robustness of circadian oscillations in cellular clock models, tissues including the liver and the SCN, as well as in whole animals. As mTOR activity is tightly coupled to the nutrient and energy status in cells, our results indicate that cellular metabolism can impact circadian timekeeping via the mTOR pathway.
In our RNAi functional genomic screen[12], we identified the human mTOR gene as a circadian modifier, whereby knockdown altered circadian rhythms in human U2OS cells. We aimed to determine whether mTOR has a similar modifier role across species and under different physiological contexts. For this study, we used lentiviral shRNA to knock down gene expression and leveraged our previously developed reporter cell lines: mouse MMH-D3 hepatocytes and 3T3-L1 adipocytes, each harboring a Per2-dLuc reporter in which the expression of a destabilized luciferase is under the control of the Per2 promoter[30]. We generated two functional shRNAs that effectively knocked down mTor, as determined by Western blotting analysis, and altered circadian bioluminescence rhythms in both cell lines (Fig 1A and 1B, left and middle panels). Compared to non-specific control, mTor knockdown caused significantly longer circadian period length in both MMH-D3 and 3T3-L1 cells (Fig 1A and 1B, right panel). mTor knockdown did not cause drastic changes in rhythm amplitude. Together with results from U2OS cells, our data suggests a ubiquitous role for mTOR in cellular clock models.
To further support the role of mTOR signaling in clock regulation, we asked whether mTOR activation and inhibition cause opposite clock phenotypes. Rheb is the upstream activator of mTOR. Previous studies show that the Rheb-Q64L mutant is constitutively active in its ability to directly interact with mTOR and stimulate its kinase activity[31], designated as CA-Rheb. CA-Rheb expression in MMH-D3 cells caused S6 hyper-phosphorylation (Fig 1C), indicative of hyperactive mTOR. As expected from the effect of mTOR inhibition, cells expressing constitutively active Rheb and consequently hyperactive mTOR have shorter period length and higher amplitude, compared to empty vector control (Fig 1C). Thus, mTOR inhibition (Fig 1A and 1B) and activation (Fig 1C) have opposite period and amplitude phenotypes, supporting a critical role of mTOR in regulating normal clock function in cells.
We reason that pharmacological inhibition of mTOR activity would have a similar phenotypic effect as RNAi knockdown. Thus, we tested the effects of rapamycin (or sirolimus), Torin1, and PP242 (or torkinib), three well-characterized mTOR inhibitors[32,33], in the MMH-D3 hepatocyte model. While rapamycin is a specific mTOR inhibitor toward mTORC1, Torin1 and PP242 target both mTORC1 and mTORC2. When incubated in the continuous presence of 50 nM rapamycin, MMH-D3 cells displayed significantly longer period length and lower amplitude, compared to DMSO control (Fig 2A). Consistent with rapamycin treatment, both Torin1 (20 nM) and PP242 (10 μM) also led to longer period lengths and reduced amplitudes (Fig 2B and 2C). Torin1 and PP242 led to stronger phenotypes than rapamycin, likely due to their different mechanisms of mTOR inhibition. Importantly, the effects of mTOR inhibition are not caused by decreased cell viability, as a medium change can reverse the effects. Given the strong inhibitory effect of these chemicals on amplitude, we suspect that the lack of an amplitude phenotype in RNAi experiments (Fig 1A and 1B) is likely due to relatively weak knockdown efficiency. Taken together, our data show that genetic knockdown and pharmacological inhibition caused similar clock phenotypes, which strongly support the role of mTOR in regulating autonomous circadian clock function.
Given the strong cellular clock phenotypes presented here, and several previous studies that support the involvement of mTOR in clock gene expression[21,34,35], we determined the levels of canonical clock proteins in fibroblasts. For this, we leveraged the fibroblast model in which knockout cells are available. Because TSC2 is a negative regulator of Rheb and therefore mTOR, in Tsc2–/–fibroblasts (in p53–/–background), Rheb is constitutively activated, leading to hyperactive mTOR[36]. As a proxy of hyperactive mTOR, both mTOR and S6 were constitutively hyper-phosphorylated throughout the experiment, whereas their expression levels remained similar (Fig 3A). Interestingly, CRY1, BMAL1, and CLOCK levels were constantly elevated in Tsc2–/–cells throughout the experiment, as compared with the Tsc2+/+ cells. The increased protein levels in Tsc2–/–cells were reduced in the presence of rapamycin (Fig 3B), indicative of mTOR-dependent upregulation of CRY1, BMAL1, and CLOCK. However, we did not detect significant changes in the levels of PER1 and PER2 in the Tsc2–/–cells. Due to the limited duration of the experiment, circadian changes in mTOR activity and clock protein levels were not detected.
The elevated clock proteins reflect their steady-state expression levels in Tsc2–/–cells where mTOR is constitutively activated. Similar to light-induced Per1 and Cry1 expression in the SCN[37,38], extracellular signals impinge on core clock genes to reset the molecular clock[39]. To evaluate the role of mTOR in inducing clock gene expression, we subjected the Tsc2+/+ cells to serum starvation for 16 hr and then treated the cells with 50% serum to stimulate mTOR activity, as reflected by the p-S6 levels (Fig 3C, left panel). We observed a rapid induction of CRY1, reaching the highest level at 5 hr following serum treatment. Treatment with rapamycin abolished serum-induced CRY1 upregulation (Fig 3C, right panel), suggesting that the CRY1 upregulation by serum is mediated, at least in part, by mTOR. In contrast, BMAL1 and CLOCK were not induced by serum stimulation and their levels were not reduced by rapamycin treatment in Tsc2+/+ cells, which could be due to relatively moderate effect of rapamycin.
Because the molecular clockwork consists of several temporally regulated negative and positive feedback loops, which complicates data interpretation, we asked whether CRY1 can be induced by serum in cells that lack a clock to intersect these feedback regulations. For this, we used fibroblasts deficient in Bmal1[40,41] and Period (Per1, 2, 3) genes[42]. We show that, following 50% serum shock, CRY1 was effectively induced in both Bmal1–/–and Per1/2/3–/–fibroblasts (Fig 3D and 3E). These data suggest that CRY1 induction mediated by mTOR signaling does not depend on the molecular clockwork, and more specifically, is independent on Bmal1 or Period genes.
The crosstalk between mTOR and clock function in hepatocytes prompted us to ask whether it also plays a role in the liver clock. To this end, we dissected liver explants from PER2::LUC fusion (Per2Luc) mice, cultured them ex vivo, and treated them with mTOR inhibitor Torin1. Our data show that mTOR inhibition reduced rhythm amplitude and lengthened period length (Fig 4A). These results are consistent with data from hepatocytes and suggest that mTOR plays a regulatory role in the intrinsic properties of the liver clock.
We then determined the role of mTOR function in the liver in vivo. Due to early embryonic lethality associated with null mice, we utilized the mTorflx/–heterozygous mice that retain 50% mTOR expression and activity[21,43]. We crossed these mice with the Per2Luc reporter line to obtain mTorflx/–;Per2Luc mice. Compared with the mTorflx/flx;Per2Luc littermates, liver explants of mTorflx/–;Per2Luc mice have significantly decreased rhythm amplitude (Fig 4B). The period length was not significantly different between the two groups. However, it is noted that the marked amplitude reduction compromised data fitting for period length analysis. Nevertheless, these data are consistent with mTOR inhibitor treatment and support the role of mTOR in regulating the liver clock.
To complement the bioluminescence data, we determined the expression patterns of endogenous core clock proteins by Western blotting analysis. Mice were entrained under the regular light/dark cycle and then released to constant darkness. Liver tissue samples were collected during the third day in constant darkness (DD), starting from circadian time hour 52 (CT52) for a complete cycle. Compared to that in mTorflx/flx mice, mTOR levels were noticeably decreased in mTorflx/–mice, and accordingly, the p-4E-BP1 levels were also lower in mTorflx/–mice (Fig 4C and 4D). p-S6 level in mTorflx/- liver was significantly decreased only at CT52, but significantly increased at CT68 as compared to the mTorflx/flx liver, which may be due to shifted phase of circadian oscillations. Importantly, while S6 and 4E-BP1 levels were constant over time, p-S6 and p-4E-BP1 levels were strongly circadian, suggesting rhythmic mTOR activity in the liver. These results are in line with previous observations about circadian rhythms of mTOR activities in the liver and SCN[7,44–48]. Notably, significant oscillations of clock proteins (PER1, PER2, CRY1, CRY2, BMAL1) were detected in the liver of mTorflx/flx and mTorflx/–mice. There was an apparent phase shift in their peak levels between the two genotype groups. For example, PER1 reached a peak at CT68 in mTorflx/flx mice but at CT64 in mTorflx/–mice. Overall, the changes in core clock protein levels were modest, which is due at least in part to mTor heterozygosity and the robustness of the circadian system. Among all the clock proteins examined, we noticed a striking reduction in CRY1 expression in mTorflx/–mice, compared to mTorflx/flx control mice, especially during peak hours (CT64-72). These results are consistent with and in support of our findings from MMH-D3 hepatocytes and liver explants cultured ex vivo, all pointing to an important regulatory role of mTOR in circadian clock function.
To accompany the Western blotting data, we performed qPCR analysis to determine the expression patterns of the transcripts of the core clock genes. No significant transcript changes for these genes were detected between the two genotypes (Fig 4E). The Cry1 levels were also not significantly increased in mTorflx/–mice, relative to mTorflx/flx mice. These data suggest that Cry1 regulation by mTOR is primarily at the posttranscriptional level.
Our finding that mTOR regulates clock function in multiple peripheral cell/tissue models (U2OS, MMH-D3, 3T3-L1, and the liver) suggested a ubiquitous modifier role and raised the possibility that mTOR also regulates the central SCN clock function. Leveraging the mTOR inhibitors, we show that treatment of Per2Luc SCN explants with rapamycin significantly lengthened the period length (Fig 5A). Similar to the inhibitory effect of rapamycin, PP242 also caused similar period lengthening effect in SCN explants and markedly decreased the amplitude (Fig 5B). Prompted by these observations, we asked whether genetic perturbation of mTor can alter the SCN clock. To this end, we show that SCN explants of mTor heterozygous mTorflx/–;Per2Luc mice have significantly longer period lengths and lower amplitudes, compared to mTorflx/flx;Per2Luc controls (Fig 5C). Taken together, our data suggest that mTOR functions not only in peripheral clock models but also in the central SCN clock.
The SCN clock generally reflects circadian animal behavior[4,49,50]. Our finding about mTOR function in the SCN prompted us to examine the effects of heterozygous mTor deletion on mouse circadian locomotor activity. As in SCN explants, we expected a similar period lengthening effect in mTor mutant mice. mTorflx/flx and mTorflx/–mice were entrained to the standard 12h/12h light/dark (LD) cycle for 10 days and then released to constant darkness (DD). Mice were housed in wheel-running cages equipped to monitor their endogenous locomotor activity under free-running conditions. Like mTorflx/flx mice, mTorflx/–mice were able to be entrained in the LD cycle and exhibited intact free-running rhythms in DD (Fig 6A). However, their circadian period was significantly longer in DD than their mTorflx/flx littermates (Fig 6B; mTorflx/–: 24 hr ± 0.03, n = 9; mTorflx/flx: 23.74 hr ± 0.01, n = 6; p = 0.03, F = 4.24). Furthermore, activity offset was less precise in mTorflx/–mice and there was an apparent rhythm splitting that usually happens under constant light conditions[51,52], all pointing to an altered circadian behavioral rhythm and compromised synchrony of the SCN clock.
After mice were kept in DD for 20 days, they were released to constant light (LL) for 40 days. As in DD, mTorflx/–mice under LL also had significantly longer period length than their mTorflx/flx littermates (Fig 6B; mTorflx/–: 25.02 hr ± 0.09, n = 9; mTorflx/flx: 24.57 hr ± 0.04, n = 6; p < 0.0001, F = 7.537). Interestingly, although the total activities were not different between WT and heterozygotes in LD and DD, mTorflx/–mice in LL did show more scattered locomotor activity, broader alpha (i.e. longer duration of active phase), and less precision in activity onset and offset, indicative of a compromised clock (Fig 6C). Taken together, our behavioral rhythm data are consistent with and in support of the reduced amplitude (Fig 6), compromised synchrony (Fig 6), and increased susceptibility to light-induced desynchrony of clock cells in the SCN of mTorflx/–mice[21].
It is well known that circadian clock regulates cellular metabolism. There has been considerable interest in studying the crosstalk between metabolism and the circadian clock[14]. However, it is not clear how various metabolic signals feed back into the clock mechanism. In the current study, we focused on the role of mTOR signaling in regulating the endogenous circadian clock function. Following our clock gene discovery through functional genomics, we employed an integrated approach combining genetic and pharmacological methods and examined the mTOR effects on circadian rhythms at multiple levels of biological organization, including fibroblasts, hepatocytes, adipocytes, the liver, the SCN, and the whole organism. We found that, while mTOR activation speeds up the clock oscillations (i.e. shorter period), mTOR inhibition lengthens circadian period. These phenotypes are consistent across multiple cell and tissue types including the central and peripheral oscillators. Moreover, mTOR also regulates rhythm amplitude: whereas mTOR inhibition dampens the amplitude, its activation increases the amplitude. Thus, certain level of mTOR activities appears to be required to maintain normal circadian oscillations in the SCN and peripheral oscillators.
The magnitude of the period changes in mTor heterozygous knockout mice (16 min in DD and 27 min in LL) was modest but significant, and is similar to that observed in other homozygous knockout mouse models, including Clock (20 min), Per3 (30 min), Nr1d1 (20 min), Npas2 (12 min), and more recently, Chrono (18 min) ([10] and references cited therein). It should be noted that the behavioral phenotypes obtained in this study were from mTor heterozygous mice, due to lethality of homozygosity. Furthermore, the SCN clock generally reflects circadian animal behavior, and due to the robustness of the SCN clock, behavioral phenotypes are usually less dramatic than those in cellular clock models[4,49,50]. These data from the central SCN clock and peripheral oscillators strongly suggest that the mTOR pathway plays an important regulatory role in the mammalian circadian system.
Light is the most important and potent environmental cue to reset the circadian clock in the SCN. In our effort of searching for signaling pathways that couple photic cues to the SCN, we uncovered the role of the mTOR signaling in resetting or entraining the SCN and circadian behavior[20]. The mTOR activity in the SCN is highly circadian (high during day and low at night)[44], and light at night rapidly stimulates mTOR activation and signaling[53]. Inhibition of mTOR by rapamycin attenuates light-induced phase delay in mice[20]. Along this line, mTOR activities were later found to show daily rhythms in various tissues and cell types[7,25–29,45–48]. At the cellular level, circadian mTORC1 activity (as indicated by p-S6) closely correlates with circadian Per1 expression in the brain[44], indicating that mTOR is important for regulating autonomous clock properties in the brain clock.
As mTOR is the key component in both mTORC1 and mTORC2, we targeted the mTOR gene to manipulate signaling of both complexes. However, as rapamycin and Rheb specifically regulate mTORC1[54,55], our results suggest that mTORC1 is a key signaling pathway that is linked to the clock machinery, consistent with findings in Drosophila[22]. However, contributions from mTORC2 cannot be excluded. Our results are also supported by a recent study showing that Tsc2 mutant cells and mice have enhanced BMAL1 translation and short free-running periods[35], which is consistent with our findings in the current study (i.e. longer period in mTOR loss-of-function, and shorter period in gain-of-function). Intriguingly, however, these phenotypes in mice are opposite to that in Drosophila, where elevated and decreased TOR activity lengthened and shortened circadian behavioral rhythm period lengths, respectively[22,23]. In these studies, the authors studied the TOR effect on animal behavior, but not on cell or tissue models. We speculate that the phenotypic discrepancy could be due to differences between the two species, particularly at the behavioral level, where they have opposite anticipatory behavior associated with diurnality. Mechanistically, our data support the involvement of CRY1, BMAL1 and CLOCK in mediating the mTOR effect. However, unlike CRY1 in mammals which is the chief repressor that regulates period length, CRY in flies doesn't function as a repressor. This may explain the opposite phenotypic difference between the two species. Future studies of the mechanisms of the TOR effect in flies may help uncover the distinct, opposite phenotypes in the two different systems.
To gain mechanistic insight into the mTOR effect, we used Tsc2–/–cells, in which mTOR is constitutively activated. We found that core clock proteins including transcriptional activators BMAL1 and CLOCK and the chief repressor CRY1 were increased in Tsc2–/–cells. This upregulation is attributable at least in part to mTOR, because mTOR inhibition by rapamycin dramatically reduced their expression levels. Our finding is consistent with the recent study that showed that mTOR regulates BMAL1 translation, ubiquitination and degradation[35]. However, while both studies detected BMAL1 elevation upon mTOR activation, our data indicate that CRY1 induction through mTOR is independent on Bmal1 or Per1/2/3 (Fig 3). Our data support the notion that time-of-day-dependent mTOR activity regulates the magnitude of BMAL1, CLOCK, and CRY1 expression, thereby enhancing the amplitude of circadian oscillations. In particular, CRY1 appears to be highly responsive to acute mTOR activation, which then provides input to affect the clock function. The physiological importance of this observation remains to be investigated in future studies.
Several studies in recent years have linked the nutrient and metabolic states of an organism to the circadian clock[56]. These studies used genetic or feeding regime manipulations, including restricted feeding, calorie restriction, high fat diet, and genetic models of type 2 diabetes, and showed that changes in animal metabolic homeostasis alter the clock function in the SCN and peripheral tissues such as the liver and heart[57–61]. Recent studies have revealed several input mechanisms: AMP-activated protein kinase (AMPK) phosphorylates CRY1 for accelerated degradation[62], NAD+-dependent deacetylase sirtuin-1 (SIRT1) promotes BMAL1 and PER2 deacetylation[63–65], poly(ADP-ribose) polymerase (PARP1) ribosylates CLOCK[66], NADP+/NADPH ratio and oxidative stress regulates BMAL1/CLOCK activity that involves the redox-sensitive antioxidative transcription factor NRF2[67,68], hypoxia-inducible factor 1-alpha (HIF1α) regulates Per2 transcription[69–71], and the tumor suppressor p53 modulates Per2 both at the transcriptional and post-transcriptional levels[72,73]. Our work provides additional mechanistic details about how the mTOR pathway links metabolic signals to the circadian clock function.
It is well established that AMPK and mTOR serve as a signaling nexus for regulating cellular metabolism[74], and AMPK was shown to regulate CRY1 phosphorylation and degradation[62]. As AMPK is a negative regulator of mTOR, it’s likely that AMPK impacts the clock function by impinging upon the mTOR pathway. However, the precise mechanism of CRY1 regulation by mTOR is not clear and warrants detailed mechanistic studies. It is worth noting that, as mTOR resides at the center of a complex signaling network, manipulation of mTOR affects both the upstream and downstream events. In particular, several pathways converge on TSC1/2, and mTORC1 and 2 and their effectors have distinct but yet overlapping downstream effectors. As such, delineating the precise regulatory mechanisms of mTOR action will require much additional work in future studies. However, given that mTORC1 plays a critical role in specific protein translation and proteolysis, it is likely that differential protein synthesis and degradation, not at the transcriptional level, underlie the mTOR effects on BMAL1, CLOCK, and CRY1 proteins and on circadian oscillations.
Thus, data from this work and previous studies demonstrate that mTOR signaling is a multifaceted regulator of the circadian clock. First, it functions as part of the photic entrainment pathway to input to the SCN clock. Second, owing to its regulation by the clock, mTOR serves to provide rhythmic outputs from the clock to regulate circadian physiological and biochemical processes such as ribosomal biogenesis[46] and mRNA translation in the liver[48]. Further, mTOR also acts as a modifier to regulate the clock function. In peripheral tissue, especially the metabolically active liver, mTOR acts to modify the local clock in response to metabolic and physiological inputs. Intriguingly, as a nutrient/energy sensor, mTOR senses cellular nutrient and energy levels and integrates the inputs to the cells from upstream pathways mediated by insulin and growth factor receptor signaling. As such, the mTOR activity is regulated not only by the endogenous, anticipatory circadian mechanism, but also by extracellular signals (e.g. the light/dark cycles and nutrient availability). Thus, the mTOR pathway interacts with the circadian system in multiple tissues, and the interplay plays a key regulatory role in mammalian metabolism and physiology. Dysregulation of mTOR under pathological and diseases states such as obesity, diabetes and cancer, could have adverse effects on the circadian clock and circadian behavioral and physiological processes. As circadian clock dysfunctions are often identified in patients with metabolic syndromes, a better understanding of how metabolic signals are transduced to control cellular clock function will provide insights into pathogenesis of these diseases.
Animals were maintained in the animal facility at the University of Memphis or University of Minnesota Duluth. All animal experiments were conducted according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee at University of Minnesota (No.1606-33864A) and the Institutional Animal Care and Use Committee at the University of Memphis (No. 0764).
mTorflx/flx mice on a C57BL/6 background (kindly provided by Dr. Sara C. Kozma, University of Cincinnati and Dr. Nahum Sonenberg, McGill University) were crossed to CMV-Cre mice to generate mTor+/–mice[21,43], which were then crossed with mTorflx/flx mice to produce mTorflx/–mice. The mTorflx/–line was crossed with mTorflx/flx line to mTorflx/flx (~50%) and mTorflx/–(~50%) mice used in the experiments. mTorflx/–mice were crossed with PER2::LUC (Per2Luc) reporter mice[75] to obtain mTorflx/flx;Per2Luc and mTorflx/–;Per2Luc mice.
About 2 months old mice were individually housed in cages equipped with running wheels and locomotor activities were recorded as previously described[21,49,34]. Briefly, mice were entrained to a standard 12hr/12hr light/dark cycle for 10 days and then released to constant darkness (DD) for 20 days, followed by release to constant light (LL) for 40 days. Wheel-running activities were analyzed using ClockLab program (Actimetrics).
Rapamycin, Torin1, PP242, and insulin were purchased from Sigma-Aldrich (St. Louis, MO) or Selleck Chemicals (Houston, TX).
For constructing lentiviral vectors expressing WT Rheb or constitutively active Rheb-Q64L mutant, two primers (forward: 5’-caccatggactacaaagaccatgacggt-3’; reverse: 5’-tcacatcaccgagcacgaagactttccttg-3’) were used to amplify the respective DNA fragments. The fragments were first sub-cloned into pENTR/D-TOPO vector (Invitrogen, Carlsbad, CA) and subsequently to the pLV7 destination vector that harbor a puromycin resistance gene via Gateway cloning to generate the pLV7-CMV-Rheb expression vector. Viral particles were generated following standard protocols in 293T cells, as described previously[76,77]. Viral particles were concentrated using Lenti-X concentrator (Clontech Lab, Mountain View, CA). Cells were infected and selected with puromycin to generate stable expression cell lines. For lentiviral shRNA vectors, we followed the procedures described in our previous study[30]. Non-specific shRNA construct (GCAACAAGATGAAGAGCAC) was described in that study. Five mTor targets were designed and tested for knockdown efficiency. The target sequences for knockdown in MMH-D3 cells were GTGGAGCCCTACAGGAAGT and GTGCTACATTGGCTGGTGT, and those for 3T3-L1 cells were GCCACACCGTGATGGAAGT and GTGCTACATTGGCTGGTGT. Viral particles were prepared as above and used to infect MMH-D3 cells. Two days post-infection, cells were selected with 2 μg/ml puromycin and stable cells lines were used for rhythm assays.
Cell culture and growth conditions for fibroblasts, MMH-D3 hepatocyte and 3T3-L1 adipocytes were performed as previously described[30]. SCN and peripheral tissue slices were dissected and cultured in explant medium as described previously[49].
For real-time bioluminescence recording, we used a Lumicycle luminometer (Actimetrics) on 35-mm culture dishes and Synergy SL2 microplate reader (Bio Tek) on 96-well plates as previously described[30,49,76]. Lumicycle Analysis Program (Actimetrics) was used to analyze Lumicycle data to determine period length and rhythm amplitude. Briefly, raw data were fitted to a linear baseline, and the baseline-subtracted data were fitted to a sine wave (damped), from which period length and goodness of fit and damping constant were determined. For samples that showed persistent rhythms, goodness-of-fit of >80% was usually achieved. Due to high transient luminescence upon medium change, the first cycle was usually excluded from rhythm analysis. For amplitude analysis, raw data from day 3 to day 5 were fitted to a linear baseline, and the baseline-subtracted (polynomial number = 1) data were fitted to a sine wave, from which the amplitude was determined. Synergy luminometer data were analyzed with the MutiCycle Analysis program (Actimetrics), in which bioluminescence data were subtracted (first-order polynomial) and fit into a sine wave to determine circadian period length and rhythm amplitude.
Liver and cell lysates were prepared as previously described[78]. Briefly, liver tissue samples were homogenized with a pestle grinder and lysed in RIPA lysis buffer containing cocktails of proteases inhibitors (Roche) and phosphatase inhibitors (Sigma). Cells were harvested by trypsinization and immediately lysed in RIPA buffer. SDS-PAGE and Western blot analysis were performed as previously described[79]. The primary antibodies used in this experiment are as following: guinea pig antibodies against BMAL1, CLOCK, PER1, PER2, CRY1, CRY2 were from Choogon Lee’s lab; rabbit or mouse antibodies against mTOR, p-mTOR, S6, p-S6, 4E-BP1, p-4E-BP1 were purchased from Cell Signaling Technology (Danvers, MA); and CRY1, CRY2, PER2, Actin and Tubulin were from Santa Cruz Biotech. Horseradish peroxidase-conjugated secondary antibodies were used for all Western detection. ECL or SuperSignal West Pico substrate (Thermo Scientific) was used for chemiluminescent detection.
RNA extraction, reverse transcription, and quantitative real-time PCR were performed as previously described[30,49,34]. SYBR Green PCR master mix (Thermo Scientific) was used in qPCR. The primers used in qPCR analysis were described in the previous study[30].
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10.1371/journal.pbio.1001490 | Parallel Evolutionary Dynamics of Adaptive Diversification in Escherichia coli | The causes and mechanisms of evolutionary diversification are central issues in biology. Geographic isolation is the traditional explanation for diversification, but recent theoretical and empirical studies have shown that frequency-dependent selection can drive diversification without isolation and that adaptive diversification occurring in sympatry may be an important source of biological diversity. However, there are no empirical examples in which sympatric lineage splits have been understood at the genetic level, and it is unknown how predictable this process is—that is, whether similar ecological settings lead to parallel evolutionary dynamics of diversification. We documented the genetic basis and the evolutionary dynamics of adaptive diversification in three replicate evolution experiments, in which competition for two carbon sources caused initially isogenic populations of the bacterium Escherichia coli to diversify into two coexisting ecotypes representing different physiological adaptations in the central carbohydrate metabolism. Whole-genome sequencing of clones of each ecotype from different populations revealed many parallel and some unique genetic changes underlying the derived phenotypes, including changes to the same genes and sometimes to the same nucleotide. Timelines of allele frequencies extracted from the frozen “fossil” record of the three evolving populations suggest parallel evolutionary dynamics driven at least in part by a co-evolutionary process in which mutations causing one type of physiology changed the ecological environment, allowing the invasion of mutations causing an alternate physiology. This process closely corresponds to the evolutionary dynamics seen in mathematical models of adaptive diversification due to frequency-dependent ecological interactions. The parallel genetic changes underlying similar phenotypes in independently evolved lineages provide empirical evidence of adaptive diversification as a predictable evolutionary process.
| The causes and mechanisms of evolutionary diversification are central issues in biology. There is well-established theory that predicts that adaptive diversification can arise because of ecological interactions between individuals, such as competition or predation, but there are no empirical examples in which this process has been observed at the genetic level. We documented the genetic basis of adaptive diversification resulting from competition for resources in populations of the bacterium Escherichia coli. The populations diversified into two coexisting ecotypes representing different physiological adaptations. We found that similar but independently evolved phenotypes often shared mutations in the same gene and, in four cases, shared identical mutations at the same nucleotide position. Timelines of allele frequencies extracted from the frozen “fossil record” of three evolving populations showed parallel evolutionary dynamics, suggesting that mutations causing one type of physiology changed the ecological environment and allowed invasion of mutations causing an alternate physiology. The results provide empirical evidence of adaptive diversification as a predictable evolutionary process.
| The causes and mechanisms of diversification are central issues in evolutionary biology. Explanations that involve the splitting of an ancestral population into geographically or otherwise isolated populations (allopatric diversification) have historically been favored because of theoretical difficulties with sympatric diversification (i.e., diversification without isolation) [1]–[5]. In the last 15 years, though, two major developments have increased the attractiveness of sympatric explanations. First, models of sympatric diversification have largely overcome earlier theoretical objections, showing that sympatric diversification can occur due to frequency-dependent selection under a wide range of conditions [6]–[9]. Second, empirical evidence from both laboratory experiments [10]–[19] and field studies [20]–[23] suggests that diversification can occur in sympatry, sometimes on time scales of hundreds of generations, and that such diversification may be an important source of biological diversity.
Sympatric diversification can be driven by frequency-dependent selection in a process called adaptive diversification and under conditions that may be quite general [7]–[9],[24]. This process can be described by the theoretical framework of adaptive dynamics [6],[25],[26]. A crucial component of this framework is the concept that the environment a population experiences, and that drives its evolutionary dynamics, depends in part on the phenotypic distribution of the population itself and the resulting ecological dynamics. Adaptive diversification occurs through evolutionary branching [6], a process in which selection drives a population to a point in phenotype space at which selection becomes disruptive. At this point, the population diverges into two lineages, which may continue to diverge.
In general, the problem of adaptive diversification and speciation is 2-fold: on the one hand, one wants to identify the ecological conditions that lead to disruptive selection and evolutionary branching, and on the other hand, one wants to understand the mechanisms interrupting gene flow between ecologically diverging subpopulations. Both of these aspects of adaptive diversification have been studied extensively in the theoretical literature (e.g., [7]–[9]). Here we experimentally address the first of these issues using asexual organisms, in which mating does not lead to recombination between diverging subpopulations, and which are therefore ideally suited to study the ecological conditions generating the frequency dependence necessary for adaptive diversification. Indeed, adaptive diversification has been documented in microbial evolution experiments [11],[12],[27]–[31] in which well-mixed populations of Escherichia coli bacteria founded with a single genotype repeatedly evolve two metabolically distinct phenotypes. When grown in well-mixed serial batch cultures in medium with glucose and acetate as carbon sources, E. coli cells preferentially metabolize glucose and excrete acetate until the glucose is depleted and then undergo a diauxic switch to acetate consumption [32]. In several populations evolving in these conditions for more than 1,000 generations, two coexisting phenotypes emerged that differ in their diauxic lag—that is, in the time required to switch to acetate metabolism: the slow switcher (SS) has a longer diauxic lag than that of the fast switcher (FS) [11],[28]. These two phenotypes reflect a tradeoff in carbohydrate metabolism: SS strains grow more quickly than FS strains when glucose is abundant, but are unable to efficiently catabolize acetate, while FS strains continue to grow rapidly on acetate after glucose is depleted [28]. The evolution of the FS and SS phenotypes in multiple replicate lines is a striking example of convergence at the phenotypic level, suggesting a deterministic adaptive process.
However, the evolutionary branching predicted by adaptive dynamics models necessarily involves changing selective pressures. Therefore, the similar outcomes of diversification across replicate populations are qualitatively different from parallel adaptation to a fixed adaptive landscape. Rather, in this case, the entire process of genetic change leading to environmental change and new selective pressures that in turn cause further genetic change has occurred in parallel. This suggests that not only the outcome of evolution is parallel but the evolutionary dynamics as well.
In spite of phenotypic evidence for adaptive diversification, there is limited information available on the genetic changes underlying this process. In fact, to our knowledge there are no examples of sympatric diversification for which the underlying genetics have been fully described. In the FS and SS example, the degree to which the similar, independently evolved phenotypes reflect similar underlying genetics in different populations is unknown. This has implications for the genotype–phenotype map: Are there few genetic ways to produce FS and SS phenotypes or many? Also unknown is the degree to which the similar evolutionary outcomes reflect similar evolutionary dynamics; the results of previous studies suggest that the degree of similarity in the type, order, and timing of adaptive changes across independently evolving populations varies widely (e.g., [33]–[35]). This in turn has implications for the degree of determinism in the evolutionary dynamics: Are there many paths or few that lead to similar phenotypic (and possibly genetic) outcomes? And are the changing selective pressures predicted by adaptive dynamics models reflected in genetic changes leading to new selective pressures that in turn cause further genetic change? If such a pattern is present in multiple replicate lines, this would provide evidence that not only the outcome of evolution is predictable, but the evolutionary dynamics as well.
To trace the dynamics of genetic change underlying adaptive diversification, we combined sequencing of FS and SS clones isolated near the end of the evolution experiment with sequencing of whole-population samples from time points in the frozen (“fossil”) record of the experiment. We sequenced two FS clones, two SS clones, and 16 time point samples for each of three replicate evolution experiments (called populations 18, 19, and 20 [28]). Sequencing of SS and FS clones allowed us to identify mutations associated with the phenotypes of interest, and sequencing of whole-population samples from the fossil record of the experiments allowed us to trace the origin, increase, and (occasionally) extinction of these and other mutations. Finally, comparing these results across three independently evolved populations allowed us to assess the degree to which a similar ecological setting led to similar evolutionary dynamics and outcomes (i.e., the degree of determinism).
Sequencing the SS and FS clones revealed striking similarities in the genetic changes underlying the derived phenotypes across the three replicate populations (Figure 1). Each of the SS clones carried a mutation in spoT, a deletion of part or all of the ribose operon (rbs), and a mutation in nadR (Figure 1). One or two additional mutations appeared in some SS clones, but these were not shared between clones. No mutations were fixed in any of the three replicate populations, and in no case was any specific genetic change shared between FS and SS clones. In population 19, the two SS clones did not share any mutations (Figure 1b), indicating that they evolved independently from the ancestral strain (although each clone has a mutation in spoT, nadR, and rbs). Thus, the six sequenced SS clones represent four separate origins of the SS phenotype, all of which evolved parallel changes to the same three loci.
Each of the FS clones carried 6–10 mutations relative to the ancestral strain, most of which were shared between the two clones from each population (Figure 1). Assuming a single origin for each mutation, we infer that these shared mutations occurred before the two sequenced clones last shared a common ancestor. Phenotypically, the FS type represents a novel metabolic strategy, while the SS type is more similar to the ancestral strain [11],[27],[28],[30], and this difference is reflected in the underlying genetics. In all three populations, the FS clones are more genetically distant from the ancestor than the SS clones (paired t test, n = 4 independent comparisons, two-tailed p = 0.0008). FS clones from different populations are also more genetically dissimilar than SS clones from different populations: in contrast to spoT, rbs, and nadR in the SS clones, there were no genes that carried mutations in the FS clones from all three populations.
Timelines of allelic invasions in the SS and FS lineages are shown in Figures 2–4. Figure 2 summarizes the evolutionary dynamics unfolding in each of the three evolution experiments, and Figures 3 and 4 show the frequencies of the mutations found in the various SS and FS endpoint clones over time. These timelines suggest that each ecotype affected the other's evolution by altering the available ecological opportunities. In all three evolving populations, nonsynonymous SS-associated spoT and rbs mutations were the first to reach high frequency and likely increased the degree of specialization on glucose [36],[37]. In population 18, for which the timeline of metabolic phenotypes has been documented [28], the rapid rise of these mutations corresponds very well with the increase in the mean switching lag shown in Figure 1B of Spencer et al. [28]. Similarly, in population 20, SS bacteria were present by generation 200 [31]. In both cases, spoT and rbs were the only SS-associated mutations present when the SS phenotype was first detected, so one or both of these mutations must have caused the SS phenotype. It is known that spoT mutations can confer a substantial advantage by reducing the lag phase before exponential growth on glucose and by increasing the maximum growth rate on glucose, both of which presumably occur through partial deactivation of the stringent stress response [37],[38]. This may in turn make it harder for the cells to switch to acetate consumption after glucose is exhausted, and hence cause the SS phenotype.
Due to an IS150 element immediately upstream of the rbs operon, deletions of all or part of rbs occur at high frequency (∼5×10−5 per cell generation) in the ancestral E. coli strains used in our evolution experiments and provide a ∼1%–2% fitness advantage in glucose minimal medium [36]. Since rbs deletions were also the first mutations to occur in two of the three FS lineages (Figure 1b, c), it is likely that rbs deletions alone do not cause either the SS or the FS phenotype, but rather that rbs deletion mutants were a common genetic background early in the experiment and that the mutations causing the SS and most FS phenotypes occurred on this background.
By generation 342, the frequency of SS-associated spoT and rbs mutations was high (>65%) in all three populations (Figure 3). If either or both of these mutations are responsible for an increase in acetate lag (as must be the case in population 18), their increased frequency would have caused a change in the daily regime of nutrient concentrations in the experimental environment, namely that more acetate was available later in the growth phase. The first FS-associated mutations began to rise in frequency at this time (Figures 2 and 4). This wave of invasion involved a different set of genes in each population, but some evidence of parallelism is apparent here as well: the mutations increasing at this time included an identical insertion in the yfbV/ackA intergenic region in populations 18 and 19, and different mutations affecting the ptsG gene in populations 19 and 20.
In all three populations, the first FS-associated mutations to reach appreciable frequency included ones in or upstream of genes related to acetate utilization and excretion and glucose metabolism. These mutations appeared either in the remaining ancestral genetic background or in rbs deletion mutants and led to coexistence between the SS and FS lineages that persisted until the end of the evolution experiments. These early FS-associated mutations occurred upstream of ackA in populations 18 and 19, in iclR in population 18, in pta in population 20, and in or upstream of ptsG in populations 19 and 20 (Figure 2). The timing of these invasions, which in all three populations only reached appreciable frequencies after SS-associated mutations had reached high frequency, is consistent with FS-like phenotypes evolving as an adaptation to the novel ecological niche of greater acetate availability generated by increased glucose specialization of the SS. These early FS invasions thus generated the basic SS-FS-polymorphism that persisted to the end of the evolution experiment. Experimental evidence demonstrates that the long-term coexistence of FS and SS is due to frequency-dependent interactions [28]–[31]. Again, in population 18 the correspondence with phenotypic change is conspicuous: clones with a short acetate lag were first detected around the same time (ca. generation 500, Figure 1B in Spencer et al. [28]) at which the first three FS-associated mutations reached appreciable frequency: a nonsynonymous substitution in yijC, an insertion upstream of ackA (yfbV/ackA+T), and a 10 bp deletion in iclR (Figures 2 and 4). Thus one or more of these must have produced the FS phenotype. By the same logic, one or more of the four FS-associated mutations present in population 20 when FS were first detected at generation 200 (rbs, pta, ptsG, and yceA) must be sufficient to produce the FS phenotype.
The functions of some genes in the initial FS invasions suggest their involvement in similar phenotypic changes across populations. The yfbV/ackA insertion in populations 18 and 19 affects a potential transcriptional recognition sequence of the global fermentation activator arcA upstream of ackA [39], suggesting that this mutation affects ackA expression, and hence acetate metabolism. In population 20, a mutation in pta rose in frequency at about the same time (Figure 4c), and all six sequenced FS clones bear one of these two mutations. Since ackA and pta catalyze subsequent reactions in the pathway of acetate utilization and excretion (acetate↔acetylphosphate and acetylphosphate↔acetyl-CoA, respectively), these two mutations may have similar metabolic effects.
The function of ackA as an important regulator of acetate metabolism and the independent origin of the identical yfbV/ackA mutation in populations 18 and 19 strongly suggest that this intergenic substitution is at least partially responsible for the reduced acetate lag in the FS clones (although FS in population 20 has a different genetic basis). Similarly, iclR is a regulator of the acetate operon aceBAK, and in an experimental population not included in this study, an insertion in iclR acting as a stop codon was previously shown to be partly responsible for the FS phenotype by derepressing the acetate operon [27]. This suggests that the iclR deletion in population 18 has contributed to the FS phenotype as well. Finally, yijC, a repressor of genes involved in fatty acid biosynthesis [38], could play a role in the FS phenotype by altering the relative amounts of acetyl-CoA used in fatty acid biosynthesis and in the citric acid cycle.
In population 20, a mutation in ptsG was one of the first FS-associated mutations to invade (Figure 4c), while in population 19, an IS186 insertion sequence appeared in the intergenic region upstream of ptsG around the same time, potentially disrupting its transcriptional regulation. The enzyme encoded by ptsG, a glucose-specific PTS permease, is involved in the uptake of glucose and its transport across the cell membrane [40], and disruption or down-regulation of these functions would be consistent with the FS phenotype.
After FS mutations had risen to intermediate frequencies (>0.15), several SS-associated nadR mutations appeared at detectable frequencies in each of the three populations (Figures 1, 2, and 5; Text S1). The proliferation of these mutations (≥5 in each population) after generation 500 is striking since no nadR mutations were present at detectable frequency before this time. nadR plays an important role in many metabolic pathways, including growth on carbohydrates [41],[42], and the observed mutations show a surprising degree of parallelism. The highest-mean frequency nadR mutation in population 20 (nadR-290) was identical to that in 19-SS1 in population 19, and a different mutation in the same codon was present in population 18. All three populations also included a mutation in codon 294 of nadR, and this was identical between populations 18 and 20 (Figure 5a, c). Thus, a different pair of mutations in these two codons is found in each of the three populations, though each mutation is shared by two populations.
The nadR mutation found in both SS clones from population 18 was only detected in a single Illumina read in the time point samples (at generation 482), indicating that it was present at very low frequency. The presence of such a low-frequency mutation in both sequenced clones suggests that it had a phenotypic effect (since we preferentially selected clones that were clearly of the SS phenotype; see Materials and Methods). The protein encoded by nadR has both enzymatic and regulatory roles in the NAD biosynthetic pathway and plays important roles in glycolysis and the citric acid cycle. The presence of nadR mutations in all six sequenced SS clones and none of the six sequenced FS clones strongly suggests that these mutations are adaptive for the SS, but not the FS, phenotype. It is interesting to note that mutations in nadR were found in 12 of 12 experimental E. coli populations after 20,000 generations of evolution in glucose minimal medium [42], and that one of these was identical to the nadR-290 mutation in populations 19 and 20.
In populations 18 and 20, invasion by SS-associated nadR mutants was followed by rapid increases in frequency of a second set of FS-associated mutations (Figure 2). In population 18, a spoT mutation identical to that in FS from population 20 (spoT-414) increased in frequency only to be replaced by another spoT mutation (spoT-369) that had previously been present at very low frequency. In population 20, the second set of FS-associated mutations included one in a global regulator (arcA) known to increase acetate consumption [31]. It is likely that the FS-associated arcA mutation in population 20 affects the expression of ackA; if so, one of the phenotypic effects of this mutation may be similar to that of the yfbV/ackA insertion in populations 18 and 19. This would explain why this mutation has a larger impact on SS clones than on FS clones [31]: if the primary phenotypic effect of the arcA mutation is to alter the rates of acetate utilization and/or excretion, the FS-associated pta mutation may have made this effect at least partially redundant in population 20.
In addition to the spoT mutations associated with FS and SS clones, one other mutation in spoT was present at ≥20% frequency at some time in each of the three populations (Figure S1). In populations 18 and 20, this mutation was lost by the end of the experiment. In population 19 this spoT mutation increased in frequency near the end of the experiment as the spoT mutation associated with 19-SS1 underwent a corresponding decline. The transient spoT mutation in population 18 was identical to that associated with the FS clones in population 20 (Figure S1a, c), and hence is likely to be FS-associated. This indicates that mutations in the stringent response can be adaptive for either the SS or the FS phenotype [43]. The phenotype associated with the spoT-316 mutation in population 20 is not known.
Several other mutations not associated with any of the FS and SS clones were present at detectable frequencies in each of the three fossil records (Figure S2). A complete list of detected mutations and the samples in which they were found is shown in Table S1.
Microbial evolution experiments are a powerful approach to understanding evolutionary dynamics, combining controlled conditions with the capability for experimental replication to allow strong inferences of causation. In addition, rapid reproduction allows laboratory experiments lasting hundreds or thousands of generations, and cryopreservation allows direct comparisons between ancestors and descendants. The recent rapid advance of nucleic acid sequencing technologies has made whole-genome sequencing feasible for both single microbial strains and whole populations containing a variety of strains. The combination of microbial evolution experiments and next-generation sequencing technologies provides an unprecedented opportunity to observe the temporal dynamics of evolutionary change across the entire genome [44],[45]. Replicating this approach in multiple independent populations can tell us whether adaptive sympatric diversification in independent populations involves similar genetic mechanisms and similar evolutionary dynamics.
Our results revealed both shared and unique genetic mechanisms underlying the evolution of pairs of metabolically distinct ecotypes in different populations. In some cases, similar phenotypes had mutations in different genes (e.g., the wecF, uppS, and arcA mutations in the FS clones from populations 18, 19, and 20, respectively; no mutations in these genes were detected in either clones or time point samples in the other populations). In some cases, mutations affected different codons of the same gene, as in the distinct spoT and nadR mutations found in the SS clones from all three populations. We also observed different changes to the same codon (e.g., codons 290 and 294 of the nadR gene; Figures 2 and 5). Finally, we found four examples of identical genetic changes in different populations: spoT-414 (populations 18 and 20; Figure 2), yfbV/ackA (18 and 19; Figure 2), and two nadR codons (290 identical in populations 19 and 20, Figure 2; 294 identical in 18 and 20, Figure 5). Of the 45 mutations shown in Figure 1, 21 (47%) occurred in a nucleotide, codon, or gene that also had a mutation associated with the same ecotype in another population.
The pattern of genetic invasions evident in the fossil records also revealed strikingly similar evolutionary dynamics: in all three evolving populations, SS-associated spoT and rbs mutations were the first to invade, followed by FS-associated mutations affecting acetate and glucose metabolism, followed by SS-associated mutations in nadR, and finally additional FS-associated mutations. In spite of several mutations showing evidence of strong positive selection, such as the SS-associated spoT mutations in all three populations, no mutation was fixed in any of the three populations. Many mutations that increased rapidly after their initial appearance later declined in frequency yet were then maintained in the populations at intermediate frequencies.
Apart from genetic drift, two separate (but not mutually exclusive) processes could explain the repeated and parallel invasions and long-term coexistence observed in these three populations. We do not consider genetic drift as an explanation because the large effective population sizes make drift implausible for allele frequency changes greater than a fraction of 1% from one time point sample to the next (see Materials and Methods).
Clonal interference, which involves the coexistence of two or more beneficial mutations on different genetic backgrounds, is one potential explanation. This process allows long transient polymorphisms to be maintained in asexual populations because several different and almost equally beneficial mutations can be present in different subpopulations [46]–[48]. Thus, clonal interference is expected to lead to longer fixation times, elevated levels of polymorphism, and generally more complex evolutionary dynamics in asexual populations such as the ones studied here. One probable example of clonal interference is the replacement of spoT-414 by fecI/insA-25, spoT-369, and wecF-244 within the FS lineage in population 18. Around generation 700, the spot-414 mutation appeared on the FS background and began to rapidly invade, while the fecI/insA-25 and spoT-369 mutations remained at low frequency. Before the spoT-414 mutation could reach fixation within the FS lineage, though, the wecF-244 mutation appeared and quickly replaced all other FS lineages, including that with spoT-414.
Another possible explanation for long-term coexistence is the coevolution of diverging phenotypes through environmental feedbacks and frequency-dependent selection. In this scenario, the adaptive landscape changes as metabolic changes in one subpopulation create a new niche, which another subpopulation evolves to fill. Since the only source of environmental change over the course of the experiments was the bacteria themselves, any such changes in the selective regime must have been generated by changes in the genetic, and hence metabolic, makeup of the bacterial populations. Such environmental feedback generates frequency dependence and is at the core of the theory of adaptive diversification [6]–[9]. An example of environmentally mediated negative frequency dependence is the interaction between the SS lineage and the wecF-244 containing FS lineage in population 18: the wecF-244 mutation invaded the FS lineage rapidly, indicating a strong selective advantage. In the absence of any frequency-dependent interactions, such an advantageous mutation would continue to invade, going quickly to fixation unless another even more advantageous mutation appeared (as in the clonal interference scenario). In this case, though, neither of these explanations is viable: after quickly fixing within the FS lineage, the wecF-244 mutation leveled off (or even declined in frequency) in the absence of any new mutations.
Taken by themselves, most of our results could be explained by either clonal interference or reciprocal niche construction. Since both processes can explain the long-term coexistence of multiple lineages, it can be difficult to distinguish between them. However, the populations in this study have also been the subject of numerous previous studies, and this prior work aids substantially in interpreting the current results. When this additional information is taken into account, it is clear that although clonal interference may explain some of the observed dynamics, it is unlikely to explain all of them.
The main reason for this is that we already know from previous experimental analyses that the coexistence between the SS and FS ecotypes involves frequency dependence, at least in populations 18 and 20 (e.g., [28]–[31]). In particular, the polymorphisms between SS and FS lineages that we observed arising early on in the evolution experiments are maintained by selective forces favoring rare ecotypes. For population 20, [31] has explicitly shown the action of frequency dependence throughout the fossil record in invasion experiments with SS and FS strains extracted at various time points. In addition, Spencer et al. [28] have already argued in detail why clonal interference is unlikely to be the main driver for the pattern of evolutionary branching observed in population 18, which is one of the populations used for the present study. Clonal interference may have played a role in generating some of the polymorphisms observed within the SS and within the FS lineages, and in the timing of the rise of various mutations. Overall, however, it seems clear that the basic coexistence between the SS and FS ecotypes are not due to clonal interference, but to frequency-dependent ecological interactions. Indeed, similar evidence has led to the conclusion that a polymorphism in one of R. Lenski's long-term experimental lines, Ara-2 [18],[49], evolved as a result of niche construction [15],[50].
It is a hallmark of frequency dependence that one type's abundance creates the niche for another type's invasion. Although we cannot rule out clonal interference, the sequence of alternating invasions observed in the fossil records of our experimental lines is consistent with this process of reciprocal niche construction. In particular, as is apparent from Figures 3 and 4, the rise of the first FS mutations consistently following in the wake of the establishment of first SS mutations is conspicuous, and so is the rise of the SS-associated nadR mutations following the appearance of the first FS mutations. We note that the limited replication of this study prevents many rigorous statistical tests, so that many of our results can only be described qualitatively, not quantitatively. With the continuing rapid decline in the cost of sequencing data, it is quickly becoming feasible to carry out studies similar to ours with higher temporal resolution and across larger numbers of populations, which will make rigorous statistical analyses possible.
Nevertheless, it seems unlikely that the consistent pattern of alternating invasions observed in our three lines is due to chance alone, and given that the endpoint FS and SS strains coexist due to frequency dependence, it is tempting to conclude that the patterns of invasion reflect the action of frequency-dependent selection in the course of the evolution experiment. The observed diversification should then be viewed in the light of the theory of adaptive diversification due to frequency-dependent interactions [9]. It is worth noting that much (but not all) of this theory is based on the assumption of many mutations of small effect, and the basic theoretical phenomenon of evolutionary branching in particular is an essentially continuous process in phenotype space [6], which moreover is often presented as a symmetric pattern of diversification. In contrast, in our experimental lines diversification is obviously due to a few mutations of large effect, and the pattern of diversification is asymmetric in phenotype space [28]. However, many aspects of the theory of adaptive diversification are robust to introducing large mutational effects, and asymmetric evolutionary branching is entirely feasible [9]. Therefore, our experimental results can be seen as proof of this robustness, and as providing a full description of adaptive diversification at the genetic level, revealing parallel evolutionary dynamics, and thus a high degree of determinism, in the sympatric origin and subsequent divergence of ecologically distinct lineages.
We isolated clones from frozen samples of populations 18 and 19 from day 156 of the evolution experiment of Spencer et al. [28]. Frozen samples were inoculated into 10 mL of the growth medium, grown overnight at 37°C with shaking, and spread onto agar plates. We arbitrarily chose 10 small colonies and 10 large colonies from each population and measured their growth profiles over 24 h as described in Spencer et al. [28]. From each population, we chose two large colonies with unambiguous SS growth profiles and two small colonies with unambiguous FS growth profiles for sequencing. For population 20, we used previously isolated clones [30], also from day 156 of the experiment. In this experiment, replicate populations were founded from isogenic lines of E. coli B and cultured in well-mixed condition for 183 d (∼1,230 generations) with daily (∼6.7 generations) transfers to fresh medium (Text S1). Populations 18 and 20 were initiated with REL606, and population 19 with REL607 [51]. REL606 and REL607 perform similarly in the growth environment of the evolution experiment [28],[51],[52].
For the time point samples, we chose 16 time points corresponding to days 0, 6, 12, 19, 30, 40, 51, 61, 72, 82, 96, 111, 124, 138, 156, and 183 of the evolution experiment for a total of 48 time point samples. We sequenced paired ends of fragments of genomic DNA samples from 12 clones (2 SS and 2 FS from each of three populations) and 48 time point samples (16 time points from each population) on an Illumina HiSeq 2000 using standard procedures. The paired t test reported for number of mutations in FS versus SS compared the mean number of mutations in FS clones to that in SS clones from the same population, considering only genealogically independent comparisons (one each for populations 18 and 20, two for population 19, since there were two independent origins of SS in this population).
Paired-end sequencing was performed on an Illumina HiSeq 2000 at the University of British Columbia's Biodiversity Research Centre. The clonal samples were prepared with the Illumina TruSeqTM DNA Sample Preparation Kit, and the time point samples with the NEXTflex DNA Sequencing Kit and DNA Barcodes by Bioo Scientific (Austin, TX). We used CASAVA 1.8 (Illumina, Inc., San Diego, CA) to demultiplex sequencing reads by barcode and generate files in FASTQ format [53] for use in all downstream analyses. All FASTQ files were deposited in the NCBI short read archive (accession: SRP017657). We identified SNPs and small (≤4 bp) indels and estimated their frequencies in the time point samples using both the main public server and local instances of Galaxy (details below) [54]–[56]. To identify larger indels and estimate their frequencies in the time point samples, we used BreSeq version 0.16 [57]. The sequence [58] of the ancestral strain REL606 (GenBank accession number NC_012967.1) was used as the reference for all mutation screens.
FASTQ files were first filtered for quality, retaining only those reads with ≤5 bases with quality scores <20. Reads were aligned to the reference genome using BWA version 0.5.9-r16 [59] with default settings and treating the reads as single-end, and variants were identified using SAMTools version 0.1.12-r862 [60]. For the 60 sequenced samples (12 clones and 48 time points) average coverage (over the 4,629,812 bp of the reference genome) ranged from 72× to 2,500×. For all 60 samples, >99% of the genome was covered by >30 aligned reads.
We report the frequencies of all variants that both appear in more than one time point sample (within the same population) and rise to at least 5% frequency in one or more of the samples. We also report the frequencies of variants that are found in the clonal samples, regardless of their frequency in the time point samples. Variants supported by a single read at a given time point are not reported unless supported by multiple reads in the next time point. We estimated the frequencies of large deletions (>4 bp) by manually inspecting all reads in which ≥10 bp matched each side of the deleted region. In a few cases, we were able to determine linkage between nearby mutations by examining individual Illumina reads that spanned both loci.
To distinguish changes in allele frequency due to selection from those due to drift, we assume an effective population size (Ne) of 3.3×107, as estimated for E. coli grown in similar conditions [51]. Under the Wright-Fisher model [61],[62], drift is a Markov chain, which generates a variance in allele frequency of pq/Ne after one generation (for haploids). After t generations, the variance is pq(1 – et/Ne). If we assume p = q = 0.5 (which yields the fastest drift), the variance after 82 generations (the average time separating our time point samples) is 6.21×10−7 (s.d. = 7.88×10−4 or 0.08%). Using the normal approximation of the binomial, the probability that drift causes an allele frequency change ≥1% from one time point sample to the next is less than 1×10−12. Thus, even accounting for multiple tests, the possibility that any of the changes in allele frequency that we discuss are caused solely by drift is remote.
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10.1371/journal.pgen.1000085 | Systematic Identification of Genes that Regulate Neuronal Wiring in the Drosophila Visual System | Forward genetic screens in model organisms are an attractive means to identify those genes involved in any complex biological process, including neural circuit assembly. Although mutagenesis screens are readily performed to saturation, gene identification rarely is, being limited by the considerable effort generally required for positional cloning. Here, we apply a systematic positional cloning strategy to identify many of the genes required for neuronal wiring in the Drosophila visual system. From a large-scale forward genetic screen selecting for visual system wiring defects with a normal retinal pattern, we recovered 122 mutations in 42 genetic loci. For 6 of these loci, the underlying genetic lesions were previously identified using traditional methods. Using SNP-based mapping approaches, we have now identified 30 additional genes. Neuronal phenotypes have not previously been reported for 20 of these genes, and no mutant phenotype has been previously described for 5 genes. The genes encode a variety of proteins implicated in cellular processes such as gene regulation, cytoskeletal dynamics, axonal transport, and cell signalling. We conducted a comprehensive phenotypic analysis of 35 genes, scoring wiring defects according to 33 criteria. This work demonstrates the feasibility of combining large-scale gene identification with large-scale mutagenesis in Drosophila, and provides a comprehensive overview of the molecular mechanisms that regulate visual system wiring.
| In the nervous system, every neuronal process should know where to grow and when to establish contacts to the next-order neurons. During development, it is known that neural circuit formation is primarily determined by the genes. To identify these genes, we focused on the Drosophila visual circuitry as a model system, and disrupted the genes randomly. From over 30,000 of these mutants, we found more than 100 mutants which have disrupted patterns of neural circuitry, which we assessed as representing about 40 genes. We have successfully nailed down which gene is disrupted in 36 of them. We provide a list of all of the genes we identified. Altogether, we performed a detailed characterization of the 35 mutant phenotypes, to assess which aspects of neural circuit formation are disrupted in each of the mutants. Summarizing and categorizing the phenotypic fingerprints of each mutant, we could see which genes are more closely related to the others. These data will be useful for clarifying the genetic program that controls neural circuit formation, not only for the Drosophila visual system, but also generally for nervous systems across the species.
| The adult visual system of Drosophila melanogaster is a powerful genetic model for exploring the molecular and cellular mechanisms involved in axon growth, guidance, and synaptic specificity [1]. The adult retina consists of some 800 ommatidia, each containing 8 photoreceptor cells (R1–R8) that form topographic connections in distinct layers of the optic lobe. These connections are established during the late larval and early pupal stages. As photoreceptors begin to differentiate in the eye imaginal disc, the R1–R8 axons from each ommatidium form a single fascicle that extends topographically into the brain. Within the optic lobe, the R1–R8 axons then defasciculate and select their individual target regions. R1–R6 cells connect to targets in the lamina region of the optic lobe as part of a circuit specialized for motion detection. R7 and R8 cells, which mediate color vision, project axons through the lamina to terminate in distinct layers of the underlying medulla.
Large-scale forward genetic screens have been used to isolate numerous mutations disrupting various aspects of visual system wiring [2]–[5]. A small subset of these mutations has been selected for positional cloning, and the genes thus identified have provided important entry points for further mechanistic studies [6]. As with most large-scale genetic screens performed in Drosophila, the selection of mutations for gene identification has often been made on an ad hoc basis. In many cases, selection has been guided in part by the strength and specificity of the mutant phenotype, but also rather opportunistically by the number of alleles recovered and any prior genetic information that might facilitate the challenging task of positional cloning.
For these reasons, the potential of this model system has not yet been fully exploited. In particular, the bias for strong and specific mutant phenotypes has evidently enriched for genes encoding regulatory proteins such as transcription factors and cell surface receptors. Mutations affecting the basic machinery of axon growth, guidance, and targeting are likely to result in more pleiotropic defects. Additionally, because of protein perdurance and possible genetic redundancy, mutations in such genes may not always lead to a pronounced wiring defect. For these reasons, we were motivated to take a more systematic approach to gene identification–one that would be robust enough to identify even those genes with only one mutant allele, and efficient enough to justify identifying those with less specific or less potent mutant phenotypes. Accordingly, we developed methods for genetic mapping using single nucleotide polymorphisms (SNPs) [7]. We have now used these methods to systematically identify the gene disrupted for nearly all the mutations recovered in a large-scale forward genetic screen for visual system connectivity defects.
Using eyFLP to generate whole-eye clones [4], we screened each of the four major autosomal arms for chemically-induced mutations that disrupt visual system wiring. Eye-Brain complexes were dissected from 3rd instar larvae harbouring the glass-lacZ reporter [8], fixed and stained by X-gal to visualize R-axon projections. Specimens were examined under a light stereomicroscope. Lines exhibiting aberrantly patterned retinas, as assessed initially from the external morphology of the adult eye and subsequently from tangential sections, were not further processed. Thus, we retained only those mutants in which the R cells appear to be appropriately specified, but their axons do not project correctly within the optic lobe [4]. Ultimately, we retained 122 mutants from a total of 32,175 lines screened (Table 1). Sporadic transheteroallelic larval or adult survivors were tested for phenotypic non-complementation either by staining of 3rd instar larval eye-brain complexes or horizontal adult head sections, respectively. Additionally, we analysed the R-cell projections in adult eyFLP mosaics of each complementation group by staining horizontal head sections to test the phenotypic consistency within the group. On this basis, mutant lines were assigned to 42 loci, 21 of which are represented by multiple alleles (Table 1).
Six genes were identified using standard positional cloning procedures, and have been reported previously [4], [9]–[12]. For the remaining loci, we used SNP mapping to identify the relevant gene [7]. The strategy was to isolate a set of ∼50 recombinants between the mutant and a reference chromosome, selecting for recombination events across the entire chromosome arm. Each of these recombinants was then scored for a visual system wiring phenotype (in eyFLP clones) and for SNP genotypes. This typically mapped the mutation to an interval of 0.5–1.5 Mb. In a second phase, a further set of 100–200 recombinants was generated within this interval, usually using a pair of flanking P-element insertions as markers. This second set of recombinants was also scored for a visual system wiring defect and SNP genotype. In some cases, rather than mapping the visual system phenotype at this second stage, we alternatively tracked a lethal mutation within this narrower interval (assuming the two to be due to the same genetic lesion). In these cases, we generated around 100 recombinants each from two P element insertion lines that were flanking the interval. This procedure gave a resolution of approximately 10–30 kb. Finally, we sequenced predicted coding regions in this region, using genomic DNA extracted from homozygous mutant and control embryos (see materials and methods). In some cases, the mutant gene was identified by a failure to complement existing alleles, in tests performed at various stages during the mapping procedure. Whenever possible, complementation was confirmed by examining visual system wiring in trans-heteroallelic animals.
Using these procedures we were able to identify a further 30 genes, two of which we have previously reported [7] and 28 of which are described here. For 12 of these loci, the gene identification was confirmed in a rescue experiment, generating transgenic animals carrying either a cDNA under the control of the eye-specific GMR or eyeless promoter, or inserting a genomic fragment. In total, we have now identified 36 of the 42 genes identified in this screen, including six genes identified by standard positional cloning. These genes are listed in Table 2, along with a summary of the evidence supporting each assignment. Of these 36 genes, visual system wiring defects have previously been reported for 11 loci: brakeless, dead-ringer/retained, dock, flamingo, misshapen, LAR, N-Cadherin, Pak, Ptp69D, golden goal and trio [3], [4], [7], [9]–[20]. Another 5 genes have been reported to have neuronal phenotypes in other developmental processes: chickadee, enoki mushroom, kinesin heavy chain, unc-104 and sequoia [21]–[26]. The remaining 20 genes have not previously been associated with neural phenotypes and for five of these no mutations have previously been reported (Br140, cdk8, wnk, ckIIα, GUK-holder).
In parallel with the systematic gene identification, we also performed a comprehensive phenotypic analysis of all mutant loci, selecting one or two representative alleles for those loci with multiple alleles. Our objective was to obtain an unbiased and semi-quantitative description of visual system wiring defects in each mutant as guide for future phenotypic and molecular studies. The screen was performed with a general R axon marker (glass-lacZ), which provides only low information content, we therefore examined each mutant using a panel of additional R-cell class-specific markers—Rh1-τlacZ (R1–R6 axons), Rh4-mCD8:GFP (R7 axons), Rh6-mCD8:GFP (R8 axons), and omb-τlacZ (polar axons)—as well as the additional general R-axon marker anti-Chaoptin mAb24B10. For each marker and mutant, visual system wiring was examined in whole-eye eyFLP clones in either 3rd instar larvae (glass-lacZ and omb-τlacZ) or in adults (Rh1-τlacZ, Rh4-mCD8:GFP, Rh6-mCD8:GFP, and mAb24B10). A total of 33 criteria of wiring defect were identified (Table S1, Table S2), and each defect was scored for each mutant using a scale of 0 (no defect) to 4 (most severe).
The dock allele D333 was excluded from our phenotypic analysis as molecular data [9], previously published reports [14] and complementation analysis suggests that it is a weak hypomorph.
For each of the data point (A score for each defect criterion of each mutant line), 2–5 hemispheres from multiple eye-brain complexes were scored independently by two investigators (T.S. and J.B.), generally from confocal microscope images. The two investigators score the same images. Wherever the larger sample size examination was possible, we prepared more than 10 samples to assess more reliably the expressivity and the penetrance of the phenotypes (e.g. omb-τlacZ (polar axons), adult gl-lacZ section and Rh1-τlacZ sections). For the confocal samples which we appreciated the resolution quality of the images that were taken, we assessed the expressivity by calculating the difference between the highest and the lowest score given within each defect criterion for each mutant allele. This reflects the variation of the scores we obtained and will help understand the expressivity of the each phenotype in each mutant allele (Figure S1). We also demonstrate the penetrance of the phenotype by checking whether each defect criterion was “fully penetrant” in our analysis (Figure S1).
For classifying the mutants, we took advantage of hierarchical clustering method. Instead of a single clustering based on all 33 defect criteria, we first selected five prominent defect criteria that gave an informative primary classification of the mutants (Figure 1). These 5 defects are axon stalling, dorsal-ventral (DV) crossing, lamina pass-through, R8 defects, and R7 undershoot. Although many mutants have more than one of these defects, these phenotypes could nevertheless be used to classify the mutants into 4 major phenotypic clusters, each representing a distinct biological step in visual system wiring: axon growth, topographic mapping, lamina targeting, and medulla targeting. With this procedure, we put more weight on these selected five criteria, which we consider of high biological importance. In the following sections, we provide a brief overview of the genes and phenotypes in each of these 4 classes, considering the full set of 33 defect criteria.
Mutations in four genes resulted in a characteristic stalling phenotype, readily visualized with the omb-τlacZ transgene at the larval stage (Figure 2). This marker labels axons from the dorsal and ventral regions of the eye disc, which target the corresponding dorsal and ventral regions of the optic lobe. In axon growth mutants, a portion of axons appear to stall within the optic stalk, or enter the optic lobe but fail to reach their normal target region. Nevertheless, these axons generally appear to remain on course, suggesting that the defect is primarily in axon growth rather than guidance.
Two of the genes in the phenotypic cluster encode conserved regulators of cytoskeletal dynamics (trio and Mbs), another encodes a conserved cytoplasmic protein of unknown molecular function (hdc), and a fourth encodes a hormone receptor co-activator (tai). For each of these mutants, we performed a rigorous quantification of the stalling phenotype (Figure 2C). For hdc, a partial rescue was obtained with an eye-specific GMR-hdc transgene (Figure 2C); a similar rescue experiment for trio has been reported previously [9].
We and others have previously characterised the axon stalling defects in trio mutants, both in the visual system [9] and in the embryonic CNS and PNS [27]–[30]. Trio is a RhoGEF that activates the three Drosophila Rac GTPases, Rac1, Rac2, and Mtl. Similar axon stalling defects occur in animals that lack multiple copies of these Rac genes [27].
Mbs also encodes a cytoskeletal regulator—the regulatory myosin-binding subunit of myosin phosphatise [31],[32]. Myosin phosphatase negatively regulates myosin II through dephosphorylation of myosin regulatory light chain (MRLC). Loss of Mbs is predicted to result in increased actomyosin contractility and hence reduced motility. Consistent with this, Mbs mutations block epithelial sheet movement during embryonic dorsal closure, accompanied by an accumulation of F-actin at the leading edge [31],[32]. Mbs mutations have also been independently isolated in an eyFLP screen for R cell differentiation, and shown to result in the occasional translocation of the R cell body toward the axon terminus [33]. We did not see this defect in our allele, perhaps because it is hypomorphic. Stalling at the axon tip, like forward translocation of the cell body, may be due to increased traction within the R cell.
hdc encodes a cytoplasmic protein without any predicted functional domains, but with highly conserved vertebrate homologs [34]–[36]. In flies, hdc regulates branching of developing tracheal tubes, and is required in cells that will branch in order to inhibit branching of their neighbours [34]. Some indicative links have been made between human hdc homologs and cancer development [35],[37].
The fourth gene in this class, tai, encodes a steroid receptor co-activator related to the mammalian AIB-1 (or SRC-3), a gene that is amplified in breast cancer [38],[39]. tai regulates the migration of border cells in the Drosophila ovary, probably in response to the steroid hormone ecdysone [38]. Similarly, AIB-1 is evidently required for mammary duct outgrowth in a mouse tumor model [40]. In the Drosophila visual system, tai might similarly function in the migration of R axon growth cones, perhaps in response to the pulse of ecdysone that accompanies pupariation. Unlike the other three mutants in this class, tai also shows an axon guidance phenotype, in that the polar R axons labelled with omb-τlacZ often innervate medial regions of the optic lobe (Figures 2A, B). However, axon stalling is more frequent in tai than in any of the other outgrowth mutants (Figure 2C), possibly indicating that this misrouting is a secondary consequence of severe stalling defects.
R-cell axons preserve their topographic arrangement as they project along the optic stalk and then fan out within the optic lobe. Topographic mapping along the dorsoventral axis is thought to involve both local R-cell axon–axon interactions and long-range positional cues, possibly involving molecular gradients [41],[42]. The omb-τlacZ marker that we used to detect axon stalling defects is an ideal marker to assess topographic mapping, as it labels the dorsal- and ventral-most R-cells in the retina and their respective projections to the dorsal and ventral regions of the optic lobe. With this marker we identified mutations in two genes with strong defects in topographic mapping: enoki mushroom (enok) and Br140 (Figure 3A).
In mutant eyFLP clones for either enok or Br140, the dorsal omb-τlacZ axons projected aberrantly to the ventral region of the optic lobe (Figures 3B(i) and 3C). They do not appear to stall, nor innervate medial regions of the optic lobe. We infer that these dorsal axons are not impaired in their growth, nor in their ability to distinguish polar from equatorial regions of the optic lobe. Rather, they are specifically disrupted in their ability to choose a dorsal rather than a ventral trajectory. The converse defect, of ventral axons mistargeting to dorsal regions, was not observed in either mutant.
The enok gene encodes a putative member of the MYST family of acetyltransferases [24]. Mutations in enok have previously been shown to disrupt proliferation of mushroom body neuroblasts [24]. We noted that enok mutant eyes are sometimes reduced in size, and suspected a similar proliferation defect might also occur in the eye. However, staining with the mitotic marker anti-phospho H3 did not reveal any defects in cell proliferation (Figure 3B(ii)), and so we conclude that the function of enok in topographic mapping of R cell axons is unrelated to its role in cell proliferation. Our two alleles are due to nonsense mutations before and within the catalytic domain, respectively, suggesting that acetyltransferase activity is essential for topographic mapping.
Mutations in Br140 have not been previously reported. This gene encodes a protein with predicted C2H2 zinc-finger, PHD, bromo, and PWWP domains. Bromodomains in other proteins bind acetylated lysines [43], and the close similarity of the enok and Br140 phenotypes suggest that Br140 might recognize Enok substrates. Br140 proteins are highly conserved throughout evolution, including the human Br140/peregrin protein [44] and C. elegans LIN-49 [45].
Because mutations in both enok and Br140 specifically disrupted dorsal and not ventral axon projections, we tested whether expression in the ventral retina might be sufficient to reroute ventral axons to the dorsal optic lobe. We prepared transgenes that drive expression of enok or Br140 in the entire eye disc with either the GMR or eyeless promoter. Introducing these transgenes into the corresponding mutants with eyFLP clones restored normal targeting of dorsal axons but did not lead to dorsal mistargeting of ventral axons (Figure 3C and data not shown). We conclude from these experiments that enok and Br140 are necessary but not sufficient for dorsal targeting.
To test whether dorsoventral patterning of the eye disc is also disrupted in these mutants, we examined the expression of mirror (mrr), a dorsal eye marker [46] and fringe (fng), a ventral marker [47]. We found that a mrr-lacZ reporter is expressed normally in the dorsal eye disc in both enok and Br140 clones (Figure 3B(iii)), but the ventral expression of a fng-lacZ reporter was significantly reduced (Figure 3B(iv)). Loss of fng in the ventral eye disc does not however account for the misrouting of dorsal axons, as these axons project normally in fng mutant clones (not shown and [41]).
Dorsal-to-ventral targeting defects do occur in mutant clones lacking all three genes of the Iroquois complex (Iro-C), to which mrr belongs [41]. However, mrr-lacZ is still expressed normally in enok and Br140 mutant clones, and enok and Br140 are ubiquitously expressed in the eye disc, including the ventral regions where Iro-C genes are absent. Thus, we infer that enok and Br140 act independently of the Iro-C genes in patterning the dorsal region of the eye disc, resulting in fng expression in the ventral region and dorsal targeting of dorsal axons.
It is also interesting to note that the reciprocal phenotype, of ventral axons targeting the dorsal region of the optic lobe, has recently been reported for mutations in Wnt4, Dfrizzled2 and dishevelled, implicating the Wnt signalling pathway in the establishment of a ventral projection [41]. We did not recover any mutations in these genes in our screen, presumably because these mutations also disrupt eye patterning and would have been discarded in our initial analysis.
R1–R6 axons terminate in the lamina in response to signals from lamina glial cells, the intermediate targets for these axons. The nature of this glial signal, and how R1–R6 axons respond to it, is unknown. However, if lamina glial cells are absent or reduced in number, then R1–R6 axons continue through to the lamina [48]–[50]. Such a “lamina pass-through” phenotype is readily visualized with the marker Rh1-τlacZ, which labels the axonal projections of R1–R6. In our screen, we identified mutations in 15 genes that exhibit a lamina pass-through phenotype. Although they formed a well-defined phenotypic cluster in our initial analysis (Figure 1A), these mutations are generally very pleiotropic (Figure 4A), suggesting that many different types of defect may result in some R1–R6 axons missing their stop signal in the lamina.
The four genes in the lamina pass-through class with the most pleiotropic phenotypes are kinesin heavy chain (khc), unc-104, Pak, and misshapen (msn) (Figure 4A). Both khc and unc-104 encode kinesins, belonging to the kinesin-1 family of conventional kinesins, and the kinesin-3 family of monomeric kinesins, respectively [26],[51],[52]. These are the major kinesin families that deliver cargo to the tips of growing axons, and so the pleiotropic wiring defects in these mutants are perhaps not surprising. Interestingly, unc-104 has been reported to be involved in retrograde transport of neurosecretory vesicles, as well as the anterograde transport [53]. In our mutant analysis, we noticed aberrant perpendicular turn of R7 axons (Figure 4A), which is indicative of a failure in retrograde transport of Smad2 protein mediated by the Drosophila Activin receptor Baboon [54].
Pak and msn both encode Ste20-like serine-threonine kinases [21],[55]. The broad range of defects seen in these mutants, as reported here (Figure 4A) and previously [9],[17],[18], may reflect functions of these two kinases in diverse signaling pathways.
Another set of genes in this class encodes regulators of gene expression, including two chromatin remodelling factors (trx, Psc) [56],[57], four putative transcription (co-)factors (bonus, brakeless [bks], dri, sequoia) [11],[19],[25],[58],[59], an RNA polymerase II C-terminal domain kinase (cdk8) [60], a splicing factor (Xe7) [61], and a translational repressor (brat) [62],[63].
The two remaining genes in this phenotypic cluster do not fit neatly into a single molecular class. These are archipelago (ago) and GUK-holder (gukh). ago encodes an F-box protein that is the substrate-specificity unit of the SCF ubiquitin ligase, and acts as a negative regulator of cell growth [64],[65]. This raises the possibility that excessive axon growth might contribute to the R1–R6 pass-through phenotype in ago mutant clones. The gukh gene was originally isolated in a two-hybrid screen for proteins interacting with Discs Large, the Drosophila ortholog of the post-synaptic scaffolding protein PSD-95 [66]. Gukh encodes two protein isoforms, Gukh-PA and Gukh-PB, which function in synaptic bouton budding at the larval neuromuscular junction [66]. Both isoforms contain an N-terminal WASP homology domain 1 (WH1), suggesting a possible role in the regulation of actin polymerisation, as well as a C-terminal PDZ-binding motif. Proteins with a similar structure are found in other species, including the human Nance-Horan syndrome protein [67]–[69]. We isolated 3 gukh alleles, all associated with nonsense mutations. One is predicted to truncate both the PA and PB isoforms, whereas the other two truncate only the PA isoform. In rescue experiments using GMR promoter, expression of Gukh-PA in the eye disc was sufficient to fully rescue the R1–R6 lamina pass-through phenotype in gukh mutant clones (Table 2).
We isolated mutations in 14 genes for which the most pronounced defect is aberrant targeting of R7 and R8 axons in the medulla (Figures 1A and 5). Most of these mutations result in a general disorganization of medulla projections, including an irregular spacing of R7 and R8 axons. As for the lamina targeting cluster, the set of genes in this group encode a diverse set of molecules, including proteins involved in gene regulation, axonal transport, cell–cell interactions, and intracellular signalling. Cell signalling molecules are more prominent in the medulla targeting cluster than in the lamina targeting cluster. This may be due to mutations in these genes displaying less dramatic effects than those involved in protein synthesis or transport, and such subtle defects are more apparent in the fine arrangement of R7 and R8 projections in the medulla than in the crowded field of R1–R6 axons in the lamina.
Four genes in this cluster are involved in gene expression or protein transport: kismet, which encodes a chromatin remodelling factor [70], single-minded, encoding a bHLH-PAS domain transcription factor [71], Hrb27c, encoding an RNA-binding protein implicated in pre-mRNA splicing [72] and mRNA localization [73], and Klp64D, encoding a member of the kinesin-2 family of heterotrimeric kinesins [74],[75].
All five of the genes identified from our screen that encode cell surface proteins fall into the medulla targeting cluster. This includes two Cadherin genes, N-cadherin [76] and flamingo [77], and two receptor tyrosine phosphatase genes, Ptp69D and LAR [78]. Detailed phenotypic analyses of these genes have been presented previously, by us [4],[10],[12] or the Zipursky lab [3],[13],[15],[16]. The fifth gene, which we call golden goal (gogo), encodes a novel single-pass transmembrane protein with extracellular region that includes a single Thrombospondin Type I and a single CUB domain. Both of these domains are also found in other proteins involved in axon guidance, such as the Neuropilin [79] and Unc-5 family receptors [80]. The cytoplasmic region of the putative Gogo protein does not contain any known protein domain or catalytic activity. gogo mutant clones result in a severe disruption of R axon projections in the medulla (Figure 5B and [20]), which we could rescue with a GMR-gogo transgene (Table 2). It is interesting to note that the gogo phenotype clusters closely with flamingo (Figure 5), potentially suggesting a function in a common or related guidance mechanism [20].
The remaining five genes in this cluster encode putative cytoplasmic signalling molecules. These are non-stop (not), a protein deubiquitinating enzyme [49], chickadee, which encodes Profilin [81], and three members of the serine-threonine kinase superfamily: basket [82],[83], casein kinase IIα (ckIIα) [84],[85], and wnk. The role of chic in axon guidance has been well documented in numerous systems [9],[14],[17],[23],[86],[87]. As not is known to be required for the migration of the lamina glia, and thus indirectly for targeting of R1–R6 axons to the lamina, we wondered whether not mutant was picked up due to occasional clones in the lamina or a true R-cell autonomous role [49]. We did not observe defects in the migration of the lamina glia in eyFLP clones of our not alleles, and we could restore normal R-axon projections with a GMR-not transgene that expresses not exclusively in the eye disc (Table 2). We conclude that not has both autonomous and non-autonomous roles in R-axon targeting.
bsk, which encodes Jun N-terminal kinase, and ckIIα, which encodes the catalytic subunit of casein kinase II, have been shown to function in a variety of developmental processes. Functions of bsk include various aspects of cellular morphogenesis, such as dorsal closure and planar cell polarity [88]. A role for bsk in R axon pathfinding has been suggested from experiments using dominant negative constructs [41]. However, the specific topographic errors observed in these experiments do not match well with the general disorganization in the medulla that we observed in bsk mutant clones allele (Figure 5A). Functions of casein kinase are even more diverse, reflecting perhaps a wider range of substrates that includes the developmental proteins Cactus, Dishevelled, Antennapedia, and Enhancer of Split proteins [89]–[91]. Casein kinase II is a critical component of the circadian clock [92], and a function in axon pathfinding has not previously been reported. We confirmed an R-cell autonomous role for casein kinase in establishing axon projections in rescue experiments using a GMR-ckIIα transgene (Table 2).
The wnk gene encodes the single Drosophila member of a recently discovered and more enigmatic family of kinases, represented in mammals by the four kinases WNK1-4. This family of serine-threonine kinases is distantly related to the Ste20-like kinases, and owes its inappropriate name (With No Lysine [K]) to the fact that the lysine required for phosphoryl transfer lies in a different position to all other protein kinases (kinase subdomain I rather than subdomain II) [93]. The best characterised role of mammalian WNKs is in the regulation of electrolyte homeostasis, and mutations in two of the WNKs have been linked to hypertension [94]. Additionally, WNK1 functions in synaptogenesis by phosphorylation of Synaptotagmin2 [95]. Our wnk alleles carry mutations either within or C-terminal to the kinase domain, suggesting that Wnk's function in R-axon targeting requires its kinase domain in addition to its long and poorly conserved C-terminal region. We could rescue the wnk mutant phenotype with a genomic transgene, confirming the role of wnk in R-axon targeting (Table 2).
We observed several mutants that have striking R-axon guidance phenotype in larvae but less severe phenotype in adults, indicating a transient nature of the defect. This is particularly evident in tai, kis, not, enok, Br140, cdk8 and wnk phenotypes (Figures 1, 2, 3, 4, 5). One possible explanation for the discrepancy between adult and larval phenotypes is that different mechanisms underlie the development of the patterning of both systems. For example, a recent study of gogo function suggested that larval bundling defects are unrelated to the later defects seen in target recognition by R8-axons [20]. Another explanation could be that these mutants still retain the lamina cartridge formation defects even in the adult, but other more discerning assays would be needed. Analysis of R1–6 superposition defects in the lamina targeting neurons in adult in these mutants might be informative.
We began this study [4] at a time when relatively little was known about the molecular mechanisms of neuronal wiring in the Drosophila visual system [96] and before the completion of the Drosophila genome sequence [97]. Our long-term goal was to systematically identify as many as possible of the genes required for axon growth, guidance, and connectivity in this model system. Initial progress in gene identification was encouraging [4], [9]–[12], but slow, prompting us to develop methods for SNP mapping in Drosophila [7].
Using this method, we have been able to identify nearly all of the genes displaying guidance defects in our screen, including those represented by just a single allele. In most cases, the genetic lesion has been mapped to a single base pair. Our systematic identification of the genes and detailed characterisation of associated mutant phenotypes will serve as a springboard for further mechanistic studies of visual system wiring. Importantly, our work also demonstrates the feasibility of large-scale positional cloning in Drosophila. The large-scale mutagenesis screen has long been the trademark of Drosophila genetics, and indeed is one of its major strengths. Using approaches such as ours, systematic mutant recovery can now be augmented with systematic gene identification.
Mutations were generated [4] and mapped [7] as described previously. In the first phase of recombination mapping, SNP genotypes were mostly determined by PLP assays [7], and in the second phase by DNA sequencing. Mapping in this second phase generally involved testing for the lethality of heteroallelic combinations, or, in the case of single alleles, failure to complement an existing deficiency. If a suitable deficiency was not available, fine mapping was performed using stocks containing two flanking EP elements [98] that had been placed in cis. Existing mutants, deficiency stocks, and EP elements were obtained from either the Bloomington or the Szeged stock centers. For sequencing, we extracted DNA from single embryos, identified homozygous mutant embryos by PCR with PLP primers [7], pooled their genomic DNA, PCR, sequenced the coding region and compared it to the parental reference chromosome.
The wnk genomic rescue transgene consisted of a 22 kb Asp718 fragment isolated from BACRP98-26P10 that was cloned into a pCaSpeR4 vector. GMR or eyeless rescue constructs were generated using standard PCR cloning techniques, using either genomic fragments containing small introns or full-length cDNAs as templates. For hdc, we used the long isoform amplified from UAS-hdcCAA [99]. For dri, brat, ckIIα, not, cdk8, and unc-104, genomic regions from the start to stop codons were amplified from genomic DNA. The gogo coding region was amplified from the EST clone RE53634, and enok from a full-length cDNA provided by Liqun Luo. Br140 was cloned as an EcoRI fragment from the EST clone GH12223.
Tangential eye sections, adult head sections, whole-mount adult brains, and whole-mount larval eye-brain complexes were prepared and stained as described previously [4],[10],[12]. Primary antibodies used were mAb24B10 (1∶50, [100]), rabbit anti-β-galactosidase (1∶2500, Cappel), and rabbit anti-GFP (1∶100–300, Torrey Pines). Secondary antibodies used were goat anti-rabbit Alexa-488 and goat anti-mouse Alexa-568 (1∶250 each, Molecular Probes). All fluorescent samples were mounted in Vectashield (Vector Laboratories). Head section stainings were performed manually for the initial characterisation, and using a Dako Autostainer plus (Dako Cytomation) for mapping and rescue experiments. Confocal images were acquired on Zeiss LSM 510 Axiovert 200M or LSM 510 Axioplan 2, or Leica SP2.
Samples were scored for each phenotypic criteria on a 0 (wild-type) to 4 (most severe) scale according to the scale described in the Table S1. For examination of confocal images with LSM5 Image Examiner or Leica LCS lite, the final score was the average from 2–5 preparations. For larval omb-τlacZ, adult glass-lacZ sections and adult Rh1-τlacZ sections were examined under normal light microscope. Sections from 10–20 heads were examined for each allele. For omb-τlacZ stainings, we examined around 50 hemispheres for each allele scored. All mutants were scored independently by T.S. and J.B. and averaged. The genes for which multiple alleles were scored were averaged. Data were clustered using a k-means clustering algorithm [101], with manual adjustment and transformed into heat map using MS Excel macro function (Designed by Georg Dietzl). The range of phenotypic scores was calculated as the subtraction of the lowest score from the highest score among the samples from the same mutant allele for each criterion. These are shown for confocal samples to provide the tendency of expressivity of the phenotype. The range of scores for two individuals was averaged and transformed into color heat maps. For the scores quantified and averaged from more than 10 samples at the same time (omb-τlacZ samples, adult DAB sections and “lamina pass through”) a range was not given, however, the score itself gives the idea of penetration of the phenotype. |
10.1371/journal.pcbi.1003380 | Time Scales in Epigenetic Dynamics and Phenotypic Heterogeneity of Embryonic Stem Cells | A remarkable feature of the self-renewing population of embryonic stem cells (ESCs) is their phenotypic heterogeneity: Nanog and other marker proteins of ESCs show large cell-to-cell variation in their expression level, which should significantly influence the differentiation process of individual cells. The molecular mechanism and biological implication of this heterogeneity, however, still remain elusive. We address this problem by constructing a model of the core gene-network of mouse ESCs. The model takes account of processes of binding/unbinding of transcription factors, formation/dissolution of transcription apparatus, and modification of histone code at each locus of genes in the network. These processes are hierarchically interrelated to each other forming the dynamical feedback loops. By simulating stochastic dynamics of this model, we show that the phenotypic heterogeneity of ESCs can be explained when the chromatin at the Nanog locus undergoes the large scale reorganization in formation/dissolution of transcription apparatus, which should have the timescale similar to the cell cycle period. With this slow transcriptional switching of Nanog, the simulated ESCs fluctuate among multiple transient states, which can trigger the differentiation into the lineage-specific cell states. From the simulated transitions among cell states, the epigenetic landscape underlying transitions is calculated. The slow Nanog switching gives rise to the wide basin of ESC states in the landscape. The bimodal Nanog distribution arising from the kinetic flow running through this ESC basin prevents transdifferentiation and promotes the definite decision of the cell fate. These results show that the distribution of timescales of the regulatory processes is decisively important to characterize the fluctuation of cells and their differentiation process. The analyses through the epigenetic landscape and the kinetic flow on the landscape should provide a guideline to engineer cell differentiation.
| Embryonic stem cells (ESCs) can proliferate indefinitely by keeping pluripotency, i.e., the ability to differentiate into any cell-lineage. ESCs, therefore, have been the focus of intense biological and medical interests. A remarkable feature of ESCs is their phenotypic heterogeneity: ESCs show large cell-to-cell fluctuation in the expression level of Nanog, which is a key factor to maintain pluripotency. Since Nanog regulates many genes in ESCs, this fluctuation should seriously affect individual cells when they start differentiation. In this paper we analyze this phenotypic fluctuation by simulating the stochastic dynamics of gene network in ESCs. The model takes account of the mutually interrelated processes of gene regulation such as binding/unbinding of transcription factors, formation/dissolution of transcription apparatus, and histone-code modification. We show the distribution of timescales of these processes is decisively important to characterize the dynamical behavior of the gene network, and that the slow formation/dissolution of transcription apparatus at the Nanog locus explains the observed large fluctuation of ESCs. The epigenetic landscapes are calculated based on the stochastic simulation, and the role of the phenotypic fluctuation in the differentiation process is analyzed through the landscape picture.
| Embryonic stem cells (ESCs) are pluripotent having the ability to differentiate into a variety of lineages, while in suitable culture conditions they proliferate indefinitely by maintaining pluripotency. These self-renewing ESCs are distinguished by the marker proteins including Sox2, Oct4 and Nanog (SON) [1]–[4]. SON are transcription factors (TFs) which directly or indirectly promote the expression of themselves by constituting an overall positive feedback network [5]–[10], among which Nanog is an essential factor working as a gatekeeper for pluripotency [11], [12]. Here, a remarkable feature is the large cell-to-cell variation of the level of Nanog in the self-renewing isogenic population of ESCs [13]–[15]. Since a distinct downregulation of Nanog is associated with the differentiation of ESCs into mesendoderm or neural ectoderm lineages [16], the heterogeneous Nanog expression can be intimately related to the process of fate decision of individual cells [14], [17]. The molecular mechanism and biological implication of this phenotypic fluctuation of ESCs, however, have not yet been clarified. In this paper we address this problem by constructing a model of the regulatory network of core genes in mouse ESCs.
One can figure out, at a glance, several scenarios which may explain the phenotypic heterogeneity. A simple scenario relies on the possible enhancement of fluctuation of the signal received by a cell: Since the reception of factors such as leukemia inhibitory factor (Lif) by a cell is stochastic, it necessarily bears fluctuation, which might be enhanced through the signal cascade to stochastically activate Nanog [18]. The other possible mechanism is based on the presumed self-activation of Nanog [6], [19], which may lead to the fluctuating pulsative expression of Nanog [14], [20]. With these mechanisms, however, another key factor, Oct4, should also exhibit the large fluctuation since Oct4 is activated by the reception of Lif and the Oct4 expression is maintained through mutually activating interactions among SON. Contrary to this expectation, the observed expression of Oct4 is rather homogeneous [14], [17]. A possible resolution of this inconsistency is to assume that some unknown factors which can bind to the Oct4 locus suppress fluctuation of the Oct4 expression [20]. There has been, however, no direct experimental observation yet for the existence of such regulatory factors, and therefore, in this paper we look for the other mechanism without relying on this assumption.
For modeling the gene regulatory dynamics, not only the topological wiring diagram among genes but also the rates of reactions in the regulatory network should be quantified. These estimated rates, however, have very different values depending on the type of reactions, and hence it is strongly desired to develop the theoretical framework to treat effects of coexistence of the distributed timescales [21], [22]. In simple bacterial cells, for example, the DNA-protein binding/unbinding is often much faster than the protein-copy number change, so that the fast DNA-state change can be regarded as equilibrated and the dynamical interference between the fluctuation of gene switching and the fluctuation of protein-copy number can be neglected. By borrowing the wording from condensed-matter physics, this separation of fast and slow processes should be referred to as the “adiabatic” separation. Theoretical studies have shown that when the adiabatic limit is not the case, the kinetic flow of the coupled stochastic dynamics of gene switching and protein-copy number change is described as “eddy current” [23], which gives rise to a variety of unexpected dynamical effects in gene regulation [23]–[28]. Indeed, it has been suggested that the transition of Bacillus subtilis into the competence period should be due to the non-adiabatic gene switching in the excitatory self-activating gene network [29]. In eukaryotic cells, processes of gene switching are much more complex including the assembly of transcriptional apparatus (TA) [30]–[33], the transition from the poised state of TA to the elongation state [34], chemical modifications of nucleosomes [35]–[39], and the structural reorganization of chromosomes [40]–[42]. Such epigenetic change of the gene state can be much slower than the bacterial DNA state change, and their timescales are often comparable with or longer than the timescale of the protein-copy number change, so that the non-adiabatic effects should play significant roles in eukaryotic cells.
Many marker genes of ESCs have been identified [43], among which SON regulate many other genes, and hence, the SON network has been regarded as the central network to maintain pluripotency [8], [9], [43]. Models of the core SON network of ESCs have been developed [14], [16], [20], [43], [44], but all of these models have been based on the assumption that the gene state is determined by the fast equilibrated binding/unbinding of TF to/from the gene locus: The assumption of the adiabatic limit has been adopted in all the previous models and the slow non-adiabatic switching dynamics has not been explicitly taken into account. In this paper, we discuss ESCs by focusing on the non-adiabatic effects, the effects of slow epigenetic processes, and we propose a hypothesis that the non-adiabatic switching in the core gene-network explains the large fluctuation of Nanog expression. By using the landscape picture, we discuss the roles of this non-adiabatic switching in the cell-fate decision of ESCs.
Before starting the explanation of the simulated results, we briefly explain the interaction network among genes considered in the present model and discuss the dynamics of each gene in the network in subsection Gene network and epigenetic dynamics. Coexistence of multiple timescales in the eukaryotic gene dynamics is the focus of the present study.
We first discuss ESCs in media containing Lif and other agents. Lif activates c-Myc [60], which activates SON by keeping the histone code of lineage-specific genes repressive [34]. We simulate this culture by adopting the null rate for turning the histone-code active as for , and (See Fig. 2 and Methods for the definition of parameters).
First simulated is the case that the formation/dissolution of TA is adiabatic with with . As a typical value to satisfy this inequality, we use for all . Distributions of the expression level of SON simulated with this parameterization are shown in Figs. 3A and 3C. We can see that the simulated distribution of the expression level of Nanog shows a single peak and the simulated distributions of Sox2 and Oct4 are double peaked at their finite values of expression level with some additional populations at the zero expression. These features are different from those observed in experiments: Compared are the distributions of cell population in a culture plotted as functions of the expression level of SON. The observed distribution of Oct4 is single peaked (Fig. 3D) [14], the distribution of Sox2 is similar to that of Oct4 [14], and the observed distribution of Nanog shows two peaks (Fig. 3D) [11], [14]. The observed two-peak distribution of Nanog indicates that the fluctuation of Nanog is dominated by transitions between two states; the high-Nanog (HN) and low-Nanog (LN) states [11], [33]. The simulated Nanog distribution with the adiabatic TA formation/dissolution apparently disagrees with this observed two-peaked Nanog distribution.
The assumption of the adiabatic TA formation/dissolution with used in the above simulation is questionable when we consider the following features of Nanog expression: First, the TA of Nanog consists of the fairly large (kb) region of DNA [61], which should make the formation/dissolution of TA a rather complex process. Second, the allelic regulation of Nanog [51] indicates that the chromosome organization on the nuclear scale regulates the Nanog expression. This observation is also consistent with the recent finding that the loci of genes of the pluripotent factors are spatially in proximity to the Nanog locus in an ESC-specific manner [62], indicating that the nuclear scale organization of chromosomes is involved in the activation of Nanog in ESCs. For such complex and spatially extended processes for TA at the Nanog locus, it should be reasonable to assume that the timescale of TA formation/dissolution is as long as the cell cycle period. To find the plausible values for the rate of TA formation () and the rate of TA dissolution () at the locus, we performed a massive parameter search by generating more than 1,000 scattered points on the two-dimensional plane of and with . The score for each of generated parameter sets was calculated by averaging 10,000 trajectories simulated with the corresponding parameter set, where the score is the number of the experimentally observed features that the simulated data reproduced. The features used to count the score include (1) bimodality of the distribution of expression level of Nanog, (2) the ratio of the copy-number at the HN state to that at the LN state, (3) the ratio of the peak height at the HN state to that at the LN state, (4) the single-peak distribution of expression level of Oct4, and so on. The score calculated in this way is plotted in Fig. 4 for 1,125 parameter sets. See Massive parameter search subsection in Methods section for more details on the definition of the score. Search of the other parameter set is shown in Fig. S1.
Results of Fig. 4 indicate that the normalized rate of TA formation should be around and the normalized rate of TA dissolution should be for to reproduce the experimentally observed heterogeneous expression levels in ESCs. Here, the result of was needed to reproduce the observed feature that the HN peak height is larger than the LN peak height. Since the biologically reasonable lower bound of is the frequency of cell cycle , we use the lowest allowed value of in the subsequent analyses by keeping for , and Oct4. The precise values of other parameters are explained in Parameters subsection in Methods section. The simulated distributions of SON with this non-adiabatic switching of Nanog are shown in Figs. 3B and 3C. The simulated width of peaks is narrower than the observed one because in simulation the extrinsic noise due to the cell-cycle oscillation and the fluctuating reception of Lif are neglected for simplicity. The overall features of the distributions, however, agree well with the experimental data [14]: Nanog shows a clear two-peak distribution and the Oct4 distribution has an asymmetric single major peak.
Shown in Figs. 5A and 5B is the temporal change of distributions of Nanog calculated by starting from the ensemble of cells either in the HN or LN state at Day 0. Within several days, the single-peaked distribution of cells in either of the HN or LN state recovers the two-peak features, which reproduces the experimentally observed temporal relaxation [11], [14]. This relaxation indicates that ESCs show dynamical transitions between HN and LN states with timescale of a few days. The agreement between the observed and simulated timescales of transitions between HN and LN states indicates the validity of the small for the slow switching at the Nanog locus, and hence the data in Fig. 5 should rule out the other hypothetical models which can yield a bimodal Nanog distribution but with the large .
A possible origin of the slow non-adiabatic switching of Nanog is the large scale chromatin reorganization in the formation/dissolution of TA of Nanog. This assumption of slow switching explains the observed two-peak distribution and the dynamical transition of the expression level of Nanog, and is also consistent with the single-peak distribution of Oct4. Thus, the assumption of the slow non-adiabatic switching of Nanog explains the observed phenotypic heterogeneity of ESCs.
Given the consistent model for the heterogeneity of ESCs, it is interesting to analyze how cells initiate differentiation. To simulate cells that can differentiate, the rate to turn the histone-code active, , is increased to have a finite value for and . for is also turned finite but kept small because in embryo, the distinct expression of Cdx2 is the event prior to the formation of inner cell mass from which ESCs are prepared, so that it is plausible to assume that the methylated DNA or the collective action of regulating factors inhibits the histone code of Cdx2 from being active in ESCs (See subsection Parameters in Methods).
Examples of trajectory simulated with this parametrization are shown in Figs. 6A and 6B. The trajectory in Fig. 6A wanders around several transient states but neither Cdx2 nor Gata6 dominates during this wandering: Cells are jumping among the states by maintaining the features of ESC. In Fig. 6B, on the other hand, the trajectory escapes from the ESC states to reach the Gata6 dominant state which is a gateway to the primitive endoderm lineage.
In both Figs. 6A and 6B, the trajectories are not the continuous drifts but consist of sojourns and jumps. This feature allows us to represent each trajectory as a sequence of transitions among “cell states”: Using the feature that the copy number of each factor, , shows a multiple-peak distribution (Fig. S2), we divide each distribution into a few parts, each of which is named in an abbreviated way as HN (high-level Nanog), MN(middle-level Nanog), LN (low-level Nanog), S (high-level Sox2), LS (low-level Sox2), etc. The thresholds used to divide the distributions are summarized in Table 1. Then, cell states are defined by thus discretized distributions and also by a set of the histone states . The trajectory is regarded as a sequence of transitions among those cell states. With this coarse-grained representation of trajectories, the mean waiting time for transition from the th to the th cell states can be estimated as , and the mean transition rate is defined by (See subsection Transition diagram in Methods for the detailed explanation on ).
In the case that the trajectory stays for a long duration at each cell state to erase its dynamical memory, this coarse-grained dynamics can be regarded as Markovian, or in other words, the transition probability from the th to th states is not affected by which state the trajectory visited before reaching the th state. It is suggested from Figs. 6A and 6B that the trajectories stay at each cell state long enough to show many oscillations during the stay, but the more quantitative test should be necessary to judge whether the coarse-grained dynamics is indeed Markovian or not. We leave this test as a future problem and proceed further in this paper to show how the transition diagram and the landscape view capture the important features of transitions among cell states.
Drawn in Fig. 6C is the diagram of transitions among thus defined cell states, where the value of is shown on the link from the th to th cell states. In Fig. 6C the cell states in which all of Sox2, Oct4, and Nanog (SON) are expressed are regarded as the pluripotent states (or the ESC states) though the level of Nanog fluctuates largely among these states and sometimes Cdx2 or Gata6 coexists with SON. These ESC states are connected by loops of transitions and hence the cells wander among ESC states to wait for a chance to escape from the ESC states. Trajectories that have escaped from the ESC states go through the network of transitions among the intermediate states in which one or two of SON are lacking. From these intermediate states, cells reach the state in which Gata6 dominates. In some cell states, Cdx2 appears as fluctuation but the small value of prevents Cdx2 from dominating the state.
It should be interesting to examine the validity of these predictions with the experimental observations: By quantifying the expression level of important factors, we will be able to define cell states from the experimental data. Then, we can check whether the differentiation is the process of jumping among these states. Though there is a global trend of kinetic flows from the ESC states to the differentiated states, the predicted pathways are not single but comprise the network of flows. It should be important to compare the predicted distribution of pathways as in Fig. 6C with the distribution of pathways experimentally observed by following the fate of individual cells in the culture.
To analyze dynamics of differentiation, the epigenetic landscape that underlies transitions among cell states should provide a useful perspective [63], [64]. Here, the landscape is derived from the transition diagram by using the analogy with the free energy surface in equilibrium dynamics. In equilibrium dynamics, by using the transition-state theory formula, the rate of transition from to th states should be proportional to where , and and are the dimension-less free-energy like quantity at the th state and at the transition state between the and th states, respectively. We use this analogy to equilibrium dynamics by fitting the calculated rates to this transition-state theory formula to obtain the free-energy like quantity and . When the transition diagram has a tree-like structure without a loop, we can determine of each state one by one by fitting the simulated rates to this formula. We use this analogy with equilibrium dynamics as far as possible to draw the landscape of non-equilibrium transitions. This method of fitting, however, apparently breaks down when the transition network contains one or more loops: When the transition network contains a loop, for example, we may attempt to determine of states in the loop one by one by starting from the th state in the loop with the landscape value , but at the end of traverse along the loop, we return to the initial th state with a different value of from the original . In this way, the fitting to the transition-state theory formula is inconsistent along loops. This inconsistency can be resolved when we explicitly consider the non-equilibrium feature of dynamics by introducing the curl flux of transition kinetics [65]–[68]. Thus, the kinetic process along each loop can be expressed by the combination of the landscape and a kinetic flow curling along the loop. Transitions, therefore, are described by the combined representation of landscape and non-equilibrium curl flux. An example of a looped diagram having curl fluxes is shown in Fig. 7. From of this diagram, the free-energy like quantity at the th cell state and at the barrier between the and th cell states are calculated for , and , and curl fluxes and are obtained simultaneously. See subsection Epigenetic landscape in Methods for the explanation on how to calculate , , and from of Fig. 7.
In Fig. 8 the landscapes and curl fluxes calculated from the simulated in the differentiation processes are shown on the two-dimensional plane with the coordinates of and . Here, is the label of the discretized expression level of the th factor, which is defined to have the larger value for the higher expression level. and , therefore, represent the degree of closeness to the trophectoderm and primitive endoderm lineages, respectively. The precise values of are chosen for obtaining good visibility of Fig. 8, and are explained in Table 1. In Fig. 8, the calculated and are plotted by assigning and for and , and and are interpolated by a smooth surface in the two-dimensional space of and .
The landscape corresponding to the diagram of Fig. 6C is shown in Fig. 8A. We see that the ESC states distribute on a flat basin in the region of small and : ESCs wander around this basin driven by both the fluctuations satisfying the balance between the forward and reverse transitions and the kinetic flow of curl flux that breaks the balance. ESCs start differentiation as they move along the valley stretching toward the Gata6 dominant state. Transitions among intermediate states along this valley are also accompanied by the weak non-equilibrium curl flux.
In Figs. 8B and 8C, the artificial depletion of Oct4 is simulated with the decreased rate of synthesis of Oct4. Since Oct4 and Cdx2 work in an antagonistic way, the depletion of Oct4 results in the stronger expression of Cdx2, which leads ESCs to the trophectoderm linage: With the decrease of the rate of Oct4 synthesis to 25% (Fig. 8B) and 10% (Fig. 8C) of the value in Fig. 8A, the landscape changes its shape by extending the valley toward the Cdx2 dominant state. In Fig. 8B two valleys to primitive endoderm and trophectoderm coexist with the curl flux on the basin of ESC states remaining, and in Fig. 8C the valley to trophectoderm dominates. These results are consistent with the experimentally observed induction of the trophectoderm lineage through the reduction of Oct4 [31].
Shown in Figs. 8D–8F are landscapes calculated with the assumption of the fast Nanog switching: . With this fast Nanog switching, the flat basin of the ESC states disappears, the curl flux in ESC states becomes localized, and ESCs quickly differentiate toward primitive endoderm (Fig. 8D). The curl flux on the ESC basin, therefore, originates from the slow Nanog switching. In other words, the eddy current associated with the non-adiabatic switching [23] manifests itself in the curl flux on the epigenetic landscape.
Difference between the slow and fast Nanog switching becomes more evident upon the reduction of Oct4 (Figs. 8E and 8F). With the fast Nanog switching, two valleys do not represent the distinct cell fate but they are directly connected to each other by the frequent transdifferentiation (Fig. 8F). This obscured differentiation arises from the averaged intermediate amount of Nanog synthesis under the fast Nanog switching. With the intermediate level of Nanog, the alleles of the lineage-specific genes tend to take the intermediate histone code as and or and . This intermediate level of activation of both Cdx2 and Gata6 increases the frequency of the transdifferentiation. With the slow Nanog switching, on the other hand, the histones of Gata6 and Cdx2 become either active with or repressive with , and such a clear-cut histone switching decreases the probability of the mixed expression of Cdx2 and Gata6. In this way the simulated results suggest that the distinct cell fate decision is based on the slow Nanog switching, so that the phenotypic heterogeneity of ESCs is necessary for the stable differentiation.
The present quantification of epigenetic landscapes showed that the model naturally reproduces the observed differentiation to primitive endoderm [10]. The model also reproduces the induced differentiation to trophectoderm observed when the Oct4 expression is artificially suppressed [3]. It should be interesting to further examine possibility of the predicted transdifferentiation due to the fast Nanog switching.
We developed a model of epigenetic dynamics and proposed a hypothesis that the timescale of formation/dissolution of TA decisively affects the self-renewal and differentiation of mouse ESCs. These effects can be checked experimentally by artificially varying the timescale of formation/dissolution of TA. The slower rate of formation/dissolution of TA for Oct4, for example, should give rise to the multi-peak distribution of Oct4, which should also affect the epigenetic landscape and non-equilibrium curl fluxes on the landscape. Further important is the application of the present ideas to engineering differentiation. Overexpression or repression of specific genes should alter the epigenetic landscape and curl fluxes, so that the calculation and observation of landscape and curl fluxes should provide a guideline for designing the process of cell differentiation.
An intriguing question is the effect of variation of the number of working alleles in a cell. In the present simulation, following the report for the single non-silenced Nanog allele in each ESC [51], only the single Nanog locus was considered in the simulation, which explained the bimodal Nanog distribution when the Nanog switching was slow. Assuming that both two alleles are working independently owing to the invalidated allelic regulation, we have three peaks in the Nanog distribution corresponding to the ‘high-high’, ‘high-low’ and ‘low-low’ levels of expression for two alleles of Nanog with the slow Nanog switching as shown in Fig. S3. This predicted three-peak distribution could be experimentally tested in ESCs, though the more careful investigation is needed on the possible correlation between the allelic regulation and the regulation of the timescale of gene switching.
The core part of the network relations among genes in the present paper was built from the experimental observations, but there are experimental suggestions still not taken into account in the present model. For example, a recent report suggested the auto-repression of Nanog [45]. This suggested interaction can affect the transition dynamics between the HN and LN states, which should be examined by simulation. The validity of the assumptions used in the present modeling of epigenetic dynamics should be checked by examining how the results are modified when the model is further extended. In the present model, three processes having the different timescales were considered; TF binding/unbinding, TA formation/dissolution, and the histone code modification. Each of these processes consists of multiple sub-processes, and therefore if the model is extended with the finer resolution, the involved timescales should have more variety [69]. The TA formation/dissolution, for example, may involve assembly of mediators and RNA polymerase, phosphorylation of these factors, chromatin looping, and the large scale change in the chromosome positioning in nucleus. In the present model, we treat them in a coarse-grained manner by representing the TA state with which takes a value between 0 and 1. By treating these multiple processes explicitly, we may be able to construct a more quantitative model that can be compared with experiments in more details, and the validity of the level of coarse-graining in the present model could be checked through such comparison.
We should stress, however, that the main conclusions on the importance of design of timescales of regulations and the usefulness of combined representation of landscape and non-equilibrium curl fluxes do not depend on the molecular details. Indeed, the simplified mathematical or statistical physical models to capture the essential features of landscapes and curl fluxes should be useful. The dynamical systems models, for example, emphasize the importance of oscillations in the gene network [70], which conforms with the view presented here on the importance of rotating curl fluxes.
Another important direction to improve the present model is to take into account the core genes that guide ESCs to primitive ectoderm which further differentiates into the primary germ layers. To develop the reliable models, the effects of cell-cell communication and cell cycles should be also taken into account. Especially, the cell-cell communication should play important roles to stabilize the cell type of colony of interacting cells [70], [71]. The model developed in the present paper was based on the assumption that the partial effect of cell-cell communication is implicitly taken into account by the mutual inhibition between Cdx2 and Gata6 (See Methods section). In order to analyze the differentiation process more quantitatively, the model needs to be extended to explicitly treat the effects of cell-cell communication. Those more elaborate models, together with the simplified statistical mechanical models, should reveal the rich phenomena in ESCs and differentiation processes.
The model consists of interactions among six genes. Those interactions are inferred from the experimental data, which are complemented with various levels of assumptions as explained below. In the following, the assumptions used are categorized into Level A, Level B, Level C, and Others. The aim of the present study is not to claim the validity of those assumptions, but to clarify the mechanisms of epigenetic dynamics by using a set of biologically consistent assumptions. The interactions considered in Fig. 1 were inferred from the discussions below, which are numbered in the same way as interactions designated in Fig. 1:
Level A. Microarray or other genetic experimental techniques revealed the correlation or anti-correlation between expression levels of two genes, and the chromatin immunoprecipitation or other biochemical data showed the binding of one factor to the locus of the other gene. These data support the assumption that the transcription factor (TF) synthesized from one gene directly regulates the other gene. The Level A assumptions give the backbone of the present model of the regulatory network.
1. Each of Oct4, Sox2 and Nanog loci has the Oct/Sox enhancer region [7], [72], on which Oct4 and Sox2 bind together to form the Oct4-Sox2 complex to activate Oct4, Sox2, or Nanog [4], [7], [72]. There are two possible ways of binding though they are not mutually exclusive; The Oct4-Sox2 complex is formed before they bind to DNA, or Oct4 and Sox2 bind to the adjacent sites of DNA to form the complex after binding. These two ways of binding are different in their cooperativity in the binding process. However, since the cooperativity of binding is masked by the cooperative formation/dissolution of transcription apparatus (TA) in the present model, these two ways of binding do not give significant difference in the switching behavior. We use, for simplicity, the latter assumption of forming complex after binding to DNA, but represent the effects of complex formation by assuming that the binding of either one of Oct4 or Sox2 is not enough but the binding of both two factors are needed for forming TA (We assume that the formation of the Oct4-Nanog complex is another route to form TA).
2. Gcnf binds to the Oct4 and Nanog loci to repress them [48].
3. The Oct4-Cdx2 complex represses both Oct4 and Cdx2 [47], [73].
4. Nanog binds directly to the Gata6 locus to repress it [13].
5. Because the binding of Oct4 to the Nanog locus is necessary for forming the higher order structure of chromatin at the Nanog locus [61] and the binding site of Oct4 is adjacent to the binding site of Nanog at the Nanog locus [6], we expect that the Oct4-Nanog complex formed on the chromatin is necessary for building the TA of Nanog.
6. Nanog promotes the expression of Oct4 [5] and both Nanog and Oct4 directly bind to the Oct4 locus [6]. Because the binding site of Oct4 is in proximity of the binding site of Nanog at the Oct4 locus [6], we assume the promotion of the formation of TA of Oct4 through the binding of Oct4-Nanog complex on the Oct4 locus.
7. Nanog is suggested to promote expression of Sox2 [8], [74] and both Nanog and Oct4 directly bind to the Sox2 locus [6]. Because the binding site of Oct4 is in proximity of the binding site of Nanog at the Sox2 locus [6], we assume that the formation of TA of Sox2 is promoted by the binding of Oct4-Nanog complex on the Sox2 locus.
Level B. Genetic experimental data showed the correlation or anti-correlation between expression levels of two genes, but the direct evidence for the physical interactions between two genes are not yet obtained. In this case, the interactions can be indirect through the other unidentified factors. Even in that case, we may assume the hypothetical direct interaction between two genes in the model. Such assumption is reasonable in the coarse-grained model, in which the multiple detailed molecular processes are summarized into one process.
8. Excess expression of Oct4 reduces the expression level of Nanog [75]. We assume in the model that the Nanog locus has multiple binding sites of Oct4 and the occupation of the part of those sites is necessary for the formation of TA, but the occupation of all sites increases the rate for making the histone-code repressive.
9. The Nanog-null ESCs differentiate into cells similar to those induced by Gata6-positive cells [2], [76]. Since Gata6 and Nanog work in an antagonistic way [73], [77], we assume that Gata6 and Nanog are mutual repressors. Though it is not clear whether the repression of Nanog by Gata6 is direct or indirect through the other factors, we represent the interaction as a direct one by following the spirit of the coarse-grained approximation.
10. Gcnf is positively regulated by Gata6 and Cdx2 [10]. We assume for simplicity that Gcnf is activated directly by Gata6 and Cdx2 in the model.
Level C. No precise genetic data is available on the correlation or anti-correlation of gene activities, but from functional or biological observations, it is reasonable to assume the relation between two genes. The assumed interaction on such phenomenological basis might be a summary of the action of the larger network, but its representation as a single hypothetical process is useful to make the model behavior biologically reasonable.
11. Upon the removal of Lif or other agents from the culture, ESCs start differentiation. Then, each cluster of differentiated cells do not return to ESCs spontaneously. This stabilization of the differentiated cells may be enhanced by the positive feedback among the lineage-specific genes as was assumed by [63]. This effect of the regulatory network is represented in the model as auto-activation of lineage-specific genes, Gata6, Cdx2 and Gcnf. These auto-activating interactions may be phenomenological or hypothetical interactions.
12. The cluster of cells differentiated into one lineage do not spontaneously transdifferentiate into the other lineages. This inhibition of transdifferentiation may arise from the reception of the external factor that is secreted or exhibited by the neighbor cells in the cluster. Such effect of the cell-cell communication is phenomenologically represented in the model by the mutual repression of genes specific to the different lineages.
Others. Other biochemical or biophysical data showed the existence of interactions.
13. Nanog dimerization is essential for the self-renewal of ESCs [19]. Nanog dimerization can be the faster process than its binding to the loci, so that we assume in the model that all the interactions between Nanog and chromatins are through the dimerized Nanog.
As assumed in the above argument, each locus of gene has multiple binding sites of TFs. From Fig. 1 we define the binding sites in the model as in Table 2.
The state of the allele of gene in the model is represented by variables , , and , where represents whether the histone code is active () or repressive (), represents whether the th TF is bound () or unbound (), and represents whether the TA of the locus is formed and ready for transcription () or unformed and not ready (). TA may be partially formed when the incomplete number of TFs are bound on a locus, and hence we write to represent such a partially formed TA. The copy number of the th protein is represented by . The temporal changes of , , , and are numerically followed by using the Gillespie algorithm [58], which simulates the reactions explained below.
The th protein is synthesized from the locus in a burst-like fashion with the rate as(1)Here, we assume that the burst size stochastically fluctuates in each burst with the probability of the distribution, , with being the average burst size. Though the distribution of the burst size was reported to obey the geometric distribution in bacteria [78], the precise distribution of the burst size in higher organisms is not known [79]. We here used the Poisson distribution to highlight the effects of the burst size, but as shown in Fig. S1, the model behavior does not sensitively depend on the burst size , and hence we expect that the difference in distribution does not much affect the results. The th protein is degraded with the rate as(2)From Eqs.1 and 2, we can see that the representative copy number of each protein is , where the factor 2 comes from two available alleles. The synthesized th protein can bind to and unbind from the th site if the locus has the binding site for the th protein (Table 2). The binding rate, , depends on the copy number of the protein to be bound. We assume the simplest linear relation by introducing a constant as . When the TF cooperatively binds in a form of oligomer, the contribution of higher orders of should be taken into account in . However, in the present model, unlike the bacterial cases, the modification of with the higher order terms of does not affect the model behaviors significantly because the cooperativity of switching is dominated by the formation/dissolution of TAs. We therefore assume(3)(4)We should note that for the frequent switching between and 0 to take place, the ratio should be around 1, or . For Nanog, dimerization should be much faster than other processes, so that we use the copy number of Nanog dimer, , as in Eq.3 instead of the total copy number of Nanog, . can be estimated from the equilibrium relation as(5)where is the equilibrium constant of dimerization. In Eq.2, the copy number of the monomeric Nanog, is used to define the degradation rate as .
The binding of activator TFs triggers the formation of TA; starting from through the intermediate state to reach as(6)and the TA is resolved stochastically as(7)These formation/dissolution of TA should be largely affected by the state of histones at that locus. The change of the histone code is simulated by switching between the active and repressive states as(8)(9)In Eqs.6–9 the rates , , , , , and are represented with a suffix to emphasize their dependences on the type of gene.
We next explain how the rates defined in Eqs.1–9 depend on the gene state, , , and . in Eq.1 depends on whether the TA is formed or not, which is represented by the variable . By using constants and , we write as(10)Notice that with this rule the protein is synthesized only when the TA is formed at least partially as .
The rates of TA formation and dissolution depend on how TFs are bound on the locus. Since TFs can be either of repressor or activator when they bind on the particular locus, we distinguish the binding sites by writing when the th TF is an activator of , and when the th TF is a repressor of . With this representation, the rate for the first step of TA forming, in Eq.6, is represented as follows;(11)Notice that the TA is formed with only when the histone code is active (). Here, is the rate when some activator TF is bound on the locus but there is no bound repressor, is the rate when some repressor is bound on the locus, and is the rate when no activator or repressor in the model is bound on the locus. Even in the case there is no bound activator in the model, other TFs which are not represented in the model may bind on the locus to promote the TA forming, and hence we assume that the basal background rate for the TA formation, , is finite. Considering these definitions, we expect . For the second step of TA forming, we expect that all possible TFs should bind on the locus to complete the TA formation, so that we have(12)For Gcnf and Sox2, we do not consider the repressor explicitly, so the rule is simplified as(13)
The rates of the histone-code modification, in Eq.8 and in Eq.9, should also depend on the state of gene. We assume that the histone code can be turned active only when enzymes to modify the histone code are recruited by activators and are not inhibited by repressors. Therefore, becomes when the gene state is similar to the situation for , i.e., when some activator TF binds and no repressor binds on the locus;(14)For , Oct4 and Nanog, the histone-code activation is promoted by binding the Oct4-Sox2 or Oct4-Nanog complex. To represent cooperativity due to this complex formation, we modify the rule of Eq.14 as(15)We assume that becomes in the opposite situation, i.e., when some repressor binds and no activator binds on the locus. For all genes except Gcnf and Sox2, we have(16)No repressor is assumed to bind on the Gcnf or Sox2 locus in the model, so that for or Sox2 we have the rule,(17)
Eqs.1–9 were simulated with the Gillespie algorithm with the rates defined by Eqs.10–17. The simulation was started from the following initial condition which represents the pluripotent state,(18)for , Oct4, and Nanog and(19)for , Gata6, and Gcnf. Starting from this initial condition, the simulation for sec was performed by keeping for , Gata6, and Gcnf to reach the steady ESC state. This first sec trajectory was discarded and the data were sampled after that for the statistical analysis by keeping for , Gata6, and Gcnf to simulate ESCs or by turning to be for , Gata6, and Gcnf to simulate the differentiation process.
The model has parameters, , , and for protein synthesis, for protein degradation, and for binding and unbinding of TF, for Nanog dimerization, , , , , and for formation and dissolution of TA, and and for the histone-code modification. For simplicity of description, the suffix of and , and the suffix 0 of and are suppressed in the previous sections of Model and Results, in Fig. 2, and in Table 3. Parameters were determined according to the following guideline: (1) Parameter values were not tuned in a precise way but only their orders of magnitude were taken care of. (2) The same parameter was set to have the same value for different genes as far as possible. In the following, in order to determine the ranges that parameters can take with this guideline, we first discuss two basic quantities, the period of cell cycle in the following items 1 and 2, and the typical copy number of each protein in a cell in the item 3.
From the above consideration, we can estimate the orders of magnitude of parameters, which are summarized in Table 3. In order to further analyze the meaning of difference in the order of magnitude of these parameters, we define the dimension-less parameters, adiabaticities, as measures of relative rates of individual processes to the rate of the protein copy-number change: We have three adiabaticities in our model of epigenetic dynamics, which measures the relative frequency of the TF binding/unbinding, which measures the relative frequency of the TA formation/dissolution, and which measures the relative frequency of the histone-code modification. From the above estimation, the orders of magnitude of adiabaticity are or . Therefore, the TF binding/unbinding is strongly adiabatic, and the histone-code modification is non-adiabatic. The TA formation/dissolution is adiabatic or non-adiabatic depending on the type of gene, which characterises the dynamic behavior of the present model.
The parameters used in Figs. 3, 5, 6, and 8 in Results section are shown in Table 4. We can see that the values of Table 4 are within the range shown in Table 3. In Table 4, the dependence of parameters on the type of genes is minimized: The specific values deviating from the most common values are for , and , , , and for . Since the differentiation to trophectoderm takes place prior to the formation of inner cell mas (ICM) in the early embryo and ESCs are prepared from ICM, it is reasonable to assume that the differentiation to trophectoderm is somehow suppressed in ESCs. We express this tendency by using the smaller value of for , the marker protein for trophectoderm. The small value of for represents the possible inherent effects of silencing Cdx2 in ESCs.
The smaller values of , , , and for are the manifestation of the slow switching dynamics of the Nanog locus, which is a main feature of the present model of epigenetic dynamics as explained in Figs. 3B, 3C, 5, 6, and 8A–8C. To clarify the effects of this slow switching, we also calculate for comparison by using the same values of , , , and for as those for Sox2 or Oct4 as shown in Figs. 3A, 3C, and 8D–8F.
From Table 3, the order of magnitude of most of parameters are determined and their typical example values can be adopted as in Table 4. In Table 3, however, values of some parameters are undetermined yet. We perform a massive parameter search for values of these parameters to find the consistent values with the experimentally observed results.
An important undetermined set of parameters are and . We adopt the values in Table 4 for other parameters, and as discussed in Parameters subsection, we impose the constraints and . Considering the constraint of , we assume a small value for as in Table 4 and also assume . Then, the parameters and are left undetermined. In Fis.3A, 3C, and 8D–8F, we used the values to represent the situation that the TA formation/dissolution is much slower than the TF binding/unbinding but faster than the protein copy-number change, i.e., . This choice of values for and , however, is not consistent with the experimentally observed distributions of expression level of SON as is shown in Fig. 3, and hence we examined the other values of and for .
We generated about 1,000 parameter sets scattered on the two-dimensional plane of and . For each of these generated parameter sets, we calculated 10,000 trajectories and obtain the distributions of expression level of SON by averaging over the trajectories. We then evaluated the score as(20)where each term of Eq.20 is when the simulated data agrees with the corresponding feature of the observed data as shown in Fig. 3D, and otherwise:
In this way, when all the observed features of the distributions of expression level of SON in ESCs are reproduced by the simulated data. In Fig. 4, for 1,125 parameter sets is plotted on the plane of and with .
The other undetermined set of parameters in Table 3 are the bare rate of protein synthesis, , the ratio of the rate of synthesis at the completed TA to that of the partially formed TA, , and the average burst size, . Since we have a constraint as in Table 3, we fixed to the value as in Table 4 and searched the values of and extensively by modifying according to the constraint . 1,125 parameter sets were generated as scattered points on the two-dimensional plane of and and were calculated by averaging over 10,000 trajectories for each of the parameter sets. The calculated is plotted in Fig. S1, which shows that should be within the range of especially to satisfy and the results are not sensitively dependent on the burst size .
The total time length, , during which the th trajectory stayed at a certain cell state is calculated. By sampling trajectories of days, the averaged frequency of the appearance of the state , , is obtained as . We can see in Fig. S4 that the small number of states appear much more frequently than the other many states. We disregard the rarely appearing states and draw the transition diagrams among the states whose are larger than a threshold value . Here, we choose different thresholds for different transition diagrams because the difficulty to solve simultaneous equations for landscape and fluxes depends on the topology of the diagram. The larger threshold makes the diagram simpler to increase the solvability of equations, but we use the smallest possible threshold; for Fig. 8A, 0.016 for Figs. 8B–8E, and 0.029 for Fig. 8F.
Then, the time of the trajectory needed for the transition from the th to th states is monitored and recorded as . is averaged along the trajectory and over the ensemble of trajectories to obtain . The transition rate is defined by . The link between two cell states and is drawn in the transition diagram when the transition is observed more frequently than the threshold times, which are 200 (Fig. 8F), 600 (Figs. 6C, 8A, 8B, and 8E), 700 (Fig. 8C), or 900 (Fig. 8D) times in the sampled ensemble of trajectories. The transition diagram of Fig. 6C is drawn by connecting the cell states with thus defined links of transitions.
The epigenetic landscape, with , is calculated with the following rules:
The last rule can be explained by using an example of Fig. 7, in which four cell states , , , and are connected by directed links that represent transitions among cell states. For the diagram of Fig. 7, we should solve the following equations:(21)These equations have 11 variables. When we fix at a certain state , the relative height at other three states, at five saddles, and two currents and are obtained by solving the above 10 equations simultaneously.
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10.1371/journal.pcbi.1005538 | Uncovering direct and indirect molecular determinants of chromatin loops using a computational integrative approach | Chromosomal organization in 3D plays a central role in regulating cell-type specific transcriptional and DNA replication timing programs. Yet it remains unclear to what extent the resulting long-range contacts depend on specific molecular drivers. Here we propose a model that comprehensively assesses the influence on contacts of DNA-binding proteins, cis-regulatory elements and DNA consensus motifs. Using real data, we validate a large number of predictions for long-range contacts involving known architectural proteins and DNA motifs. Our model outperforms existing approaches including enrichment test, random forests and correlation, and it uncovers numerous novel long-range contacts in Drosophila and human. The model uncovers the orientation-dependent specificity for long-range contacts between CTCF motifs in Drosophila, highlighting its conserved property in 3D organization of metazoan genomes. Our model further unravels long-range contacts depending on co-factors recruited to DNA indirectly, as illustrated by the influence of cohesin in stabilizing long-range contacts between CTCF sites. It also reveals asymmetric contacts such as enhancer-promoter contacts that highlight opposite influences of the transcription factors EBF1, EGR1 or MEF2C depending on RNA Polymerase II pausing.
| Chromosomal DNA is tightly packed in three dimensions (3D) such that a 2-meter long human genome can fit into a microscopic nucleus. Recent studies have revealed that such packing of DNA is not random but instead structured into functional DNA loops. Those loops are essential to numerous key processes in the cell, such as genome expression and DNA replication. In addition, disruption of DNA loops can lead to genetic diseases and cancers. Understanding how DNA loops are formed and what are their molecular determinants is thus a fundamental issue. In this work, we propose a computational model to identify the molecular determinants of loops, including protein and DNA sequence. Most notably, the model offers insights in the different mechanistic scenarios behind loop formation. Using this model, we uncover numerous novel DNA loops and underlying mechanisms in Drosophila and human. We find that the orientation-dependent specificity between CTCF motifs is conserved in metazoans. We show how loops between DNA-binding proteins can be mediated by additional cofactors. Our analyses further reveal opposite influences of transcription factors depending on RNA Polymerase II pausing.
| Chromosomal DNA is tightly packed in three dimensions (3D) such that a 2-meter long human genome can fit into a nucleus of approximately 10 microns in diameter [1]. Such 3D structure of chromosome has recently been explored by chromosome conformation capture combined with high-throughput sequencing technique (Hi-C) at an unprecedented resolution [2–4]. Multiple hierarchical levels of genome organization have been uncovered such as compartments A/B [5] and topologically associating domains (TADs) [2, 3]. In particular, TADs represent a pervasive structural feature of the genome organization and are highly conserved across species. Functional studies revealed that spatial organization of chromosome is essential to numerous key processes such as for the regulation of gene expression by distal enhancers [4] or for the replication-timing program [6].
The comprehensive analysis of 3D chromatin drivers is currently a hot topic [7]. A growing body of evidence supports the role of insulator binding proteins (IBPs) such as CTCF, and cofactors like cohesin, as mediators of long-range chromatin contacts [3, 8, 9]. In human, high-resolution Hi-C mapping has recently revealed that loops that demarcate domains were often marked by asymmetric CTCF motifs where cohesin is recruited [10]. Depletions of CTCF and cohesin decreased chromatin contacts [11]. However the impact of these depletions was limited suggesting that other proteins might be involved in shaping the chromosome in 3D. For instance, numerous IBPs, cofactors and functional elements were shown to colocalize at TAD borders [9, 12]. The identification of 3D chromatin drivers is thus an active avenue of research. Computational approaches that integrate the large amount of available protein binding data (chromatin immunoprecipitation followed by high-throughput DNA sequencing, ChIP-seq), functional elements (promoters and enhancers), and DNA motifs, with Hi-C data may be well-suited to identify novel factors that participate in shaping the chromosome in 3D [13].
In this paper, we propose a model to comprehensively analyze the roles of genomic features, such as DNA-binding proteins or motifs, in establishing or maintaining chromatin contacts. The proposed model offers insights in the different mechanistic scenarios behind loop formation, because of its ability to rigorously assess the effect of protein complex on long-range contact frequency. Using real data, the model successfully predicted numerous long-range interactions involving motifs and proteins as highlighted in previous independent studies. Moreover, our model outperformed current approaches to identify architectural proteins and motifs, and to detect the effects of single nucleotide polymorphisms (SNPs) in the dCTCF motif. In addition, our model is the only approach able to assess the effect of a cofactor in mediating long-range contacts between distant protein binding sites, such as cohesin with CTCF. Using recent Drosophila and human Hi-C data at high resolution, combined with a large number of ChIP-seq, RNA-seq, CAGE-seq and DNA motif data, we revealed numerous novel motifs, insulator binding proteins, cofactors and functional elements that positively or negatively impact long-range contacts depending on transcriptional activity or motif orientation.
We propose to use a generalized linear model with interactions (GLMI) to analyze the effects of genomic features such as architectural protein co-occupancies on chromatin contacts at genome-wide level:
log ( E [ y | X ] ) = β 0 + β X = β 0 + β d d + β B B + β C C + β g g (1)
Variable y denotes the number of Hi-C contacts for any pair of bins on the same chromosome. Variable set X = {d, B, C, g} comprises several variable subsets: the log-distance variable d, the bias variables B, the confounding variable set C and the genomic variable of interest g. The log-distance variable d accounts for the background polymer effect (log-log relation between distance and Hi-C count) [14]. Bias variables B = {len, GC, map} are known Hi-C biases including fragment length (len), GC-content (GC) and mappability (map) that are computed as in [15] (S1 Appendix, Bias variable computation). Including those bias variables into the model allows to correct for biases in Hi-C data. Bias normalization by matrix balancing methods [16] is avoided, because these methods might remove effect of genomic variable of interest. Variable g represents the genomic feature of interest, whose associated βg parameter value reflects its effects on chromatin contacts. Variable set C comprises confounding variables included to properly estimate βg. Model (1) is very general and can be developed in multiple versions depending on the variable g of interest. In the following paragraphs, we will see the different kinds of variables g. The corresponding models are detailed in Subsection Materials and Methods, The different models.
We illustrate the different model variables in Fig 1. For simplicity, we illustrate our model with protein binding sites, yet the same model is applicable to many other genomic features such as motifs or promoters. Let consider a pair of bins that we call left bin (L) and right bin (R). The attribution for left and right bins is arbitrary. Let also consider 3 genomic features Fi (whose binding is colored in blue in Fig 1), Fj (in red) and Fk (in green) that represent binding sites of 3 different proteins. For the genomic feature Fi, occupancy variables ziL and ziR denote the occupancies of Fi on left and right bins, respectively. For an occupancy variable, a value of 0/1 means absence/presence of the corresponding feature on the bin, e.g. absence/presence of the protein on the bin (a value between 0 and 1 means partial overlap of the feature). Occupancy variables are used to build 4 main kinds of model variables as follows.
A “homologous interaction” variable nii is the product of ziL and ziR (nii = ziL × ziR). The associated β n i i parameter reflects the extent by which the genomic feature Fi interacts with itself through chromatin contacts (Fig 1a). For instance, distant CTCF binding sites were shown to form loops in human [10, 17].
A “heterologuous interaction” variable nij is the average of the product ziL × zjR and the product zjL × ziR (n i j = 1 2 ( z i L × z j R + z j L × z i R )), because both products are identically associated to y. The associated β n i j parameter reflects the extent by which the genomic feature Fi interacts with another genomic feature Fj through chromatin contacts (Fig 1b). For instance, enhancers are in long-range contacts with promoters to regulate target gene expression [14, 18].
A “homologous interaction cofactor” variable ciik is the product of an interaction variable nii and an interaction variable nkk (ciik = nii × nkk = ziL × ziR × zkL × zkR). Here we consider the cofactor Fk as a protein that does not directly bind to DNA, but which is instead bound by an insulator binding protein Fi (IBP) to DNA, such as cohesin is recruited by CTCF to DNA. Hence we expect that a cofactor will be found at both bins L and R in contact, e.g. cohesin ring entraps both chromatin fibers and is thus observed at both bins [10, 17]. That explains why ciik is the product of nii and nkk. The associated β c i i k parameter reflects the extent by which chromatin contacts between genomic feature Fi and itself are mediated by a genomic feature Fk, the cofactor (Fig 1c).
A “heterologous interaction cofactor” variable cijk is the product of an interaction variable nij and an interaction variable nkk (c i j k = n i j × n k k = 1 2 ( z i L × z j R × z k L × z k R + z j L × z i R × z k L × z k R )). Here we consider the cofactor Fk as a protein that does not directly bind to DNA, but which is instead bound to two IBPs Fi and Fj. For instance, a loop can be mediated by CP190 that binds to BEAF-32 and GAF sites that are distant [19]. The associated β c i j k parameter reflects the extent by which chromatin contacts between genomic features Fi and Fj are mediated by a third genomic feature Fk, the cofactor (Fig 1d).
In the previous paragraphs, we introduced numerous variables that were the products of simpler variables, namely the occupancy variables. In (generalized) linear regression, those product variables are called “interaction” terms. To detect such interaction effects, one usually needs a large number of observations. We will see in the next subsections that the tremendous amount of data provided by Hi-C experiments allows to detect such interaction effects with accuracy. The model and the different variables will be illustrated with real world scenarios in the next subsections.
We first sought to validate our model using experimental data. For this purpose, we focused on the Drosophila model because several insulator binding proteins (IBPs) that mediate long-range interactions have been well characterized in this organism. Drosophila IBPs comprise suppressor of hairy wing (Su(Hw)), Drosophila CTCF (dCTCF), boundary-element-associated factor of 32 kDa (BEAF-32), GAGA binding factor (GAF), Zeste-White 5 (ZW5) [20], the general transcription factor dTFIIIC [9] and DNA replication-related element factor (DREF) [7]. We analyzed Kc167 Hi-C data at 10 kb resolution and focused on 20kb-1Mb distances for which contact frequencies were accurately measured experimentally [21]. At this distance range, the log-log relation between Hi-C count and distance was linear (R2 = 0.99, S1 Fig), supporting the use of the log-distance term in the model. The data comprised approximately 1 million of observations, which allowed to detect higher-order interactions with enough precision (tight parameter confidence intervals reflected by low p-values, see below). Because of Hi-C count overdispersion, we used negative binomial regression as the most appropriate specification of the generalized linear model.
It has been shown that BEAF-32 motifs can form long-range interactions with each other using both fluorescence cross-correlation spectroscopy [22] and high-resolution microscopy [23]. Following this observation, we first validated our model by successfully estimating long-range contacts between the BEAF-32 CGATA motifs using model (2) (β ^ n i i = 6 . 7 × 10 3, p < 10−20; Fig 2a; model (2) and all other models used in the following are described in Subsection Materials and Methods, The different models). This result was confirmed as we observed that the Hi-C count increased with co-occupancy of BEAF-32 motifs (variable nii) (Fig 2b). We also observed long-range contacts between dCTCF motifs (β ^ n i i = 2 . 4 × 10 4, p = 3 × 10−14), highlighting their important roles in loop formation in Drosophila as observed in human [10, 17]. Over the 7 known IBPs, the model correctly identified all IBP motifs as involved in long-range contacts among themselves (Fig 2c). Next the same approach was used to evaluate the model’s ability to discriminate between the 7 IBP motifs (true positives) and 83 other DNA-binding protein motifs (false positives). This approach obtained good predictions (area under the curve (AUC) = 0.855; Fig 2d). Among the motifs that we considered as false positives, M1BP and Ttk69K motifs presented high and significant interaction effects (M1BP: β ^ n i i = 1 . 7 × 10 5; Ttk69K: β ^ n i i = 2 . 3 × 10 4, p < 10−12, resp.). These results suggested that M1BP and Ttk69K might represent new insulator-binding protein candidates. Accordingly, M1BP protein binds to the promoters of paused genes that were shown to be involved in long-range contacts [18, 24]. Ttk69K protein has a homomeric dimerization BTB/POZ domain that could help bridging two distant proteins through long-range contacts [22].
We then used GLMI to study the role of cofactors that cannot directly bind to DNA, but are instead recruited by IBPs, and are required to mediate or stabilize long-range contacts between two IBP binding sites. In Drosophila, well-known cofactors include condensin I, condensin II, Chromator, centrosomal protein of 190 kDa (CP190), cohesin [19–22], Fs(1)h-L [25] and lethal (3) malignant brain tumor (L(3)Mbt) [7]. Most notably, fluorescence cross-correlation spectroscopy (FCCS) experiments have shown that CP190 is required to bridge long-range contacts between two BEAF-32 binding sites [22]. Using ChIP-seq peak data with model (4), we estimated a significant and positive effect of CP190 in mediating long-range contacts between BEAF-32 sites (β ^ c i i k = 878, p < 10−20; Fig 2e), in complete agreement with recent work [22]. Similar result was obtained for Chromator in mediating long-range contacts between BEAF-32 sites (β ^ c i i k = 3 . 4 × 10 3, p < 10−20) [22]. In addition, previous BEAF-32 mutation by our group has revealed that cofactor CP190 is also required to bridge long-range contacts between BEAF-32 and GAF binding sites [19]. Using ChIP-seq peak data with model (5), we estimated a significant and positive effect of CP190 in bridging distant BEAF-32 and GAF sites (β ^ c i j k = 1 . 3 × 10 3, p < 10−20; Fig 2e) [19]. We applied the same modeling approach to the 6 other known cofactors and found that all were associated with significant positive effects in mediating contacts between BEAF-32 and GAF binding sites (all betas β ^ c i j k > 326, all p-values p < 10−20; Fig 2f). Because CP190 was also shown to mediate long-range contacts between BEAF-32 and dCTCF, and between BEAF-32 and Su(Hw) [19], we estimated the corresponding cofactor effects. We again found significant positive effect of CP190 between BEAF-32 and dCTCF (β ^ c i j k = 892, p < 10−20), but our method only detected a slightly significant mediating effect of CP190 between BEAF-32 and Su(Hw) (β ^ c i j k = 175, p = 0.02). In human, the most studied cofactor is cohesin that is able to entrap two chromatin fibers thereby stabilizing long-range contacts between CTCF sites [10, 17]. Hence we assessed the impact of cohesin in mediating long-range contacts between two dCTCF binding sites in Drosophila. We found a significant and positive effect of cohesin (β ^ c i i k = 105 . 8, p < 10−20; Fig 2g), thus supporting a conserved function of cohesin in stabilizing long-range contacts between CTCF sites in metazoans.
We further tested our model for cofactor effects using perturbed conditions such as the removal of these cofactors, as obtained through knocking-down (KD) followed by Hi-C experiment. Of note, Hi-C experiments are expensive and complex to carry out, and the possibility to predict long-range contacts upon such KD is of major importance. We compared the impact of cohesin in the context of long-range contacts bridging CTCF sites in WT and Rad21 (cohesin subunit) KD Hi-C data. Our model estimated a significant but lower cofactor effect of cohesin in Rad21 KD (β ^ c i i k = 75 . 7, p = 9 × 10−12), compared to WT (β ^ c i i k = 105 . 8, p < 10−20). The difference between WT and Rad21 KD associated coefficients was negative and significant (beta difference = −30.1, p = 0.027), corresponding to a beta decrease of 28% (Fig 2h). This result therefore validated the estimated effect of cohesin in mediating distant dCTCF binding sites, which decreased upon cohesin depletion as expected.
Using real data, we concluded that our model successfully predicted the roles of IBP motifs in long-range contacts between distant loci, as well as the roles of known cofactors in bridging distant IBP binding sites. The GLMI predictions were validated in the literature and using protein KD followed by Hi-C experiment.
We then compared GLMI with existing methods for their ability to identify genomic features known to be involved in long-range contacts. For this purpose, we compared GLMI with (1) enrichment test (ET) on highly confident chromatin interaction pairs as previously [26], (2) correlation (Cor) on highly confident chromatin interaction pairs [27] and (3) random forests (RF) discriminating highly confident chromatin interaction pairs from non-interacting pairs [28]. As a first and simple benchmark, we assessed the different methods to identify long-range contacts between protein binding sites of the same proteins (model (2)). We evaluated the ability to discriminate between architectural proteins known to be involved in long-range contacts (13 true positives including IBPs and cofactors) and random protein peaks (100 false positives) using receiver operating characteristic (ROC) curves. We observed that all four methods were very efficient to detect long-range contacts between known architectural protein binding sites (Fig 3a). In particular, GLMI and Cor showed perfect predictions (AUC = 1). RF and ET were also very accurate (AUC > 0.94). Previous benchmark was an easy task because it relied on random protein peaks whose binding was very different from real protein binding. For a more realistic benchmark, we then evaluated the ability to discriminate between motifs whose proteins are known to be involved in long-range contacts (7 true positives) and other DNA-binding protein motifs (83 false positives) using ROC curves. Using this benchmark, all the four methods performed less well (Fig 3b). However we found that GLMI clearly outperformed the three other methods to detect long-range contacts between DNA motifs known to be involved in chromatin interactions (AUCGLMI = 0.855).
Another benchmark consisted in identifying long-range contacts between binding sites of a protein and active promoters. Here, as previously, we evaluated the ability to discriminate between architectural proteins known to be involved in enhancer-promoter contacts (13 true positives including IBPs and cofactors) and random protein peaks (100 false positives) using ROC curves. We observed that all four methods were very efficient to detect long-range contacts between known architectural protein binding sites and active promoters (Fig 3c). In particular, GLMI and Cor showed excellent predictions (AUCGLMI = 0.985 and AUCCor = 1). We then evaluated the ability to discriminate between motifs whose proteins are known to be involved in enhancer-promoter contacts (7 true positives) and other DNA-binding protein motifs (83 false positives) using ROC curves. Both GLMI and Cor performed well (AUCGLMI = 0.797 and AUCCor = 0.807; Fig 3d). Conversely, ET and RF showed lower perfomance (AUCET = 0.728 and AUCRF = 0.601).
We next analyzed the impacts of mutations in the consensus dCTCF motif. Single nucleotide polymorphisms (SNPs) play an important role in common genetic diseases and recent works have uncovered differential long-range contacts due to variations in the CTCF motif in human [17, 29, 30]. Hence we evaluated the methods to detect the impacts of single nucleotide mutations in the dCTCF motif. For this purpose, we considered the dCTCF consensus motif AGGTGGCG (wild-type motif) [31] and generated dCTCF motifs with single nucleotide mutations for each position (mutated motifs). For instance, for the first position, the mutated motifs were TGGTGGCG, GGGTGGCG and CGGTGGCG. Over the 24 possible mutated motifs (8 positions × 3 alternative nucleotides), GLMI detected 17 motifs (71%; Fig 3e) with homologous interaction variable betas that were lower than the one of the wild-type motif, indicating that the corresponding mutations diminished the ability of dCTCF to bridge long-range contact. Compared to GLMI, other approaches showed lower performance (Cor: 14/24; RF = 10/24; ET = 8/24).
In addition to its better prediction performances, our model presents several theoretical advantages over the three other methods as summarized in Fig 3f. All the methods can assess long-range contacts between protein binding sites. However, GLMI is the only model that, at the same time, (1) accounts for the contact frequency which can vary among highly confident loops, (2) can deal with the presence of colocalization among proteins using conditional independence, (3) allows variable selection using lasso or stepwise, and (4) can assess the effect of cofactors by including higher-order interaction terms.
Given the biological validation of our model, we next sought to address the roles of IBP motifs in establishing or maintaining long-range interactions in Drosophila. We first assessed how IBP motifs were coupled to form loops (i.e. for all combinations of distant IBP motifs). For this purpose, we estimated homologous and heterologous interaction variable effects for any couple of IBP motifs using models (2) and (3), and using the same Hi-C data, distance range and resolution as above (Fig 4a). The strongest long-range contacts were between dCTCF and DREF motifs (β ^ n i j = 2 . 8 × 10 4, p < 10−20), between dCTCF motifs (β ^ n i i = 2 . 4 × 10 4, p < 10−20) and between DREF motifs (β ^ n i i = 2 × 10 4, p < 10−20). High levels of long-range contacts were also found between BEAF-32 and DREF motifs (β ^ n i j = 1 . 9 × 10 4, p < 10−20) and between BEAF32 and dCTCF motifs (β ^ n i j = 1 . 9 × 10 4, p < 10−20). Thus in Drosophila, chromatin loops not only involve dCTCF motifs but also DREF and BEAF-32 motifs that all work together. We then explored if these long-range contacts depended on the distance between motifs. At short distance (<100kb), long-range contacts were mainly detected between DREF motifs (β ^ n i i = 1 . 8 × 10 4, p < 10−20), whereas at long distance (> 750kb), they were more frequent between dCTCF and DREF motifs (β ^ n i j = 3 . 5 × 10 4, p = 7 × 10−9) (Fig 4b). In addition, long-range contacts between dCTCF motifs peaked at 500 kb. Our results therefore raise the possibility that long-range contacts between IBP motifs could be distant-dependent. This observation might provide a molecular explanation for the observed hierarchical nature of 3D chromatin structure [32, 33], for which loops could be formed at different scales by the interplay of specific proteins.
Next we sought to comprehensively test whether motif orientation could influence long-range contacts, as originally shown for CTCF motifs in human [10] and more generally in mammals [34]. We distinguished the motifs that were on the positive DNA strand (denoted +), from those that were on the negative DNA strand (denoted -). Then it was possible to compute four types of homologous interaction variables: nii+− = ziL+ × ziR− (orientation →←), nii−+ = ziL− × ziR+ (orientation ←→), nii−− = ziL− × ziR− (orientation ←←), nii++ = ziL+ × ziR+ (orientation →→). The corresponding models are detailed in Subsection Materials and Methods, The different models. Here we processed data at 1 kb resolution for better accuracy in distinguishing the different orientations. Similarly to in human and mammals, we found significant long-range contacts for motifs in convergent orientation (β ^ n i i = 570, p = 2 × 10−3), and no significant contacts for the 3 other possible orientations (←→, →→ and ←←; Fig 4c), revealing conservation of convergent CTCF mediated loops in agreement with 4C analyses [35]. We then assessed motif orientation for all other IBP motifs. Of note, the orientation of DREF TATCGATA motifs could not be assessed because of its palindromic property. For BEAF-32, dTFIIIC and Su(Hw) motifs, we could not detect any strong orientation effect (Fig 4c). Conversely, for GAF and ZW5 motifs, we found stronger contacts for motifs in divergent orientation (←→) compared to convergent orientation (→←), suggesting a different mode of binding of the corresponding protein to DNA or a different constraint depending of its interaction with cofactors. Thus motif orientation in loops depends on the protein involved, and the dependence on convergent orientation of motifs does not apply to all insulator binding proteins.
IBP binding sites might significantly vary depending on the cell type and stage. Hence we reanalyzed the roles of IBP binding in Kc167 Drosophila cells using available ChIP-seq data (same cell type with Hi-C data; ZW5 data were not available). As in the previous subsection, we estimated interaction effects for any couple of IBP motifs using models (2) and (3). Similarly to the analysis of IBP motifs, we observed high levels of long-range contacts involving DREF and dCTCF (Fig 5a). In particular, we found strong long-range contacts between distant DREF binding sites (β ^ n i i = 147, p < 10−20) and between dCTCF and DREF binding sites (β ^ n i j = 133, p < 10−20). However, we also observed strong long-range contacts between DREF and dTFIIIC (β ^ n i j = 119, p < 10−20), and between DREF and GAF (β ^ n i j = 112, p < 10−20), which could not be detected by previous analysis of IBP motifs. We then built a graph using estimated betas by adding an edge between two proteins Fi and Fj with a weight β ^ n i j, and by adding an edge between a protein Fi and itself with a weight β ^ n i i (Fig 5b). Analysis of the graph clearly revealed the role of DREF as a hub, i.e. DREF was involved in many long-range contacts with other IBPs, such as BEAF-32, DREF, dTFIIIC and GAF. Such DREF-mediated loops might be in apparent contradiction with recent experiments showing that DREF motifs tag proximal activation of housekeeping genes, in contrast to long-range activation of developmental genes [36]. However such DREF-mediated loops can be explained by long-range contacts between promoters (β ^ n i i = 203, p < 10−20).
Previous results should be carrefully interpreted since IBPs often linearly colocalize (i.e. correlate) with each other on the chromosome [31]. Such correlations can lead to “indirect” long-range contacts between IBPs. For instance, if a loop is maintained by two distant dCTCF binding sites, and that BEAF-32 colocalizes to dCTCF, then it is likely that we will also observe loops between distant BEAF-32 and dCTCF sites, and even between BEAF-32 sites. The impact of such correlations between proteins in the study of 3D chromatin has been discussed in details [12]. Models (2) and (3) could not account for such correlations between IBPs because only one interaction variable term was included. Instead one should use another model that includes all possible interaction variable terms between IBPs (model (10), see Subsection Materials and methods, The different models). To better discard indirect long-range contacts between the 6 IBPs, we thus re-estimated interaction variable beta parameters using model (10) that included all marginal variables (6 variables, one for each IBP) and all interaction variables (21 variables, one for each combination of IBPs). Using model (10), we obtained rather different results (Fig 5c). We still observed strong long-range contacts between DREF binding sites (β ^ n i i = 25, p < 10−11). However other long-range contacts were observed such as between BEAF-32 sites (β ^ n i i = 30, p < 10−20). In turn, such analysis showed that an IBP tended to interact more with itself (homologous interactions) than with another IBP (heterologous interactions) (p = 0.018; Fig 5d), in agreement with insulator bodies observed by microscopy [37]. In addition, the model (10) allowed to infer negative and significant interaction effects, such as between distant DREF and BEAF-32 (β ^ n i j = - 25, p < 10−11), which could not be detected before. This negative effect means that BEAF-32 and DREF tend to avoid each other in long-range contacts, i.e. they tend to have a repulsive effect. This might reflect the known antagonistic relationship between BEAF-32 and DREF in competing for binding to overlapping binding sites [38, 39]. As previously, we built a graph of betas and could detect groups of IBPs that may cluster together through long-range contacts as found for the two connected components BEAF-32/dTFIIIC/GAF and DREF/Su(Hw)/dCTCF, respectively (Fig 5e). Interestingly, these two classes of IBPs that worked together in 3D were different from the two classes that were previously identified by 1D analysis: dCTCF/BEAF-32 and Su(Hw), respectively [40]. Such observations strenghtened the importance of analyzing protein complexes in 3D in complement to 1D analysis (see Discussion).
In human and mammals, the main model of loop formation involves CTCF and cohesin [10, 17]. According to this model, a loop may form by the homodimerization of two CTCF proteins bound to two distant CTCF motifs that are in convergent orientation [10]. The loop also involves cohesin that is recruited by CTCF and that has the ability to entrap the two DNA fibers inside a ring. In addition to CTCF and cohesin, other architectural proteins have been recently uncovered such as ZNF143 [41] and PcG proteins [42]. In order to systematically analyze proteins mediating loops, we considered integrating available protein binding data (73 proteins) together with high-resolution Hi-C data in human GM12878 cells using our GLMI model. As previously done for Drosophila, we analyzed Hi-C data at 10 kb resolution and focused on 20kb-1Mb distances [10]. At this distance range, the Hi-C data comprised a very large number of bin pairs (around 22 millions), and hence, its analysis often required subsampling to few million pairs to achieve tractable regression parameter estimation. As for Drosophila, the log-log relation between Hi-C count and distance was linear at this distance range (R2 = 0.992, S2 Fig), supporting the use of the log-distance term in the model.
We first investigated contacts between distant CTCF binding sites using model (2). As expected, we observed strong long-range contacts (β ^ n i i = 37, p = 6 × 10−12) [10]. Moreover high levels of long-range contacts were detected between cohesin subunit Rad21 binding sites as expected (β ^ n i i = 89, p < 10−20; Fig 6a) [10], as well as between cohesin subunit SMC3 (β ^ n i i = 75, p < 10−20). We then used the same approach to estimate long-range contacts for all 73 proteins available (S1 Table). Among the proteins that significantly interacted among themselves, we found several proteins known to colocalize to CTCF binding sites including YY1 (β ^ n i i = 31, p < 10−20), MAZ (β ^ n i i = 16, p < 10−20) and JUND (β ^ n i i = 258, p = 10−9) [7]. We also found P300, an important transcriptional coactivator [43] (β ^ n i i = 264, p < 10−20). In addition, histone marks including H3K27me3, H3K36me3, H3K4me2, H3K4me3, H3K9ac and H3K9me3 showed homologous long-range contacts, as previously shown by polymer simulations [44] (all β ^ n i i > 0 . 05, p < 10−20). Curiously, H4K20me1 sites presented repulsive effects with each other (β ^ n i i = - 0 . 07, p < 10−20), indicating that distant H4K20me1 marked sites may avoid each other. We further estimated the well-known influence of cohesin in mediating long-range contacts between distant CTCF binding sites in human using model (4) [8, 10]. Interestingly, we found that the effect of cohesin depended on the distance between CTCF binding sites, with no significant contacts for short distances (20-300kb: β ^ c i i k = - 3 × 10 3, p = 0.63; 300-700kb: β ^ c i i k = - 1 × 10 4, p = 0.15) and significant contacts for long distances (700-1000kb: β ^ c i i k = 4 × 10 4, p = 3 × 10−6) (Fig 6b). This suggested that cohesin is required for stabilizing CTCF-mediated loops for long distances, but is not necessary for short distances for which homodimerization of CTCF might be sufficient. We also sought for other proteins whose loops could be mediated by cohesin for long distances (S2 Table). Most notably, we found that cohesin positively influences long-range contacts between architectural protein ZNF143 binding sites (β ^ c i i k = 4 . 8 × 10 4, p = 2 × 10−9), between PolII binding sites (β ^ c i i k = 446, p = 6 × 10−16), and between transcriptional factor binding sites (EGR1, ELF1, FOXM1, MAZ, MXI1, NRF1, YY1), which suggests a wider role for cohesin in mediating long-range contacts.
Further analyses of long-range contacts for every couple of proteins were performed using model (10) that included together all possible interaction variables. We considered 73 proteins, 7 histone modifications, active enhancers and active promoters. The model thus comprised (82 × 83)/2 = 3403 interaction variables. To deal with such a large number of interaction variables, we used a Poisson lasso estimation [45]. An interaction variable beta of zero was expected to reflect the absence of direct long-range contact between two proteins. From the estimated betas, we built a first graph that we called “attraction graph” by adding an edge between two proteins Fi and Fj if β ^ n i j > 0, and by adding an edge between a protein Fi and itself if β ^ n i i > 0 (Fig 6c). To identify hubs in the graph, we used eigenvector centrality that reflected how central is a node (Fig 6d). Both active and repressed chromatin marks as well as enhancers were the most central nodes (H3K9ac: score = 1; H3K9me3: score = 0.98; H3K4me3: score = 0.948; Enhancer: score = 0.84). Among DNA-binding proteins, CTCF and Rad21 showed high values (CTCF: score = 0.619; Rad21: score = 0.555). Surprisingly, however, other proteins MEF2C and FOXM1 presented the highest values (MEF2C: score = 0.725; FOXM1: score = 0.692). Previous studies showed that MEF2C is necessary for bone marrow B-lymphopoiesis (GM12878 is a lymphoblastoid cell line) [46], and that FOXM1 has an important role in maintenance of chromosomal segregation [47]. We then looked for cliques in the graph, i.e. a group of nodes that were all connected to each other (complete list in S3 Table). As expected, we found a clique composed of CTCF and the cohesin subunits Rad21 and SMC3, that are known to mediate together loops [10]. But we also found novel protein complexes that were specific to lymphocyte B such as the clique IKZF1/RFX5/PolII. IKZF1 plays a role in the development of lymphocytes [48], RFX5 is involved in bare lymphocyte syndrome [49] and polymerase II catalyzes gene transcription. In addition, we found many cliques involving Polymerase III (PolIII) such as the cliques MEF2C/RUNX3/PolIII and MEF2C/WHIP/PolIII, which might reflect the influence of architectural protein RNA polymerase III-associated factor (TFIIIC) at tRNA genes [2, 50].
Very little is known about repulsion effects between distant binding sites. Such repulsive effects could result from allosteric effects of loops [51], or factors that disassociate protein complexes involved in loops [52]. To investigate repulsive effects, we built a second graph that we called “repulsion graph” by adding an edge between two proteins Fi and Fj if β ^ n i j < 0, and by adding an edge between a protein Fi and itself if β ^ n i i < 0 (Fig 6e). The repulsion graph was very different from the attraction graph. Different histone marks were central in the repulsion graph, including H3K36me3 (score: 1) and H4K20me1 (score: 0.974), except histone mark H3K9me3 (score: 0.798) that was central in both the attraction and repulsion graphs (Fig 6f). Interestingly, we found that enhancers presented a high centrality score in the repulsion graph (score: 0.766), as found in the attraction graph. This result highlights the ability of enhancers to specifically interact with distant protein partner binding sites while avoiding others. Supporting this interpretation, we found enhancers to be in attraction with CFOS, NRF1 or POU2F2, and in repulsion with RXRA, NFE2 or P300. We then looked at pairs of proteins that were in repulsion. Most notably, we found CTCF to be in repulsion with EZH2, which might result from steric effects of CTCF-mediated loops [10] with Polycomb-mediated loops [42].
Enhancer-promoter (EP) interactions play an essential role in the regulation of gene expression [14, 18]. Therefore, we explored the roles of DNA-binding proteins in establishing or maintaining EP interactions. Before assessing the role of proteins, we first measured long-range contacts between active enhancers and promoters depending on gene expression using model (3) (Fig 7a). We observed an attraction effect between active enhancers and highly expressed gene promoters (β ^ n i j = 2, p = 3 × 10−5), and conversely, a repulsion effect between active enhancers and low expressed gene promoters (β ^ n i j = - 1 . 7, p < 1 × 10−20), in complete agreement with the established positive influence of long-range contacts on gene expression [53]. To identify the influence of DNA-binding proteins, we then assessed the presence of long-range contacts between lymphocyte B transcriptional activator binding sites (ChIP-seq data) and promoters using the same model (3). All lymphocyte B transcriptional activators including BCL11A, EBF1, EGR1, MEF2C, PAX5 and TCF12 showed long-range contacts with highly expressed gene promoters, compared to weakly transcribed gene promoters (Fig 7b). This clearly showed that lymphocyte B transcriptional activators regulate expression of target genes through long-range contacts. Among the proteins available, we could not identify any that acted as silencers, i.e. proteins whose long-range contacts are high with low expressed gene promoters and low with highly expressed gene promoters. However when we focused on histone modifications, we found that long-range contacts of H3K27me3 mark were stronger to weakly transcribed gene promoters (β ^ n i j = 0 . 06, p < 10−20), compared to highly expressed gene promoters (β ^ n i j = - 0 . 2, p < 10−20) (Fig 7c). This suggested that H3K27me3 mark not only acts as a transcriptional silencer in linear proximity [54], but could also repress target genes at distance through loops. Conversely, active marks such as H3K4me3 and H3K9ac interacted more with highly expressed genes. Because enhancer-promoter contacts were previously shown to be associated with Polymerase II pausing [18], we then assessed enhancer-promoter interactions depending on gene transcription pausing. As expected, we found higher EP contacts at paused genes (β ^ n i j = 62 . 2, p = 10−3), compared to genes in elongation (β ^ n i j = 49 . 3, p = 2 × 10−3). We then looked at the influence of DNA-binding proteins (Fig 7d). For instance, EBF1 sites showed higher long-range contacts with promoters of genes in pause (β ^ n i j = 39 . 7, p = 1 × 10−13), compared to those in elongation (β ^ n i j = 17 . 8, p = 3 × 10−5), in agreement with [18]. But, surprisingly, we also found that BCL11A sites showed higher long-range contacts with promoters of genes in elongation (β ^ n i j = 72 . 8, p < 10−20) than with genes in pause (β ^ n i j = 60 . 9, p = 2 × 10−11). These observations suggest that, depending on the protein involved, long-range contacts with promoters are not always associated with pausing, but could also be linked to elongation.
Here, we propose to use a generalized linear regression with interactions (GLMI) to study the roles of genomic features such as DNA-binding proteins, motifs or promoters to bridge long-range contacts in the genome, depending on transcriptional status or motif orientation. GLMI has multiple assets over existing approaches such as enrichment test, correlation and random forests. Compared to enrichment test [2, 55] or correlation [27] that respectively assesses the protein enrichment or correlation at highly confident loops, GLMI quantitatively links the frequency of all long-range contacts to complex co-occupancies of proteins while accounting for known Hi-C biases and polymer background. Moreover, GLMI accounts for colocalizations among protein binding, a strong issue when analyzing protein binding sites known to largely overlap over the genome. In contrast to random forests [28] which are efficient predictive models but sometimes poor explanatory ones, GLMI allows to identify key chromatin loop driver proteins and motifs. GLMI can also uncover numerous mechanisms behind loop formation using higher-order interaction terms and proper confounding variables. For instance, GLMI can determine if a cofactor is necessary to mediate long-range contacts between distant protein binding sites.
Using real Drosophila Hi-C and ChIP-seq data, we validate numerous GLMI predictions of long-range contacts that involve insulator binding proteins, cofactors and motifs, and which were confirmed by previous microscopy and mutational studies. For instance, our model estimates long-range contacts between distant BEAF-32 motifs, which were previously observed with both fluorescence cross-correlation spectroscopy [22] and high-resolution microscopy [23]. In addition, our model finds a mediating role of CP190 in bridging long-range contacts between distant BEAF-32 and GAF binding sites, in agreement with mutational experiments [19]. Of interest, GLMI analyses highlight a role of cohesin in stabilizing long-range contacts between CTCF sites in Drosophila, similarly to its role in human [7]. Supporting this role, we show that such influence is reduced upon cohesin subunit Rad21 depletion. It has to be noted that the absence of complete loss of contacts between CTCF sites after Rad21 depletion can be explained by the fast turnover of chromosome-bound cohesin in interphase [56]. Moreover, GLMI outperforms enrichment test, correlation and random forests in the identification of known architectural proteins and motifs, and in the detection of the effects of mutations in the dCTCF motif.
The proposed model also uncovers several novel results. In Drosophila, GAF and ZW5 motifs are shown to act in divergent orientation to form loops, in contrast to CTCF motifs that are found in convergent orientation in Drosophila and human [10, 17], suggesting a different mode of action of corresponding proteins. In addition, we identify two groups of proteins that act in 3D to form loops. The first group comprises BEAF-32, dTFIIIC and GAF, and the other group includes DREF, Su(Hw) and dCTCF. Those groups are different from the ones observed with 1D analysis only (i.e. linear colocalization on the genome) [40], highlighting the importance of 3D analysis using GLMI. In human, we identify numerous long-range contacts between protein binding sites. In addition to the well-known protein complex CTCF/RAD21/SMC3, we uncover new protein complexes that are specific to lymphocyte B such as IKZF1/RFX5. We also found that enhancers could be either in long-range contact or repulsion with certain protein binding sites, highlighting potential specificity in selecting protein partners for long-range contacts. Our observations therefore support the idea that enhancer-promoter contacts are not solely driven by insulators or TAD borders that physically constrain such long-range interactions [29, 36, 57]. Rather, enhancer-promoter contacts may also be encoded by the specificity of protein-protein interactions. In addition, our results suggest that repressive mark H3K27me3 does not only repress genes that are contigous [54], but it could also repress from a distance through the juxtaposition of H3K27me3 with genes in 3D. We also find that, depending on the protein involved, long-range enhancer-promoter contacts are not always favored by PolII pausing [18], which may highlight distinct mechanisms by which proteins can influence transcription-associated long-range contacts.
There are several limitations of the proposed approach. First, the present analysis is restricted to a 10-kb resolution because of the quadratic complexity of Hi-C data. Second, our analysis is limited by the amount of higher-order interaction variable parameters that can be learned within the same model (full model) using current parameter learning programs. Most notably, all possible interaction cofactor variables cannot be included in the same model because of the cubic complexity of such model, and hence they are learned separately instead (using models (4) and (5)). In addition, although generalized linear models can include interactions of any order involving large protein complexes (for instance, complexes of more than 4 proteins), parameter learning is limited by the availability of data and computational resources. Increasing depth of Hi-C data will allow inference of more complex models in the near future. Moreover the development of new big data learning algorithms could be used to process the data at a higher resolution that would allow in-depth analysis of 3D chromatin drivers [58]. An alternative to the exploration of all possible higher-order interactions together might be to guide the search using prior information, such as protein-protein interaction network [55]. Lastly, in order to explore all possible higher-order interaction variables within the same model (full model), one should use a lasso regression model with hierarchically constrained interactions [59].
We used publicly available high-throughput chromatin conformation capture (Hi-C) data from Gene Expression Omnibus (GEO) accession GSE62904 [21]. Hi-C experiments have been done for Drosophila melanogaster wild-type and Rad21 knock-down Kc167 cells with DpnII restriction enzyme. Hi-C data were binned at 1 and 10 kb resolutions.
For human data analysis, we used publicly available Hi-C data of lymphoblastoid cells GM12878 cells from Gene Expression Omnibus (GEO) accession GSE63525 [10]. We used Hi-C data binned at 10 kb resolution.
For Drosophila analysis, we used publicly available binding profiles of chromatin proteins of Drosophila melanogaster wild-type embryonic Kc167 cells. ChIP-seq data for CP190, Su(Hw), dCTCF and BEAF-32 were obtained from GEO accession GSE30740 [60]. ChIP-seq data for Barren (condensin I), Cap-H2 (condensin II), Chromator, Rad21 (cohesin), GAF and dTFIIIC were obtained from GEO accession GSE54529 [9]. ChIP-seq data for DREF and L(3)Mbt were obtainted from GEO accession GSE62904 [21]. ChIP-seq data for Fs(1)h-L and Fs(1)h-LS were obtained from GEO accession GSE42086 [25]. Peak calling was done using MACS 2.1.0 (https://github.com/taoliu/MACS).
For human analysis, we used publicly available binding peaks of 73 chromatin proteins (RAD21, CTCF, YY1, ZBTB33, MAZ, JUND, ZNF143, EZH2, ATF2, ATF3, BATF, BCL11A, BCL3, BCLAF1, BHLHE40, BRCA1, CEBPB, CFOS, CHD1, CHD2, CMYC, COREST, E2F4, EBF1, EGR1, ELF1, ELK1, FOXM1, GABP, IKZF1, IRF4, MAX, MEF2C, MTA3, MXI1, NFATC1, NFE2, NFIC, NFKB, NFYA, NFYB, NRF1, NRSF, P300, PAX5, PBX3, PML, POL2, POL3, POU2F2, RFX5, RUNX3, RXRA, SIN3A, SIX5, SMC3, SP1, SPI1, SRF, STAT1, STAT3, STAT5, TBLR1, TBP, TCF12, TCF3, TR4, USF1, USF2, WHIP, ZEB1, ZNF274, ZZZ3) and histone marks (H3K27me3, H3K36me3, H3K4me2, H3K4me3, H3K9ac, H3K9me3, H4K20me1) of GM12878 cells from ENCODE [61]. We downloaded peaks that were uniformly processed (Uniform Peaks).
For human analysis, we divided promoters into quartiles of gene expression using RNA-seq data [61]. We also divided promoters into quartiles of gene pausing and into quartiles of gene elongation using PolII ChIP-seq data [61]. For enhancer mapping, we used lymphocyte of B lineage differentially expressed enhancers identified from the Fantom5 project [62].
For both Drosophila and human analyses, we used transcription factor binding site (TFBS) motifs from the MotifMap database (http://motifmap.ics.uci.edu/).
The proposed GLMI assumed a linear relation between logarithm of Hi-C counts and the logarithm of distance between bins as previously shown in [5]. This assumption only holds locally, i.e. for a specific distance scale. Hence we restricted GLM modeling to a certain range of distances, e.g. for 20kb to 1Mb. In addition, we tested this assumption on data before using GLMI. We considered that this assumption holds when the R2 > 0.95.
Before computing variables for the GLMI presented above, intermediate variables from the genomic features such as DNA-binding proteins needed to be calculated. Intermediate “occupancy” variable zi denoted the presence (zi = 1) or absence (zi = 0) of the protein Fi within the genomic bin. If the protein only overlapped 60% of the genomic bin, then zi = 0.6.
Here are described the different models derived from model (1) that we used. In order to assess a homologous interaction variable nii = ziL × ziR (here g = nii), model (1) becomes:
log E y | X = β 0 + β d d + β B B + β C C + β g g = β 0 + β d d + β B B + β m i m i + β n i i n i i (2)
Following the hierarchy principle in (generalized) linear models, the assessment of a statistical interaction variable, such as nii = ziL × ziR, must include both ziL and ziR as confounding variables. Because ziL and ziR are identically associated to y (the attribution for left and right bins is arbitrary), their values are averaged to give m i = 1 2 ( z i L + z i R ). Hence C = mi is used as a confounder of nii.
In order to assess a heterologous interaction variable n i j = 1 2 ( z i L × z j R + z j L × z i R ) (here g = nij), model (1) becomes:
log E y | X = β 0 + β d d + β B B + β C C + β g g = β 0 + β d d + β B B + β m i m i + β m j m j + β n i j n i j (3)
Following the hierarchy principle, ziL, ziR, zjL and zjR have to be included as confounding variables. As previously, ziL and ziR are averaged to give m i = 1 2 ( z i L + z i R ). Similarly, zjL and zjR are averaged to give m j = 1 2 ( z j L + z j R ). Hence C = {mi, mj} is used as confounder of nij.
In order to assess a homologous interaction cofactor variable ciik = nii × nkk (here g = ciik), model (1) becomes:
log ( E [ y | X ] ) = β 0 + β d d + β B B + β C C + β g g = β 0 + β d d + β B B + β m i m i + β m k m k + β m i k m i k + β n i i n i i + β n k k n k k + β n i k n i k + β n i i × m k ( n i i × m k ) + β n k k × m i ( n k k × m i ) + β c i i k c i i k , (4)
Here variable ciik is a four-way interaction term and hence there are a large number of confounding variables included in variable set C = {mi, mk, mik, nii, nkk, nik, nii × mk, nkk × mi}. We need to introduce a new type of variable, noted mij, the average of product ziL × zjL and product ziR × zjR (m i j = 1 2 ( z i L × z j L + z i R × z j R )). For a detailed explanation of the confounder set C, see S1 Appendix, Confounder sets.
In order to assess a heterologous interaction cofactor variable cijk = nij × nkk (here g = cijk), model (1) becomes:
log ( E [ y | X ] ) = β 0 + β d d + β B B + β C C + β g g = β 0 + β d d + β B B + β m i m i + β m j m j + β m k m k + β m i k m i k + β m j k m j k + β n i j n i j + β n j k n j k + β n i k n i k + β n k k n k k + β n i j × m k n i j × m k + β n k k × m i n k k × m i + β n k k × m j n k k × m j + β c i j k c i j k .
(5)
Here variable cijk is a four-way interaction term and hence there are a large number of confounding variables included in variable set C = {mi, mj, mk, mik, mjk, nij, njk, nik, nkk, nij × mk, nkk × mi, nkk × mj}. For a detailed explanation of the confounder set C, see S1 Appendix, Confounder sets.
In addition, we formulated models for homologous interaction variables, depending on motif pair orientation. For a pair of motifs in convergent orientation (→←), model (1) becomes:
log ( E [ y | X ] ) = β 0 + β d d + β B B + β C C + β g g = β 0 + β d d + β B B + β z i L + z i L + + β z i R − z i R − + β n i i + − n i i + − (6)
with nii+− = ziL+ × ziR−. Symbol “+” denoted motifs that were on the forward DNA strand, while symbol “-” denoted motifs that were on the reverse DNA strand. For instance, variable ziL+ was the occupancy of a motif on the forward DNA strand within genomic bins.
For a pair of motifs in divergent orientation (←→), model (1) becomes:
log ( E [ y | X ] ) = β 0 + β d d + β B B + β C C + β g g = β 0 + β d d + β B B + β z i L − z i L − + β z i R + z i R + + β n i i − + n i i − + , (7)
with nii−+ = ziL− × ziR+.
For a pair of motifs in same orientation (→→), model (1) becomes:
log ( E [ y | X ] ) = β 0 + β d d + β B B + β C C + β g g = β 0 + β d d + β B B + β z i L + z i L + + β z i R + z i R + + β n i i + + n i i + + , (8)
with nii++ = ziL+ × ziR+.
For a pair of motifs in same orientation (←←), model (1) becomes:
log ( E [ y | X ] ) = β 0 + β d d + β B B + β C C + β g g = β 0 + β d d + β B B + β z i L − z i L − + β z i R − z i R − + β n i i − − n i i − − , (9)
with nii−− = ziL− × ziR−.
Moreover, we formulated an additional “full” model where all possible homologous and heterologous interaction variables were included. For instance, if we study two proteins Fi and Fj that tend to linearly colocalize, then the following “full” model would be:
log ( E [ y | X ] ) = β 0 + β d d + β B B + β C C + β G G , = β 0 + β d d + β B B + β m i m i + β m j m j + β n i i n i i + β n j j n j j + β n i j n i j ,
(10)
where G is the set of all possible homologous and heterologous interaction variables. Here G = {nii, njj, nij} for two proteins Fi and Fj. The confounder set C = {mi, mj} includes all marginal variables.
The general linear regression with interactions is implemented in R language. The model is available in the R package “HiCglmi” which can be downloaded from the Comprehensive R Archive Network.
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10.1371/journal.pgen.1006766 | Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes | In nature, microbes often need to "decide" which of several available nutrients to utilize, a choice that depends on a cell’s inherent preference and external nutrient levels. While natural environments can have mixtures of different nutrients, phenotypic variation in microbes’ decisions of which nutrient to utilize is poorly studied. Here, we quantified differences in the concentration of glucose and galactose required to induce galactose-responsive (GAL) genes across 36 wild S. cerevisiae strains. Using bulk segregant analysis, we found that a locus containing the galactose sensor GAL3 was associated with differences in GAL signaling in eight different crosses. Using allele replacements, we confirmed that GAL3 is the major driver of GAL induction variation, and that GAL3 allelic variation alone can explain as much as 90% of the variation in GAL induction in a cross. The GAL3 variants we found modulate the diauxic lag, a selectable trait. These results suggest that ecological constraints on the galactose pathway may have led to variation in a single protein, allowing cells to quantitatively tune their response to nutrient changes in the environment.
| In nature, microbes often need to decide which of many potential nutrients to consume. This decision making process is complex, involving both intracellular constraints and the organism’s perception of the environment. To begin to mimic the complexity of natural environments, we grew cells in mixtures of two sugars, glucose and galactose. We find that in mixed environments, the sugar concentration at which cells decides to induce galactose-utilizing (GAL) genes is highly variable in natural isolates of yeast. By analyzing crosses of phenotypically different strains, we identified a locus containing the galactose sensor, a gene that in theory could allow cells to tune their perception of the environment. We confirmed that the galactose sensor can explain upwards of 90% of the variation in the decision to induce GAL genes. Finally, we show that the variation in the galactose sensor can modulate the time required for cells to switch from utilizing glucose to galactose. Our results suggest that signaling pathways can be highly variable across strains and thereby might allow for rapid adaption in fluctuating environments.
| The nutrient composition of natural environments can fluctuate and organisms must induce metabolic pathways that allow them to utilize the available nutrients [1–3]. Recent studies have found that closely related microbes vary in both the types of nutrients they can utilize and the efficiency at which they do so [4,5]. However, most studies have focused on differences in growth in single-nutrient environments (e.g., growth in “pure” glycerol). Natural environments, on the other hand, often contain multiple nutrients that cells need to choose between, and suboptimal nutrient decisions can have severe fitness consequences [6–9]. Hence, it is likely that cells have been selected not only to utilize nutrients efficiently, but to decide which subsets of nutrients to utilize.
Signaling pathways sense which nutrients are present and control the decision of which transcriptional network to activate. The majority of plasticity in gene expression patterns has been linked to changes in transcriptional regulatory networks. While transcription factor binding sites are typically conserved, the location of the binding sites in the genome can rapidly evolve [10]. Chromatin immunoprecipitation followed by sequencing in yeast [11–13], mice and human [14], and flies [15] have shown a surprisingly small conservation in the genes and sites that were bound by transcriptional regulators between species. Even when the regulated genes are conserved, the transcription factors that regulate them can change [16–19]. The development of genomic tools has greatly aided the interspecific comparison of regulatory binding sites.
There are relatively few cases where adaptive changes in signaling networks have been linked to molecular and genetic variation. By contrast, changes in transcription regulatory networks have been easier to identify due to the development of high-throughput genomic and computational approaches. Additionally, studies are often biased towards finding changes in transcriptional regulatory networks based on the phenotypes assayed, i.e. fitness in 'extreme' environments. Still, there are multiple situations where upstream signaling changes must have occurred. For instance, in the galactose-utilization pathway (GAL) in C. albicans, Rtg1p and Rtg3p activate GAL genes while Gal4p is involved in glucose regulation; in S. cerevisiae, Gal4p activates GAL genes, while Rtg1p and Rtg3p are involved in glucose regulation [20]. This implies that the upstream signaling networks that sense and transduce galactose and glucose signals have also changed. Furthermore, duplication and divergence can shape signaling networks. For example in the GAL pathway in yeast, duplication and divergence allowed the sensing and catabolic activity of a single ancestral protein to be separated into two paralogous proteins [21]; this divergence likely had profound consequences for how yeast were able to 'perceive' galactose. Hence, it is likely that cellular decision-making can also evolve, but the degree of variation, its molecular and physiological basis, and the evolutionary timeframe at which it occurs has yet to be resolved.
To begin to address these questions we characterized differences in natural isolates of the budding yeast, S. cerevisiae in the decision to induce the GAL pathway in mixtures of glucose and galactose. In the presence of high concentrations of glucose, the preferred carbon source, yeast cells repress the GAL pathway [22,23]. In the presence of galactose alone, cells activate GAL genes. In mixtures of both glucose and galactose, cells must "decide" whether to induce GAL-associated genes. In such mixed environments, cells show a complex response [24] where the induction of the pathway is dependent on the ratio of glucose and galactose [25]. These observations, combined with the deep molecular understanding in the literature [26,27], make the GAL pathway an excellent model for studying natural variation in cellular decision-making.
Here, we use single-cell measurements to quantify differences in GAL decision-making across closely related natural isolates of S. cerevisiae, followed by bulk segregant analysis and allele replacements to find the genetic determinants of this variation. We found that the glucose concentration needed to induce GAL genes varies ~100-fold across yeast strains. Even though this phenotypic variation is continuous, a large proportion of it can be explained by differences in a single gene, the galactose sensor GAL3. Changing the GAL3 allele produces a measurable difference in the diauxic lag length, a trait that was previously shown to be selectable [8]. These results highlight the fact that cellular decision-making has the potential to be rapidly shaped by selective pressures in the environment.
To enable measurement of the GAL signaling response, we generated a fusion of the GAL1 promoter from S. cerevisiae and yellow fluorescent protein (GAL1pr-YFP) (Fig 1A). GAL1 is the first metabolic gene in the galactose utilization pathway [28] and this promoter has been used by numerous studies as a faithful readout of pathway activity [9,25,29,30]. The reporter construct was integrated into the neutral HO locus [31] in 42 different S. cerevisiae strain backgrounds (S1 Fig) [32,33]. These 42 strains span a range of phylogenetic and ecological diversity [32,33]. Six of these strains either did not grow in galactose, likely due to inactivation of the pathway [4], and thus were not characterized further. We focused on determining the GAL response phenotype of the remaining 36 strains (S1 Table).
To survey the natural variation in the inducibility of GAL genes in mixtures of glucose and galactose, we measured the GAL reporter response in a titration of glucose concentrations from 2% to 0.004% w/v on a background of constant 0.25% galactose (Fig 1B, Materials and methods). Cells were first pre-grown for 14–16 hours in 2% raffinose (which does not induce or repress the GAL pathway), and then transferred to glucose + galactose and grown for 8 hours at low densities. We previously showed that this protocol is sufficient for cells to reach steady-state without depleting the carbon sources [25]. Finally, single-cell YFP fluorescence was measured by flow cytometry. To account for well-to-well variability or variability in our glucose titration, each of the 36 query strains were co-cultured with a reference strain, YJM978, containing TDH3pr-mCherry (this constitutive fluorophore allowed us to distinguish the query and reference strains) and GAL1pr-YFP (Materials and methods).
Qualitatively, there were large strain-to-strain differences in the concentration of glucose at which cells induced the GAL pathway (Fig 1, S4 Fig). We also observed bimodal expression in some strains and conditions, a likely consequence of cellular heterogeneity and ultrasensitivity in the GAL circuit [30,34,35]. This complicates quantitative analysis, because a metric such as the mean expression (which is implicit, for example, in a bulk assay) would convolute both the number of cells that are inducing and the expression level of the cells that have 'decided to' induce, two factors that may vary independently in bimodal responses. Hence, to compare the GAL pathway response between natural isolates, we defined a metric, the “decision threshold”, as the concentration at which 50% of cells have greater-than-basal expression of the GAL reporter (Materials and methods). This metric is similar to those used in previous work [29,36], and focuses on when a cell decides to induce a pathway while differentiating it from how strongly a cell responds once induced. The decision threshold is highly reproducible across replicate measurements for all of our natural isolates (S3 Fig).
Quantitatively, the decision threshold varies over a range of 108 ± 0.7-fold glucose concentrations across our strains (Fig 1, S4 Fig). The Hawaiian cactus strain UWOPS87-242.1, was most inducible, with a decision threshold of 0.74±0.2% glucose (mean ± standard error mean), while the clinical isolate, YJM421, was least inducible, with a decision threshold of 0.01±0.01% glucose (mean ± S.E.M.). Half of the strains have decision thresholds within a 8.1-fold range centered at 0.25% glucose (Fig 1C). This glucose concentration corresponds to a galactose+glucose ratio of ~1:1. The distribution of decision thresholds appears continuous; there are significantly more than two distinct decision thresholds given the reproducibility of our measurements (Materials and methods).
Strain differences in decision threshold could be due to differences in sugar signaling, utilization, or both. If sugar utilization is a factor, we expect the decision threshold to be correlated to growth rates in glucose or galactose. We measured the growth rates of the 36 natural isolates during mid-exponential growth in either 0.5% glucose or 0.5% galactose (S5 Fig, [6]). Despite substantial variation in single-sugar growth rates across our strains (0.23-fold in glucose and 0.16-fold in galactose), neither growth in pure galactose or glucose are correlated with the decision threshold (glucose r2 = 0.2, galactose r2 = 0.001). This implies that while both sugar utilization and signaling can vary between strains, evolution has the potential to select these two traits independently.
Previous studies have determined the correlation between genotypic diversity and either phenotypic diversity or ecological niche. For example, analysis of 600 traits in yeast by Warringer et al. identified a correlation between phylogeny and phenotype [4]. These studies can be used to assess whether traits are more likely to be neutral or undergoing selective constraint. To determine if the decision threshold is correlated with phylogeny, we began by comparing the 13 closely related strains of the wine/European clean lineage. Despite the close phylogenetic relationship of these strains, this lineage represents the most phenotypic diversity. The two most phenotypically distinct strains in this lineage, YJM978 and DBVPG1373, have a 48±0.3-fold difference (mean ± S.E.M.). More broadly, we compared the pairwise genetic distances (determined by RAD-SEQ [37]) to pairwise phenotypic distance (Materials and methods). We did not find a significant correlation (r2 = -0.08) between genetic distance and decision threshold. This level of correlation with genetic distance is comparable to that of many other traits [4] (S6 Fig, p-value = 0.17 by ANOVA). Finally, we tested for and found a signification association between ecological niche and decision threshold (S7 Fig, p-value = 2.95e-5 by ANOVA).
To investigate the genetic basis of the observed variation in GAL decision threshold, we performed bulk-segregant analysis using a variant of the X-QTL method (Fig 2A) [38–40]. We crossed eight strains that span the phenotypic and phylogenetic diversity of S. cerevisiae in a round-robin design (Fig 2). This design is known to efficiently sample parental genetic variation and allow downstream linkage analyses to detect loci with a range of effect sizes [38]. Pools of segregants from each cross were grown in a glucose + galactose condition that maximally differentiates the parental phenotypes. The 5% least and 5% most induced cells (“OFF” and “ON” segregant pools) were isolated by fluorescence-activated cell sorting (FACS) and sequenced in bulk to determine the parental allele frequencies in each pool. We used the MULTIPOOL software [41] to determine statistical significance for allele frequency differences between OFF and ON pools across the genome (Materials and methods), and called significant loci as regions where the peak log-odds-ratio was greater than 10 (Fig 2B). This cutoff had a low false-discovery rate in a previous study, and correlated well with allele frequency difference, a proxy for locus effect size, in our data [38] (S8 Fig).
Over all 8 crosses, we found 16 loci where segregant pools differ in allele frequency at LOD > 10 (Fig 2B). One locus centered at 460 kb on chromosome IV (henceforth, “chrIV:460”) was the only locus to exceed the LOD cutoff in all 8 crosses, as well as the most significant locus in each cross (Fig 2B). The 2-LOD support interval for this locus in the YJM978 x BC187 cross, defined as the genomic region where LOD decreases by 2 from its peak, is 10 kb wide and contains six genes (Fig 2C). This includes GAL3, whose product directly binds galactose and positively regulates the GAL pathway [42]. The support interval for chrIV:460 looked similar in other crosses (S2 Table). One other locus, at chrXIV:462, reached LOD > 10 in two crosses; the remaining significant loci were confined to a single cross. We did detect additional loci in multiple crosses using a less stringent cutoff of LOD > 5; however, chrIV:460 remained the only locus significant in all crosses (S8 Fig, S2 Table, Materials and methods).
In principle, a round-robin cross design is expected to detect each locus in more than one cross. The fact that we identified several alleles in only one cross is potentially explained by a lack of statistical power, epistasis, or gene-by-environment effects [38]. Indeed, a potential caveat to the apparent importance of chrIV:460 is that in a pooled segregant analysis, large effect QTLs might mask the presence of smaller-effect genes [43]. Furthermore, low sequencing depth of some of our segregant pools may have limited our power to detect small-effect alleles (Materials and methods). However, even the lowest sequencing depth we obtained (25x) is still sufficiently powered to map alleles with effects as low as 5% of phenotypic variance [44]. More importantly, we performed a complementary analysis of segregants to directly measure the contribution of GAL3 to the phenotypic variance in a cross (see below). Finally, alleles that were identified in only one cross may arise from the different conditions used for sorting each cross (Materials and methods), i.e. gene-by-environment effects. Given its importance, we chose to focus on the chrIV:460 locus for further characterization.
To determine if GAL3 was the causative allele on chrIV:460 with a predictable and quantitative impact on the decision threshold, we replaced the endogenous GAL3 allele of strains YJM978, BC187, and S288C with alleles from eleven natural isolates spanning the observed range of phenotypic variation (Fig 1). Allele replacements were constructed by deleting the 3283bp GAL3 locus, which includes 890 bp upstream, 911 bp downstream, and the 1563 bp GAL3 ORF in haploid parental strains and then replacing the deleted locus with the homologous ~3283bp GAL3 locus from other strains using the CRISPR-Cas9 system [45] (Materials and methods). Replacement of GAL3 alleles in the YJM978 background recapitulated the ~95-fold range of decision threshold of the natural isolates that served as GAL3 allele donors. Additionally, the decision thresholds of allele-replacement and GAL3 donor strains were well-correlated in this background (r2 of 0.58). Similarly, GAL3 alleles in the S288C background had a ~55-fold range and r2 of 0.60; GAL3 alleles in the BC187 background had a ~138-fold range and r2 of 0.63. In total, this confirms the significant impact that the GAL3 locus has on variation in the decision threshold (Figs 1 and 3A–3C, S9 Fig).
While different GAL3 alleles were able to confer a range of phenotypes in a particular strain background, the three strain backgrounds also displayed different decision thresholds for a given GAL3 allele. This suggests that genes other than GAL3 also affect the decision threshold, even for the BC187xYJM978 cross. To assess the magnitude of this background effect, we measured the decision threshold in seven different strain backgrounds where the GAL3 locus has been replaced with an allele from YJM978, S288C, or BC187 (Fig 3D, S10 Fig). Across the seven backgrounds, GAL3YJM978 allele-replacement strains varied in decision threshold over a ~14-fold range, GAL3S288C strains over ~20-fold, and GAL3BC187 strains over ~49-fold (Fig 3), and the correlation (r2) in decision threshold between these allele-replacement strains and their strain background donors was 0.60, 0.28, and 0.12, respectively. These results confirm that strain background strongly influences decision threshold. However, it is also clear that GAL3 allele still has a stronger effect, because both the phenotypic range and correlations to donor strain were lower for strain background than for GAL3 allele. This can also be seen by the fact that the GAL3BC187 and GAL3S288C strains have decision thresholds that are similar to each other but systematically higher than GAL3YJM978, regardless of strain background.
The allele replacements show that GAL3 is a major driver of natural variation in the decision threshold, but also suggests that other genes play a significant role. To quantify the relative contribution of GAL3 allele versus other genes to variation in decision threshold, we analyzed the variance in decision threshold across meiotic segregants from YJM978 x BC187 hybrids with different combinations of GAL3 alleles. This method is relatively insensitive to the metric chosen and potential non-linear relationships between genotype and phenotype. We chose this cross because the GAL3 locus was the only significant locus from BSA, and thus our calculation should yield a rough upper bound on the GAL3 contribution in other strains. We constructed three hybrid strains: 1) a ‘wild-type’ hybrid (YJM978 x BC187), 2) a hybrid with GAL3 only from YJM978 (YJM978 x BC187 gal3Δ::GAL3YJM978) and 3) a hybrid with GAL3 only from BC187 (YJM978 gal3Δ::GAL3BC187 x BC187). The decision threshold of at least 58 meiotic segregants was measured for each hybrid in duplicate (Fig 4, S11 Fig). Consistent with GAL3 having a large effect, we found that converting a single allele in each hybrid greatly reduced the phenotypic variation of the segregant populations.
To quantify the effect of GAL3, we used a variance-partitioning model with additive effects. We assumed that the total variance of each segregant population (VP) can be separated into several contributions: VP = VG + VE + VEG + VD + VI. We assumed no interactions between gene and environment (VEG = 0) and no epistatic interactions (VI = 0). Additionally, there is no dominance as we used haploid strains (VD = 0) and the environmental variability is equal to the measurement noise because the strains are isogenic and are grown in identical environments (VE = ε2). Since we know that GAL3 is a major driver of the decision threshold phenotype, we partitioned VG into two components: the variance due to the background (VBG) and the variance due to GAL3 (VGAL3). Hence the total variance could be simplified to VP = ε2 + VGAL3 + VBG (Fig 4, S11 Fig). By definition, in the allele swap segregants (hybrids 2 and 3) VGAL3 = 0.
Based on this variance-partitioning model (Materials and methods), we can estimate the contribution of the GAL3 allele by dividing VGAL3 with the sum of VGAL3 and VBG or the total genetic variance. We can estimate the VBG by comparing segregants from hybrid 2 and 3 or from the ‘wild-type’ hybrid, which will give us an upper and lower bound of GAL3 allelic contribution. Using the segregant population from hybrid 2 and 3, the GAL3 allele contributes 86% of the genetic variance between YJM978 and BC187. Two segregants from hybrid 1 (‘wild-type’ hybrid) have a decision threshold lower than what we would have expected from segregants of hybrid 2 (S11 Fig). These two strains increase the background variance, which ultimately reduces the effect of GAL3. Using the segregant population from hybrid 1, we estimate that GAL3 explains 67% of the variance between YJM978 and BC187. These two 'outliers' could potentially result from a rare combination of alleles between the strains, implying that we undersampled the distribution from hybrid 2. These calculations suggest that GAL3 could contribute anywhere from 70–90% to the variance of the decision threshold phenotype.
To further explore how polymorphisms in GAL3 might contribute to the decision threshold phenotype, we analyzed the sequences of 55 natural isolates of S. cerevisiae [32,46,47]. We identified 8 synonymous and 19 nonsynonymous polymorphisms in the coding region of GAL3, which represent 26 unique haplotypes (S3 Table). The natural isolates that we assayed (Fig 1) included 21 of these unique haplotypes, where we excluded the haplotypes from the 6 strains that cannot utilize galactose. To determine whether the GAL3 haplotype is predictive of the decision threshold, we tested for and found a significant association between decision threshold and GAL3 haplotype (S12 Fig, p-value = 0.04 by ANOVA). However, strains that share GAL3 haplotypes also tend to share population history (i.e. genomic background) and ecological niche. In particular, YPS163, YPS606, YSP218, and T7 were all isolated from North American oak trees and make up the North American lineage; S288C and FL100 are both mosaic lab strains; YJM978, YJM981, and YJM975, are clinical isolates in the Wine/European lineage. Due to the correlation between phylogeny and GAL3 haplotype, follow-up investigations using a larger and more diverse set of strains are needed to determine the extent to which decision threshold can be determined solely from the GAL3 haplotype.
The GAL3 polymorphisms we observed can in principle affect the expression level, regulation, or function of the protein. Using mutfunc, a database that predicts the consequences of mutations in a protein, we found that 13 of the 19 nonsynonymous SNPs are predicted to affect protein function (S4 Table). This includes nonconservative amino-acid substitutions in the Gal3p dimerization interface and the Gal3-Gal80p interface [48]. Gal3p and Gal80p are both homodimers and the Gal3p-Gal80p interaction, which is crucial to the mechanism of GAL pathway activation, is thought to depend on this homodimerization [49,50]. We are less able to predict the impact of promoter variation, but we found 13 SNPs in the promoter (500 bp upstream of the start codon), none of which were in known transcription factor binding sites (S3 Table). Furthermore, we did not find a significant association between the GAL3 promoter haplotype and decision threshold (S12 Fig, p-value = 0.98 by ANOVA). Follow-up investigations to characterize the effects of each SNP in GAL3 will provide mechanistic insight into how the GAL response can be tuned quantitatively by polymorphisms in a single gene.
To determine if GAL3 or any other genes in the canonical pathway are subject to adaptive evolution, we performed a McDonald-Kreitman analysis [51] using DnaSP [52]. The McDonald-Kreitman test compares intraspecies variation with the divergence between two species. If the ratio of nonsynonymous to synonymous variation between species is equal, there is neutral selection, while any act of natural selection will result in a shift of these two ratios. This test suggests that GAL3, GAL80, and GAL5 are under strong purifying selection (S5 Table). Our analysis is consistent with two studies that analyzed polymorphism and divergence data between S. cerevisiae and S. paradoxus, which suggested that there is strong evidence for purifying selection across the yeast genome [53,54].
We next asked whether variation in GAL3 produces selectable variation in phenotype. Diauxic growth is a classical phenotype observed when cells are grown in two sugars [3]. Cells undergo two phases of growth separated by a period with little growth, known as the “diauxic lag”, during which cells induce the genes required to metabolize the second sugar. Previously, our lab has shown that diauxic lag length varies across natural yeast isolates vary, and that GAL1 transcriptional reporter level before the lag is negatively correlated with lag length [6]. Here, we further show that decision threshold is correlated to GAL reporter expression (S13 Fig), and likely as a result, also negatively correlated with diauxic lag length (Fig 5A). This suggests that changing GAL3 alleles will also change the diauxic lag.
To determine if GAL3 allele affects diauxic lag across our natural isolates, we performed diauxic shift experiments on allele replacement strains representing six GAL3 alleles (I14, YJM421, Y12-WashU, BC187, and S288c) in three strain backgrounds (YJM978, S288C and BC187) (Fig 5B). As expected, simply changing the GAL3 allele in either the YJM978, BC187, or S288C background was sufficient to change the diauxic lag (Fig 5B, S14 Fig). Additionally, GAL3 alleles from short-lag strains S288C, BC187, and I14 (which also have higher decision thresholds) tended to reduce diauxic lag length when introduced into long-lag strain backgrounds YJM978, DBVPG1106, and YJM421, and vice versa. Previously, strains evolved to have an altered diauxic lag in glucose+maltose also had altered lag in glucose+galactose [8]. To determine if the GAL3 alleles we identified had a specific effect on GAL regulation, we also measured diauxic lag in glucose+maltose. This showed that GAL3 allele only affects diauxic lag in glucose+galactose and not in glucose+maltose (Fig 5B, inset).
Genetically and phenotypically diverse natural isolates of yeast have become a powerful system to determine the genetic basis of complex traits. Analyzing natural variation in the well-characterized GAL pathway has the potential to allow us to connect molecular variation, phenotypic variation, and selection. Two recent studies also explored variation in the GAL response across budding yeasts. Peng et al. used combinatorial promoter swaps of GAL regulatory components (GAL2, GAL3, GAL4, GAL80) between S. cerevisiae and S. paradoxus to show that GAL80 promoter variation was responsible for differences in the GAL response [36]. Roop et al. used a combination of promoter and ORF swaps to show that variation in multiple GAL pathway genes underlies regulatory differences between S. cerevisiae and S. bayanus [55]. In our study, we used bulk-segregant linkage mapping across diverse S. cerevisiae strains to find that most of the variation in GAL regulation is caused by polymorphisms in a single gene, GAL3.
Why did each of the three studies identify different genetic loci? A potential explanation is the difference in genetic distance between the strains/species analyzed. We analyzed variation within S. cerevisiae, Peng et al. analyzed variation between the closely related species S. cerevisiae and S. paradoxus [36], and Roop et al. analyzed variation between the more distantly related species S. cerevisiae and S. bayanus [55]. One hypothesis, based on studies of evolution of development, holds that phenotypic changes on short timescales (i.e. between closely related organisms) are more likely to be caused by nonsynonymous coding-sequence mutations [56,57]. These are favored because of their large phenotypic effects, but come at a cost of increased pleiotropy. On a longer timescale, cis-regulatory mutations are enriched, presumably because they are less pleiotropic and allow finely tuned regulation of fitness-enhancing activities [56,57]. Results from Peng et al., Roop et al., and our study are largely inconclusive or weakly inconsistent with pleiotropy being the driving force between the sources of variation. Variation in the GAL response between S. cerevisiae and S. paradoxus is driven by promoter variation in GAL80 [58], while variation between S. cerevisiae and S. bayanus was driven by a combination of promoter and ORF changes [55]. Furthermore, many causative genes were identified between S. cerevisiae and S. bayanus, while a single gene drove most of the variation within S. cerevisiae and between S. cerevisiae and S. paradoxus. Based on genome-wide expression profiles, there is no evidence that a GAL80 or GAL3 variants should be more pleiotropic than simultaneously varying all pathway components [59]. Broader investigations of multiple Saccharomyces species will help clarify the relationship between evolutionary distance and the repertoire of mutations.
There are possibilities other than pleiotropy that could cause the difference in genes identified. The different phenotypes assayed in each study could be controlled by different components in the GAL pathway. However, we believe our assays measure highly correlated underlying traits. Peng et al. supplemented their media with mannose to avoid the confounding effects of carbon limitation at low galactose concentrations [36]. The effect of mannose on galactose utilization has not extensively been studied in S. cerevisiae, but in other systems mannose can be utilized as a preferred carbon source [60]. Therefore, we expect that the decision threshold in mannose and glucose are likely correlated. Roop et al. compared batch growth in a mixture of glucose and galactose a condition that leads to a diauxic lag in S. cerevisiae but not in S. bayanus. While natural variation in diauxic growth could have involved many pathways, we showed previously that glucose-galactose diauxic lag is driven by the timing of GAL pathway induction [6]. Here we extended this by showing that diauxic lag is correlated with the decision threshold (Fig 5A) and primarily modulated by variation in GAL3 (Fig 5B). Overall, our work here and recent findings in the literature suggest that all these traits are highly interconnected.
Is the observed variation in the GAL pathway the result of neutral drift or selection? There are three lines of evidence that suggest the GAL pathway is under selection. First, previous analysis has used the QTL cis-regulatory sign test [61] to argue that the GAL pathway has undergone selection between S. cerevisiae and S. bayanus [55]. Second, we show via the McDonald-Kreitman test that several of the genes in the GAL pathway within S. cerevisiae are significantly enriched for nonsynonymous polymorphisms, and therefore likely under purifying selection (S5 Table). Third, there are at least five genes that affect variability in the GAL response [29,35,36,55]. Given this knowledge we can ask whether the GAL pathway is under selection in a manner similar to the cis-regulatory sign test. Instead of looking for concordant expression changes, we look for enrichment of independent functional mutations in an unexpectedly small subset (i.e. one gene) of multiple possible target genes. Specifically, what is the chance of eight independent alleles of GAL3 being the main driver of variation in all eight of our crosses given a mutational target size of 5 genes (p-value<1e-6, permutation test). While there are caveats with this method, e.g. what is the true number of potential QTN for each gene, the potential mutational target size is probably much larger than five genes. A recent study of deletion mutants found that upwards of 40% of genes in the yeast genome have the potential to influence the GAL response [62]. Together, we believe these lines of evidence support the hypothesis that the GAL pathway is under selection.
The interplay between selection, pleiotropy, and natural variation is further highlighted by experimental evolution studies in yeast [8] and E. coli [63,64]. In mixtures of carbon sources, microbes first consume a preferred nutrient, followed by a “diauxic lag” where cells must induce the genes necessary to metabolize the second, less preferred nutrient [3]. New et al. found that yeast strains evolved in rapid shifts between glucose and maltose also had a shorter diauxic lag in a mixture of the two sugars [8]. Similarly, E. coli passaged in glucose-acetate mixtures evolved into both short-lag and long-lag subpopulations [63,64]. These results show that diauxic lag length is a readily evolvable trait. However, in both previous cases, the evolved phenotypes were due to mutations in global metabolic regulators. For example, New et al. obtained evolved isolates with weakened catabolite repression, via mutations in the glucose-sensing genes HXK2 and STD1, while maltose regulatory genes were unchanged [8]. These mutations are pleiotropic, and thus the evolved strains had shorter diauxic lags in both galactose and maltose. By contrast, we did not find a strong role for general catabolite repression underlying natural variation in GAL regulation, even though the potential mutational target size is large. Instead, our GAL3 allele replacements specifically tune the glucose-galactose diauxic lag and do not affect the glucose-maltose lag (Fig 5B, inset). This raises the possibility that in natural environments, where evolution has had longer to act, mutations that perturb global metabolic regulation (as in STD1 or HXK2) may be more detrimental than mutations that tune a particular sugar preference (as in GAL3). Hence, as predicted, the frequency of pleiotropic mutations may be an important difference between evolution at short versus long timescales [56,57].
A number of labs have analyzed phenotypic variation in response to a range of environmental conditions [4,65,66] and delved into the genetic basis of variation in specific traits such as heat tolerance [67], gene/protein expression [68,69], sporulation efficiency [70–72], colony morphology [73–76], sulfur uptake [77], and carbon regulation [36,55]. The vast majority of these studies used growth or expression level [68] as readout. Collectively these studies have yielded insight into the nature of quantitative traits [78]. But, these readouts can potentially miss the complexities of response to fluctuating environments. For example, cells grown in mixtures of glucose and galactose must choose when to induce GAL genes, a property that varies between natural isolates and is distinct from the growth rate on pure glucose and pure galactose (S5 Fig). Is the decision to induce the GAL pathway similar to other phenotypic traits?
In principle, multiple different pathways and genes can shape natural variation of a trait. A round-robin BSA analysis of MAPK-pathway-mediated stress tolerance in yeast showed that genes both inside and outside the assayed pathway can have large effects on intraspecies phenotypic variation [38]. Previous X-QTL analyses of various traits in yeast have identified a handful of QTLs per trait [38,39,79]. The largest throughput single study found a median of 12 loci for 46 phenotypic traits [80]. While neither previous analysis of the GAL pathway performed a BSA, the number of causative alleles identified through swaps and the total amount of variation explained by these alleles suggests a similar number of genes affect the GAL pathway as other traits [36,55]. Similar to other traits [81], despite the strong correlation between the decision threshold of GAL3 allele-replacements and their corresponding GAL3 donor strains, the genetic background still plays a strong role in phenotypic variation (Fig 3). QTLs outside the GAL pathway might explain why swapping the main regulators of the GAL pathway between S. cerevisiae and S. bayanus was only able to partially interconvert the phenotypes [55]. Taken together, these results suggest the GAL pathway is similar to other quantitative traits. But, taken alone, our BSA of the GAL pathway suggests that the GAL phenotype is a simpler genetic trait than many of the previously analyzed traits. While other studies have found a small number of QTLs drive the majority of variation in a cross, e.g. sporulation efficiency, when these traits are analyzed in a different cross unique QTLs are often found [71]. Our variation stands out in that there appears to be an allelic series of a single gene, GAL3, driving the variation. Similar to the genes whose alleles drive variation in sporulation efficiency, GAL3 is positioned in a 'signal transduction bottleneck' [70]. Unlike sporulation where multiple genes critical for decision making were identified [70], we found variation is driven by a subset, i.e. one, of potential decision making proteins. Taken collectively with the previous analysis of variation in the GAL pathway, an intriguing possibility is this difference might not arise from 'genetic simplicity' of the GAL response. Instead, GAL3 might control the variability in a subset of the phenotype of the GAL response, i.e. the decision threshold, while other members of the GAL pathway might control different aspects of a broader phenotypic response, e.g. diauxic lag. Our future work will directly test this hypothesis.
Genetic interactions, often referred to as epistasis, play a role in many QTL-mapping studies [82,83]. Our study highlights two types of epistasis. First, similar to many other systems [81], the quantitative effect of each GAL3 allele is influenced by the genetic background. While, the directional effect of the GAL3 alleles from S288C, BC187, and YJM978 are largely preserved, in the YJM978 and S288C background, the effects of the allele replacements are diminished and the background dominates the resulting phenotype, which is highly compressed (Fig 3D). This suppression of variation in certain strain backgrounds has been seen in other systems, such as colony morphology [84,85], and can result from the interaction of two or many genes. Second, there appears to be a 'maximum' achievable decision threshold of around 1% glucose in 0.25% galactose. Hence, when GAL3 alleles with decision thresholds above 0.25% glucose in 0.25% galactose are placed into a background with a high decision threshold (e.g. S288C or BC187), the effect of the GAL3 allele appears to saturate. This behavior is phenomenologically reminiscent of epistatic interactions in peaked fitness landscapes where beneficial mutations have diminishing effects [86,87] or the apparent saturating interaction between gene expression and fitness [88]. Further elucidation of these examples of epistasis in the GAL pathway will likely provide new insights into basic principles of quantitative genetics.
In conclusion, while other genes contribute, the repeated and sizeable role of GAL3 in this study stands out compared to other QTL analyses in yeast. Using BSA, we identified the galactose sensor GAL3 as a major driver of this phenotypic variation, accounting for 70–90% of the variation in a single cross. Hence, polymorphisms in a single gene in the canonical GAL pathway are sufficient to create a continuum of natural variation. An intriguing possibility is that in S. cerevisiae, variation in GAL3 may allow strains to vary the diauxic lag in a non-pleiotropic manner. Environments that fluctuate in a 'predicable' manner might be expected to select for a pathway architecture that allow strains to evolve on this fluctuating axis [56]. Further analysis of the GAL pathway should help to elucidate the interplay of molecular variation, phenotypic variation, and selection.
Strains were obtained as described in [6]. Strains used in this study can be found in S1 Table. All strains from the collection and those assayed in Fig 1 were homozygous diploids and prototrophic. An initial set of 42 strains were assayed in a gradient of glucose (2% to 0.004% by two-fold dilution) in a background of 0.25% galactose. Strains W303 and YIIC17-E5 were excluded from downstream analysis due to poor growth in our media conditions. Strain 378604X was also excluded due to a high basal expression phenotype that was an outlier in our collection. All experiments were performed in synthetic minimal medium, which contains 1.7g/L Yeast Nitrogen Base (YNB) (BD Difco) and 5g/L ammonium sulfate (EMD), plus D-glucose (EMD), D-galactose (Sigma), or raffinose (Sigma). Cultures were grown in a humidified incubator (Infors Multitron) at 30°C with rotary shaking at 230rpm (tubes and flasks) or 999rpm (600uL cultures in 1mL 96-well plates).
GAL induction experiments were performed in a 2-fold dilution series of glucose concentration, from 1% to 0.004% w/v, with constant 0.25% galactose. 2% glucose and 2% galactose conditions were also included with each glucose titration experiment. To assess and control for well-to-well variation, experiments were performed as a co-culture of a “query” strain to be phenotyped and a “reference” strain that was always SLYB93 (natural isolate YJM978 with constitutive mCherry segmentation marker).
To start an experiment, cells were struck onto YPD agar from -80C glycerol stocks, grown to colonies, and then inoculated from colony into YPD liquid and cultured for 16–24 hours. Query and reference strains were then co-innoculated at a 9:1 ratio by volume in a dilution series (1:200 to 1:6400) in S + 2% raffinose medium. The raffinose outgrowths were incubated for 14–16 hours, and then their optical density (OD600) was measured on a plate reader (PerkinElmer Envision). One outgrowth culture with OD600 closest to 0.1 was selected for each strain, and then washed once in S (0.17% Yeast Nitrogen Base + 0.5% Ammonium Sulfate). Washed cells were diluted 1:200 into glucose + galactose gradients in 96-well plates (500uL cultures in each well) and incubated for 8 hours. Then, cells were processed by washing twice in Tris-EDTA pH 8.0 (TE) and resuspended in TE + 0.1% sodium azide before transferring to a shallow microtiter plate (CELLTREAT) for measurement.
Flow cytometry was performed using a Stratedigm S1000EX with A700 automated plate handling system. Data analysis was performed using custom MATLAB scripts, including Flow-Cytometry-Toolkit (https://github.com/springerlab/Flow-Cytometry-Toolkit, https://github.com/springerlab/Induction-Gradient-Toolkit). All experiments were co-cultured with a reference strain and were manually segmented using a fluorescent channel (mCherry or BFP) and side scatter channel (SSC). GAL1pr-YFP expression for each segmented population was collected and the induced fraction for each concentration of sugars was computed as shown previously in Escalante et al. [25]. The decision threshold for each glucose titration was calculated from the induced fraction of the ten sugar concentrations. The decision threshold was reported as the glucose concentration were 50% of the cells were induced.
To prepare parent strains for crossing and sporulation, diploid natural isolates bearing the hoΔ::GAL1pr-YFP-hphNT1 reporter cassette were sporulated and random spores were isolated. Mating type was determined by a test cross. We then introduced a constitutive fluorescent marker in tandem with the GAL reporter, to obtain MATa; hoΔ::GAL1pr-YFP-mTagBFP2-kanMX4 or MATα; hoΔ::GAL1pr-YFP-mCherry-natMX4 parent strains. To the MATa parent we also introduced a pRS413-derived plasmid bearing STE2pr-AUR1-C and hphNT1. This plasmid is maintained by hygromycin selection but also allows selection for MATa cells by Aureobasidin A [89]. This plasmid design is inspired by a similar mating-type selection plasmid used in a recent study [38].
To generate segregant pools, we prepared a diploid hybrid and sporulated it as follows. We crossed a parent with BFP-kanMX with the mating type selection plasmid to a parent with mCherry-natMX4 and isolated a G418RNatRHygR diploid hybrid with the plasmid. We sporulated the hybrid by culturing it to saturation in YPD, diluting 1:10 in YP+2% potassium acetate and incubating at 30C for 8 hours. Cell were then washed and resuspended in 2% potassium acetate and incubated at 30C until >20% of cells were tetrads, or about 72 hours. We incubated ~5x106 tetrads in 100uL water with 50U of zymolyase 100T (Zymo Research) for 5 hours at 30C, and then resuspended tetrads in 1mL of 1.5% NP-40 and sonicated for 10 seconds at power setting 3 on a probe sonicator (Fisher Scientific Model 550).
To reduce the size of recombination blocks and improve the resolution of linkage mapping [90], we then performed the following “intercross” protocol 4 times: 1) Spores were isolated using the Sony SH800 Cell Sorter selecting for 4x106 BFP+ or mCherry+ (but not +/+ or -/-). 2) The sorted cells were grown into 100uL YPD + 40ug/mL tetracycline. 3) Cells were incubated for 16 hours at 30C without shaking. 4) 5mL of YPD + 200ug/mL G418 + 100ug/mL ClonNat + 200ug/mL Hygromycin B was added and cells were incubated for 48 hours at 30C with shaking. 5) Cultures were sporulated and spores were isolated by zymolyase treatment and sonication as described above. Steps 1–5 were repeated 4 times, resulting in a sonicated suspension of spores that had undergone 5 generations of meiosis since the parents. These spores were resuspended in YPD + 0.5ug/mL AbA and incubated at 30C for 16 hours to select for MATa haploids. This haploid culture was split to create a frozen glycerol stock, and was used as the inoculum for phenotypic isolation by FACS (as described above).
To sort segregant pools for bulk genotyping, the intercrossed MATa-selected segregants were inoculated from a saturated YPD culture into S + 2% raffinose + AbA at dilutions of 1:200, 1:400, 1:800, and 1:1600, and incubated at 30C for 16–24 hours. The outgrowth culture with OD600 closest to 0.1 was selected for each strain, washed once in S, and diluted 1:200 into S + 0.25% glucose + 0.25% galactose + AbA. The glucose-galactose culture was incubated at 30C for 8 hours, and then a Sony SH800 sorter was used to isolate pools of 30,000 cells with the 5% lowest (“OFF”) and highest (“ON”) YFP expression, among cells whose Back Scatter (BSC) signal was between 105 and 3x105. This BSC gate was used to minimize the effects of cell size on expression level as cell with similar BSC have similar cell size. The sorted cells were resuspended in YPD + AbA and incubated at 30C until saturation, about 16–24 hours. An aliquot of this culture was saved for -80C glycerol stocks, and another was used to prepare sequencing libraries.
To sequence the segregant pools, genomic DNA was extracted from 0.5mL of saturated YPD culture of each segregant pool using the PureLink Pro 96 kit (Thermo Fisher K182104A). From these genomic preps, sequencing libraries were made using Nextera reagents (Illumina FC-121-1030) following a low-volume protocol [91]. The input DNA concentration was adjusted so that resulting libraries had mean fragment sizes of 200-300bp as measured on a BioAnalyzer. Libraries were multiplexed and sequenced in an Illumina NextSeq flow cell.
Non-S288C parental genomes for the bulk segregant analysis were obtained from the literature: I14 from [38]; BC187, YJM978, DBVPG1106, and Y12 from [92]; YPS606 from [93]. We sequenced our parent strains at ~1x depth and verified their SNP patterns against these datasets. We initially obtained an unpublished sequence for YJM421 from the NCBI Sequencing Read Archive (accessions SRR097627, SRR096491), but this did not match our strain (it appeared similar to YJM326 instead). A RAD-seq SNP profile of YJM421 [37] partially matched our YJM421, but the RAD-seq data displayed heterozygosity. Because we crossed our YJM421 strain to both I14 and DBVPG1106, for which we have high-quality genomes, we could do the linkage mapping given only one parental genome. However, we confirmed that the YJM421 parent used for both crosses were the same strain, by looking at SNPs in the segregant pools of the two crosses that did not match the other parent. Our current hypothesis is that the YJM421 isolate we obtained from the Fay lab (and which was genotyped by RAD-seq in Cromie et al. [37]) was a heterozygous diploid, a haploid spore of which we used as the parent in our round robin cross.
To perform linkage analysis, we aligned raw reads for parent strains (from the literature) and segregant pools (from our experiments) to the sacCer3 (S288C) reference genome using BWA-MEM on the Harvard Medical School Orchestra cluster (http://rc.hms.harvard.edu, see Orchestra High Performance Compute Cluster note below). We identified SNPs between cross parents and determined allele counts at each SNP in segregant pools using samtools mpileup and bcftools call -c. Using custom MATLAB code, we removed SNPs where read depth was less than 2 or higher than 1000 to avoid alignment artifacts. After filtering, average sequencing depth per pool ranged from 25x to 71x, with a median of 48x.
To calculate LOD scores for allele frequency differences between OFF and ON pools, we input filtered allele counts to the mp_inference.py script (MULTIPOOL Version 0.10.2; [41]) with the options -m contrast -r 100 -c 2200 -n 1000, following previous practice [38]. A value of n = 1000 likely underestimates our segregant pool size and will lead to conservative LOD estimates. An exception to this is the I14xYJM421 cross, which displayed unusually low spore viability (~20%), possibly due to a Dobzhansky-Muller incompatibility [94]. Thus we used n = 200 for this cross.
We defined significant loci as LOD peaks where LOD > 10 (Fig 2B). Previous bulk segregant analyses using MULTIPOOL used a less stringent cutoff of LOD > 5 [38,39]. This corresponded to a false discovery rate of 5% in one study [39], but led to a much higher number of unreplicated locus calls in another study [38]. Given that our segregant pools underwent multiple rounds of meiosis (and potentially diversity-reducing selection), we chose to use the more conservative LOD > 10. The choice of LOD does not affect our main conclusions about GAL3; even the lowest LOD for the chrIV:460 locus (in YJM978 x Y12) is 24 and thus highly significant (S2 Table). Besides this locus, other moderately significant loci may still be biologically relevant, and so we provide a list of LOD peaks and their corresponding support intervals at LOD > 5 (S2 Table). We clustered these peaks as a single locus if they occur within 20kb of each other from different crosses (S8 Fig, S2 Table).
Allele replacement strains were constructed by knocking out GAL3 (-890bp from start to +911bp from the stop) with KANMX4 followed by CRISPR/Cas9-mediated markerless integration of the heterologous allele. Initially, strains were prepared by introducing Cas9 on a CEN/ARS plasmid (SLVF11); this plasmid is derived from a previous one [95], but the auxotrophic URA3 marker was replaced with AUR1-C to allow Aureobasidin A selection on prototrophic natural isolates. Then, a donor DNA, a guide RNA insert, and a guide RNA backbone were simultaneously transformed into the strain [45]. The donor DNA contained the new allele (PCR amplified from the desired natural isolate genome), its flanking sequences, and an additional 40bp of homology to target it to the correct genomic locus. The guide RNA insert was a linear DNA containing a SNR52 promoter driving a guide RNA gene containing a 20bp CRISPR/Cas recognition sequence linked to a crRNA scaffold sequence, plus 40bp of flanking homology on both ends to a guide RNA backbone. The guide RNA backbone was a 2u plasmid containing natMX4 (pRS420). This was linearized by NotI + XhoI digestion before transformation. Allele re-integration transformations were plated on cloNAT to select for in vivo assembly of the guide RNA into a maintainable plasmid, and Aureobasidin A to select for presence of Cas9. Successful re-integration was verified by colony PCR and Sanger sequencing was performed on a subset of strains and on all donor DNAs to verify the sequence of allelic variants.
To estimate the effect of GAL3 allele on decision threshold, we performed a variance partitioning analysis on decision thresholds of segregants from each of 3 hybrids (Fig 4). Two heterozygous hybrids with homozygous GAL3 alleles were constructed by mating CRISPR/Cas9 generated allele replacement strains (YJM978 GAL3BC187 or BC187 GAL3YJM978) to either BC187 or YJM978 wildtype haploids. A “wildtype” hybrid heterozygous at all loci (BC187 x YM978) was also analyzed. These 3 hybrids were sporulated as described above, and the resulting segregants phenotyped for decision threshold in duplicate.
We assumed a model VP = VGAL3 + VBG + ε2, where phenotypic variance VP is a sum of contributions from the variance due to GAL3 VGAL3, the variance due to strain background VBG, and measurement error ε2. We estimated measurement error by assuming a Gaussian form N(μ,σ) and fitting it to the differences between replicate measurements across all segregants. The variance in inter-replicate differences should be twice the measurement variance, and thus ε=σ2. To filter out poor-quality data, we removed segregants where half the inter-replicate difference was greater than 1.5 (S3 Fig). We calculated the mean of each allele population (μa or μ−a), where the two allelic variants of GAL3 are denoted by a and–a. To estimate the effect of the GAL3 allele EGAL3, we divided the difference of the mean of the two populations by 2. The variance due to GAL3 is the square of EGAL3.
Finally, the phenotypic variance of a segregant population (VP) is composed of the measurement noise (ε2) and the genotypic variance (VG). VP was calculated for the YJM978 x BC187 segregants and for both of the hybrid conversion segregants. Since GAL3 is a major driver of the decision threshold phenotype, VG was partitioned into two components: the contribution to variance of the background (VBG) and the contribution to variance of GAL3 (VGAL3). The background variance was estimated by subtracting ε2 and GAL3 variance from the variance of the segregant population. The GAL3 contribution (VGAL3VG) was reported as the ratio of the variance in GAL3 and the genotypic variance (VG).
Growth curves were obtained as described in Wang et al. [6]. In short, growth curves were obtained by manually measuring the absorbance at 600 nm (OD600) on a plate reader (PerkinElmer EnVision) for each plate approximately every 15 min for up to 20 h in a room maintained at 30°C and 75% humidity. Strains to be assayed were pinned into 500 μl of liquid YPD and incubated for 16 h, then diluted 1:200 into 500 μl of synthetic minimal medium + 0.5% glucose and grown for 6–8 h, and finally diluted 1:150 into synthetic minimal medium + 0.25% glucose + 0.25% galactose or synthetic minimal medium + 0.25% glucose + 0.25% maltose for growth curve measurements. The final inoculation was performed into two different plates (with 2 replicates per plate); these replicate growth curves were nearly indistinguishable for all strains. Analysis of growth curve data was performed in MATLAB using custom-written code [6].
To obtain growth rates in glucose or galactose, additional growth curves were performed as above, except the final culture medium contained 0.5% glucose alone or 0.5% galactose alone. The exponential growth rate was extracted from these data as the mean growth rate between when OD600 = 2−6 and OD600 = 2−4 (or, equivalently, when culture density was approximately 1/16 and 1/4 of saturation, respectively).
Sequences for the SGRP strains were downloaded from SGRP website. Sequences for the strains in the Liti library [96] were downloaded from https://www.sanger.ac.uk/research/projects/genomeinformatics/sgrp.html. For the remaining strains with multiple distinct isolates reporter in the literature, a single genetic distance that matched the strain in our collection was selected. Using these sequencing databases, we extracted the GAL3 region and aligned sequences using MUSCLE (S3 Table, S12 Fig). Based on the identified SNPs, we used mutfunc (http://mutfunc.com/) to predict the consequences of nonsynonymous SNPs in the GAL3 variants (S4 Table). These sequences were used for the McDonald Kreitman analysis using DnaSP [52] (S5 Table). A neighbor-joining phylogenetic tree was generated using the seqneighjoin function on MATLAB (S7 Fig) and genetic distances [37].
Portions of this research were conducted on the Orchestra High Performance Compute Cluster at Harvard Medical School. This NIH supported shared facility consists of thousands of processing cores and terabytes of associated storage and is partially provided through grant NCRR 1S10RR028832-01. See http://rc.hms.harvard.edu for more information.
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10.1371/journal.pcbi.1003110 | A Design Principle of Group-level Decision Making in Cell Populations | Populations of cells often switch states as a group to cope with environmental changes such as nutrient availability and cell density. Although the gene circuits that underlie the switches are well understood at the level of single cells, the ways in which such circuits work in concert among many cells to support group-level switches are not fully explored. Experimental studies of microbial quorum sensing show that group-level changes in cellular states occur in either a graded or an all-or-none fashion. Here, we show through numerical simulations and mathematical analysis that these behaviors generally originate from two distinct forms of bistability. The choice of bistability is uniquely determined by a dimensionless parameter that compares the synthesis and the transport of the inducing molecules. The role of the parameter is universal, such that it not only applies to the autoinducing circuits typically found in bacteria but also to the more complex gene circuits involved in transmembrane receptor signaling. Furthermore, in gene circuits with negative feedback, the same dimensionless parameter determines the coherence of group-level transitions from quiescence to a rhythmic state. The set of biochemical parameters in bacterial quorum-sensing circuits appear to be tuned so that the cells can use either type of transition. The design principle identified here serves as the basis for the analysis and control of cellular collective decision making.
| Although the genetic circuits underlying state switching at the single-cell level are well understood, how such circuits work in concert among many cells to support the population-level switching of cellular behaviors is not fully explored. Experiments using microbial signaling systems show that group-level changes in cellular state occur in either a graded or an all-or-none fashion. We show that the type of group-level decision making used by populations is uniquely determined by a single dimensionless parameter that compares the quorum-signaling molecules accumulated within the cells with those secreted by the population. Bacterial quorum-sensing circuits appear to be tuned so that the cells can convert between the two types of decision-making in response to slight biochemical variations. Furthermore, the role of the parameter is universal such that it not only applies to the autoinducing circuits typically found in bacteria but also to the more complex gene circuits involved in transmembrane receptor signaling and negative feedback. The design principle that we describe thus serves as the basis for the analysis and control of collective cellular decision making in general.
| Cells often switch their state autonomously, either individually or as a group [1]–[3]. The cell-autonomous switch is exemplified by the classical molecular switch in Bacteriophage Lambda: the cI and cro genes mutually repress one another and thus operate as a genetic toggle switch between the lytic and lysogenic cycles [4]. A common network topology [5], [6] that realizes either the positive autoregulation of inducing signals [7] or the mutual repression of inhibitory signals [8] is generally responsible for the all-or-none responses of individual cells. Bistable behavior at the single-cell level does not, however, necessarily translate into an all-or-none response at the group-level. Because of stochasticity in gene expression [9] and variability among cells in their sensitivity to environmental change [10], [11], the switch is graded at the population level [12], [13]; i.e., cells in the ON state coexist with cells in the OFF state [1]–[3], [7], [8] (Fig. 1A). There are many cases; e.g., bacterial quorum sensing (QS) [14], [15]; however, where the transition is abrupt and occurs in an all-or-none fashion even at the group level (Fig. 1B). In QS, cells secrete inducing molecules that signal neighboring cells to synthesize and secrete more of the same inducing molecules; thus, global positive feedback is realized (Fig. 1C). The autoinducer Acyl-homoserine lactone (AHL) is an inducing molecule [16]–[19] in populations of the luminescent symbiotic bacterium Vibrio fischeri and of other bacteria species [14], [15]. In animal development, a collective state change within a differentiating tissue is referred to as ‘community effect’ [20], [21]. Generally, a group-level transition between cellular states manifests itself via a combination of cell-autonomous and group-level mechanisms; these two modes of transition, however, have not been clearly distinguished from one another thus far.
In QS, both the graded and the all-or-none types of transitions are observed at the group level [22]–[27]. In a graded transition, cells in the ON and OFF states coexist within a population; thus, the state of the cells follows a bimodal distribution. Such a behavior is observed in populations of the free-living bacterium V. harveyi, the virulent pathogen Salmonella typhimurium, and Listeria monocytogenes; in these populations, the percentage of cells in the ON state increases gradually as cell density increases or other environmental factors change [25], [26], [28]. Similar behavior occurs in engineered E. coli that harbors synthetic luxI and luxR genes encoding AHL synthetase and a transcriptional activator [22], [23]. When the regulation of the lux genes is synthetically rewired, however, the entire population synchronously switches its pattern of gene expression when cell density reaches a certain threshold [24]. Such sharp population-level transitions underlie important biological phenomena such as bioluminescence and virulence in a wide range of species from V. fischeri to the opportunistic pathogen Pseudomonas aeruginosa (see Fig. 1 in [29]; Fig. 2 in [30]; Fig. 2 in [31]). Interestingly, when V. fischeri cells are isolated in a chamber while continuously being cleared of AHL by dilution, their response to exogenously applied AHL is heterogeneous [10]. Thus when making the all-or-none switch as a population, cell-cell variability must be somehow suppressed by cell-cell communication. Because most existing mathematical models of QS are formulated either entirely at the single-cell [10], [32], [33] or the population level [16]–[19], the relationship between the graded and the all-or-none transitions and the underlying bistability of cellular states have not received a full theoretical treatment.
To clarify the mechanisms of group-level transitions, we numerically and analytically studied general classes of mathematical models that describe QS across two levels of organization; i.e. single-cell and cellular-ensemble. We show that graded transitions occur when the intracellular positive feedback, mediated by the synthesis and accumulation of autoinducer molecules within the cells, alone can support bistability. Conversely, we show that all-or-none transitions occur when the secreted signal within the population serves predominantly to realize bistability at the group-level. We identify a unique dimensionless parameter, representing the respective relative contributions to the regulatory feedback of the intracellular and extracellular autoinducer molecules, that determine the type of transition and the underlying bistability. We find that in many bacterial species, this parameter is near the optimal value for allowing the bacteria to select between the two transition types depending on environmental conditions. We explored this common design principle in a basic circuit with negative feedback. The types of cells harboring such circuits range from particle-based chemical reactions [34] to engineered E. coli [35], [36], yeasts [37], and the social amoeba Dictyostelium discoideum [38]. These systems are known to exhibit density-dependent transitions from quiescence to an oscillatory state [39]. We show that the same unique parameter determines whether the transition from quiescence to oscillation occurs gradually or synchronously.
To analyze group-level transitions at both the single-cell level and the group level, we studied three basic circuit topologies (Fig. 1C–E). For simple autoinduction (Fig. 1C) and a dual positive-feedback circuit (Fig. 1D), we employed a previously described quantitative model [27]. First, for the simple autoinduction circuit (Fig. 1C), when the extracellular and intracellular synthesis and degradation of the autoinducer are rapid compared with changes in the synthase concentration, the autoinducer concentration can be approximated by the steady-state. Accordingly, the equations can be simplified to (1)(See Supporting Information Text S1 1.1 for a detailed derivation), where xi, , si, and ki are the normalized intracellular concentration of the synthetase, the population mean concentration of the synthetase, the normalized intracellular concentration of the autoinducer and the normalized the threshold concentration for the induction (; See Table 1 for representative examples in bacterial QS). ρV represents the volume fraction ρV = NcellVcell/Vtot, where Ncell, Vcell, and Vtot denote the number of cells, volume of a single cell and the total volume including both intracellular and extracellular space, respectively. Based on experimental data (Table S1), Hill coefficient is set to 2 which supports bistability. When the Hill coefficient is equal to 1, bistability does not exist (Text S1 2.1). The amplification factor λ determines the ratio of basal to maximal rate of QS molecule synthesis (Eq. S1-4 in Text S1) when si is above a certain concentration ki (Eq. S1-3 in Text S1) [14], [15], [18]. The dimensionless parameter ε is given by(2)(See Eq. S1-13 in Text S1 for derivation), where , and γex and csec denote the degradation and secretion rates, respectively. ε essentially compares synthesized autoinducer concentration with the threshold (Eq. S1-14 in Text S1). In the present study, intracellular degradation of the autoinducer was not taken into account. This approximation holds as long as the intracellular degradation rate is much smaller than csec. The overall results are not affected by this assumption, because ε is independent of the ratio between γin and γex (see Text S1 1.5 for a detailed calculation). The advantage of this simplification is that, besides the volume fraction of cell density , the model is left with only two parameters, ε and λ, both of which can be experimentally measured (Tables S1 and S6) and manipulated [27], [40] (Table S2).
In this model and those described below, the value of ki is randomly distributed [11] around the mean to account for cell-cell variability in the response to exogenously applied autoinducer [10] (Fig. S1A–B). In addition, to reflect heterogeneous gene expression within the population [9], [25], the model assumes intrinsic stochasticity in the rate of synthetase production, which follows Gaussian white noise ηi [41], [42]. The molecules are passively transported into and out of the cells at the rate csec, and degraded extracellularly at the rate γex (Eqs. S1-1 and S1-2 in Text S1) [43]. Here, we assumed that the autoinducer molecules diffuse rapidly so that they are well mixed in the extracellular space. The extracellular concentration of the autoinducer is proportional to the cell density ρV (Eq. S1-1 in Text S1) [17] and can be considered almost uniform in space for systems smaller than 1 mm (Text S1 1.6).
The second model that we shall study here describes a circuit with an additional intracellular positive feedback (Fig. 1D): (3)where zi is the normalized intracellular concentration of a transcriptional activator (Fig. 1D; Table 1). In addition to the three parameters already described; i.e. ρV, λ, and ε (Eq. 2; Eq. S1-18 in Text S1); m and n denote the Hill coefficients of binding between the signal molecule and the transcriptional activator and between the transcriptional activator and its target promoter, respectively (see Text S1 1.2 for a derivation). Such dual positive-feedback loops are common in bacterial QS [15], [27] (Table 1). In the lux operon of V. fischeri, the autoinducer AHL binds to the transcriptional regulator LuxR, forming a complex that binds to a promoter of both the luxR and the luxI genes, which encode the LuxR and the synthetase, respectively [14], [15], [18], [27]. Because LuxR cannot be exported outside the cell, the LuxR feedback mechanism only works intracellularly; the autoinduction mediated by the LuxI feedback, however, works both intracellularly and extracellularly.
Our third model describes a basic circuit with positive and negative feedback loops (Fig. 1E). The model is given by(4)where yi denotes the normalized concentration of an inhibitor, and ρ = ρV/(ρV+γex/csec) (see Text S1 1.3 for a derivation and Table 2 for representative examples). Here, the mean threshold corresponds to the inverse of the order parameter ε (Eq. 2; Eq. S1-24 in Text S1). This type of circuit has been previously modeled and implemented in a synthetic circuit where AHL activates the production of its own synthetase, LuxI (X), and of lactonase AiiA (Y); and AiiA degrades AHL [35].
Numerical integration of Eqs. 1, 3, and 4 was performed using the fourth-order Runge-Kutta algorithm. All programs were written using the C programming language. The cell-density dependence was examined by decreasing the volume of extracellular space exponentially while keeping the number of cells at 1,000 (Fig. 2). Accordingly, cell density ρV and extracellular autoinducer concentration increase exponentially thereby effectively implementing the growth phase of a population. The rate of volume decrease is set to 1/40 of the degradation rate of the synthetase (Eqs. 1 and S1-1 in Text S1) for the phase diagrams (Figs. 3A and S3.). As long as this ratio is small, the present results do not depend on the exact rate of volume reduction, as will be described in the Results section. Except where we study the phase diagrams and the time course of the negative-and-positive-feedback circuit (Fig. 4A–B), plots were obtained at the steady state. The initial concentrations of xi, yi, zi, and si were set randomly between 0 and 0.01.
To examine cell density dependence, we first defined the threshold cell density for each cell. For the autoinduction and dual positive-feedback circuit (Eqs. 1 and 3), the threshold density for the i-th cell ρi was determined by the volume fraction of density ρV at which the normalized concentration xi () took the half maximum xi = 0.5. In case of the positive-and-negative-feedback circuit (Eq. 4), the threshold density ρi was defined by the density at which the temporal evolution of xi switched from quiescence to oscillations. As a measure of cell-cell variability at the onset of the transition, the standard deviation of ρi normalized by its population mean was denoted CVρ (coefficient of variation of ρi). Likewise, CVk was defined by the standard deviation of ki normalized by the mean . ki follows lognormal distribution with CVk = 0.5.
Following the formulation of a chemical Langevin equation [44], the variance in noise |ηi|2 at the steady states of Eqs. 1, 3, and 4 is given by |ηi|2 = 2 xi/γX NON (Eq. S1-11 in Text S1), where NON and γX are the number of synthetase molecules within a cell that is in the ON state and the degradation rate of the synthetase, respectively (Eqs. S1-12 and S1-3 in Text S1). We set γXNon to 140. γX = 1 corresponds to Non = 140 molecules, or a 60 nM synthetase concentration with cell volume of approximately 3.6×10−15 L [45].
First, we will numerically study the cell-state transitions that depend on the cell density ρV in the simple autoinduction circuit (Eq. 1). The key parameter that distinguishes the transitions at the group level is the concentration of intracellular autoinducing signal (si = γex xi/csec+ in Eqs. 1 and S1-6 in Text S1): γex xi/csec represents the intracellular feedback on signal synthesis caused by the cell itself, and measures the strength of the feedback mediated by the secreted signal. When the threshold concentration ki and the secretion rate of autoinducer molecules csec are low, the secretion-mediated feedback is relatively weak, so the induction depends mainly on the feedback from intracellular synthesis. In this case, the cells turn themselves on individually, provided that the concentration of the autoinducer accumulated inside the cell (si∼γex xi/csec) is higher than the threshold ki. The switch gives rise to two stable states that are each self-enforcing. The OFF state at xi∼λ−1 keeps cells in a state of low autoinducer synthesis (upper panel of Fig. 2A). Likewise, once the cells are in the ON state (xi∼1), the high rate of autoinducer synthesis will keep them in the ON state (upper panel of Fig. 2A). Because the two stable states do not require secreted signal from other cells (see coexistence of ON and OFF cells in Fig. S1A), we shall refer to this as “cell-autonomous bistability”. The word “group-level” is a relative term; and more accurately, it is the volume fraction of cell density, rather than the absolute number of cells, that essentially controls the level of extracellular autoinducer molecules. Indeed, a single cell confined to a small chamber has been shown to turn on its QS genes [46], [47].
The signature of group-level transitions that are driven by the single-cell-level switch is the coexistence of cells in the ON state and cells in the OFF state within the population (low density in the upper panel of Fig. 2A). Here, the percentage of cells in the ON state gradually increases as a function of the cell density (lower panel of Fig. 2A). This is clearly demonstrated by the bimodal distribution of cellular states xi when the cell density ρV is in the intermediate range (Fig. 2C). Individual cells switch in an all-or-none manner, however, at different densities (upper panel of Fig. 2A; see also Fig. S1C for the coexistence in the nullclines). Thus, at the population level the transition becomes graded. Recent experimental observations of bimodal distributions of cell states and graded group-level transitions [22], [23], [25], [26] suggest that, in many bacterial QS systems, the contribution of cell-cell communication is rather weak and the switch is caused by cell-autonomous bistability.
In contrast, at high ki and csec the amount of inducing molecules secreted extracellularly ( in Eq. 1) becomes profound. When the secretion-mediated feedback becomes negligible in the isolated condition, because of the continuous clearance of extracellular signals via degradation or dilution [10], [38], the cells cannot exhibit bistability (Fig. S1B). Fig. 2B shows that above a cell-density threshold, all cells change their state simultaneously. Although the ON state (xi∼1) and the OFF state (xi∼λ−1) are identical to the states that appear in the case of cell-autonomous bistability, the entire population must now either be ON or OFF (Fig. 2B and 2D). The two states cannot coexist within the population (see also Fig. S1D for a nullcline analysis). This group-level all-or-none transition is mediated by the feedback from the secreted autoinducer molecules in the extracellular space ( in Eq. 1). Because the concentration of the synthesized signal within the ON-state cells (γex/csec) is below the threshold ki, the ON state cannot be self-sustaining unless a sufficient amount of signaling molecules are synthesized and secreted by other cells. To distinguish this form of bistability from the cell-autonomous bistability described above, we shall hereafter refer to it as “group-level bistability”. Frequent experimental observations of such all-or-none transitions in many bacterial systems [14], [15], [30], [31] suggest that the occurrences of group-level bistability are widespread.
Graded transitions and all-or-none transitions both involve a combined action by the cells. Although cell-autonomous bistability underlies the graded transition, the switch is nonetheless density dependent, and there is a cooperative effect within the group of cells. Whether a cell can switch its state depends on its position in the state space relative to the basin of attraction (Fig. S1C, low density). By plotting the synthetase production rate dxi/dt as a function of the synthetase concentration xi, we see that the range of initial concentrations that converge to the ON state expands as the density is increased (Fig. S1E). Because the concentration of the autoinducer in the OFF state (si∼γexλ−1/csec+ρV λ−1; xi∼λ−1) is close to the threshold ki, the probability that a cell switches from the OFF state to the ON state increases with the density ρV. On the other hand, cell-cell communication is absolutely essential for group-level bistability. At intermediate cell densities, the concentration of secreted autoinducer () exceeds the average threshold (i.e., , Fig. 2B), so cells that have not yet switched are forced to do so (Fig. S1F). Likewise, when the synthesized concentration is insufficient to sustain the cells in the ON state, the whole population converges to the OFF state at the steady state. Thus, although there is a difference of degree, both types of bistability depend on the interactions among the cells within the population.
To help identify the design principle underlying graded and all-or-none transitions, we analytically derived a unique dimensionless parameter ε (Eq. 2) that determines the nature of the bistability (Text S1 2.1 and 2.2). Essentially, ε compares the magnitude of the inducing signal that is synthesized intracellularly (γex/csec) with the response threshold (Eq. S1-13 in Text S1). A solid line in Fig. 3A indicates the analytically obtained boundary (ε∼2) in the parameter space (ε, λ) that separates autonomous bistability from group-level bistability (Text S1 2.2). The border matches well with the results of numerical simulations (Fig. S2A). For ε>2, even isolated cells can take two stable fixed points (Eq. S2-15 in Text S1), which indicates cell-autonomous bistability (closed circles in Fig. S1C). For ε<2, the autonomous bistability disappears; instead, the whole population can only be at one of the two stable fixed points (red lines in Fig. 2B upper panel and closed circles in Fig. S1D). When the group average of the synthetase concentration is greater than the value of the unstable fixed point (yellow line in Fig. 2B and open circle Fig. S1D), the entire population immediately jumps to the ON state. Otherwise, all of the cells converge to the OFF state. Thus, the value of ε determines the origin of the bistability and the form of the resulting group-level transition.
When ε is increased above (Eq. S2-15 in Text S1), the intracellular signal concentration always exceeds the threshold regardless of the extracellular autoinducer concentration, so the cells are constitutively in the ON state at all cell densities (right of dashed line in Fig. 3A). Thus, autonomous bistability appears when is satisfied (Eq. S2-15 in Text S1). The condition indicates that the region of the parameter ε that supports autonomous bistability (between the solid and dashed lines in Fig. 3A) broadens as λ is elevated, meaning that the bistability becomes less sensitive to variation in ε. In addition, when λ is decreased below λ = 9, the two stable states disappear and the system undergoes a pitchfork bifurcation. The cells thus become monostable at all cell densities (dotted line in Fig. 3A; Text S1 2.1). In summary, the analytical calculations indicate that autonomous bistability requires , whereas group-level bistability requires both λ>9 and ε<2.
To clarify whether the above conditions for the two types of bistability directly translate into the conditions for group-level transitions, we examined whether group-level bistability always results in an all-or-none response and, similarly, whether autonomous bistability always gives rise to a graded response. This can be verified by checking whether or not the variability of the response is reduced by the secreted signal. To this end, we numerically measured the ratio between the coefficient of variation (CV) of the threshold cell density ρ (CVρ; Fig. 2A–B; see Models) and the CV of the intrinsic heterogeneity of ki (CVk; Fig. S1A–B). Consistent with the above analysis, we see that CVρ/CVk>1 for cell-autonomous bistability, indicating graded transitions (red and pink region in Fig. 3A). In contrast, for group-level bistability, CVρ/CVk is almost always lower than unity, indicating a reduction in the variation (blue and cyan region in Fig. 3A). The condition CVρ/CVk = 1 marks the borderline between the cell-autonomous and group-level switch for a wide range of growth rate (Model; Fig. S4). In addition, CVρ/CVk decreases further as ε decreases and λ increases (Figs. 3A and S2B–C). At high λ, a state change within a small fraction of the population can elicit a sufficient increase in the extracellular signal concentration to override cell-cell variability in response sensitivity. Thus, the simulations show that while the effect of cell-cell variability is deleterious to simultaneous switch at low λ (Fig. S2D for λ = 10), the switch becomes more abrupt when λ is elevated (Fig. 1B for λ = 100). In group-level bistability, a large λ promotes all-or-none transitions by reducing the intrinsic heterogeneity (CVρ/CVk<1). Thus, the conditions for autonomous () bistability and group-level (λ>9 and ε>2) bistability directly translate into the necessary conditions for the all-or-none and graded transitions, respectively.
The parameter region of autonomous bistability (between the solid and dashed lines in Fig. 3A) indicates robustness to variation in ε, while CVρ/CVk<1 indicates robustness of group-level bistability to intrinsic variation of threshold ki (Figs. 3A, 2B and S2C–D). The robustness is further enhanced when we include an additional positive feedback in the model circuit (Eq. 3). First, in experimental observations of the synthetic lux gene circuits [23], the region of ε that supports autonomous bistability for the dual positive-feedback circuit (Eq. 3) is wider than that for the autoinduction circuit (Eq. 1). The bistable region further expands when the Hill coefficients, m and n, of AHL-LuxR and LuxR-promoter binding are increased (Figs. 3B and S3). We derived analytically that the boundary between autonomous bistability and the constitutively monostable state (dashed lines in Fig. 3B) is given by , which monotonically increases with m and n (Eq. S2-23 in Text S1 2.3). In contrast, the boundary between autonomous bistability and group-level bistability is almost independent of m and n (ε = 2∼3; solid line in Fig. 3B; Eq. S2-23 in Text S1). Second, the value of CVρ/CVk for the group-level bistability decreases further (Fig. S3) than that for the simple autoinduction circuit (Fig. 3A; e.g., at λ = 10∼100). Thus, the dual positive-feedback is highly effective in reducing the intrinsic variation. Such strengthening of group-level bistability explains the observation that a group-level switch of the rewired lux operon occurs much more abruptly in a dual positive-feedback circuit than in a simple autoinduction circuit [24]. In summary, in both the simple autoinduction and the dual positive-feedback circuits, a large amplification factor λ increases the robustness of both the graded and the all-or-none transitions.
Although microbial populations exhibit either graded [22], [23] or all-or-none [24] transitions, little is known about their benefit. Depending on the nature of environmental fluctuations, the coherence of cell-state transitions could significantly affect the chance of survival. When the environment varies more rapidly than the cellular response, the autonomous switch of individual cells could be more beneficial, because survival strategies can be diversified due to the heterogeneous response [48]: e.g., bistability in the expression of the lac gene in E. coli under certain growth conditions [49] and in the lysis/lysogeny decision of Lambda phage. In other words, the autonomous bistability is a bet-hedging or risk-spreading strategy in the population [50]. On the other hand, when the cells are able to respond as quickly as the environment changes, an all-or-none switch of the whole population allows more cells to survive and therefore could be a better strategy. Thus, depending on the time-scale of environmental fluctuations, being able to choose between autonomous and group-level switches provides an added advantage over a fixed survival strategy. The selection is more feasible when the order parameter ε of the population is close to the borderline; i.e. ε = 2. There, cells can choose between the two types of bistability by only slightly adjusting either the signal threshold, the maximum signal synthesis rate, or the transport rate (Eqs. 2 and S1-18 in Text S1).
To examine the survival strategy of bacterial species, we estimated the values of the parameter ε for four gene circuits in three bacterial species: the rhl and las operons in P. aeruginosa, the car operon in the plant pathogen Erwinia carotovora, and the lux operon in V. fischeri (Text S1 3). Each system has a dual positive-feedback network topology with cooperative gene regulation (Eq. 3; Table 1) [15]. We estimated the csec and γex in Eq. 2 from the export and hydrolysis rates of AHL, respectively. We estimated the normalized threshold from the threshold signal concentration for gene expression within the operon with the extracellular signal concentration above a threshold density (Eq. S3-3 and Table S6). We found that not only do all QS systems analyzed fall within the appropriate range of ε that supports group-level or autonomous bistability (Fig. 3B), they also appear to converge on the boundary between the two types of bistability; i.e. ε∼2. The results suggest that bacteria could be adjusting the coherence of their state transitions in response to environmental conditions.
Several lines of evidence suggest that the parameters that determine ε are in fact being exploited in microbial populations. According to our estimate of the lux system (ε = 20∼30), the system should have a preference for a graded transition (Fig. 3B). Although this is true in E. coli harboring the synthetic lux system [22], [23], [27], all-or-none transition is observed in V. fischeri [29], [30]. This discrepancy could be caused by the fact that our estimate of the threshold concentration of an AHL 3-oxo-C6-HSL (corresponding to ki in Eq. 1) was based on a synthetic lux system in E. coli. In the real lux system of V. fischeri, an antagonist C8-HSL (HomoSerine Lactone) is endogenously synthesized and competitively binds to LuxR [51]. A microfluidic study of single V. fischeri cells showed that the presence of 100 nM C8-HSL increases the threshold concentration for 3-oxo-C6-HSL by as much as 10-fold [52]. Based on this evidence, we predict that, the addition of C8-HSL to synthetic lux systems should decrease ε by at least 10-fold and, as a consequence, would result in an all-or-none type transition. Likewise, the real V. fischeri lux system should exhibit a graded transition by eliminating C8-HSL or suppressing its synthesis.
Similarly, in the las system of P. aeruginosa, addition of an antagonist furanone, which eukaryotic cells produces to interfere with the bacterial QS [53], [54], suppresses the las gene expression [55] so that the concentration of the autoinducer 3-oxo-C12-HSL decreases. Conversely, the concentration of 3-oxo-C12-HSL is increased four-fold by the addition of a nutrient amino acid [31] which leads to inhibition of RNA synthesis [56] – bacterial survival strategy to avoid exhausting nutrients. Between P. aeruginosa stains that were clinically isolated from patients with severe polytrauma or congestive heart failure, there were large variations in the synthesized concentrations of 3-oxo-C12-HSL [57]. In addition, there was nine-fold decrease in threshold concentration of the rhl system in the absence of an antiactivator QslA [58]. The increase in autoinducer synthesis and the decrease in the threshold act to increase ε (Eqs. 2, S1-14 and S1-18 in Text S1) so that the graded transition is likely to emerge in the las and rhl systems. The heterogeneous response is in line with the fact that, in P. aeruginosa biofilms, the las and rhl systems are utilized for cell differentiation [59], [60]. Unlike laboratory conditions, nutrient conditions in natural habitats such as those surrounding biofilms inside animal hosts tend to fluctuate at various time scales [61]. Thus, by maintaining ε∼2, many bacterial populations may have the option of choosing between the two modes of transition by slightly changing their kinetic parameters.
To further explore the applicability of the design principle (Fig. 3A) of group-level decision making, we introduced a negative feedback loop into the simple autoinducing circuit (Fig. 1E; Eq. 4). When the negative feedback takes place at a much slower time scale than the positive feedback does, qualitatively different dynamics may appear; the cells become oscillatory or excitable – ability to respond transiently to changes in the signal concentrations [62], [63]. Excitatory responses appear during the differentiation of Bacillus subtilis into the state of competence [40], the stress response of bacterial and mammalian cells [64], [65], the relay response of chemoattractant cyclic-AMP (cAMP) of Dictyostelium discoideum [38], the Ca2+ concentration response of pancreatic β cells [66], and the decision of the fate of embryonic stem cells [67]. When the cells are confined to a small chamber, the secreted signal becomes large that cells switch from a quiescent state to a rhythmic state as a group (Fig. 4).
The oscillatory transition is referred to as dynamical quorum sensing (DQS) [34]–[39]. The presence of quiescent cells at low density in DQS is a marked contrast to the Kuramoto-type transition [68]–[71], where all cells are independently oscillatory and the transition to a collective state is realized by phase synchronization. While such a transition is believed to take place in populations of fireflies [72] and in the neurons of the mammalian suprachiasmatic nucleus [73], other examples have shown a state of quiescence at low cell density [38], [74], [75]. Individual Dictyostelium cells do not exhibit cAMP oscillations at low density, and they only become oscillatory above a certain density [38]. A slightly different case is found in the NADH oscillations of Saccharomyces cerevisiae, where the fraction of oscillatory cells gradually increases when the dilution rate of secreted factors is decreased [75].
The parameter ε (Models; Eqs. 2 and S1-24 in Text S1) in DQS also determines whether the transition is graded or all-or-none. As shown by the numerical simulations, when ε is high the transition is graded (Fig. 4A); a fraction of cells oscillate individually, whereas the others remain quiescent. As in the bistable circuits, cells become autonomously oscillatory when the intracellular autoinducer concentration (γex xi/csec) exceeds the threshold ki (Eq. 4). Because of intrinsic cell-cell heterogeneity in the sensitivity threshold ki (Fig. S5A), a small fraction of the population is already oscillatory even at low cell densities (Figs. 4A and S5C). As we have seen in the bistable system (Fig. 2A), the proportion of oscillatory cells gradually increases with increasing cell density (Fig. 4A). Accordingly, while the amplitude of a single cell is kept constant (local maximum of the blue line in Fig. 4A), the amplitude of the cellular ensemble gradually increases (red line in Fig. 4C). Such gradual increases in the mean amplitude have been observed in engineered E. coli [35] and in the glycolytic oscillations of yeasts [37].
The oscillatory transition is all-or-none when ε is low: all cells simultaneously switch to the oscillatory state above a threshold cell density (Fig. 4B; see also Fig. S5D for the density dependence of the nullclines). Moreover, at the onset of oscillations, the pulse is highly synchronized among the cells (black dots in Fig. 4B). Note that this occurs despite the presence of cell-cell heterogeneity in the response threshold (Fig. S5B). An all-or-none transition is observed as both an abrupt increase in the oscillation amplitude averaged over the population (red line in Fig. 4B) as well as an increase in the fraction of oscillatory cells (Fig. 4D). A group-level excitatory response to a common level of signaling molecule is responsible for the all-or-none transition in DQS. There are almost no cells that oscillate below the threshold density, because the synthesized concentration γex xi/csec is below the threshold ki regardless of xi (Eq. 4). When a certain fraction of the population is excited because of cell-cell variability in ki, a subsequent increase in the secreted signal invokes the excitation of the remaining population. Thus, the positive feedback supports a chain reaction of excitatory responses, because the secreted signals mutually enhance the excitation of other cells (time ∼3600 in Fig. 4B). Such group-level excitation captures the essence of what has been observed in the abrupt transition from quiescence to highly synchronized oscillations in particle-based Belouzov-Zhabotinsky reactions [76] and in the cAMP signaling of Dictyostelium [38].
Following the argument for the coupled bistable circuits described above (Fig. 3A), the nature of oscillatory transitions in the coupled excitable circuits could also be numerically classified by CVρ/CVk (Fig. 5A); i.e., whether or not the intrinsic variability of threshold ki is reduced: all-or-none when CVρ/CVk<1 (blue and cyan in Fig. 5A) and graded when CVρ/CVk>1 (red and pink in Fig. 5A). The boundary between the transition types (CVρ/CVk = 1; green line in Fig. 5A) is located between ε = 2 and ε = 10. ε>10 roughly corresponds to the necessary condition for cell-autonomous oscillations (black dashed line in Fig. 5C), while ε>2 is shown analytically to be the necessary condition for cell-autonomous excitation in isolated cells (black solid line in Fig. 5C; Fig. S6A; see Text S1 2.4 for a derivation). To examine the role of autonomous excitability, intrinsic noise is introduced into the kinetics of the synthetase (ηi in Eq. 4), as was done for the bistable circuit. At ε>2, the cells are repetitively excited by the intrinsic signal noise rather than by the secreted signal, so there are cell-autonomous stochastic pulses frequently observed in excitable systems [38], [77]. Thus the transition becomes graded (right of the yellow line indicating CVρ/CVk = 1 in Fig. 5B–C). The convergence of the boundary to ε = 2 in the presence of noise occurs irrespective of the remaining free parameter g (yellow line in Fig. S6B–E). Thus, the autonomous excitation and oscillation mediated by the intracellular feedback lead to graded transitions; whereas the group-level excitation mediated by the secreted autoinducer invokes all-or-none transitions to highly synchronized oscillations. Moreover, the position of the boundary (ε∼2, Figs. 5B–C and S6B–E; Eq. S2-27 in Text S1) agrees well with that obtained for the bistable circuits (Fig. 3A; Eq. S2-15 in Text S1), indicating that the relative contributions to the feedback from the autoinducer that is synthesized and accumulated within the cell and that which is secreted and shared with other cells are the key determinants of the group-level transition. In the engineered E. coli with a positive-and-negative-feedback (Fig. 1E; Eq. 4) mediated by the lux system [35], it is reasonable to expect ε>2 (SI Text 3.2.4), since the expression of ε is identical with that of the autoinduction circuit (Eq. 2; Eq. S1-24). As a result, the mean amplitude increases gradually with cell density (Fig. 4A). This suggests that the oscillatory transition in the engineered E. coli. [35] is graded.
Future works should clarify the limit and applicability of the common design principle elucidated in this study by exploring more complex circuit topologies in a wide variety of biological contexts. Our models did consider spatial heterogeneity of the extracellular autoinducer concentration that could potentially form a spatial gradients [78] or propagating waves [35], [79] (Models). The spatial heterogeneity becomes important, for example when we consider spatial structure of microbial colonies, aggregates or biofilms with a diameter of more than 1 mm (Text S1 1.6). The autonomous bistability presented here faithfully reproduces microbial group-level dynamics such as the bimodal distribution (Fig. 2C; [22], [23]) and the continuous increase in the fraction of ON cells as cell density increase (Fig. 2A upper panel; [25], [28]). We should note, however, that there may also be other types of bistability. In V. harveyi, the maximum fraction of the ON-state cells never reaches 100% even at high densities [25]. It also appears that not all V. fischeri cells can exhibit state transition when isolated in a chamber and perfused with high dosages of autoinducer [10]. Such a property could be due to either a large variability in the threshold value ki, presence of an antagonist [52] that suppresses autoinducer synthesis, or another negative feedback that adds a repressive cell-cell interaction [80]–[82] so as to render coexistence of ON and OFF cells (Fig. 1E) more likely in a wide range of model parameters. Delineating these possibilities will be an important avenue for future studies.
To further test applicability of the common design principle, we expanded the simple transport system for the autoinducer (Fig. 1C–E) to describe transmembrane signal recognition and transduction [15], [32], [83]. For transmembrane recognition systems, in addition to the extracellular feedback of the autocrine signaling, an intracellular positive feedback is required for a graded transition (Text S1 1.4), as in the simple autoinduction (Eq. 1) and the dual positive-feedback circuits (Eq. 3). Consistently, the parameter ε tunes the graded and all-or-none transitions in QS (Fig. S7 and Eq. S1-33 in Text S1) as well as in DQS (Fig. S8). Hence, the design principle should be widely applicable to cell density-dependent fate decisions [84] in a broad spectrum of cell populations; e.g., in animal embryogenesis [20], [21], stem-cell differentiation in tissue engineering [85], [86], influenza virus infection [87], and cancer metastasis [88].
We have seen that when individual cells alone can harbor dynamic stabilities, the transition at the group level becomes graded (Figs. 2A and 4A). These dynamic stabilities are cell-autonomous bistability, in the case of autoinducing circuits, and cell-autonomous excitability, in case of negative-feedback circuits (Figs. 2B and 4B). In contrast, group-level all-or-none transitions between cellular states are supported when these stabilities require a sufficient number of cells. In both bistable circuits and excitable circuits (Fig. 1C–E), the two parameters ε and λ determine the transition type (Figs. 3 and 5). For the cells to switch their states, inducing molecules need to accumulate to a certain level within the group. ε compares the contribution of intracellular local feedback with that of secretion-mediated global feedback. For ε>2, bistability or excitability can be reduced to a single-cell property. For ε<2, the switch requires group-level cooperation mediated by secreted signaling molecules. The necessary conditions for autonomous and group-level stabilities are directly translated into those for graded and all-or-none transitions, respectively (Figs. 3A and 5C). The greater the amplification factor λ is, the more robust the transitions are to cell-cell variability (Figs. 3A and S2C) and parameter variations (Fig. 3B). Future studies should be able to experimentally verify this design principle by tuning λ and ε with inducible promoters [27], [40] or by applying agonists and antagonists to the system [52], [54], [55].
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10.1371/journal.ppat.1000914 | Mouse Senile Amyloid Fibrils Deposited in Skeletal Muscle Exhibit Amyloidosis-Enhancing Activity | Amyloidosis describes a group of protein folding diseases in which amyloid proteins are abnormally deposited in organs and/or tissues as fine fibrils. Mouse senile amyloidosis is a disorder in which apolipoprotein A-II (apoA-II) deposits as amyloid fibrils (AApoAII) and can be transmitted from one animal to another both by the feces and milk excreted by mice with amyloidosis. Thus, mouse AApoAII amyloidosis has been demonstrated to be a “transmissible disease”. In this study, to further characterize the transmissibility of amyloidosis, AApoAII amyloid fibrils were injected into transgenic Apoa2cTg+/− and normal R1.P1-Apoa2c mice to induce AApoAII systemic amyloidosis. Two months later, AApoAII amyloid deposits were found in the skeletal muscles of amyloid-affected mice, primarily in the blood vessels and in the interstitial tissues surrounding muscle fibers. When amyloid fibrils extracted from the skeletal muscles were subjected to Western blot analysis, apoA-II was detected. Amyloid fibril fractions isolated from the muscles not only demonstrated the structure of amyloid fibrils but could also induce amyloidosis in young mice depending on its fibril conformation. These findings present a possible pathogenesis of amyloidosis: transmission of amyloid fibril conformation through muscle, and shed new light on the etiology involved in amyloid disorders.
| “Amyloidosis”, a group of protein folding diseases characterized by deposition of fine fibrils in tissues, is a common disorder of protein metabolism and can be acquired, inherited and/or age-associated. Recently, prion-like transmission has been found in various amyloidoses. AApoAII amyloid fibrils in mouse senile amyloidosis have exhibited transmissibility. For instance, ingested AApoAII amyloid fibrils, which were excreted from mice and contained in feces or milk, function as seeds for changing apoA-II amyloid precursor protein to the fibrillar form and cause mouse senile amyloidosis. However, transmissibility through other pathways has not yet been established. Here, we induced AApoAII systemic amyloidosis in transgenic Apoa2cTg+/− and normal R1.P1-Apoa2c mice to analyze the transmissibility of mouse senile amyloidosis through muscle tissues. In this study, we not only detected AApoAII deposited in various skeletal muscles, but also found that it could induce secondary transmission of AApoAII amyloidosis. This is the first evidence of transmission through skeletal muscles in non-prion systemic amyloidosis. This pathway of transmission provides new insight into the potential for food-borne pathogenesis and etiology of systemic amyloidosis.
| Amyloidosis refers to a group of protein folding disorders. Various proteins that are harmless and soluble under normal physiological conditions can undergo marked conformational changes and subsequent self-assembly outside of the cell into highly stable, insoluble amyloid fibrils with a high content of ß-sheet structures. Currently, twenty-eight different kinds of human proteins, in intact or fragmented forms, have been found to be amyloidogenic in vivo and to be associated with pathological disorders such as prion diseases, Alzheimer's disease, type II diabetes, dialysis-related amyloidosis, and familial, systemic, and sporadic amyloidosis [1], [2].
Many factors, such as aging and epigenetic factors, including lifestyle and types of food ingested, may influence fibril formation and deposition in organs and/or tissues. Transmission of amyloid fibrils might act as an important etiological factor of amyloidosis. Exogenous amyloid fibrils could act as a seed (nuclei) and change the conformation of endogenous amyloid protein into that of fibrils, such as PrPSc, reactive (AA) and AApoAII amyloid fibrils [3], [4], [5]. Transmissible spongiform encephalopathies (TSEs) comprise a group of infectious neurodegenerative diseases that affect humans and other animals and are characterized by accumulation of the misfolded, protease-resistant prion protein PrPSc in the central nervous system [6], [7]. It is hypothesized that TSEs are transmitted from one species to another through ingestion of urine, saliva, and/or infected meat [7].
Apolipoprotein A-II (apoA-II) is present in the plasma of humans, mice, rats, and fish [8], [9]. In mice, apoA-II is the second most abundant apoprotein in serum high density lipoprotein (HDL), and accumulates to form amyloid fibrils (AApoAII) in many organs, leading to senile amyloidosis [10]. In laboratory mice, three major alleles (Apoa2a, Apoa2b and Apoa2c) of the apoA-II gene encode three variants of the apoA-II protein [11], [12]. Several genetic analyses have indicated that the Apoa2c allele markedly accelerates age-associated deposition of AApoAII [13]. Mouse AApoAII amyloidosis has been demonstrated to be a transmissible disease by a prion-like infectious process occurring through a seeding-nucleation mechanism [4], [14]. Our group found that a single intravenous injection of a very small amount of AApoAII amyloid fibrils (∼10−13g) led to systemic deposition of amyloid in young mice [15], [16]. AApoAII amyloidosis can also be transmitted by the feces [17] and milk [18] excreted by mice with AApoAII amyloidosis. Furthermore, transmission of AApoAII amyloidosis shows a ‘strain phenomenon’ analogous to the prion strains [10]. Thus, the fibrillar nuclei or amyloid fibrils formed by the aggregation of misfolded protein monomers (rich in ß-sheet structures) act as seeds to induce and stabilize conversion of the native monomeric protein [19], [20]. This mechanism provides a plausible explanation for the transmissible nature of AApoAII amyloidosis.
In the present study, we found AApoAII amyloid fibrils in the skeletal muscles of AApoAII amyloid-affected mice. Unexpectedly, amyloid fibrils isolated from the muscles were demonstrated to be sufficient for the transmission of amyloidosis. These findings provide important implications for assessing the potential risk of consuming amyloid-deposited skeletal muscles in the transmission of amyloidosis.
To confirm whether apoA-II mRNA is expressed in mouse skeletal muscle, total RNA was extracted from the triceps brachii muscles in the forelimbs, the femoral quadriceps muscles in the hindlimb, the longissimus thoracis muscle in the back and the greater pectoral muscles from the breast of Apoa2cTg+/− mice and R1.P1-Apoa2c mice. ApoA-II mRNAs were detected in several muscles obtained from these mice. Interestingly, expression levels in muscle tissues were lower than those seen in liver (Figure 1A). Quantitative real time PCR analysis revealed that the expression levels of apoA-II mRNA in the muscles were about one tenth and one thirtieth of the expression levels observed in the livers from Apoa2cTg+/− and R1.P1-Apoa2c mice, respectively, and expression levels were significantly higher in Apoa2cTg+/− mice (Figure 1B).
To determine whether amyloid fibrils exist in the muscles of AApoAII-deposited mice, we intravenously injected 1 µg of isolated AApoAII fibrils into six 2-month-old female Apoa2cTg+/−mice. Two months later, amyloid deposition was detected by the presence of green birefringence in Congo Red-stained tissue from four muscles of Apoa2cTg+/− mice displaying heavy amyloid deposits throughout the body. Histological examination revealed that muscles of all Apoa2cTg+/− were deposited with amyloid (6/6; Table 1). In amyloid fibril-injected normal R1.P1-Apoa2c mice, obvious amyloid deposition was observed only in one of three mice at two months after injection (1/3), and all three had amyloid deposits at four months after injection (3/3); that is, in skeletal muscles, the deposition of AApoAII amyloidosis increased with age. Amyloid deposits were found mainly in the blood vessels of muscle tissues, but were also found in connective tissues around muscle fibers (endomysium) both in Apoa2cTg+/− mice (Figure 2A, B) and R1.P1-Apoa2c mice (Figure 2E, F). AApoAII amyloid deposition, which was observed in muscles, was further confirmed with anti-apoA-II staining (Figure 2C, D, G, and H). However, no deposits of AApoAII amyloid fibrils were found in muscle tissues of the R1.P1-Apoa2c mice (0/3) without induction by AApoAII fibrils.
Amyloid fibril fractions were isolated from various muscles and apoA-II protein was detected by Western blot analysis. ApoA-II proteins were detected in amyloid fibril fractions of femoral quadriceps muscles in the hindlimb of Apoa2cTg+/− mice with AApoAII-deposition but similar results were not observed in the muscles of R1.P1-Apoa2c control mice lacking AApoAII-deposition (Figure 3A). Moreover, apoA-II was also detected in all four kinds of muscle of Apoa2cTg+/− mice (Figure 3B). In R1.P1-Apoa2c mice, two months after injection of amyloid fibrils, apoA-II was detected in greater pectoral muscles in the breast of all three mice. Four months after injection, apoA-II deposition expanded to other muscles and amounts of apoA-II increased (Figure 3C). The amount of deposition was different among different muscles: greater pectoral muscles from the breast > longissimus thoracis muscle in the back > triceps brachii muscles in the fore-limbs > femoral quadriceps muscles in the pelvic-limb.
To further confirm the existence of amyloid fibrils in muscle tissues, the amyloid fibril fractions of muscle tissues from the AApoAII-deposited Apoa2cTg+/− mice and R1.P1-Apoa2c mice without AApoAII-deposition were observed by transmission electron microscopy. We found amyloid fibrils extracted from muscles only in the fractions of mice with AApoAII-deposition (Figure 4A), but not in the fractions of mice without deposition. Ultrastructural analysis of cross sections of AApoAII-deposited muscle was performed by transmission electron microscopy. Bundles of amyloid fibrils deposited in endomysiums and capillary walls of skeletal muscles were observed (Figure 4B and C).
To elucidate whether AApoAII amyloid transmissibility existed in skeletal muscle, amyloid fibril fractions were isolated from femoral quadriceps muscles of Apoa2cTg+/− mice with AApoAII deposition (Figure 5) and were injected into 2-month-old female R1.P1-Apoa2c mice. Two months following injection, amyloid deposits were observed in the tongue (6/6), lungs (6/6), stomach (6/6), heart (6/6), intestine (4/6), and skin (1/6) of six injected mice (Table 2). The mean AI was 1.19. In contrast, significantly less amyloid deposition was observed when the mice were injected with fractions isolated from R1.P1-Apoa2c mice without AApoAII-deposition; the mean AI was 0.26 (p = 0.0062). Additionally, we found that injection of fibril fractions extracted from no AApoAII deposited muscles from R1.P1-Apoa2c mice induced a small amount of amyloid deposition (2/5 mice had AApoAII deposits and mean AI = 0.10). To confirm this, amyloid fibril fractions were extracted from muscles of young (2-month-old) female R1.P1-Apoa2c mice. Although these fractions induced secondary transmission in mice, neither apoA-II nor AA could be detected in the fraction by Western blot analysis (Figure 5). Unexpectedly, slight amyloid depositions were observed in the tongue (5/7) and stomach (3/7) of seven injected mice two months after injection; mean AI = 0.20.
Amyloid fractions denatured by guanidine hydrochloride were injected into six 2-month-old female R1.P1-Apoa2c mice. No amyloid deposition was detected in any of these mice two months later (0/6) (Table 2).
Our previous work in senescence-accelerated mice determined that AApoAII amyloid fibrils are deposited throughout the body, including in the liver, spleen, stomach, intestine, heart, kidneys, lungs, tongue, skin, gonads, adrenal glands, salivary and thyroid glands, thymus, mesenteric lymph nodes, epineurium of the sciatic nerve and blood vessels in various tissues; however, no evidence of amyloid deposition was found in brain parenchyma or bone marrow in the vertebral body of the lumbar spine [21]. Although amyloid fibrils were also detected in the musculoskeletal systems, there were no detailed descriptions nor further studies on AApoAII amyloid deposition in the skeletal muscles [21].
The current study demonstrates that skeletal muscle tissue is capable of propagating AApoAII amyloidosis in mice. We first detected AApoAII amyloid fibrils in four skeletal muscles in different body regions using immunohistochemistry, Western blot analysis and electron microscopy. Amyloid deposits were observed in blood vessels and interstitial tissues surrounding muscle fibers. However, no AApoAII was observed in muscle cells or in the nerve fibers in which prion proteins were previously detected [22]. Although apoA-II mRNA was detected in muscle tissues (Figure 1), it is unclear whether apoA-II protein in AApoAII fibrils around muscle fibers originates from muscle cells or blood. Interestingly, the amounts of amyloid deposition in the skeletal muscles differed among body regions; that is, breast > back > forelimb > hindlimb. This order is different from that observed in mice inoculated with prion protein [23]. First, we observed amyloid deposition in Apoa2cTg+/− mice in which apoA-II protein is overexpressed in various tissues under the control of a ubiquitous promoter, and found that serum levels of apoA-II increased the susceptibility to induction of AApoAII [24]. As a result, AApoAII deposits in muscles were found in Apoa2cTg+/− mice. Second, we also observed AApoAII deposits in the muscles of R1.P1-Apoa2c mice in which apoA-II was expressed under an endogenous promoter/enhancer. According to the above results, it was found that AApoAII is deposited in the skeletal muscles as part of a universal phenomenon. Next, we demonstrated that intravenous injection of amyloid fibrils extracted from muscle tissues could transmit amyloidosis depending on fibril-conformation. That is, transmissibility was lost following denaturation with 6 mol/L guanidine hydrochloride. In previous studies, AApoAII amyloid fibril was extremely efficient in inducing amyloidosis following doses of less than 1 pg; moreover, amyloidosis could be initiated after oral ingestion of AApoAII fibrils [16], [17], [25]. Thus, the infectious ability of skeletal muscle raised the possibility that mouse AApoAII amyloidosis may result, in part, from dietary exposure to amyloid fibrils through consumption of muscle/meat containing amyloid materials.
Animal muscles, an important food component for most humans, have been examined in several studies for the presence of TSE transmissibility [23], [26]. Recently, it was reported that high prion titers and the disease-causing isoform of the prion protein PrPSc appear in the skeletal muscles of mice, hamsters and sheep inoculated with prion agents [27], [28], [29], [30], and in deer infected with chronic wasting disease [31]. Furthermore, PrPSc is also present in skeletal muscle samples of sporadic Creutzfeldt-Jacob disease (CJD) in humans [32], and has been demonstrated to be present in the nerve fibers of skeletal muscles tissue [23]. Although some findings are contradictory to the above reports [33], elucidation of the contribution of muscle tissues to transmission is important for the prevention of prion-related disorders. Prion-like transmission has been reported in mouse inflammation-associated amyloid A (AA) amyloidosis [34]. Dietary supply of amyloid fibrils might also be a trigger in the development of AA amyloidosis, especially for a susceptible population. Notably, it was reported that 71.4% of skeletal muscles from cows with systemic AA amyloidosis stained positive with anti-AA antibody [35]. Although an unexpectedly high incidence of visceral AA-amyloidosis in aged slaughtered cattle in Japan was reported, and isolated AA amyloid fibrils exhibited amyloid-enhancing factor activity, amyloid deposition in the skeletal muscles was rare [36], [37].
Thus, these studies support the idea of the transmissibility of systemic AApoAII and AA amyloidosis from skeletal muscle. Additionally, we found that injection of fibril fractions extracted from either control non-AApoAII injected or, no AApoAII deposited muscles of young R1.P1-Apoa2c mice induced a small amount of amyloid deposition, although these fractions contained neither apoA-II nor AA detectable by Western blot analysis. It is possible that extracts contain trace amounts of AApoAII amyloid fibrils or oligomers that could not be detected by available techniques and these undetectable AApoAII peptides might be transmissible like PrPres [38]. Alternatively, components other than AApoAII amyloid fibrils might induce amyloid deposition. In R1.P1-Apoa2c mice, AApoAII amyloid can be seeded by various heterogeneous amyloid fibrils (cross-seeding) [39] and unexpectedly many kinds of proteins have been reported to form amyloid fibril-like structures [40], [41]. For example, collagen fibrils and glycosaminoglycans, supportive structures in skeletal muscle, appear to be actively involved in the induction of ß2-microglobulin amyloid fibril formation [42].
Elevated levels of prion protein in muscle lead to myopathy and neurogenic muscle atrophy in affected patients [43], [44], [45]. Accumulated amyloid proteins have been found in inclusion-body myositis and are toxic to skeletal myoblasts [46]. Although we observed amyloid deposits around skeletal muscle fibers after inducing amyloidosis, further studies will be necessary to examine possible myopathy and/or toxicity of these deposits.
In summary, apoA-II, which is present in the plasma of humans, mice, rats, and fish [6], [7], has been demonstrated in the form of amyloid fibrils with transmissibility in mouse muscle. AApoAII amyloid fibrils were detected in various skeletal muscles, especially in the pectoral muscles. The verification of this transmission pathway is valuable for understanding the pathogenesis and etiology of amyloidosis.
All experimental procedures were pre-approved by Division of Laboratory Animal Research of Shinshu University and were performed according to the guidelines of Division of Laboratory Animal Research of Shinshu University.
R1.P1-Apoa2c is a congenic strain of mice with the amyloidogenic Apoa2c allele from the SAMP1 strain in the genetic background of SAMR1 [13]. Apoa2c transgenic mice (Apoa2cTg+/−) were established in the genetic background of R1.P1-Apoa2c [24]. These strains were maintained by sister-brother mating in the Division of Laboratory Animal Research, Research Center for Human and Environmental Science, Shinshu University. Mice were raised under specific pathogen-free (SPF) conditions at 24±2°C with a light-controlled regimen (12-hour light/dark cycle). A commercial diet (MF; Oriental Yeast, Tokyo, Japan) and tap water were provided ad libitum. In this study, only female mice were used to avoid AA amyloidosis and/or other adverse impacts caused by fighting or other behaviors among mice reared in the same cage. All experiment procedures were carried out in accordance with the Regulations for Animal Experimentation of Shinshu University.
Total RNAs were extracted from the skeletal muscles and liver (as control) using RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. First-strand cDNA was synthesized from 1 µg total RNA of each muscle tissue (First-strand cDNA Synthesis Kit; Amersham Pharmacia Biotech, Piscataway, NJ) and subjected to PCR amplification with Taq DNA polymerase (Promega; Madison, WI). The reverse transcriptase-polymerase chain reaction (RT-PCR) amplification was carried out in a 50-µl reaction mixture containing 200 µM each dNTP, 1× buffer containing 1.5 mM MgCl2, 0.1 µM each primer, and 1.25 U of Taq DNA polymerase. The cycling parameters for RT-PCR were initial denaturation for 1 minute at 94°C followed by 23 cycles of 30 sec at 94°C, 30 sec at 55°C, and 1 min at 72°C. A 5-µl aliquot of the PCR product was subjected to 3% agarose (Takara Bio, Otsu, Japan) gel electrophoresis. SYBR Premix Ex Taq II (Takara Bio) was used as the fluorescent marker to monitor DNA accumulation in quantitative real time PCR analysis with the 7500 Real-Time PCR System (Applied Biosystems Life Technologies, Tokyo Japan). The primers used for PCR amplification of apoA-II mRNA were as follows: A2/acc-F (5′-AAGAGACAGGCGGACGGACA-3′) and A2/acc-R (5′-GAGGTCTTGGCCTTCTCCAC-3′).
The AApoAII amyloid fibril fraction was isolated as a water suspension from the livers of a 20-month-old R1.P1-Apoa2c mouse as described previously [10]. Purified amyloid fibrils were re-suspended at a concentration of 1.0 mg/ml in distilled water (DW). One milliliter of this solution was put into an Eppendorf tube and sonicated on ice for 30 sec with an ultrasonic homogenizer VP-5S (Tietech Co., Ltd., Tokyo, Japan) at power level 4. This procedure was repeated five times at 30 sec intervals. Sonicated amyloid samples were then immediately injected into the caudal vein of mice to induce AApoAII amyloidosis.
Six 2-month-old female Apoa2cTg+/− mice were injected intravenously with 1 µg of AApoAII fibrils to induce AApoAII amyloidosis. Two months later the mice were sacrificed and the triceps brachii muscles in the forelimbs, the femoral quadriceps muscles in the hindlimb, the longissimus thoracis muscles in the back, and the greater pectoral muscles from the breast were dissected. Half of the tissue was kept at −80°C and the other half was fixed in 10% neutral buffered formalin, embedded in paraffin, and cut into 4-µm sections. Six 2-month-old female R1.P1-Apoa2c mice were injected intravenously with 100 µg of AApoAII fibrils; three were sacrificed after two months and the other three were sacrificed after four months. Muscle tissue was dissected and either stored or fixed and embedded in the same fashion as the Apoa2cTg+/− transgenic mice. Three female R1.P1-Apoa2c littermates not injected with fibrils, were sacrificed at two months as controls.
AApoAII amyloid fibril fractions were isolated from the muscle of amyloid fibril-injected mice by Pras' method [47]. Thawed muscles (0.1 g) were sonicated twice for 30 sec with a 30-second rest interval in 1.0 ml of 0.15 M NaCl on ice using an ultrasonic homogenizer VP-5S (Tietech Co., LTD, Tokyo, Japan) at power level 4. The homogenate was centrifuged at 40,000×g for 20 min at 4.0°C, after which the supernatant was discarded and the pellet was re-suspended in 1.0 ml 0.15 M NaCl. The sonication and centrifugation were repeated two more times, and the pellet was suspended in 1.0 ml deionized DW (DDW) and centrifuged after sonication once more. The pellet was re-suspended with DDW and sonicated. Following centrifugation at 30,000×g for 20 min at 4.0°C, the supernatant containing amyloid fibrils was collected and used for Western blotting, transmission electron microscopy analysis and for the secondary transmission experiment.
Deposition of amyloid fibrils was identified by the appearance of green birefringence in Congo Red-stained sections [48] visualized under polarizing microscopy. AApoAII amyloid fibril proteins were identified immunohistochemically using the avidin-biotinylated horseradish peroxidase complex method with specific antiserum against mouse apoA-II (1∶3000) [49].
Isolated amyloid fibril fractions (25 µg) from the muscles were separated on Tris-Tricine sodium dodecyl sulfate-polyacrylamide (16.5% [w/v] acrylamide) electrophoresis (SDS-PAGE) gels [50]. Proteins on the gel were electrophoretically transferred to Immuno-Blot polyvinylidene difluoride membrane (0.2 µm pore size; Bio-Rad, Hercules, CA, USA). Proteins on the membrane were detected with rabbit anti-mouse apoA-II antiserum (1∶3000), followed by peroxidase-conjugated goat IgG against rabbit immunoglobulin (1∶1000; ICN Pharmaceuticals, Inc., Aurora, OH, USA). Immunoreactive proteins were visualized with ECL reagents (Amersham Biosciences, Buckinghamshire, England). The film (Amersham Biosciences, Buckinghamshire, England) was exposed for 3 min.
Aliquots (20 µl; 0.5 µg/µl) of amyloid fibril fractions isolated from the muscles were applied to 400-mesh collodion-coated copper grid (Nissin EM Co., Ltd., Tokyo, Japan) for 1 min and subjected to negative staining with 1% phosphotungstic acid (pH 7.0) for 1 min. The negatively stained samples were observed with a JEOL 1200 EX electron microscope (JEOL, Tokyo, Japan) operated at 80 kV. Electron micrographs were taken with a Gatan multiscan camera model 791 with Gatan digital micrograph software version 3.6.4 (Gatan, Pleasanton, CA, USA). For ultrastructural analysis by electron microscopy, greater pectoral muscles were thinly sliced and placed in 2.5% glutaraldehyde at 4°C overnight. The tissue was rinsed twice with 0.1 M phosphate-buffered saline (PBS) and post-fixed with 1% osmium tetroxide on ice for 1 hour. Then, the tissue underwent a graded ethanol dehydration series and was infiltrated using a mixture of one-half propylene oxide and one-half resin for one hour. One hour later, the tissue was embedded in resin for four hours and then polymerized at 37°C for 10 hours followed by 60°C for 24 hours. One hundred nanometer sections were cut and stained with 4% uranyl acetate for 20 minutes and 0.5% lead citrate for five minutes. The sections were observed with a JEM-1400 transmission electron microscope (JEOL, Tokyo, Japan) operated at 80 kV. Electron micrographs were taken with a Gatan multiscan camera with Gatan digital micrograph software version 1.81.78 (Gatan, Pleasanton, CA, USA).
Amyloid fibril fractions were isolated from the muscles of AApoAII-deposited Apoa2cTg+/− mice or R1.P1-Apoa2c mice without AApoAII-deposition. 100 µl (2.5 µg/µl) of the amyloid fibril fractions were injected into two-month-old female R1.P1-Apoa2c mice, and after two months, the mice were sacrificed and the intensity of AApoAII amyloid deposition was determined semi-quantitatively using the amyloid index (AI). The AI was determined by taking the mean value of the scores of amyloid deposition (graded from 0 to 4) in the seven major organs (liver, spleen, tongue, heart, intestine, stomach, and skin) stained with Congo Red as described previously [10].
Amyloid fibril fractions were denatured in a solution of 6 mol/L guanidine hydrochloride, 0.1 mol/L Tris-HCl (pH 10.0), and 50 mmol/L dithiothreitol (1.0 mg/mL) for 24 hours at room temperature with gentle stirring. Denatured amyloid fractions were dialyzed quickly against 10 mmol/L NH4HCO3 solution. The solution was injected into two-month-old female R1.P1-Apoa2c mice that were treated as described above.
We used the StatView software package (Abacus Concepts, Berkeley, CA, USA) to perform statistical analyses. Significant differences in the value of AI among the various groups of mice were examined using the Mann-Whitney U-test.
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10.1371/journal.ppat.1000947 | Coexpression of PD-1, 2B4, CD160 and KLRG1 on Exhausted HCV-Specific CD8+ T Cells Is Linked to Antigen Recognition and T Cell Differentiation | Exhausted CD8+ T cell responses during chronic viral infections are defined by a complex expression pattern of inhibitory receptors. However, very little information is currently available about the coexpression patterns of these receptors on human virus-specific CD8+ T cells and their correlation with antiviral functions, T cell differentiation and antigen recognition. We addressed these important aspects in a cohort of 38 chronically HCV infected patients and found a coexpression of inhibitory receptors such as 2B4, CD160 and KLRG1 in association with PD-1 in about half of the HCV-specific CD8+ T cell responses. Importantly, this exhaustive phenotype was associated with low and intermediate levels of CD127 expression, an impaired proliferative capacity, an intermediate T cell differentiation stage and absence of sequence variations within the corresponding epitopes, indicating ongoing antigen triggering. In contrast, a low expression of inhibitory receptors by the remaining HCV-specific CD8+ T cells occurred in concert with a CD127hi phenotype, an early T cell differentiation stage and presence of viral sequence variations within the corresponding epitopes. In sum, these results suggest that T cell exhaustion contributes to the failure of about half of HCV-specific CD8+ T cell responses and that it is determined by a complex interplay of immunological (e.g. T cell differentiation) and virological (e.g. ongoing antigen triggering) factors.
| About 170 million people are infected with hepatitis C virus (HCV), which may cause severe liver disease and liver cancer. Upon acute infection, only about 30% of patients are able to eliminate the virus spontaneously while about 70% of patients develop chronic infection. It is known that a successful immune response against HCV depends on virus-specific CD8+ T cells. However, during chronic infection, these cells are impaired in their antiviral function. In this study, we found that the exhaustion is characterized by the expression of multiple inhibitory receptors, such as PD-1, 2B4, CD160 and KLRG1. Of note, the coexpression of these receptors depends on the ongoing recognition of the viral antigen and the maturation stage of the T cell. The remaining virus-specific T cell responses that are not exhausted do not recognize the virus present in the patients any more due to viral mutations, indicating viral escape. Thus, they fail to exert antiviral activity, although they share characteristics of fully functional memory T cells. In sum, we have found that T cell exhaustion contributes to the failure of about half of HCV-specific CD8+ T cell responses and that it is determined by a complex interplay of immunological and virological factors. These findings will be important to consider in the design of new antiviral vaccination strategies.
| Virus-specific CD8+ T cells play a central role in the outcome of HCV infection. Indeed, several human and animal studies have shown associations between strong and multispecific T cell responses and viral clearance [1]. During chronic HCV infection, viral escape and an impairment of HCV-specific CD8+ T cell antiviral functions, e.g. the ability to proliferate or to secrete antiviral cytokines such as interferon-γ (IFN-γ) contribute to virus-specific CD8+ T cell failure. The underlying mechanisms for the functional impairment of HCV-specific CD8+ T cells have not been clarified in detail, although lack of CD4+ T cell help, action of regulatory T cells and expression of immunomodulatory cytokines, such as Il-10, have been suggested to contribute [1]. In addition, expression of the inhibitory receptor PD-1 has been postulated to characterize a state of exhaustion of HCV-specific CD8+ T cells in chronic HCV infection in analogy to murine models of chronic viral infections [2]. Indeed, analysis of patients with chronic HCV infection identified high levels of PD-1 expression on HCV-specific CD8+ T cells in blood and liver [3] and blockade of PD-1 signaling resulted in the functional restoration of blood-derived HCV-specific CD8+ T cell responses in chronic infection [3], [4]. However, the relevance of PD-1 in defining exhausted HCV-specific CD8+ T cells has not been unchallenged. For example, PD-1 blockade alone was unable to restore the function of liver-derived HCV-specific CD8+ T cells [5] while targeting additional inhibitory signaling pathways reinvigorated the antiviral function [6]. In addition, PD-1 expression did not necessarily identify exhausted HCV-specific CD8+ T cells during acute HCV infection in humans [7] and chimpanzees [8]. Thus, PD-1 expression alone may not be sufficient to determine exhaustion of HCV-specific CD8+ T cells during HCV infection. In this context, it is interesting to note that a recent study identified coexpression of additional inhibitory receptors next to PD-1 as a critical determinant of CD8+ T cell exhaustion in a murine model of chronic viral infection. For example, expression of several inhibitory receptors including 2B4 and CD160 next to PD-1 was identified on strongly exhausted virus-specific CD8+ T cells in severe LCMV infection [9]. 2B4 is a coregulatory receptor of the SLAM-receptor family that is capable of mediating both activatory and inhibitory signals upon ligand CD48 binding [10]. High levels of 2B4 expression are associated with 2B4 inhibitory receptor function [10] and indeed, 2B4 and CD160, a glycoylphosphatidylinositol-anchored receptor that coinhibits T cells upon ligand HVEM binding [11], both highly correlated in gene expression analysis with PD-1 expression [12]. The positive correlation with PD-1 expression was also observed for other inhibitory receptors, such as LAG-3, a protein closely related to CD4, and CTLA-4, a member of the CD28 family of receptors [9]. Of note, expression of the inhibitory receptor Killer-cell-lectin-like receptor G1 (KLRG1) showed no correlation with exhaustion in severe LCMV infection [9]. KLRG1 mediates proliferative dysfunction of differentiated CD8+ T cells upon ligand E-cadherin binding [13], [14], [15] and its upregulation is thought to be mediated by ongoing antigen triggering [16].
Despite these insights into inhibitory receptor expression on CD8+ T cells, very little information is currently available about the coexpression of these receptors on virus-specific CD8+ T cells during chronic human infection such as HCV and their association with T cell function, differentiation and antigen recognition. Here, we show that the coexpression of inhibitory receptors such as PD1, 2B4, CD160 and KLRG1 occurs in about half of HCV-specific CD8+ T cell responses and that it is linked to low or intermediate levels of CD127 expression, impaired proliferative capacity, an intermediate T cell differentiation stage and ongoing antigen-recognition. In contrast, the absence of inhibitory receptor expression is associated with a CD127 high phenotype and the presence of virus sequence variations in the respective epitopes, indicating viral escape. These results indicate that two different mechanisms contribute to the dysfunction of the HCV-specific CD8+ T cell pool in chronic HCV infection. These results also indicate that different factors are involved in the development of HCV-specific CD8+ T cell exhaustion.
In a first set of experiments, we screened HCV-specific CD8+ T cells for the expression of a large set of inhibitory receptors such as PD-1, 2B4, CD160, KLRG1, LAG-3 and CTLA-4 in a cohort of 10 chronically HCV infected patients. We compared the expression of these receptors on HCV-specific CD8+ T cells with the expression on FLU-specific CD8+ T cells that represent a memory population. As shown in Fig. 1, we found an elevated expression of PD-1, 2B4, CD160 and KLRG1 by HCV-specific CD8+ T cells, however, no significant increase of LAG-3 and CTLA-4 expression was observed. Based on these results, we focused in the following on the analysis of the expression of PD-1, 2B4, CD160 and KLRG1 by HCV-specific CD8+ T cells in a cohort of 38 patients with chronic HCV that displayed HCV-specific tetramer responses (Table 1). As shown in Fig. 2A, we found a high expression of PD-1 in our cohort (median: 88.9%). Although PD-1 expression was high on most HCV-specific CD8+ T cell responses, it was not detectable on all HCV-specific CD8+ T cells (Fig. 2). 2B4 was expressed by some HCV-specific CD8+ T cells with a median expression of 62.0% (Fig. 2A and B), whereas CD160 was detectable only on a fraction of virus-specific CD8+ T cells (median: 7.4%) (Fig. 2A and B). Expression of KLRG1 was found to be quite variable, with a median expression of 40.8% (Fig. 2A and B).
Previously, we have shown that HCV-specific CD8+ T cells consist of subsets with distinct phenotypical and functional properties that can be defined by CD127 expression [17]. Thus, we asked whether the expression of CD127 is also linked to the expression of the inhibitory receptors PD-1, 2B4, CD160 and KLRG1 by HCV-specific CD8+ T cells. To address this question, we costained the inhibitory receptors and CD127 on HCV-specific CD8+ T cells. Depending on the expression level of CD127 (Fig. S1), we defined three groups of HCV-specific CD8+ T cells as displayed in Fig. 3A: a CD127 high (hi) group with more than 80% of epitope-specific CD8+ T cells expressing CD127, a CD127 intermediate (mid) group with 50–80% of CD127 expression and a CD127 low (lo) group with less than 50% expression of CD127. Importantly, we found a clear association between the expression of CD127 and the expression of inhibitory receptors on HCV-specific CD8+ T cells. Indeed, CD127lo cells highly expressed PD-1 (median 97.5%), 2B4 (median 80.0%) and CD160 (median 19.3%) (Fig. 3B). CD127mid cells expressed slightly reduced levels of PD-1 (median 86.7%), 2B4 (median 66.4%) and CD160 (median 8.9%). In contrast, CD127hi cells showed a significantly lower expression of PD-1 (median 48.3%), 2B4 (median 35.7%) and CD160 (median 3.3%) (Fig. 3B). KLRG1 was also higher expressed on HCV-specific CD8+ T cells with a CD127lo phenotype compared to a CD127mid and CD127hi phenotype (67.1% vs 40.8% vs 25.0%) (Fig. 3B). In contrast to PD-1, 2B4 and CD160, however, the range of KLRG1 expression varied more strongly between both groups. Next, we analyzed the coexpression of inhibitory receptors on HCV-specific CD8+ T cells in 10 patients at the single cell level in a multi-inhibitory receptor staining (Fig. S2). As shown in Fig. 3C, a CD127lo phenotype was associated with a high frequency of HCV-specific CD8+ T cells coexpressing multiple inhibitory receptors. The inhibitory receptor coexpression profile of CD127mid HCV-specific CD8+ T cells was reduced compared to CD127hi cells. For example, in our cohort, more than 80% of CD127lo cells coexpressed 2 or more inhibitory receptors while coexpression was found in less than 55% of CD127mid cells (Fig. 3C). In contrast, a CD127hi phenotype was associated with low or absent levels of inhibitory receptor coexpression. These results clearly illustrate that the coexpression of multiple inhibitory receptors by HCV-specific CD8+ T cells is linked to low levels of CD127 expression.
Next, we asked whether the coexpression of multiple inhibitory receptors on CD127lo CD8+ T cells defines functionally impaired HCV-specific CD8+ T cells, indicating exhaustion. We addressed this by stimulating CD127lo, CD127mid and CD127hi HCV-specific CD8+ T cells antigen-specifically for one week and by analyzing the subsequent proliferation in the presence or absence of PD-L1 blockade. Of note, we found significant differences regarding the proliferative capacity and the effect of PD-L1 blockade between these groups (Fig. 4). Indeed, the proliferative capacity of CD127hi HCV-specific CD8+ T cells was much higher upon antigen stimulation compared to CD127mid and CD127lo HCV-specific CD8+ T cells. As shown in Fig. 4A and 4E, CD127lo HCV-specific CD8+ T cells that expressed multiple inhibitory receptors proliferated poorly upon antigen stimulation. However, these cells could be reinvigorated partially upon blockade of the inhibitory PD-1/PD-L1 signaling pathway, indicating that they were in a functional reversible state of exhaustion. CD127mid HCV-specific CD8+ T cells displayed an improved proliferative capacity upon antigen stimulation (Fig. 4B). In contrast to CD127lo and CD127mid cells, CD127hi HCV-specific CD8+ T cells proliferated vigorously (Fig. 4C) upon stimulation with peptide alone. Additional PD-L1 blockade resulted in only a minor increase in proliferation. In a subsequent analysis of 15 patients, we found that the increase in HCV-specific CD8+ T cells due to PD-L1 blockade was about 2.2fold for CD127lo cells. This increase was significantly higher compared to CD127mid (1.5fold) and CD127hi cells (1.2fold) (p<0.01) (Fig. 4D). These results indicate that CD127lo HCV-specific CD8+ T cells are functionally impaired in vivo and are functionally dependent on inhibitory receptor blockade, indicating exhaustion.
It has previously been shown that the expression of inhibitory CD8+ T cell receptors, e.g. PD1 is linked to the stage of T cell differentiation [18], [19]. Thus, we asked whether the coexpression of CD127 and all inhibitory receptors analyzed in our study was linked to certain stages of T cell differentiation. For this analysis, we assessed post-thymic human CD8+ T cell differentiation using a linear model based on CD27, CCR7 and CD45RA expression as displayed in Fig. 5A [18]. CD127 expression was highest in naïve and early T cell subsets (subsets 1 and 2), but reduced in late differentiated CD8+ T cells, indicating that CD127 expression was linked to early stages of T cell differentiation (Fig. 5B). In agreement with a previous study, the expression of PD-1 was highest on intermediate differentiated CD8+ T cells (subset 3) and lowest on naïve and late differentiated CD8+ T cells [19] (Fig. 5C). Interestingly, the expression patterns of 2B4, CD160 and KLRG1 did not mirror the expression pattern of PD-1 but displayed unique, individual profiles. 2B4 expression was lowest on naïve CD8+ T cells but progressively increased along the differentiation subsets reaching almost 100% expression on late-differentiated CD8+ T cells (Fig. 5D). CD160 expression was lowest in naïve T cells and increased in further differentiated subsets with two expression peaks in intermediate subset 3 and late differentiated T cell subset 5 (Fig. 5E) while KLRG1 showed a similar expression pattern as 2B4 being low in naïve and early subsets but enriched in late differentiated subsets (Fig. 5F). Overall, these results indicate that inhibitory receptors are increasingly expressed by further differentiated CD8+ T cells. However, since very distinct patterns of expression were observed for each marker, the expression of one inhibitory receptor does not necessarily indicate coexpression of others. Thus, we asked whether coexpression of the inhibitory receptors 2B4, CD160 and KLRG1 together with PD-1 occurred in a specific T cell differentiation subset. Indeed, as shown in Fig. 6, we identified the highest level of coexpression of all markers in the intermediate T cell subset 3. These results indicate that inhibitory receptors exhibit individual and distinct expression patterns linked to T cell differentiation and that coexpression of 2B4, CD160 and KLRG1 with PD-1 indicating exhaustion occurs in intermediate rather than late differentiated CD8+ T cell subsets.
Finally, we asked whether the same association can also be found within the HCV-specific CD8+ T cell population. As shown in Fig. 7 and in agreement with previous findings, we observed that HCV-specific CD8+ T cells consisted mostly of early and intermediate differentiated T cells. Interestingly, the differentiation of HCV-specific CD127hi, CD127mid and CD127lo cells was significantly different (p<0.0001). The majority of CD127hi cells belonged to the early differentiation subset 2, whereas CD127mid cells were distributed at comparable levels in subset 2 and subset 3. In contrast, HCV-specific CD127lo cells were prominently found within the intermediate subset 3. Keeping in mind that the coexpression of inhibitory receptors is highest in the intermediate subset 3 and associated with a CD127lo phenotype, these results clearly suggest that there is a complex link between CD127 expression, T cell differentiation and T cell exhaustion.
In a final set of experiments, we set out to determine whether the differential expression of inhibitory receptors by phenotypically and functionally distinct HCV-specific CD8+ T cell populations is related to the autologous viral sequence present in a given patient. To address this issue, we sequenced the autologous viral sequences of genotype 1 patients corresponding to recognized CD8+ T cell epitopes (Table 2). Importantly, sequence variations from the genotype consensus sequence were significantly more prevalent within epitopes targeted by CD127hi HCV specific CD8+ T cells with a low expression of inhibitory receptors (p<0,0001)(Fig. 8A). We confirmed that these sequence variations represented viral escape in several patients by stimulating HCV-specific CD8+ T cells with peptides matched to both the autologous and consensus sequence. Indeed, as shown for six representative patients in Fig. 8B, HCV-specific CD8+ T cells produced high levels of IFN-γ upon stimulation with the consensus sequence peptide, but not when stimulated with the variant autologous peptide. Furthermore, proliferation of these HCV-specific CD8+ T cells was significantly reduced upon stimulation with the variant autologous peptide, indicating viral escape (data not shown). These results indicate that viral escape mutations are linked to a CD127hi phenotype whereas a CD127mid or CD127lo phenotype is linked to ongoing antigen recognition of the autologous virus sequence. To further test this hypothesis, we analyzed HCV-specific CD8+ T cells from patients with multiple immunodominant T cell responses that harbored consensus and variant sequences in different epitopes. Indeed, different expression profiles of inhibitory receptors and CD127 could be observed within the same patient depending on the virus sequence (patient 4, Fig. 8C, Table 2). Indeed, although both immunodominant responses were detectable at a comparable frequency (Fig. S3), all NS3-1406-specific CD8+ T cells expressed CD127, but only low levels of PD-1 (14.3%), 2B4 (4.17%), CD160 (2.09%) and KLRG1 (25.0%), whereas an opposite phenotype was observed on NS5-2594 specific CD8+ T cells, of which only 9,09% expressed CD127, but high levels of PD-1 (86.7%), 2B4 (81.8%), CD160 (27.3%) and KLRG1 (86.7%) (Fig. 8C, 8D). These results underline that the mechanisms responsible for inhibitory receptor coexpression operate in an epitope-specific manner depending on the autologous virus sequence. To further test the hypothesis that the low expression of inhibitory receptors and high expression of CD127 are indeed caused by absence of antigen triggering in vivo, we stimulated CD127hi HCV-specific CD8+ T cells with the consensus peptide in vitro. Importantly, this peptide specific stimulation induced a downregulation of CD127 and an upregulation of inhibitory receptor expression after a seven day culture (shown in Fig. 9). In sum, the downregulation of CD127 expression and upregulation of inhibitory receptors upon antigen stimulation suggest that the absence of antigen triggering in vivo is responsible for a CD127hi phenotype with a low expression of inhibitory receptors. In agreement with this hypothesis, we found a low expression of CD127 and a high expression of inhibitory receptors by virus-specific CD8+ T cells that targeted epitopes without sequence variations, indicating ongoing antigen recognition (Fig. 8A). These results also underscore the importance of evaluating autologous viral sequences in studies aimed at investigating the mechanisms of virus-specific CD8+ T cell failure.
Here, we have investigated the expression of inhibitory receptors by virus-specific CD8+ T cells during chronic HCV infection and their association with CD127 expression, proliferative capacity, T cell differentiation and antigen recognition. We found a specific pattern of expression of inhibitory receptors by HCV-specific CD8+ T cells. Indeed, a large fraction of the HCV-specific CD8+ T cell responses detectable in our cohort coexpressed 2B4, CD160 and KLRG1 next to PD1. These results are interesting in the light of a recent study by Blackburn et al. demonstrating a clear association between a specific expression pattern of inhibitory receptors including 2B4, CD160, LAG-3 and CTLA-4 next to PD-1 and severe exhaustion of virus-specific CD8+ T cells in LCMV infection in mice [9]. Interestingly, we did not observe a significant expression of LAG-3 and CTLA-4 on HCV-specific CD8+ T cells in our study. However, these results are not surprising since it has previously been shown that CTLA-4 expression by HCV-specific CD8+ T cells is only detectable to some degree in the liver, but not in the blood of chronically infected patients [5]. In addition, a gene expression analysis of exhausted CD8+ T cells in mice revealed that LAG-3 and CTLA-4 coexpression showed a weaker association with PD-1 compared to 2B4 and CD160 coexpression [12]. Thus, these combined results indicate a dynamic hierarchical pattern of inhibitory receptor expression by exhausted CD8+ T cells with prominent roles of 2B4 and CD160 [12]. Accordingly, our results indicate that a significant fraction of HCV-specific CD8+ T cells display typical markers of exhaustion. Importantly, HCV-specific CD8+ T cell exhaustion defined as a loss of CD8+ T cell function, e.g. proliferation and cytokine secretion has been reported by several groups [4], [5], [20], [21], [22]. Our findings implicate that therapeutic targeting of exhausted CD8+ T cells during chronic HCV infection may require not only the blockade of one inhibitory receptor pathway, but rather a cocktail of antibodies targeting multiple inhibitory pathways [6]. In line with these findings, we observed only a 2.2fold increase in proliferation of exhausted CD127lo HCV-specific CD8+ T cells upon PD-1/PD-L1 blockade (Fig. 4D). An even smaller increase in proliferation due to PD-1/PD-L1 blockade was seen in CD127hi HCV-specific CD8+ T cells, however, this may be explained by the finding that these cells proliferate vigorously in the absence of blockade, indicating that they are not significantly inhibited (Fig. 4C–E and [17]). Clearly, additional studies should address the effect of 2B4, CD160 and KLRG1 receptor blockade on exhausted T cells as soon as suitable blocking reagents are available.
2B4 is a coregulatory receptor capable of mediating activatory and inhibitory signals in CD8+ T cells [23]. It was previously considered as a predominantly activating receptor in humans [24]. However, a recent study addressed the dual roles of murine and human 2B4 receptor signaling and found that both human and murine 2B4 can exert both inhibitory or activatory receptor functions [10]. Importantly, high levels of 2B4 surface expression were linked to inhibitory 2B4 function, whereas low levels of 2B4 surface expression promoted activatory 2B4 signals [10]. Based on the data analyzed in our study, we cannot exclude that ligation of 2B4 on HCV-specific CD8+ T cells may result in a costimulatory signal. However, we used a strict gating strategy to determine 2B4 positivity only on cells with a high surface expression level of 2B4 (Fig. 2B and data not shown), indicating that 2B4 may act as an inhibitory receptor on these cells. Further studies will be needed to clarify whether 2B4 expression on virus-specific CD8+ T cells, indeed, promotes inhibitory signals.
Of note, we observed significant levels of KLRG1 expression on HCV-specific CD8+ T cells (Fig. 1,2). KLRG1 is up-regulated by virus-specific CD8+ T cells upon repetitive antigenic stimulation and frequently used as a marker for T cell differentiation [16], [25]. Although KLRG1 expression was not shown to be strongly associated with PD-1 coexpression by exhausted CD8+ T cells in mice it was, however, identified on a significant fraction of exhausted CD8+ T cells [9], [12]. Since the up-regulation of KLRG1 is induced by repetitive antigen stimulation, it is well possible that KLRG1 expression might not directly reflect T cell exhaustion but rather ongoing antigen recognition, that, however, by itself is thought to be the prerequisite for the development of CD8+ T cell exhaustion [26], [27].
Our results also show that the expression level of the inhibitory receptors PD-1, 2B4, CD160 and KLRG1 is linked to a specific stage of cell differentiation. Recently, a linear model of human CD8+ T cell differentiation has been developed that distinguishes between naïve, early, intermediate and late differentiated cells [28]. The differentiation status of CD8+ T cells is influenced by the history of antigenic stimulation, clonal cell division, telomere length and proliferative capacity of CD8+ T cells [18]. Here, we found that the expression of PD-1 was highest on CD8+ T cells with an intermediate T cell differentiation phenotype. In contrast, 2B4, CD160 and KLRG1 were differentially expressed by late differentiated CD8+ T cells (Fig. 5). However, the highest level of coexpression of all inhibitory receptors was observed within the intermediate T cell differentiation stage (Fig. 6), indicating that exhaustion of CD8+ T cells is most likely linked to this specific subset. It remains unclear whether the coexpression of inhibitory receptors 2B4, CD160 and KLRG1 by late differentiated cells in the absence of PD-1 may also define a specific subset of exhausted CD8+ T cells. However, this is rather unlikely since coexpression of inhibitory receptors 2B4 and CD160 by late T cell differentiated T cells has been shown to define a functional cytotoxic CD8+ T cell population [24], [29].
Another important finding of our study is that coexpression of inhibitory receptors on HCV-specific CD8+ T cells is linked to the expression level of CD127. We have previously described the existence of subsets of HCV-specific CD8+ T cells with distinct functional properties that can be distinguished by the level of CD127 expression [17]. Here, we classified HCV-specific CD8+ T cells depending on the level of CD127 expression into three groups: CD127lo, CD127mid and CD127hi cells. CD127lo HCV-specific CD8+ T cells express markers associated with effector cells and are dysfunctional in terms of proliferation whereas CD127hi cells phenotypically and functionally resemble memory CD8+ T cells [17]. These memory-like properties are also reflected by an increased expression of antiapoptotic molecules [30], [31](Fig. S3). Here, we found that high coexpression of inhibitory receptors next to PD-1 was restricted to CD127lo HCV-specific CD8+ T cells with impaired proliferative capacity that could be reinvigorated upon PD-1/PD-L1 signaling blockade, thus, correlating well with the impaired functional state of these cells. We also found that HCV-specific CD8+ T cells do not represent a homogenously differentiated CD8+ T cell population. This novel finding was made possible through the use of polychromatic flow cytometry that allowed us to simultaneously stain HCV-specific CD8+ T cells for the set of differentiation markers recommended by Appay et al. [18] and extends previous reports regarding the differentiation of HCV-specific CD8+ T cells that used conventional 4-color flow cytometry [18], [28]. Of note, the differentiation stage of HCV-specific CD8+ T cells was significantly different for CD127hi, CD127mid and CD127lo cells (Fig. 7). In addition, the intermediate differentiation of HCV-specific CD8+ T cells characterized by the coexpression of multiple inhibitory receptors (as observed prominently for CD127lo cells) correlates with the coexpression of inhibitory receptors in intermediate differentiation subsets observed on the general CD8+ T cell population. Since these results underline the existence of phenotypically and functionally very different HCV-specific CD8+ T cell populations during chronic HCV infection that can be defined by the differential expression of CD127 and inhibitory receptors, respectively, it will be important to distinguish between these populations in future studies analyzing HCV-specific CD8+ T cells.
Another important finding of our study is that the expression of inhibitory receptors by virus-specific CD8+ T cells is associated with ongoing antigen recognition and the absence of viral escape. Since we could not analyze the virus sequence that initially infected our patients, and we thus could not observe the development of escape mutations as has been elegantly shown in recent longitudinal studies in acute HCV infection [32], [33], we assumed that the majority of patients was infected with the genotype consensus sequence (table 2). This approach has some limitations, for example it cannot be ruled out that remnants of a prior infection with an alternate genotype or varying epitope sequence confounded our analysis (for example, the sequence of the autologous epitope matches the genotype consensus sequence in one CD127hi patient (the NS3-1406 epitope in subject 9)). In addition, it is impossible to dissect whether a variant epitope that matches the heterologous genotype sequence that is targeted by a CD127hi T cell response represents viral escape or whether the T cell response represents the remnant of a prior resolved infection with the alternate genotype (e.g., the NS5-2594 epitope in subject 32). Clearly, these issues can only be addressed in cohorts of HCV-infected patients, in which the sequence of the inoculum and information about the T cell response prior to infection is known. However, the clear association of high levels of CD127 expression on virus-specific CD8+ T cells with the presence of autologous sequence variations from the consensus epitopes, the reduced IFN-γ production and reduced proliferation of CD8+ T cells upon stimulation with variant but not with consensus peptides supports our approach in the majority of cases. Of note, we also found a clear association between the expression level of inhibitory receptors next to PD1 and the absence of sequence variations indicating ongoing antigen recognition. These results are in agreement with recent studies showing that high levels of specific antigen drive CD8+ T cell exhaustion [27] and that exhaustion of CD8+ T cells can be prevented by the emergence of viral escape mutations that abrogate epitope recognition [34]. In line with these findings, emergence of viral sequence variations was associated with a reduction of PD-1 expression and upregulation of CD127 expression in acute and chronic HCV infection [32], [33] and HIV infection [35]. It is also interesting to note that we observed sequence variations in about half of the targeted T cell epitopes. Although the number of HCV-specific CD8+ T cell epitopes analyzed in our study was limited, and it is thus hard to make firm conclusions about the general frequencies of viral escape among HCV-specific CD8+ T cell epitopes, our results are in agreement with recent studies that also have shown the presence of viral escape mutations in about 50% of targeted CD8+ T cell epitopes [36], [37]. Thus, both mechanisms of CD8+ T cell failure, viral escape and T cell exhaustion, defined by the coexpression of multiple inhibitory receptors, seem to contribute significantly to the ineffective viral control and they can be easily identified by specific surface expression patterns.
In sum, our results show that the coexpression of inhibitory receptors by exhausted HCV-specific CD8+ T cells is linked to CD127 expression, proliferative capacity, the stage of T cell differentiation and ongoing antigen recognition. Thus, a complex interplay of immunological and virological factors determines T cell exhaustion in human chronic viral infection. These findings have also important implications for the rational design of immunotherapeutic treatment strategies in chronic HCV infection since only exhausted CD8+ T cells still recognize the present viral antigens and should thus be targeted by immunomodulatory strategies.
38 patients with chronic HCV infection presenting at the outpatient hepatology clinic of the University Hospital Freiburg with detectable HCV-specific CD8+ tetramer responses were included in the study after obtaining written informed consent from the patients and approval by the ethics committee of the Albert-Ludwigs-Universität, Freiburg. All investigations have been conducted according to the principles expressed in the Declaration of Helsinki. The characteristics of the study population are included in table 1.
Detection of HCV viral load and epitope sequencing was performed as previously described [37].
Lymphocytes were analyzed from patient blood as previously described [17].
Peptides corresponding to immunodominant HLA-A2- and HLA-B27- epitopes (sequences CINGVCWTV, KLVALGINAV, ALYDVVTKL, ARMILMTHF) and variant autologous patient sequences were obtained from Biosynthan, Berlin, Germany. These peptides were dissolved in 100% dimethyl sulfoxide (Sigma-Aldrich, Germany) at 20 mg/ml and further diluted to 1 mg/ml with RPMI 1640 (Gibco) before use. HLA-A2- and HLA-B27-tetramers were obtained from the National Tetramer Core Facility at Emory University, Atlanta. Influenza-specific pentamers containing the immunodominant GILGFVFTL peptide were purchased from ProImmune, Oxford, UK.
The following reagents were used for polychromatic stainings: anti-2B4-FITC, anti-CD27-APC-eFluor780, anti-CD45RA-PerCP-Cy5.5, anti-PD-1-PE, anti-CD127-APC Alexa Fluor 750, anti-CD127-APC-eFluor 780, anti-CD127-Pacific Blue, anti-IgM-PerCPeFluor710, streptavidin-eFluorV450 (Ebioscience), anti-Bcl-2-PE, anti-CCR7-PE-Cy7, anti-CD28-FITC, anti-CD38-PE, anti-CD8-APC H7, anti-CD8 AmCyan (BD Biosciences), anti-CD127-PE, anti-CD160-PE, anti-CD57-FITC (Beckman Coulter), anti-CTLA-4-FITC (R&D), anti-CD3-PerCP, anti-CD160-FITC, purified anti-CD160 (BioLegend), anti-LAG-3-Atto488 (Alexxis), anti-LAG-3 FITC (LifeSpan BioSciences), anti-LAG-3-PE (R&D). Viaprobe (7-AAD, BD Biosciences) was used for dead cell exclusion. The anti-KLRG1-AlexaFluor488 and anti-KLRG1-biotin antibodies were generated as previously described [16].
Tetramer and antibody staining was performed as previously described [17]. For the multi-inhibitory receptor staining, the cells were first stained with the CD160-IgM-pure antibody, then with the anti-IgM-PerCP secondary antibody, then stained with the KLRG1-biotin antibody followed with the streptavidin-eFluorV450 antibody. Cells were then incubated with the tetramer followed by the staining with the fluorophore-conjugated antibodies (CD8-AmCyan, 2B4-FITC, PD-1-PE, CD127-APC-eFluor780). Incubation time was 15 min for each staining step and cells were washed twice in between the addition of staining reagents.
Samples were acquired on a FACS Canto II flow cytometer (BD Biosciences) and analyzed with FlowJo v8.8.6 software (TreeStar Inc.). Gates for positivity in polychromatic panels were determined by fluorescence-minus-one control stains, as recommended [38].
2*10∧6 freshly isolated PBMCs were labelled with 40 µM Pacific Blue succinimidyl ester (PBSE, Invitrogen), and stimulated with 10 µM peptide in the presence of 20 IU/ml rhIl-2 (Roche) in 1 ml complete medium (RPMI 1640 containing 10% fetal calf serum, 1% streptomycin/penicillin, and 1.5% Hepes buffer 1 mol/L). Cells were incubated at 37°C in the presence or absence of 10 µg/ml anti-PD-L1 (Ebioscience) for one week, subsequently stained and analyzed by polychromatic flow cytometry. PD-L1 blockade-dependent gain of proliferation was determined by the ratio: frequency of HCV-specific CD8+ T cells after peptide stimulation and PD-L1 blockade divided by the frequency after peptide stimulation alone. The proliferation index was calculated by dividing the frequency of HCV-specific CD8+ T cells after stimulation by the frequency ex vivo.
Statistical analysis was performed using GraphPad Prism 5 software (GraphPad Prism Software, Inc.). The statistical tests used were ANOVA 1way analysis of variance followed by Newman-Keuls Multiple Comparison Test (Fig. 3,4,7), and Mann-Whitney U test (Fig. 8).
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10.1371/journal.ppat.1000527 | Production of Superoxide Anions by Keratinocytes Initiates P. acnes-Induced Inflammation of the Skin | Acne vulgaris is a chronic inflammatory disorder of the sebaceous follicles. Propionibacterium acnes (P. acnes), a gram-positive anareobic bacterium, plays a critical role in the development of these inflammatory lesions. This study aimed at determining whether reactive oxygen species (ROS) are produced by keratinocytes upon P. acnes infection, dissecting the mechanism of this production, and investigating how this phenomenon integrates in the general inflammatory response induced by P. acnes. In our hands, ROS, and especially superoxide anions (O2•−), were rapidly produced by keratinocytes upon stimulation by P. acnes surface proteins. In P. acnes-stimulated keratinocytes, O2•− was produced by NAD(P)H oxidase through activation of the scavenger receptor CD36. O2•− was dismuted by superoxide dismutase to form hydrogen peroxide which was further detoxified into water by the GSH/GPx system. In addition, P. acnes-induced O2•− abrogated P. acnes growth and was involved in keratinocyte lysis through the combination of O2•− with nitric oxide to form peroxynitrites. Finally, retinoic acid derivates, the most efficient anti-acneic drugs, prevent O2•− production, IL-8 release and keratinocyte apoptosis, suggesting the relevance of this pathway in humans.
| Acne vulgaris is a chronic inflammatory disorder of the sebaceous follicles. It is the most common skin disease, affecting up to 80% of individuals at some point between the ages of 11 and 30 years. Propionibacterium acnes (P. acnes) plays a role in the development of inflammatory acne lesions, but whether it causes inflammation by itself or through indirect mechanisms is not clear yet. Therefore, by exposing epidermal cells to P. acnes in vitro, we tested whether reactive oxygen species (ROS) production (oxidative burst) was involved in the inflammatory process. We found that one particular ROS, superoxide anion, was generated by epidermal cells following P. acnes stimulation. This phenomenon is associated with the production of a soluble pro inflammatory molecule, IL-8, and epidermal cell death. The abrogation of P. acnes-induced oxidative burst by the most commonly used and most efficient treatments of acne suggests that superoxide anions produced by epidermal cells are critical in the development of acne inflammatory lesions.
| Acne vulgaris is a chronic inflammatory disorder of the sebaceous follicles. Acne is the most common skin disease, estimated to affect up to 80% of individuals at some point between the ages of 11 and 30 years. Despite its common occurrence, the pathogenesis of acne is not fully understood. Excessive shedding of epithelial cells from the walls of follicles combined with increased amounts of sebum produced by associated sebaceous glands are two important factors that contribute to follicular obstruction. This obstruction leads to the formation of microcomedos, which are believed to precede lesions of acne. These microcomedos may evolve into clinically visible comedos and/or inflammatory lesions.
Propionibacterium acnes (P. acnes), a gram-positive anaerobic bacterium part of the normal skin flora, plays a critical role in the development of inflammatory lesions in acne [1]. Various mechanisms can explain the role of P. acnes in skin inflammation. First, it is widely accepted that inflammation may be induced by the immune response of the host to P. acnes. Chemotactic substances released from the bacteria attract polymorphonuclear leukocytes to the site of inflammation. Those cells are activated locally to produce inflammatory cytokines such as TNF-α, IL-1β, and IL-8 [2]. After phagocytosis of the bacteria, the attracted neutrophils are thought to release lysosomal enzymes and produce reactive oxygen species (ROS) that can damage the follicular epithelium.
Beside the immune response of the host, a direct effect of P. acnes on keratinocytes has also been suspected in the initiation of the inflammatory process. Indeed, P. acnes interacts with toll-like receptors TLR-2 and TLR-4 on keratinocytes [3]. This interaction induces the release of inflammatory cytokines such as IL-1α, IL-1β, IL-8, GM-CSF, and TNF-α [4],[5]. Although nothing is known about the interaction between P. acnes or any other bacteria with keratinocytes in terms of reactive oxygen species (ROS) production, purified tuberculine has been shown to activate TLR-2 on keratinocytes, leading to the production of ROS during tuberculosis infection [6]. In addition, Vitreoscilla filiformis has been identified to activate MnSOD as an inducible free-radical scavenger in keratinocytes [7]. Furthermore, keratinocytes are known to produce ROS upon exposure to toxic compounds such as inorganic arsenic [8] or to ultraviolet radiations [9],[10]. Whatever the mechanism implicated in the induction of skin inflammation by P. acnes, ROS are probably involved in that process since the production of hydrogen peroxide (H2O2) is increased in neutrophils from acne patients [11]. Moreover, the decrease in superoxide dismutase (SOD) activity in patients with acne lesions [12] is correlated with the severity of acne [13].
ROS are short-lived small molecular structures that are continuously generated at low levels during the course of normal aerobic metabolism. They are also part of the inflammatory process that aims at killing or eliminating invasive microorganisms and/or eliminating damaged tissular structures. Among the large number of ROS that have been described, superoxide anion (O2•−) and hydrogen peroxyde (H2O2) play prominent roles. On the other hand, the interaction between O2•− and nitric oxide (NO), leads to the formation of highly reactive peroxynitrites (ONOO•−). ROS interact strongly with a variety of molecules including lipids, proteins, and nucleic acids. Produced in large amounts, ROS can lead to apoptotic or necrotic cell death. To counteract the overproduction of ROS, skin is equipped with antioxidant mechanisms including anti-oxidant enzymes such as superoxide dismutase (SOD) that detoxifies O2•−, catalase, and glutathione peroxidase (GpX) that uses reduced glutathione (GSH) to detoxify H2O2 into water [14].
In this work, we have used an in vitro model to investigate ROS production by keratinocytes upon P. acnes stimulation. We have dissected the control mechanisms of this production, and investigated how they fit into the general inflammatory response induced by P. acnes.
P. acnes increased the production of O2•−, NO and H2O2 by the immortalized keratinocyte cell line HaCaT in a dose-dependent manner (Figure 1A, B and C). At the highest concentration of P. acnes, O2•−, NO and H2O2 levels were increased by 85% (P<0.05), 44.5% (P<0.05) and 41% (P<0.05), respectively. We then evaluated the kinetics of ROS production (Figure 2). The production of O2•−, was significantly increased 15 min after P. acnes stimulation (P<0.05). The production reached its peak one hour after the stimulation, then progressively declined (Figure 2A). In contrast, both NO and H2O2 productions increased slowly and reached their highest levels after 24 h of incubation with P. acnes (Figures 2B and C). Since keratinocytes stimulated by P. acnes can produce IL-8 [3], we next compared the kinetics of ROS production with that of IL-8 production upon P. acnes stimulation (Figure 2D). Significant levels of IL-8 protein appeared 2 h after incubation with P. acnes (P<0.05) and increased along with ROS production. Altogether, these results indicate that the production of ROS, and especially of O2•−, is a very early event occurring almost immediately after the stimulation of keratinocytes with P. acnes. The production of O2•− by HaCat keratinocytes was identical whether the cells had been stimulated by an extract of P. acnes surface proteins or by the whole bacteria. O2•− production was measured using DHE, and cell death estimated using YO-PRO-1 were dose-dependent (Figure 3).
Superoxide anions can originate from the mitochondrial complex I or III of the respiratory chain, or from the cytosolic enzymes NAD(P)H oxidase or xanthine oxidase. Incubation of P. acnes-stimulated keratinocytes with rotenone and antimycin that inhibit the mitochondrial respiratory chain complexes I and III, respectively, did not significantly alter the production of O2•− (Figure 4A). Incubation of P. acnes-stimulated keratinocytes with DPI (a NAD(P)H oxidase inhibitor) significantly decreased O2•− production (P<0.03), while incubation with allopurinol (a xanthine oxidase inhibitor) had no effect (Figure 4A). To confirm that Nox is the main source of O2•− in keratinocytes stimulated by P. acnes, the level of Nox1 was knocked down using RNA interference. The small interfering RNA (siRNA) Nox1A-siRNA was used as described previously [15]. Nox1A-siRNA dramatically decreased the production of O2•− upon stimultion by P. acnes in transfected-keratinocytes, with nearly 100% inhibition after 3 h of stimulation (Figure 4C). Keratinocytes treated with scrambled sequence siRNA produced similar levels of O2•− as non-transfected cells. These results demonstrated that O2•− is mainly produced by NAD(P)H oxidase in P. acnes-stimulated keratinocytes.
In order to determine the pathways implicated in the detoxification of ROS produced by P. acnes-stimulated keratinocytes, we used specific modulators of the enzymatic systems involved in ROS metabolism. Superoxide anions are converted into hydrogen peroxide by SOD. Inhibiting SOD by the specific inhibitor DDC significantly increased O2•− production by P. acnes-stimulated keratinocytes (P<0.003) (Figure 4A). By contrast, incubation of keratinocytes with MnTBAP or CuDIPS, two SOD mimics, significantly decreased O2•− production by P. acnes-stimulated keratinocytes (P<0.05 and P<0.04, respectively) (Figure 4A). Hydrogen peroxide is converted into H2O by two sets of enzymes, catalase and the GSH/GPx system. The elevation of hydrogen peroxide levels can be caused either by an increase in superoxide dismutation as observed following incubation with MnTBAP (P<0.004) or CuDIPS (P<0.02) or by a decrease in the detoxification pathways (Figure 4B). Specific inhibition of catalase by aminotriazol (ATZ) or addition of exogenous catalase, had no effect on the levels of hydrogen peroxide (Figure 4B). Thus, the catalase pathway is not involved in the control of hydrogen peroxide detoxification in our system. Depleting GSH with BSO, inhibited GPx and significantly increased H2O2 production (P<0.05), while adding exogenous GSH or its precursor NAC, significantly decreased H2O2 levels (P<0.004 and P<0.01, respectively) (Figure 4B). Those results highlight the role of GSH/GPx in keratinocytes to counteract the overproduction of ROS induced by P. acnes: superoxide anions are dismuted by SOD into hydrogen peroxide, which is further detoxified into H2O through the GSH/GPx pathway.
Given the high toxicity of ROS, we were prompted to investigate if the levels of O2•− produced by keratinocytes could impact cellular viability. The apoptosis of keratinocytes induced by P. acnes alone was estimated by YO-PRO-1 (Figure 5A) and TUNEL staining (Figure 5B). In order to determine the nature of ROS involved in P. acnes-induced cellular toxicity, we pre-treated keratinocytes wih specific modulators of the enzymes involved in the production of O2•− and H2O2 and measured the death of keratinocytes upon stimulation with P. acnes. Inhibition of superoxide anions by allopurinol, DPI or MnTBAP, significantly decreased P. acnes-induced keratinocyte apoptosis (P<0.005, P<0.005, P<0.001, respectively), whereas DDC and antimycin, two compounds that increase O2•− production, increased cell death (P<0.007 and P<0.01, respectively) (Figure 5A). CuDIPS, a mimic of the cytosolic superoxide dismutase, increased cell death. This is explained by the cytotoxic properties of this molecule on the cells. No major effect was observed on the rate of cell death with molecules modulating H2O2 production, such as ATZ, BSO, NAC, GSH or catalase.
Peroxinitrites result from the combination of O2•− and NO. They are highly reactive metabolites that create nitrosyl residues on proteins and alter their functions. Therefore, the levels of nitrosyl residues not only reflect the intensity of the oxidative attack but are also markers of the cellular damages created by the oxidative burst. As shown by flow cytometry, 3-nitrosotyrosyl residues were dose-dependently increased from with very low concentrations of P. acnes (Figure 6A). This result is in agreement with the observation that the nitric oxide synthase (NOS) is activated in keratinocytes stimulated with P. acnes. The expression of iNOS was steady in keratinocytes during a period of time of 24 h as determined by RT-PCR (Figure 6B) and by RT-qPCR (data not shown). This data is consistent with those presented in Figure 1, showing that NO was produced early after P. acnes incubation, making its interaction between O2•− and NO possible. Altogether, those experiments suggested that the toxicity of O2•− produced by keratinocytes stimulated with P. acnes was dependent on the combination with NO and the production of nitrosyl residues.
In order to evaluate the role of O2•− in the production of IL-8 by P. acnes-stimulated keratinocytes, we measured the levels of IL-8 produced in presence of the various ROS modulators (Figure 7). All the molecules that inhibited O2•− production also decreased IL-8 synthesis, but only the decrease induced by DPI reached statistical significance (P<0.03). If all the molecules that increased O2•− levels also increased IL-8 production, only the massive increase caused by DDC reached significance (P<0.04) (Figure 7). Both ATZ and NAC significantly decreased IL-8 production. Since it has previously been shown that ATZ has no effect on H2O2 production while NAC and GSH do (Figure 4), these results suggest that the effects of ATZ and NAC on IL-8 production are independent of the regulation of H2O2 and are more likely linked to intrinsic properties of those products. Altogether these results suggest that the toxicity of ROS on P. acnes-stimulated keratinocytes is mainly caused by O2•− which also exerts a positive effect on IL-8 production.
Since O2•− elicits IL-8 production by keratinocytes stimulated with P. acnes, we investigated which surface proteins could be implicated in the recognition of P. acnes. P. acnes-stimulated keratinocytes were incubated with antibodies to TLR-2 or CD36, and IL-8 and O2•− productions measured (Figure 8). The antibody directed to TLR-2 was known as a blocking agent for the production of IL-8 by keratinocytes after stimulation by P. acnes [3]. This was confirmed by the reduction in IL-8 production by 65% (P = 0.01) (Figure 8A), whereas no change was observed in O2•− production (Figure 8B). However, when P. acnes-stimulated keratinocytes were incubated in the presence of the antibody to CD36, the production of O2•− was reduced by 51% (P = 0.03) and the production of IL-8 was completely abolished.
We first compared the relative sensitivity of HaCaT cells and P. acnes to the toxic effect of O2•−. HaCaT cells and P. acnes were incubated separately with a solution containing O2•−. The growth of P. acnes was dose dependently inhibited by O2•− while the HaCaT cells appear to be more resistant than P. acnes at the same O2•− concentration (Figure 9A). We then tested the hypothesis that the ROS produced by keratinocytes, and particularly O2•−, could be responsible for the inhibition of the growth of P. acnes (Figure 9B). When P. acnes-stimulated keratinocytes were preincubated with MnTBAP, a MnSOD mimic that detoxifies O2•−, or with DPI that inhibits NAD(P)H oxydase, the growth of the bacteria was restored. Reciprocally, when keratinocytes where preincubated with DDC, a SOD inhibitor, the bacterial growth was decreased.
In order to evaluate the effects of the most common drugs used in the treatment of acne, HaCaT cells were stimulated by P. acnes in the presence of ZnSO4, doxycycline, nicotinamide, nitroimidazol, retinol, retinoic acid, or isotretinoin (Figure 10). The production of superoxide anions was reduced by all the drugs tested, at least at the highest concentration (0.05%), except for nicotinamide (Figure 10A). IL-8 production was reduced neither by ZnSO4 at low concentration (0.01%) nor by nicotinamide, but all the others drugs tested were effective. This is particularly the case for retinoic acid derivates that completely abolished IL-8 production (Figure 10B). The percentage of cells present in the wells after incubation ranged from 68 to 91% (Figure S1). All the drugs except ZnSO4 and nicotinamide reduced the apoptosis of keratinocytes stimulated by P. acnes at least at the highest concentration tested (0.05%). This is particularly the case for retinoic acid derivates (P<0.03 in all cases) and for the antibiotics doxycycline (P<0.02) and nitroimidazole (P<0.03) (Figure 10C). The rate of cell death in the presence of the various compounds alone ranged from 0 to 26% (Figure S2). Altogether, these results suggest that the anti-acne drugs are active on the production of O2•− and IL-8 as well as on the decrease in the death rate of keratinocytes.
This report describes the production of ROS by keratinocytes upon bacterial infection by P. acnes. The production of superoxide anions takes place at least one hour prior to that of nitrix oxide and hydrogen peroxide. The same kinetics is observed following UV radiation or arsenite intoxication [8].
Superoxide anions can originate from the cytosolic enzymes NAD(P)H oxidase, or xanthine oxidase, or from the complexes I or III of the mitochondrial respiratory chain. The use of DPI, an inhibitor of NAD(P)H oxidase and more specifically knocking down Nox1 by small RNA interference clearly shows that, in P. acnes-stimulated keratinocytes, O2•− is produced by NAD(P)H oxidase. This data is in line with a recent report showing that NAD(P)H oxidase is the major source of UVA-induced ROS in human keratinocytes where mitochondria are rapidly damaged after UVB exposure [16]. However, to date, no link between a specific damage of the mitochondrial respiratory chain and the production of O2•− has been established [15]. Under our experimental conditions, superoxide anions are dismuted by superoxide dismutase to form H2O2, which is further detoxified into water by the GSH/GPx system and not by the catalase pathway. In contrast, H2O2 generated by UVB applied to keratinocytes is detoxified through both the catalase and the GPx pathways. Usually, catalase finely tunes down H2O2 levels, while the glutathione system (GPx and reduced glutathione) is more specialized in buffering acute oxidative stress. This is probably what happens in the case of P. acnes infection.
However, the key-element for P. acnes-induced apoptosis of keratinocytes is O2•− and not H2O2. O2•− can be toxic per se or following its combination with NO to form peroxynitrites (ONOO•−), a phenomenon that requires the activation of inducible nitric oxide synthase (iNOS). We confirm that P. acnes induces the formation of nitrotyrosine residues on proteins, a footprint of in vivo peroxinitrite production [17]. Similarly, keratinocytes exposed to UVB or arsenite produce both O2•− and NO, potentially leading to peroxinitrite formation [8],[18]. In our model, the production of NO by P. acnes-stimulated keratinocytes is correlated with the steady expression of iNOS, as already observed in keratinocytes [18]. Those data suggest that the cytotoxicity mediated by ROS in our model involves the overproduction of O2•− and also the nitrosylation of amino acid residues on proteins.
Keratinocytes are the first line of defense against external aggressions; they participate in the innate immune response by secreting soluble factors with chemotactic activity for leukocytes and neutrophils. Thus, P. acnes triggers the secretion of IL-1α, TNF-α [4], and the chemokine IL-8 [3] which have been implicated in the inflammatory process of acne. Using activators and inhibitors of the O2•− production, we have been able to modulate the production of IL-8 upon stimulation by P. acnes. Particularly, DPI an inhibitor of the NADPH oxidase, significantly decreases IL-8 production, whereas DDC, a SOD inhibitor that increases O2•− levels, dramatically increases IL-8 production by keratinocytes. The question was then to determine the pathway through which P.acnes stimulates keratinocytes. Several previous observations suggested the implication of the Toll-like receptor (TLR) pathway. TLRs can recognize conserved molecular structures at the surface of bacteria. TLR-2, present at the surface of keratinocytes [19],[20], is upregulated in acne lesions [21] and is potentially involved in the recognition of P. acnes during the inflammatory process [22]. Moreover, P. acnes-stimulated TLR-2 induces IL-8 release by keratinocytes [3],[23]. We have observed a time-lag between the early production of O2•− and the secretion of IL-8 that occurs 2 h later, that probably corresponds to the activation of the TLR-signaling mediated pathway. Therefore, we hypothesized that the molecular mechanism responsible for O2•− production is TLR-independent. Indeed, whereas blocking TLR-2 with a monoclonal antibody decreases the production of IL-8 as described previously [3], it has no effect on O2•− production. We also tested the role of CD36, a scavenger molecule expressed on keratinocytes [24]. The generation of ROS by the NAD(P)H oxidase-NOX system has already been observed following the activation of scavenger receptors in vitro [25] and in vivo in a murine model of cerebral ischemia [26]. This receptor is a sensor of microbial diacylglycerides that signals via the TLR-2/6 heterodimer. In response to bacterial lipoteichoic acid (LTA) and diacylated lipoproteins, CD36 associates with TLR-2/6 [24],[27]. Although it does not express LTA, P. acnes expresses a closely related amphiphilic antigen, a lipoglycan containing mannosyl, glucosyl, galactosyl residues, and an amino sugar, diaminohexuronic acid [28],[29]. We observed that, blocking CD36 with a monoclonal anti-CD36 antibody in P. acnes-stimulated keratinocytes, significantly decreases both the level of O2•− and that of IL-8. In our model, IL-8 secretion is triggered by the binding of P. acnes to TLR-2 and modulated by the generation of superoxide anions resulting from the binding of P. acnes to CD36. In phagocytic cells, Nox1 oxidizes NADPH on the cytosolic side of the cellular membrane and reduces oxygen across the membrane to generate O2•− which contributes to the killing of P. acnes [30]. On the other hand, in keratinocytes, Nox1 is localized in the nucleus [31] and could release O2•− into the cytoplasm. Therefore, we hypothesized that nuclear Nox1 could generate O2•− which combine with steadily NO to form peroxinitrites. Peroxinitrites activate p38 and ERK in the MAPK pathways, contributing to the tight regulation of IL-8 production by O2•− [32],[33] (Figure 11). In addition, O2•− produced by keratinocytes upon stimulation with P. acnes, counteract the growth of the bacteria. Those results highlight a new mechanisms by which keratinocytes participate in the innate immune response to pathogens.
Finally, the inhibition of O2•− production, IL-8 release and keratinocyte apoptosis by retinoic acid derivates, the most efficient anti-acneic drugs, demonstrates the relevance of these pathways in vivo. In addition, our data are in agreement with the observations that , retinoic acid can induce MnSOD mRNA in a human neuroblastoma cell line and decrease TPA-induced O2•− production in mouse keratinocytes [34]. In conclusion, keratinocytes are not mere targets of the innate immune response but are directly involved in the defence mechanisms aiming at eliminating pathogens. In response to P. acnes, keratinocytes can produce massive amounts of ROS that, in return, inhibit bacterial growth. Those ROS do not only eliminate the bacteria but also generate inflammation. Thus, we hypothesize that the severity of acne depends on the balance between the ability of the P. acnes strain to induce a potent immune response [3] and the capability of the host to generate and to detoxify the ROS produced [11],[13]. Therefore, inhibiting this inflammatory reaction using appropriate antioxidant molecules could be considered as a potential treatment of acne.
P. acnes strain 6919 was obtained from the American Type Culture Collection (Manassas, VA) and grown under anaerobic conditions in reinforced clostridial liquid and solid medium (RCM) (Difco Laboratories, Detroit, MI) at 37°C during 5 days in order to reach stationary phase. Typically, 100 ml of RCM were used and bacteria were harvested after centrifugation at 7,000 g for 10 min at 4°C. Pellets were pooled and washed in about 30 ml of cold PBS and centrifuged again as described above. Finally, the bacterial pellet was suspended in PBS or DMEM. From this suspension, dilutions of 105 to 108 CFU/ml were prepared, resulting in a multiplicity of infection (MOI) of 0.05 to 50 bacteria per cell in 0.1 ml of inoculum. To obtain total surface protein extract, the bacteria were scraped in the presence of 2 ml of PBS [1.5 mM KH2PO4, 2.7 mM Na2HPO4.7H2O, 0.15 M NaCl (pH 7.4)] from the solid RCM. The bacterial suspension was heated at 60°C for 20 min and the bacteria removed by centrifugation at 16,000 g for 20 min at 4°C. The supernatant containing total surface proteins was subjected to ammonium sulfate precipitation at 60% of saturation for 1 h under stirring. The precipitated proteins were recovered after centrifugation at 22,000 g for 30 min at 4°C, then resuspended in PBS, and extensively dialyzed against PBS. Protein concentration was determined by the method of Lowry using BSA as standard described by Peterson [35].
The human keratinocyte cell line HaCaT was grown in Dulbecco's modified Eagle's medium-Glutamax-I (DMEM) (Invitrogen, Cergy Pontoise, France) supplemented with 10% heat-inactivated fetal calf serum (Invitrogen), 20 mM L-glutamine, 1 mM sodium pyruvate, and antibiotic/antimycotic solution (10 U/ml Pencillin, 10 µg/ml Streptomycin, 0.25 µg/ml Amphoterin) (Invitrogen) at 37°C in humidified atmosphere containing 5% CO2 as described [36]. The cell line was routinely tested to assess the absence of Mycoplasma infection. For stimulation experiments, HaCaT cells were incubated with the P. acnes suspension adjusted at the appropriate concentration in buffer solution for the desired period of time at 37°C in 5% CO2.
HaCaT cells (2.104/well) were seeded in 96-well plates (Corning Costar, Brumath, France). After 18 h, cells were washed three times in PBS and incubated with 100 µl per wells of 5 µM DHE (for determination of O2•−) or 5 µM H2-DCFDA (for determination of H2O2) or 5 µM DAF2-DA (for determination of NO) for 30 min as described previously [37],[38],[39]. Fluorescent probes were purchased from Molecular Probes (Eugene, OR, USA). After three washes, cells were incubated with 100 µl of a suspension of P. acnes in PBS (Abs at 600 nm = 0.5) and fluorescence intensity was recorded every hour over a period of 5 h. Fluorescence excitation/emission maxima were for DAF2-DA: 495/515 nm, for DHE: 480/610 nm and for H2-DCFDA: 507/525 nm. At the end of the experiment, the number of adherent cells was evaluated by the crystal violet assay as described below. O2•−, NO and of H2O2 were assayed by spectrofluorimetry on a Fusion spectrofluorimeter (PackardBell, Paris, France). Levels of ROS were calculated in each sample as follows: reactive oxygen species rate (arbitrary units/min/106 cells) = (fluorescence intensity [arbitrary units] at T5h – fluorescence intensity [arbitrary units] at To/300 minutes/number of adherent cells as measured by the crystal violet assay, and were expressed as arbitrary unit (A.U.).
HaCaT cells (2.104/well) were seeded in 96-well plates and incubated for 18 h in complete medium alone or with the following molecules: 2 mM diethyldithiocarbamate (SOD inhibitor), or 400 µM CuDIPS (Cu/Zn SOD mimic), or 100 µM MnTBAP (MnSOD mimic), or 40 µM rotenone (inhibitor of mitochondrial complex I) or 40 µM antimycine (inhibitor of mitochondrial complex III), 40 µM diphenyliodonium (inhibitor of NADPH oxidase), or with 40 µM allopurinol (inhibitor of xanthine oxidase). Cells were then washed three times in PBS and incubated with 100 µl per well of 5 µM DHE for 30 min. After three washes, cells were incubated with 100 µl of a suspension of P. acnes (Abs at 600 nm = 0.5) and fluorescence intensity was recorded every hour over a period of 5 h as previously described. At the end of the experiment, the number of adherent cells was evaluated by the crystal violet assay. The levels of O2•− were calculated as described above.
HaCaT cells (2.104/well) were seeded in 96-well plates and incubated for 18 hours in complete medium alone or with the following molecules: 3200 µM reduced glutathione, 800 µM N-acetylcysteine, or 400 µM CuDIPS, or 100 µM MnTBAP, or 100 µM D,L-buthionine-[S,R]-sulfoximine (inhibitor of glutathione reductase), or 400 µM aminotriazol (inhibitor of catalase), or 20 U PEG-catalase (cell permeable catalase). Cells were then washed three times in PBS and incubated with 100 µl per wells of 5 µM H2-DCFDA for 30 minutes. After three washes, cells were incubated with 100 µl of a suspension of P. acnes (Abs at 600 nm = 0.5) and fluorescence intensity, was read at a fluorescence excitation wavelength of 507 nm and at an emission wavelength of 525 nm, and was recorded every hour over a period of 5 hours. At the end of the experiment, the number of adherent cells was evaluated by the crystal violet assay. The levels of H2O2 were calculated in each sample as described above.
Nox1 silencing was performed as previously described [15]. We used the Nox1-A siRNA primer with the sequence sense 5′-ACAAUAGCCUUGAUUCUCAUGGUAA-3′, anti-sense 5′-UUACCAUGAGAAUCAAGGCUAUUGU-3′, located at 750 bp. A scrambled siRNA duplex as negative control was used with the sequence sense 5′-ACACCGAAGUUUCUUGUACGUAUAA-3′, anti-sense 5′-UUAUACGUACAAGAAACUUCGGUGU-3′ (MWG Biotech, Les Ulis, France). At 24 h before transfection, HaCaT cells were transferred onto 96-well plates at the density of 1.104 cells/well and transfected with 10 nM of each siRNA duplex using INTERFERin™ transfection reagent (Polyplus transfection, Illkirch, France) for 4 h in serum free DMEM without antibiotics. Then, complete DMEM medium was added and the cells were incubated for 48 h. Western blot using specific antibody against Nox1 (Santa Cruz Biotechnology Inc., Santa Cruz, CA) was used to assess the reduction of Nox1 protein production as previously described [15]. The level of Nox1 using Nox1A-siRNA was decreased by 86%, whereas scrambled siRNA did not affect the Nox1 level (Figure S3).
Cell death was estimated spectrofluorometrically using the fluorescent probe YO-PRO-1 (Molecular Probes) on a Fusion spectrofluorimeter (Packard Bell). HaCaT cells (2.104/well) were seeded in 96-well plates and incubated for 18 h in complete medium alone or with the following molecules: 2 mM diethyldithiocarbamate, or 40 µM rotenone, or 40 µM antimycine, 40 µM diphenyliodonium, or with 40 µM allopurinol, or 1600 µM reduced glutathione, 3200 µM N-acetylcysteine or 400 µM CuDIPS, or 100 µM MnTBAP, or 800 µM D,L-Buthionine-[S,R]-sulfoximine, or 400 µM aminotriazol, or 20 U PEG-catalase. Cells were then washed three times in PBS and incubated with 100 µl per well of a suspension of P. acnes (Abs at 600 nm = 0.5) for 24 h in complete medium. After three washes in PBS, cells were incubated with 10 µM YO-PRO-1 for 30 min. Cell death was measured by reading at an excitation wavelength of 480 nm and an emission wavelength of 525 nm. The level of cell death was estimated in each sample by the fluorescence intensity [arbitrary units] reflecting the disruption of the cell membranes.
HaCaT cells incubated or not with P. acnes were fixed in 3.7% buffered formaldehyde directly onto the 96-well plate. Cells were then subjected to TUNEL assay using the TACS™ TdT-Fluorescein In situ apoptosis detection kit (R&D Systems Inc., Minneapolis, MN) following the manufacturer's intructions. Briefly, after fixation, cells were permeabilized by Proteinase K and incubated with the reaction mixture containing Terminal deoxynucleotidyl Transferase (TdT) and biotinylated-conjugated dNTPs for 1 h at 37°C. After washing, biotinylated nucleotides were detected by incubating cells with a streptavidin-fluorescein conjugate for 20 min at room temperature in the dark. After removing the excess of fluorescein conjugate by washing in 0.1% Tween 20 in PBS, labeled DNA was examined under a fluorescence microscope.
Crystal violet staining was used to determine the number of adherent cells in 96-well plates. Briefly, after incubation with the test compound, the culture medium was discarded and the cells were incubated with a 0.05% crystal violet solution (Sigma) for 30 min at room temperature. After washing with PBS, 100% methanol was added, and the absorbance was measured spectrophotometrically at 540 nm on an ELISA multiwell reader.
The MTT (1-(4,5-dimethylthiazol-2-yl)-3,5-diphenylformazan) assay was performed to test cell viability in 96-well plates. The cells were incubated with a 0.2% MTT solution in cell culture medium for 4 h at 37°C. The MTT solution was then discarded and DMSO added to solubilize the MTT-formazan cristals produced in living cells. After thorough mixing, the absorbance was measured at 540 nm.
HaCaT cells were incubated in presence of two P. acnes concentrations (Abs at 600 nm = 0.2 and 1.0) for 18 h at 37°C. Cells were washed twice with cold PBS, harvested after trypsinization and fixed with 3.5% paraformaldehyde in PBS for 15 min at 4°C. After washing in PBS, cells were permeabilized in 1% NP-40 and incubated with FITC-labelled anti 3-nitrotyrosine monoclonal antibody (Clone 1A6, Upstate Cell Signalling Solutions, Lake Placid, NY, USA) at 6.4 µg/ml for 1 h at 4°C. After three washes, cells were pelleted and suspended in 1 ml of PBS, then analyzed by flow cytometry (FACScalibur, Becton Dickinson, Mountain View, CA). Control experiments were perfomed by incubating the cells with a FITC-labelled irrelevant IgG of the same isotype under the same conditions as described above.
Human IL-8 protein concentration was measured in the supernatants of HaCaT cells using the Quantikine® human IL-8 immunoassay kit (R&D Systems Inc., Mineapolis, MN) according to the manufacturer's instructions. We used serial dilutions of recombinant human IL-8 for standard curve. The optical density was determined at 450 nm at a wavelength correction of 540 nm.
Total RNA was isolated with TRIzol® reagent (Invitrogen) according to the manufacturer's instructions and treated with DNAse I (Roche Molecular Biochemical). RNA concentration was determined by reading the absorbance at 260 nm. Complementary DNA (cDNA) was generated from 2 µg total RNA using the oligo(dT) primer (MWG Biotech, Les Ulis, France) and 1.6 unit of AMV reverse transcriptase (Promega, Madison, WI, USA) and then used as template for standard PCR. Standard amplification was carried out using Taq DNA polymerase (Invitrogen) in 25 µl final volume with the cycling conditions set at 94°C for 5 min followed by 35 cycles of 94°C for 1 min, 62°C for 1 min and 72°C for 1 min and ending by an elongation at 72°C for 7 min. Primers amplified a 259 and 113 bp fragment of iNOS and GAPDH cDNA, respectively. Primers used were: iNOS sense 5′-CGGTGCTGTATTTCCTTACGAGGCGAAGAAGG-3′, iNOS reverse 5′-GGTGCTGTCTGTTAGGAGGTCAAGTAAAGGGC-3′; GAPDH sense 5′-GTGAAGGTCGGAGTCAACG-3′, GAPDH reverse 5′-TGAGGTCAATGAAGGGGTC-3′.
HaCaT cells were grown on two separate 96-well plates and pre-incubated with neutralizing anti-human TLR-2 mAb TLR-2.1 (10 µg/ml) (eBioscience, San Diego, California) and anti-human CD36 monoclonal antibody FA6-152 (Hycult biotechnology b.v) or isotype-matched control-purified mouse IgG antibodies (10 µg/ml) (Caltag) (10 µg/ml) diluted in supplemented DMEM media for IL-8 measurement, and in sterile PBS pH 7.4 for O2•− quantitation at 37°C in 5% CO2 atmosphere. After 2 h, cells were incubated for 30 min with 100 µl DHE at 5 µM final concentration. After three washes, cells were incubated with 100 µl of a suspension of P. acnes (Abs at 600 nm = 1.0) in PBS and fluorescence intensity was recorded every 30 min over the 3 h time-frame stimulation. After 3 h of incubation, supernatants were collected and used for IL-8 quantitation as described below.
A 10 mM O2•− solution was obtained by mixing 16 mM dicylohexano-18-crown-6 with 9.8 mM KO2 in DMSO. The solution was allowed to stabilize for 1 h at room temperature with stirring and protected from light before use. The relative sensitivity of HaCaT and of P. acnes was then tested against serial dilution of the O2•− solution.
The statistical significance of differences between data from experimental groups was analyzed by paired Student's-test. A level of P≤0.05 was accepted as significant. Statistical significance is indicated by * (P≤0.05), ** (P≤0.01), and *** (P≤0.001), respectively.
Catalase (# P04040), CD-36 (# P16671), ERK (# P28482), GpX (# P07203), GM-CSF (# P32927), IL-1α (# P01583), IL-1β (# P01584), IL-8 (# P10145), iNOS (# P35228), MnSOD (# Q7Z7M6), NOX1 (# Q9Y5S8), p38 (# Q16539), TLR2 (# O60603), TLR4 (# O00206), TLR6 (# Q9Y2C9), TNF-α (# P01375).
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10.1371/journal.ppat.1006937 | The interferon-stimulated gene product oligoadenylate synthetase-like protein enhances replication of Kaposi’s sarcoma-associated herpesvirus (KSHV) and interacts with the KSHV ORF20 protein | Kaposi’s sarcoma-associated herpesvirus (KSHV) is one of the few oncogenic human viruses known to date. Its large genome encodes more than 85 proteins and includes both unique viral proteins as well as proteins conserved amongst herpesviruses. KSHV ORF20 is a member of the herpesviral core UL24 family, but the function of ORF20 and its role in the viral life cycle is not well understood. ORF20 encodes three largely uncharacterized isoforms, which we found were localized predominantly in the nuclei and nucleoli. Quantitative affinity purification coupled to mass spectrometry (q-AP-MS) identified numerous specific interacting partners of ORF20, including ribosomal proteins and the interferon-stimulated gene product (ISG) oligoadenylate synthetase-like protein (OASL). Both endogenous and transiently transfected OASL co-immunoprecipitated with ORF20, and this interaction was conserved among all ORF20 isoforms and multiple ORF20 homologs of the UL24 family in other herpesviruses. Characterization of OASL interacting partners by q-AP-MS identified a very similar interactome to that of ORF20. Both ORF20 and OASL copurified with 40S and 60S ribosomal subunits, and when they were co-expressed, they associated with polysomes. Although ORF20 did not have a global effect on translation, ORF20 enhanced RIG-I induced expression of endogenous OASL in an IRF3-dependent but IFNAR-independent manner. OASL has been characterized as an ISG with antiviral activity against some viruses, but its role for gammaherpesviruses was unknown. We show that OASL and ORF20 mRNA expression were induced early after reactivation of latently infected HuARLT-rKSHV.219 cells. Intriguingly, we found that OASL enhanced infection of KSHV. During infection with a KSHV ORF20stop mutant, however, OASL-dependent enhancement of infectivity was lost. Our data have characterized the interaction of ORF20 with OASL and suggest ORF20 usurps the function of OASL to benefit KSHV infection.
| The herpesviruses are a family of large double-stranded DNA viruses that cause a variety of illnesses from chicken pox to cancer. Kaposi’s sarcoma-associated herpesvirus (KSHV) is a cancer-causing herpesvirus and can lead to development of Kaposi’s sarcoma, a major form of cancer in HIV-positive patients. As for all herpesviruses, infection with KSHV is lifelong. Exactly how KSHV initiates and maintains its infection is still not well understood, but it must manipulate the host cell to establish favorable conditions. Likewise, the host has developed a complicated system to fight off invaders, which includes the production of interferon-stimulated gene products. We have now found that KSHV exploits one such host cell protein, the oligoadenylate synthetase-like protein (OASL). Rather than OASL acting as an antiviral protein as it does during many other viral infections, KSHV appears to have found a way to utilize OASL for its own benefit. The KSHV protein ORF20 interacts with OASL, they co-localize in nucleoli, and both ORF20 and OASL associate and purify with components of the cellular translational machinery. This may help viral infection by selectively controlling protein production.
| The oncogenic human herpesvirus 8, also known as Kaposi’s sarcoma-associated herpesvirus (KSHV), belongs to the Gammaherpesvirinae subfamily of the Herpesviridae [1]. KSHV is the etiological agent of multiple malignancies, including Kaposi’s sarcoma, which is a common form of cancer in HIV-infected individuals [2], and primary effusion lymphoma, an aggressive non-Hodgkin’s lymphoma [3]. After successful primary infection of the host, KSHV, like all herpesviruses, can undergo lytic replication or establish latency in infected cells [4]. While the mechanisms leading to transformation and oncogenesis are still not completely understood, the current view is that both lytic replication and latency are important for KS tumor development [4].
KSHV has a large dsDNA genome with more than 85 open reading frames (ORFs) encoding proteins at least 100 amino acids (aa) in length. In addition, it utilizes mechanisms like splicing, mRNA editing, and alternative start codons to further maximize genomic capacity [5]. The poorly characterized ORF20 is an example of this additional complexity. Wildtype ORF20 (ORF20WT) encodes 3 colinear isoforms: the 320 amino acid long full length isoform ORF20FL, the 297 amino acid isoform ORF20A, and the 257 amino acid isoform ORF20B. The sequences for ORF20FL (AAC57101.1) and ORF20B (ABD28871.1), both of which start with a methionine, were submitted to Genbank as part of the genomic annotations for the minor and predominant (M and P) strains of KSHV, respectively, despite the presence of the sequences encoding ORF20FL and ORF20B in both genomes. ORF20A was identified much more recently and its translation utilizes a noncanonical leucine via a CTG start codon [5].
ORF20 is a member of the herpesviral core UL24 gene family and is conserved in the Alpha-, Beta-, and Gammaherpesvirinae. Transiently expressed HSV-1 UL24, KSHV ORF20WT, HCMV UL76, and MHV68 ORF20 have been reported to affect the mitotic cdc2/cyclinB complex, thereby inducing cell cycle arrest and apoptosis [6, 7]. However, studies with MHV68 WT and ORF20 mutant viruses found no effect on the cell cycle [8]. Multiple UL24 family members, including HSV-1 UL24, HCMV UL76, and KSHV ORF20WT, localize to the nucleoli of transfected cells [9, 10]. KSHV ORF20 mRNA is expressed late upon de novo infection of primary human umbilical vein endothelial cells. ORF20 mRNA is also detected upon reactivation of a latently infected body cavity based lymphoma cell line (BCBL-1) [11]. To date, the function of ORF20 is not well understood, and furthermore, the biological relevance of the different ORF20 isoforms is unclear.
Interferon-stimulated gene (ISG) transcription, including that of 2’-5’-oligoadenylate synthetase-like protein (OASL), is activated upon pattern recognition receptor and type I interferon (IFN) receptor signaling [12–15]. ISGs have evolved as a host defense mechanism and fall into three different categories: broadly acting antiviral effectors, ISGs with targeted antiviral specificity, and proviral ISGs that enhance the replication of certain viruses [15]. OASL expression is induced by viral infection. For example, both Sendai virus and influenza virus infection upregulate OASL in an early IFN regulatory factor 3 (IRF3)-dependent manner [13]. In a screen of almost 400 ISGs that characterized their antiviral activity against six different viruses, OASL was identified as an ISG with targeted antiviral specificity [15].
Lentiviral expression of OASL inhibited replication of hepatitis C virus, but not replication of human immunodeficiency virus type-1, yellow fever virus, West Nile virus, chikungunya virus, or Venezuelan equine encephalitis virus [15]. In a subsequent study that also primarily focused on RNA viruses, OASL inhibited poliovirus, equine arterivirus, and Newcastle disease virus, mildly inhibited influenza A virus and measles virus, but did not inhibit coxsackie B virus, Sindbis virus AR86, Sindbis virus Girdwood, o’nyong-nyong virus, human parainfluenza virus type 3, respiratory syncytial virus, bunyamwera virus, nor did it inhibit the DNA virus vaccinia [16]. The role of OASL for HSV-1 is more controversial; in one study, OASL expression was shown to have no effect on HSV-1 replication [17], and in another, OASL inhibited HSV-1 [18].
OASL is a member of the oligoadenylate synthetase protein family that includes the prominent enzymes cyclic GMP-AMP (cGAMP) synthase (cGAS) and 2’-5’-oligoadenylate synthase 1 (OAS1), as well as OAS2 and OAS3. The OAS enzymes and cGAS are activated by cytosolic double-stranded nucleic acids and produce 2’-5’-linked second messenger molecules [19]. While OASL shares a highly conserved N-terminal OAS-like domain with the OAS enzymes, it lacks enzymatic activity and has a unique C-terminus composed of two ubiquitin-like domains (UBL) [20]. Additionally, OASL has a double-stranded RNA (dsRNA) binding groove and the OAS-like domain binds dsRNA [21].
OASL is conserved between mice and humans; murine OASL1 (mOASL1) is the most similar to human OASL (hOASL), and like hOASL, mOASL1 lacks enzymatic activity and has a UBL [22]. mOASL2 is more distantly related to hOASL; it possesses enzymatic activity, unlike hOASL, but like OASL has a UBL [22]. mOASL1 has been shown to inhibit IRF7 translation by binding to the 5’ untranslated region (UTR) of IRF7, and compared to wildtype mice, mOASL1 knockout mice are more resistant to HSV-1 and EMCV infection, likely due to increased interferon production [23]. However, unlike mOASL, hOASL does not seem to bind to the IRF7 UTR [18]. A yeast two-hybrid screen that utilized a human leukocyte cDNA library identified the transcriptional repressor methyl CpG binding protein 1 (MBD1) as an interacting partner of OASL [24]. Another study found that hOASL enhanced RIG-I signaling, and furthermore showed that OASL co-immunoprecipitated with RIG-I [18]. To date, however, the function of OASL is not completely understood and no viral binding partners have previously been identified. Furthermore, its role during gammaherpesvirus infections is unknown.
We have now characterized all three isoforms of KSHV ORF20 and show that they localize to the nuclei and nucleoli of transiently transfected or transduced cells, including HeLa, 293T, primary HFF, HuARLT2, and HuARLT2-rKSHV.219. In an unbiased quantitative affinity purification coupled to mass spectrometry (q-AP-MS) approach, we identify OASL as an interaction partner of ORF20. We show that the interaction with OASL is conserved among ORF20 isoforms and UL24 family members present in other herpesviruses. We analyze the OASL interactome by q-AP-MS and find that OASL and ORF20 share numerous ribosomal interaction partners. Furthermore, both proteins copurify with 40S and 60S ribosomal subunits. Moreover, when expressed together they associate with polysomes, but do not have a global effect on translation. Interestingly, ORF20 upregulates OASL mRNA expression downstream of RIG-I in an IRF3-dependent manner, but independently of IFNAR signaling. During reactivation of latently infected HuARLT2-rKSHV.219, both OASL and ORF20 mRNA levels are upregulated early. Lastly, OASL enhances KSHV infection in an ORF20-dependent manner, suggesting that KSHV has commandeered this ISG to benefit KSHV infection.
KSHV ORF20 is a member of the conserved UL24 family but to date it has not been extensively characterized. The ORF20 genomic locus encoded on the minus strand of the genome is 660 nucleotides in length and although the 5’ and 3’ untranslated regions are not known [5], it is anticipated that one mRNA is transcribed (Fig 1A). Next generation ribosomal footprinting data identified initiating ribosomes at three positions, giving rise to three colinear isoforms of ORF20: full-length ORF20 (ORF20FL) as well as two shorter isoforms, ORF20A, which starts at an alternative leucine start codon at amino acid 24 of ORF20FL, and ORF20B, which starts at an internal methionine at amino acid 64 of ORF20FL (Fig 1A) [5]. The anticipated sizes of all three protein isoforms are 35, 32, and 28 kDa, respectively (Fig 1A). A plasmid construct encoding genomic ORF20WT [10], equipped with a C-terminal myc epitope tag, can potentially express ORF20FL, ORF20A, and ORF20B (Fig 1B), but we only observed two distinct bands upon immunoblotting cell lysates with an anti-myc antibody (Fig 1C). To identify the isoforms responsible for these two bands, we cloned all isoforms singly and in combination (Fig 1B) and analyzed their expression by immunoblotting (Fig 1C). We found that the two bands observed upon expression of ORF20WT correspond to ORF20FL and ORF20B (Fig 1C). We could not detect expression of ORF20A from ORF20WT or ORF20FLgA plasmid constructs that contain the genomic leucine start codon, but could drive expression of ORF20A by addition of an N-terminal methionine in ORF20A and ORF20AB constructs (Fig 1B and 1C).
A previous study has shown that KSHV ORF20WT localizes to the nucleus [10]. However, the localization of all individual ORF20 isoforms has not yet been studied. We determined the subcellular localization of ORF20WT as well as the individual ORF20 isoforms ORF20FL, ORF20A, and ORF20B in HeLa cells by immunofluorescence (IF) and confocal microscopy. First, we analyzed the localization of ORF20 isoforms in whole cell IF. All isoforms localized predominantly to the nuclei and nucleoli, where they co-localized with Hoechst and the nucleolar protein fibrillarin, but in some cells ORF20 forms could also be detected in the cytoplasm (Fig 2A). To enhance visualization of the nucleoli, we performed nuclear IF. For this, we extracted the cytoplasm by incubation with 1% NP-40 extraction buffer prior to fixation. By labeling the nucleolar marker fibrillarin, we confirmed that all KSHV ORF20 isoforms localized to the nucleoli (Fig 2B).
To better understand the function of ORF20, we utilized q-AP-MS [25, 26] to identify cellular binding partners of ORF20. HeLa S3 cells were metabolically labeled with stable heavy or light isotopes of arginine and lysine, then transfected with myc-tagged ORF20WT or LacZ as a control. In the forward experiment, heavy labeled cells were transfected with ORF20-myc, and light labeled cells with LacZ-myc (Fig 3A). In the crossover experiment, the labels were exchanged. Lysates were subjected to anti-myc immunoprecipitation, then combined and subjected to LC/MS-MS before peptide analysis (Fig 3A). In the forward experiment, specific interaction partners of ORF20 were more abundant in the heavy form than in the light form (Fig 3A), while in the crossover experiment the label switch resulted in an inverted abundance of the same protein. In contrast, nonspecific binding partners were equally abundant in the forward and crossover experiment. Proteins identified in both the forward and crossover experiments were analyzed for relative ratios and graphed. Interacting partners of LacZ are displayed in the upper left quadrant, and nonspecific interactions or contaminants are located around the origin (Fig 3B). We identified multiple specific interacting partners of ORF20 (Fig 3B, Table 1, and S1 Dataset), which cluster in the lower right quadrant of the graph (Fig 3B). These interacting partners included numerous 40S and 60S ribosomal proteins, ribosome-binding protein 1, and OASL, an interferon-stimulated gene product (ISG) (Table 1, S1 Dataset, S1 Supporting Information). The 40S and 60S ribosomal subunits, which contain many individual proteins as well as the 5.8S, 18S, and 28S ribosomal RNAs (rRNAs), form the mature 80S eukaryotic ribosome. Multiple 80S ribosomes associate to form actively translating ribosomes or polysomes [27, 28].
We next wanted to confirm the interaction of ORF20 with endogenous OASL in wildtype 293T and HeLa S3 cells, using 293T OASL-/- cells as a specificity control (Fig 3C). As OASL is an ISG, we transfected cells with a constitutively active mutant of the pattern recognition receptor RIG-I, RIG-I N, to enhance expression of endogenous OASL. Endogenous OASL was specifically expressed in RIG-I N transfected 293T cells, but not in the absence of RIG-I N transfection or in 293T OASL-/- cells. In HeLa S3 cells, OASL was detected both in the presence and absence of RIG-I N. We immunoprecipitated ORF20WT with an anti-myc antibody, then analyzed immunoprecipitates for the presence of endogenous OASL by anti-OASL immunoblotting. OASL co-immunoprecipitated with ORF20WT from both 293T and HeLa S3 cells co-transfected with RIG-I N, but not from 293T OASL-/- cells, as expected. OASL also co-immunoprecipitated with ORF20WT from HeLa S3 cells in the absence of RIG-I N, validating our q-AP-MS results (Fig 3C, Table 1).
As detection of endogenous OASL was challenging, we used co-transfections for further interaction studies. To determine whether OASL interacted with all ORF20 isoforms, we co-transfected 293T cells with V5-tagged OASL and various ORF20-myc isoform constructs or LacZ-myc as a control. All proteins were appropriately expressed in the input lysates (Fig 3D). We then performed an anti-myc immunoprecipitation and immunoblotted first against V5-OASL, then verified successful immunoprecipitation of the various myc constructs (Fig 3D). We found that OASL co-immunoprecipitated with all ORF20 isoforms (FL, A, and B) individually as well as with ORF20WT, but not with the LacZ control.
We then analyzed the subcellular localization of OASL and ORF20 by immunofluorescence. We tested several antibodies for detection of endogenous OASL and could detect endogenous protein by immunoblotting but not by immunofluorescence. Furthermore, there are no commercial antibodies available against ORF20. Thus, to analyze localization, we transfected HeLa S3 cells with either myc-tagged ORF20WT or V5-tagged OASL, or co-transfected them with both plasmids. When expressed alone, ORF20WT was located predominantly in the nuclei and nucleoli (Fig 3E). When OASL was expressed alone, it was located in the cytoplasm as well as in the nucleoli (Fig 3E). When ORF20WT and OASL were co-expressed, their subcellular localization was unaltered (Fig 3F). ORF20WT and OASL co-localized in the nucleoli but not elsewhere in the cells (Fig 3F).
Next, we wanted to verify the subcellular localization of OASL and ORF20 in additional cell types. First, we utilized HuARLT2-rKSHV.219 cells, a conditionally immortalized human endothelial cell line latently infected with recombinant KSHV rKSHV.219 [29–32]. In cells infected with rKSHV.219, latently infected cells express GFP from the cellular EF-1α promoter and upon reactivation RFP from the KSHV lytic gene PAN promoter. We transduced HuARLT2-rKSHV.219 cells with lentiviruses encoding OASL-V5, ORF20WT-myc, ORF20FL-myc, or ORF20B myc, then analyzed the localization in latently infected cells (Fig 4A). We found that the localization was similar to that observed in HeLa cells; OASL was present in the cytoplasm and nucleoli, and ORF20 forms were present in the nuclei and nucleoli (Fig 4A). To determine whether the localization remained the same in reactivated cells, we reactivated transduced HuARLT2-rKSHV.219 cells with sodium butyrate and a baculovirus carrying the RTA gene (Fig 4B). As in latently infected cells, in reactivated cells, OASL was present in the cytoplasm and nucleoli, and ORF20 forms were identified in the nuclei and nucleoli (Fig 4B). We then analyzed the subcellular localization of OASL and ORF20 forms in transiently transduced primary human foreskin fibroblasts (S1A Fig) and conditionally immortalized HuARLT2 endothelial cells (S1B Fig). The subcellular localization was similar to that observed in other cell types; OASL was detected in the cytoplasm and nucleoli, and ORF20 forms in the nuclei and nucleoli.
Next, to determine whether the localization of ORF20 was dependent upon OASL expression, we transfected 293T or 293T OASL-/- cells with ORF20WT and analyzed the localization by immunofluorescence. In both cell types, ORF20WT was detected in the nuclei and nucleoli (S1C Fig). In parallel, we co-transfected 293T cells with ORF20WT and RIG-I N to induce expression of endogenous OASL. Due to lack of specific antibodies for IF of endogenous OASL, we verified nuclear translocation of endogenous IRF3 as a marker for OASL expression. In ORF20WT expressing cells with nuclear translocation of IRF3, ORF20WT localized to the nuclei and nucleoli (S1D Fig), similar to what we saw in HeLa cells, where endogenous OASL was detected even in the absence of RIG-I N (Fig 3C).
In summary, ORF20 forms were consistently detected in the nuclei and nucleoli in primary and immortalized cells and the subcellular localization of ORF20 was independent of OASL expression. OASL was observed in the cytoplasm and nucleoli in a variety of cell types, and its localization was independent of ORF20 expression, and ORF20 and OASL co-localized in the nucleoli of transiently transfected cells.
As ORF20 and OASL co-localized in the nucleoli, we wanted to verify whether their interaction detected by IP was specific or only due to their subcellular co-localization. To do so, we cloned an ORF20WT-myc-GFP fusion and compared it to GFP-NS1A, in which GFP is fused to amino acids 203–237 of influenza A/Udorn/72 NS1, conferring nucleolar localization of GFP [33]. As a positive control, we utilized GFP-tagged RIG-I, which localizes to the cytoplasm, as RIG-I has previously been shown to interact with OASL under mild lysis conditions [18]. We co-transfected OASL-V5 with two independent ORF20WT-myc-GFP clones, RIG-I-GFP, or GFP-NS1A, as well as all singly-transfected controls, and performed an anti-V5 IP (S2A Fig). We found that ORF20-GFP co-immunoprecipitated with OASL-V5, but GFP-NS1A did not. RIG-I-GFP also weakly co-immunoprecipitated with OASL-V5. Next, we performed an anti-GFP IP on similar samples and found that OASL-V5 strongly co-immunoprecipitated with ORF20-myc-GFP, but not with GFP-NS1A (S2B Fig). Under our stringent lysis conditions, OASL-V5 did not co-immunoprecipitate with RIG-I-GFP (S2B Fig). These data confirm the interaction of ORF20 with OASL and verify that the detected interaction is not solely due to nucleolar co-localization.
We next wanted to determine if the interaction with OASL was specific for KSHV ORF20 or if it was conserved among UL24 family members present in the Alpha-, Beta-, and Gammaherpesvirinae. We compared the amino acid sequences of the UL24 family members HSV-1 UL24, HCMV UL76, MCMV M76, KSHV ORF20FL, KSHV ORF20A, KSHV ORF20B, and MHV68 ORF20, and found that all KSHV ORF20 homologs aligned to each other and all forms of ORF20 (S3 Fig). We cloned myc-tagged constructs utilizing the genomic nucleotide sequence of several KSHV ORF20 homologs: HSV-1 UL24, HCMV UL76, and MCMV M76. As MHV68 ORF20 was poorly expressed, we codon-optimized its sequence and added a 3x myc tag. First, we analyzed the subcellular localization of all homologs in transiently transfected HeLa cells. Similarly to ORF20, all UL24 family members were located in the nuclei and nucleoli (Fig 5A). We verified expression of all homologs by immunoblotting, and found that UL24, M76, and ORF20B were of similar sizes, while UL76 and MHV68 ORF20 were more similar to the size of ORF20WT (Fig 5B and 5C). While codon-optimized MHV68 ORF20 expression was low compared to KSHV ORF20, it was expressed and detection of MHV68 ORF20 was improved by anti-myc immunoprecipitation prior to immunoblotting (Fig 5C).
To determine whether OASL interacted with UL24 family members, we co-transfected 293T cells with V5-tagged OASL and myc-tagged KSHV ORF20FL, MCMV M76, HCMV UL76, or HSV-1 UL24, or LacZ-myc as a control. OASL co-immunoprecipitated strongly with KSHV ORF20FL and MCMV M76. HCMV UL76 and HSV-1 UL24 also immunoprecipitated OASL, but much more weakly than KSHV ORF20 (Fig 5D). OASL-V5 did not co-immunoprecipitate with the negative control LacZ (Fig 5D). Our results show that the interaction with OASL is conserved among ORF20 isoforms and UL24 homologs.
We next analyzed the interaction of ORF20 with a variety of V5-tagged OASL mutants to determine if specific OASL domains or functions are required for the interaction to occur. The mutants included ΔUBL, lacking the ubiquitin-like domain of OASL; P-loop mutants V67G and N72K with reduced nucleic acid binding; three RNA binding mutants, R45E/K66E/R196E/K200E (RKRK), K63E, and K66E; and three catalytic triad mutants, E81A, E83A, and T152A, based on homology with the catalytic site of the OAS family enzymes [21] (Fig 6A). We also used OAS1, which is enzymatically-active and lacks the UBL domains, as a control in our interaction studies. We co-transfected myc-tagged ORF20WT with V5-tagged OAS1, WT OASL, or the various OASL mutants and verified their expression in input lysates (Fig 6B). All constructs were expressed, although OAS1 and OASLΔUBL were weakly expressed compared to WT OASL. We then performed an anti-myc immunoprecipitation and found that WT OASL and all OASL mutants co-immunoprecipitated with ORF20WT, but not with the negative control LacZ (Fig 6B). OASLΔUBL, despite its weaker expression compared to WT OASL, also co-immunoprecipitated with ORF20WT, but OAS1 did not, confirming the specificity of the interaction between ORF20 and OASL. We next verified the subcellular localization of all OASL constructs by whole-cell and nuclear immunofluorescence, and found that with the exception of OASLΔUBL, which was weakly expressed and detected in the cytoplasm and nuclei, all constructs were localized to the cytoplasm and nucleoli (S4 Fig).
As all three ORF20 isoforms immunoprecipitated OASL, we next wanted to identify the region of ORF20B required for interaction with OASL. Based on secondary structure and nuclear and nucleolar localization sequence predictions (Fig 6C), we created three ORF20B deletion mutants: ORF20B 1–235, 1–220, and 1–186 (Fig 6C). We co-transfected the myc-tagged ORF20 isoforms WT, FL, A, or B, ORF20B deletion mutants, or LacZ as a control with V5-tagged OASL. OASL and all ORF20 isoforms and truncation mutants were expressed in input lysates as expected (Fig 6D). We then performed an anti-myc immunoprecipitation and found that OASL co-immunoprecipitated with all ORF20B deletion mutants, suggesting that the first 186 amino acids of ORF20B are important for the interaction with OASL (Fig 6D). We then verified the subcellular localization of all ORF20B deletion mutants and found that all mutants localized to the nuclei and nucleoli (S5 Fig). These data suggest that the predicted nuclear and nucleolar localization sequences (Fig 6C) are not exclusively required for ORF20 localization. In summary, neither the ubiquitin-like domains nor RNA binding functions of OASL are required for the interaction with ORF20, and the interaction of ORF20 with OASL can be mapped to amino acids 1–186 of the smallest isoform, ORF20B.
To better understand why ORF20 and OASL may interact, we utilized unbiased q-AP-MS to identify interaction partners of OASL. Using an experimental setup similar to that for ORF20 (Fig 3A), we identified numerous interacting partners of OASL (Fig 7A, Table 2, S2 Dataset), of which many were 40S or 60S ribosomal proteins or nucleolar proteins. We also identified proteins with anticipated functions in ribosome biogenesis (Table 2, S2 Dataset). Next, we compared the proteins identified as interacting partners of ORF20 and OASL (Table 1 and Table 2). We identified 14 proteins that copurified with ORF20 only and 49 proteins that copurified exclusively with OASL. Interestingly, 33 proteins were identified as interacting partners for both ORF20 and OASL (Fig 7B and S1 Supporting Information).
The nucleoli are the sites of ribosome biogenesis. Based on the nucleolar localization of ORF20 and OASL, as well as on their numerous shared nucleolar and ribosomal interacting partners, we wanted to verify whether ORF20 and OASL co-sediment with ribosomal subunits and/or polysomes. We performed sucrose gradient fractionation of ribosomes from cells expressing ORF20WT, OASL, or both ORF20WT and OASL together. Ribosomes were purified either in the presence of EDTA, which causes the dissociation of ribosomes into small (40S) and large (60S) subunits, or in the presence of MgCl2 to stabilize 80S ribosomes and polysomes. The absorbance at 254 nm was measured to identify the fractions containing subunits, ribosomes, and polysomes (Fig 8). The fraction identification was further verified by analyzing the 18S and 28S rRNA content; 18S rRNA was present in the 40S subunit fractions and 28S rRNA in the 60S subunit fractions. We found that when expressed alone, ORF20WT and OASL individually copurified with 40S and 60S ribosomal subunits (Fig 8A and 8C). ORF20WT weakly copurified with 80S ribosomes and polysomes (Fig 8B) and OASL copurified with 80S ribosomes when expressed alone (Fig 8D).
When co-expressed, ORF20WT and OASL copurified with 40S and 60S ribosomal subunits (Fig 8E), as well as with polysomes (Fig 8F). In summary, the co-sedimentation of ORF20WT and OASL with the 40S and 60S ribosomal subunits verifies our identification of 40S and 60S ribosomal subunit proteins as specific interaction partners of ORF20WT and OASL. Furthermore, the association of ORF20WT and OASL with polysomes suggests that these proteins may affect protein translation.
To analyze whether ORF20WT and OASL have global effects on cellular translation, we utilized a puromycin incorporation assay [34]. 293T cells were transfected with EV, ORF20WT, ORF20FL, or ORF20B, and either EV as a control or RIG-I N to induce expression of endogenous OASL. 24 h post transfection, cells were treated for 15 minutes with puromycin to allow incorporation into nascent proteins, then immediately lysed in sample buffer. Lysates were then subjected to anti-puromycin, anti-OASL, anti-myc, and anti-actin immunoblotting. Expression of ORF20 did not affect translation rates, as incorporation of puromycin was similar across all samples both in the absence and presence of RIG-I N (Fig 9A). As expected, we did not detect endogenous OASL unless RIG-I N was transfected. In addition, we did not observe global changes in translation upon OASL expression, as translation rates were similar in EV and RIG-I N transfected cells (Fig 9A). Interestingly, the amount of OASL protein was slightly increased when ORF20 forms were present (Fig 9A).
To determine whether ORF20 affects OASL mRNA expression, we used quantitative reverse transcriptase polymerase chain reaction (q-RT-PCR) to measure OASL mRNA levels in transfected 293T cells. As expected, OASL mRNA levels were upregulated in the presence of RIG-I N (Fig 9B). In addition, we observed a further significant increase in OASL mRNA levels when ORF20WT and RIG-I N were expressed (Fig 9B). As specificity controls, we included additional nuclear KSHV ORFs in this assay. Unlike for ORF20WT, compared to EV with RIG-I N, we observed no upregulation of OASL mRNA levels in cells co-expressing RIG-I N and either ORF59, K8.1, ORF27, or RTA (S6A Fig). We also included vIRF1 as a known inhibitor of IRF3 [35], and observed reduced OASL levels as expected (S6A Fig). Next, we determined whether ORF20 also enhanced OASL expression when endogenous RIG-I was stimulated. For this, we utilized HEK293 cells with functional RIG-I signaling, transfected them with empty vector or ORF20WT, and stimulated the cells with 5’pppRNA to activate the RIG-I signaling cascade (Fig 9C). We found that OASL mRNA levels were significantly upregulated upon 5’pppRNA stimulation, and as with RIG-I N (Fig 9B), OASL mRNA levels were significantly increased in the presence of ORF20WT (Fig 9C).
As OASL expression can be induced directly by IRF3 as well as via IFNAR signaling [13], we wanted to elucidate the mechanism required for enhanced expression of OASL in the presence of ORF20. To do so, we used siRNA to knockdown IRF3, IFNAR, or STAT1 expression, then transfected cells with combinations of empty vector, ORF20WT, and/or RIG-I N. In control siRNA-transfected cells, we observed upregulation of OASL in the presence of ORF20WT and RIG-I N (Fig 9D), as shown previously (Fig 9B). In cells transfected with siRNA targeting IRF3, IRF3 was efficiently knocked down (S6B Fig) and upregulation of OASL was completely abrogated (Fig 9D). Next, we analyzed the effect of IFNAR and STAT1 knockdown on the ability of ORF20WT to upregulate OASL mRNA levels and found that neither IFNAR nor STAT1 knockdown affected upregulation of OASL mRNA levels (Fig 9D), although expression of both was efficiently inhibited (S6C and S6D Fig, respectively).
In summary, these results suggest that although ORF20 does not globally affect translation, it affects expression of endogenous OASL at both the mRNA and protein levels, and ORF20-mediated upregulation of OASL mRNA expression is IRF3- but not IFNAR-dependent.
As OASL has been shown to interact and co-localize with RIG-I [18], we next analyzed whether ORF20 affected the interaction or co-localization of OASL with RIG-I. First, we analyzed interactions in transiently transfected 293T cells lysed under milder conditions than those used for S2 Fig. OASL co-immunoprecipitated with RIG-I in both the presence and absence of ORF20WT (S7A Fig). ORF20WT did not interact with RIG-I in the absence of OASL, but did co-immunoprecipitate with RIG-I in the presence of OASL, suggesting the detected interaction was due to OASL binding both RIG-I and ORF20WT (S7A Fig).
Our analysis of the subcellular localization of RIG-I, OASL, and ORF20WT in HeLa S3 cells showed that RIG-I was located in the cytoplasm, as expected, OASL was in the cytoplasm and nucleoli, and ORF20WT in the nuclei and nucleoli (S7B Fig) as shown previously (Fig 3E and 3F, Fig 4, S1 Fig). When RIG-I and OASL were co-expressed, their subcellular localization did not change, and OASL and RIG-I co-localized in the cytoplasm (S7B Fig) as reported [18]. Co-expression of ORF20WT and RIG-I did not alter their subcellular localization, nor did they co-localize (S7B Fig). When RIG-I, OASL, and ORF20WT were co-expressed, neither their individual subcellular localization, nor the co-localization between ORF20WT and OASL in the nucleoli, nor the co-localization between RIG-I and OASL in the cytoplasm, was affected (S7C Fig). Co-localization between ORF20WT and RIG-I was also not observed in the presence of OASL (S7C Fig).
ISGs can have pro- and anti-viral effects [15]. Although OASL has been identified as an antiviral protein against multiple viruses, it has targeted antiviral specificity [15, 16]. To verify the antiviral function of OASL in a reconstitution assay, we infected 293T OASL-/- cells reconstituted with EV or hOASL with vesicular stomatitis virus expressing GFP (VSV-GFP) (Fig 10A and 10B) and determined the GFP signal by flow cytometry 16 h post infection. Compared to the mean fluorescence intensity (MFI) of GFP in EV-transfected cells, we observed a decrease in MFI when hOASL was expressed (Fig 10A). We observed a corresponding significant decrease in the number of GFP-high cells when hOASL was expressed (Fig 10B), confirming the antiviral effect of OASL during VSV infection [18].
OASL has not been characterized for its role during gammaherpesviral infection. We therefore determined the effect of OASL expression on gammaherpesviral growth and infectivity. First, we reconstituted 293T OASL-/- cells with the murine homolog of OASL, mOASL1 (Fig 10C), human OASL (Fig 10D), or empty vector (EV) as a control. We then infected cells with MHV68-GFP at a low multiplicity of infection (MOI) and performed growth curves over several days. When either mOASL1 or hOASL was expressed, viral growth was enhanced compared to cells transfected with EV (Fig 10C and 10D). We then reconstituted 293T OASL-/- cells with mOASL1 or hOASL, infected them with MHV68-GFP at a high MOI, and quantified the number of infected GFP-positive cells by flow cytometry 20 h post infection. We found that the number of GFP-positive cells was significantly increased in samples expressing mOASL1 or hOASL, compared to cells transfected with EV (Fig 10E).
We next performed a similar experiment with KSHV. As KSHV infection in vitro frequently defaults to latency and ORF20 is a lytic gene, we used a genetically modified form of KSHV. In KSHVLYT, the viral replication and transcription activator RTA is under control of a constitutively active PGK promoter, leading to exclusively lytic replication [36, 37]. We infected 293T OASL-/- cells reconstituted with either EV or hOASL with wildtype KSHVLYT and used flow cytometry to quantify the number of infected cells 24 h post infection. We found that the number of GFP-positive cells was significantly increased in the presence of hOASL (Fig 10F). Our data show that OASL is beneficial for both MHV68 and KSHVLYT infection.
To determine whether the proviral effect of OASL is dependent on the presence of ORF20, we used en passant mutagenesis to generate an ORF20stop virus on the KSHVLYT background. To avoid affecting the ORF21 protein sequence or identified ORF21 promoter transcription factor binding sites, we changed the codon for E69, GAG, to the stop codon TAG (Fig 10G). While the first 69 amino acids of ORF20FL may be expressed, only 5 amino acids of ORF20B can be expressed. We infected 293T OASL-/- cells reconstituted with either EV or hOASL with KSHVLYT ORF20stop. We found that the number of GFP-positive cells was similar in both EV and hOASL reconstituted cells (Fig 10H), in contrast to the enhancement of infection observed for KSHVLYT wildtype (Fig 10F). These data suggest that ORF20 contributes to the beneficial effect of hOASL on KSHVLYT.
OASL is expressed upon de novo infection with KSHV [38], but to date, the expression of OASL upon reactivation of KSHV-infected cells has not been well characterized. To determine whether OASL is expressed upon reactivation, we treated HuARLT-rKSHV.219 cells with sodium butyrate and RTA-expressing baculovirus to induce lytic reactivation, then collected samples for RNA isolation at 6 and 24 h. As a control, we included uninfected HuARLT2 cells. We then measured OASL and lytic transcript levels by q-RT-PCR. Compared to uninfected cells, OASL mRNA was increased in latently infected cells (Fig 11A). Upon reactivation, OASL mRNA expression significantly increased (Fig 11A). To analyze expression of lytic viral genes, we compared mRNA levels of ORF20, ORF16, OF46, and K8.1 in latently infected and reactivated cells. Based on de novo infection of endothelial cells and reactivation of latently infected BCBL-1 cells, ORF16 is classified as immediate early, ORF46 as early, K8.1 as late, and ORF20 as late [11, 39, 40]. We found that in HuARLT2-rKSHV.219 cells, ORF20 (Fig 11B) and ORF16 (Fig 11C) expression was already increased as early as 6h post reactivation, with expression further increasing at 24h. In contrast, very little ORF46 (Fig 11D) or K8.1 (Fig 11E) was detected at 6h post reactivation, but levels were greatly increased at 24h. Our data show that OASL and ORF20 mRNA expression are increased concomitantly during reactivation of HuARLT2-rKSHV.219 cells.
In summary, we have identified the cellular protein OASL as an interacting partner of KSHV ORF20 by q-AP-MS and identified novel interactions of ORF20 and OASL with ribosomal and nucleolar proteins. We characterized the conserved interaction of ORF20 isoforms and homologs with OASL, and we used a variety of mutants to further characterize the interaction of ORF20 with OASL. We found that both ORF20 and OASL co-sediment with ribosomal subunits. While ORF20 did not globally enhance translation, ORF20 expression strongly increased expression of endogenous OASL in an IRF3-dependent manner. Both OASL and ORF20 are expressed upon lytic reactivation of cells latently infected with KSHV. Finally, the presence of OASL enhances KSHV infectivity in an ORF20-dependent manner, suggesting a novel viral mechanism for usurping control of the host cell environment.
In this study, we have characterized the three protein isoforms of KSHV ORF20WT: ORF20FL, ORF20A, and ORF20B. We have found that like ORF20WT [10], all three ORF20 isoforms predominantly localize to the nuclei and nucleoli of transiently transfected cells. ORF20 is classified as a late lytic protein based on detection of ORF20 mRNA at late time points post de novo infection of endothelial cells or lytic reactivation of BCBL-1 cells [40] [11, 39]. We analyzed the expression of ORF20 mRNA upon lytic reactivation of endothelial cells, and found that it was expressed similarly to ORF16, which is classified as an immediate-early gene. This difference in expression may be due to reactivation versus de novo infection of endothelial cells, or due to differences in expression upon reactivation of endothelial versus B cells. This is not unusual for KSHV proteins; for example, K15 expression is very different in B cells and endothelial cells [29]. ORF20 is a member of the herpesviral UL24 family and the subcellular localization of all ORF20 isoforms was similar to the localization reported for multiple members of the UL24 family [9, 10].
As ORF20 is a poorly characterized protein, we wanted to better understand its function. We utilized an unbiased q-AP-MS approach to identify specific interacting partners of ORF20. During the course of our experiments, a global mapping study identified interacting partners of 89 KSHV proteins [41]. The authors identified high-confidence interactions using a mass spectrometry interaction statistics scoring algorithm that they developed, and identified one interacting partner for KSHV ORF20: coiled-coil domain containing protein 86 (CCDC86), also known as cytokine-induced protein with coiled-coil domain or cyclon [41]. The function of CCDC86 is not well understood. It localizes to the nuclei and nucleoli, is induced by IL-3 expression, and is involved in maintenance of T-cell homeostasis [42–44]. In our analysis of ORF20 interacting partners, we did not identify CCDC86. This may be due to differences in the cell type used, as we used HeLa S3, not 293T cells, or differences in the analysis method, as we used stable isotope labeling in cell culture for our q-AP-MS.
We found that ORF20 specifically interacted with the ISG oligoadenylate synthetase-like protein OASL. This is the first known interaction of a viral protein with OASL. We characterized the interaction of ORF20WT with a variety of OASL mutants and found that the interaction occurred independently of RNA-binding and the ubiquitin-like domain of OASL. Whether nucleolar localization of OASL is exclusively required for its interaction with ORF20WT is still unclear, as the expression of the OASLΔUBL mutant was low and thus nucleolar localization may have been obscured. We used C-terminal truncation mutants of the shortest ORF20 isoform, ORF20B, and found that the shortest mutant, ORF20B 1–186, was still able to bind wild-type OASL and localize to nucleoli.
Although the role of OASL during KSHV infection has not previously been studied directly, microarray analysis of cellular mRNA expression upon KSHV infection has shown that OASL expression is induced at late time points upon de novo KSHV infection [38]. We now show that OASL mRNA expression is increased in latently infected HuARLT2-rKSHV.219 cells compared to uninfected cells, and OASL mRNA expression is further increased upon reactivation with concomitant ORF20 mRNA expression. The exact mechanism of OASL induction by de novo KSHV infection or reactivation is not known, but OASL is induced by Sendai and influenza infection in an IRF3-dependent manner [13]. OASL is also expressed as an ISG downstream of the type I IFN receptor [13].
Until now, the effect of OASL on gammaherpesviral infection was not studied. When we infected OASL-reconstituted cells with MHV68-GFP or KSHVLYT, we found that the number of infected cells was significantly increased compared to EV-transfected cells. When we infected hOASL-reconstituted 293T OASL-/- cells with KSHVLYT ORF20stop, we did not observe enhancement of infection. However, when we infected OASL-reconstituted OASL-/- cells with VSV-GFP, we observed inhibition of infection as expected. Taken together, these results indicate a unique proviral role for OASL during gammaherpesviral infection.
Two previous studies have analyzed protein-protein interactions of OASL and identified interactions with the transcriptional repressor methyl CpG binding protein 1 (MBD1) and RIG-I [24] [18]. However, no q-AP-MS data was available. When we analyzed the OASL interactome, we found that it was very similar to what we identified for ORF20, and in agreement with their nucleolar localization. We found that 40S and 60S ribosomal proteins were robustly represented among the identified interaction partners of both ORF20 and OASL. Ribosomal proteins are often considered contaminants in standard affinity purification mass spectrometry analysis and many ribosomal proteins are listed in the “contaminant repository for affinity purification”, or CRAPome [45]. However, q-AP-MS allows identification of ribosomal proteins as specific interaction partners. True interaction partners are identified by their specific abundance ratio for the bait protein, while non-specific contaminants can be identified based on their equal binding affinity for the bait and control proteins. The identification of ribosomal proteins for both ORF20 and OASL is thus of interest for elucidation of the functions of ORF20 and OASL.
To further characterize the interactions of ORF20 and OASL with ribosomes, we utilized a ribosomal sedimentation assay to analyze the association of both proteins with ribosomes. Both proteins sedimented with 40S and 60S ribosomal subunits when expressed individually and in combination with each other, further validating the protein interactions we identified by q-AP-MS. Interestingly, we found that when ORF20 and OASL were co-expressed, both proteins associated with polysomes. This may be due to increased expression when both were co-expressed, although recruitment to polysomes cannot be excluded. As association with polysomes may globally or selectively affect translation, we used a puromycin termination assay to examine the effect of ORF20 and OASL on cellular translation [34]. We found that puromycin was incorporated similarly in all samples. Taken together, the association with polysomes and similar incorporation of puromycin suggest selective control of gene expression. We found that endogenous OASL and ORF20 mRNA expression were increased concomitantly during reactivation of HuARLT2-rKSHV.219 cells. Additionally, we found that the OASL mRNA level was further upregulated upon RIG-I N overexpression or stimulation of endogenous RIG-I when ORF20 was present. We used siRNA knockdown to analyze the signaling components required for upregulation of OASL mRNA, and found that upregulation of OASL mRNA levels by ORF20 was IRF3- but not IFNAR-dependent. It is plausible that ORF20 enhances OASL expression at the mRNA level to allow sufficient protein levels for ORF20 to utilize OASL.
Regulation of gene expression occurs at many levels in eukaryotic cells. Transcriptional and post-transcriptional regulation are two major factors. Additionally, emerging evidence suggests that rather than possessing intrinsic translational capabilities, ribosomes contribute to control of gene expression. A recent publication has shown that translating ribosomes vary in their ribosomal protein composition, bestowing translational selectivity [46]. There are at least 80 different ribosomal proteins, and furthermore ribosomal RNA bases and ribosomal proteins can be modified. The number of possible ribosomes is therefore enormous [47]. Viral modulation of ribosome formation to form specialized ribosomes is one potential mechanism that viruses could use to enhance production of viral proteins and necessary cellular factors. By manipulating the incorporation of specific ribosomal proteins, a virus could promote translation of desirable mRNAs. It is possible that ORF20 increases expression of OASL to allow both proteins to contribute to formation of specialized ribosomes and translational control, which is an exciting topic for future study.
Human embryonic kidney 293T (CRL-3216), human embryonic kidney 293 (CRL-1573), primary human foreskin fibroblast HFF-1 (SCRC-1041), human epithelial adenocarcinoma HeLa (CCL-2) and HeLa S3 (CCL-2.2), immortalized retinal pigment epithelial hTERT RPE-1 (CRL-4000), murine bone marrow fibroblast M2-10B4 (CRL-1972), African green monkey kidney Vero (CCL-81), and baby hamster kidney BHK-21 (CCL-10) cells were originally obtained from the American Type Culture Collection (ATCC). 293T OASL-/- and corresponding control 293T cells were kindly provided by Veit Hornung (Ludwig Maximilians Universität, Munich, Germany) [18]. Conditionally immortalized human endothelial cells stably infected with rKSHV.219, HuARLT2-rKSHV.219, and corresponding uninfected HuARLT2 control cells [29, 31, 48] were kindly provided by Thomas F. Schulz (Hannover Medical School, Hannover, Germany). HuARLT-rKSHV.219 and HuARLT2 cells were maintained in Cellovations enhanced microvascular endothelial cell growth medium (PB-MH-100-4099, PELOBiotech, Planegg, Germany) supplemented with 2 μg/ml doxycycline and for HuARLT2-rKSHV.219 additionally 5 μg/ml puromycin. All other cell lines were maintained in basal medium of Gibco high glucose (4.5 g/L) DMEM containing L-glutamine (ThermoFisher Scientific) supplemented with 8% fetal calf serum (FCS), penicillin/streptomycin (p/s), and sodium pyruvate; medium for HeLa and HeLa S3 additionally included 1% non-essential amino acids (NEAA), and medium for M2-10B4 and BHK-21 cells lacked sodium pyruvate. hTERT-RPE1 were cultured in high glucose DMEM with 5% FCS and p/s. HEK 293 were cultured in high glucose DMEM with 10% FCS and p/s. HFF-1 were cultured in high glucose DMEM supplemented with 15% FCS and p/s. Vero cells were cultured in MEM supplemented with 7.5% FCS, glutamine, NEAA, and p/s. All cell lines were cultured at 37°C in a humidified, 7.5% CO2 environment.
Antibodies against fibrillarin (# 2639), IRF3 (# 4302 for immunoblotting), STAT1 (# 9172), and the anti-myc tag mouse monoclonal 9B11 (# 2276) were from Cell Signaling. Rabbit anti-c-myc (# C3956), mouse anti-tubulin (# T6199-200UL), and mouse anti-beta-actin (#A5441) antibodies were purchased from Sigma-Aldrich. The anti-puromycin antibody clone 12D10 (# MABE343) was purchased from Merck Millipore. Anti-V5 antibodies were purchased from Invitrogen (# R960-25) and Biolegend (# 680601). Rabbit anti-GFP antibody ab290 was purchased from Abcam and HRP-coupled mouse anti-GFP antibody (sc-9996 HRP) and anti-IRF3 (sc-9082 for immunofluorescence) were purchased from Santa Cruz. Two rabbit polyclonal antibodies, anti-OASL 7 and anti-OASL 8, were raised in individual rabbits against a C-terminal peptide of OASL, KQQIEDQQGLPKKQ, which corresponds to amino acids 460–473 within the OASL ubiquitin-like domain.
pcDNA4/myc-His B, pcDNA4/myc-His/LacZ (LacZ-myc), and pcDNA3.1(+) were purchased from Invitrogen. The LacZ-myc plasmid encodes the beta-galactosidase protein and is referred to by the LacZ construct name throughout the text. ORF20WT was cloned into pcDNA4/myc-His B and encodes genomic KSHV ORF20 with a C-terminal myc-tag [10]. The ORF20WT plasmid construct can potentially express myc-tagged ORF20FL from its ATG (methionine) start codon at nucleotide position (nt) 1, myc-tagged genomic ORF20A (gA) from its CTG (leucine) start codon at nt 70, and myc-tagged ORF20B from its ATG start codon at nt 190. pcDNA4/myc-His B constructs to express myc-tagged ORF20FL, ORF20A, or ORF20B individually were made using ORF20WT as a template and utilized site-directed mutagenesis and/or PCR based cloning. To enhance expression of ORF20A individually, an ATG was added 5’ of the genomic leucine CTG start codon. pcDNA4/myc-His B constructs expressing two myc-tagged isoforms were also made using mutagenesis and/or PCR based cloning. ORF20FLgA and ORF20FLB potentially express ORF20FL and ORF20A or ORF20FL and ORF20B, respectively, from their genomic start codons. ORF20AB, which can express ORF20A and ORF20B, includes the genomic ORF20B start codon and a 5’ ATG upstream of the genomic ORF20A start to enhance plasmid-based expression.
MHV68 ORF20 (WUMS strain, NCBI NC_001826.2) with two sequential C-terminal myc tags was codon optimized for human expression and synthesized by Integrated DNA Technologies, then cloned into pcDNA4/myc-His B to make an MHV68 ORF20 construct with three C-terminal myc tags. Other homologs were cloned into pcDNA4/myc-His B and have one C-terminal myc tag. MCMV M76-myc (MCMV Smith strain, NCBI GU305914.1) and HSV-1 UL24-myc (NCBI NC_001806.1) were cloned using plasmid templates and HCMV UL76-myc (NCBI AY446894.2) was cloned by direct PCR of an E. coli colony containing the HCMV Merlin BAC, kindly provided by Martin Messerle (Hannover Medical School, Germany) [49]. pEGFP-RIG-I, expressing a GFP-RIG-I fusion, and pCAGGS Flag-RIG-I N (RIG-I N), expressing a constitutively active truncation mutant of RIG-I, were kindly provided by Andreas Pichlmair (Technical University of Munich, Germany). pCMV GFP-NS1A(203–237), referred to as GFP-NS1A, expresses GFP fused to amino acids 203–237 of influenza A/Udorn/72 encoding a nucleolar localization sequence and localizes to the nucleoli of transfected cells [33]. GFP-NS1A was kindly provided by Ilkka Julkunen (University of Turku, Finland). pcDNA3.1 human OASL-V5 WT (OASL-V5 or hOASL) and mutants have been previously described [17, 21]. pcDNA3.1 murine OASL1-V5 (mOASL1) encodes the murine homolog of human OASL (mOASL1: NM_145209.3). pcDNA3.1(+) OASL-myc (OASL-myc) expresses human OASL with a C-terminal myc tag. When not otherwise specified in the text, OASL refers to hOASL. ORF20WT-myc, ORF20B-myc, ORF20FL-myc, and OASL-V5 were subcloned into the lentiviral expression vector pWPI puro, kindly provided by Didier Trono, and lentiviral particles were prepared using pCMV gag-pol and pVSV-G.
Inserts of novel constructs were fully sequenced; other constructs were partially sequenced for verification. Primer and cDNA sequences are available upon request.
We used en passant BAC mutagenesis [50] to construct an ORF20stop mutant on the constitutively lytic KSHV (KSHVLYT) backbone [36, 37]. We mutated the ORF20FL GAG codon for E69 into the stop codon TAG to stop translation of ORF20FL, ORF20A, and ORF20B without altering the ORF21 promoter. KSHVLYT-BAC mutant clones were fully sequenced to confirm successful mutagenesis and to exclude additional undesired mutations in the viral genome.
BAC-derived MHV68-GFP [51] was amplified in M2-10B4 cells, concentrated by centrifugation for 2 h at 26,000 ×g and 4°C, purified by pelleting through a 10% Nycodenz cushion for 3 h at 36,000 ×g, and resuspended in sterile virus standard buffer (50 mM Tris-HCl, pH 7.8, 12 mM KCl, 5 mM Na2-EDTA) essentially as previously described [36]. For reconstitution of KSHVLYT wildtype or ORF20stop, BAC DNA was transfected into hTERT RPE-1 cells using Polyfect (Qiagen), propagated in the same cells, and concentrated by centrifugation for 4 h at 15,000 ×g at 4°C. Viral stock titers were determined using the median tissue culture infective dose (TCID50) method on M2-10B4 cells and hTERT-RPE1 cells for MHV68 and KSHVLYT, respectively, as previously described [36, 37]. Vesicular stomatitis virus with GFP inserted between the G and L genes [52] was kindly provided by Andrea Kröger (Otto von Guericke University Magdeburg, Germany). VSV-GFP was amplified in BHK-21 cells and the stock titer was determined by plaque assay on Vero cells. Lentiviral particles were prepared by co-transfection of low-passage 293T cells in 6 well plates with 2 μg of the appropriate pWPI expression vector, 1.3 μg pCMV gag-pol, and 700 ng VSV-G; DNA was complexed with 10 μl Lipofectamine 2000.
Approximately 16 h post transfection, medium was changed to viral harvest medium (high glucose DMEM containing 20% FCS, p/s, and 10 mM HEPES). The following day, approximately 40–44 h post transfection, supernatants were filtered through 0.45 μm filters and used immediately for transduction.
Transfected cells were lysed in mild NP-40 lysis buffer, containing 50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1% IGEPAL CA-630 (NP-40 substitute), 0.25% sodium deoxycholate, and 1x complete protease inhibitors without EDTA (Roche), or stringent radioimmunoprecipitation (RIPA) buffer containing 20 mM Tris-HCl, pH 7.5, 100 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS, and 1x complete protease inhibitors (Roche). One-tenth of the lysate was reserved as input lysate, the remainder was pre-cleared with protein A agarose beads (Repligen), immunoprecipitated (IP) with anti-myc, anti-V5, or anti-GFP antibodies, and then incubated with protein A agarose beads. For anti-FLAG IPs, anti-FLAG antibody and Pierce protein G magnetic beads (ThermoFisher) were used. Following extensive washing, bound protein was eluted by heating samples in Laemmli or NuPAGE sample buffer. Input lysates and IP samples were then processed for immunoblotting.
Cell lysates were combined with Laemmli or NuPAGE sample buffer, heated, and separated by SDS-PAGE or Bolt Bis-Tris Plus PAGE (Thermo Fisher Scientific). Both methods were used interchangeably for most proteins; for endogenous OASL, exclusively Bis-Tris PAGE was used. Proteins were transferred to nitrocellulose or PVDF membranes using wet transfer and either Towbin or NuPAGE transfer buffer, as appropriate. After blocking with 5% milk in TBST, membranes were incubated with appropriate primary and secondary antibodies, developed using Lumi-Light (Roche Applied Science) or SuperSignal West Femto (Thermo Scientific) chemoluminescence substrates, and signal was detected using either film exposure or an Intas Chemidoc system.
HFF, HuARLT2-rKSHV.219, or HuARLT2 were seeded in 6 well plates, then transduced with 1 ml filtered lentiviral-containing supernatant, 2 ml appropriate cell-specific medium, and 8 μg/ml polybrene. Plates were centrifuged at 690 × g for 90 min at 30°C, incubated 1–3 h at 37°C, then medium was exchanged. After 4 days, cells were seeded onto glass coverslips.
HeLa cells were transfected in plastic multiwell plates and seeded onto glass coverslips approximately 24 h post transfection. For whole-cell staining, cells were fixed with 4% paraformaldehyde in PBS (PFA) approximately 48 h post transfection. For nuclear staining, 48 h post transfection, HeLa cells were washed once with PBS, incubated 5 minutes in cold 1% NP-40 extraction buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% NP-40), washed once with TBS, then fixed with 3% PFA [53]. HeLa cells and nuclei on coverslips were washed, blocked and permeabilized with 5% FCS and 0.3% Triton-X 100 in PBS, then stained with the indicated primary antibodies, followed by staining with Alexa Fluor 488 or Alexa Fluor 594 conjugated secondary antibodies. Antibodies were diluted in PBS containing 0.1% BSA and 0.3% Triton-X 100. Transiently transduced HFF, HuARLT2-rKSHV.219, and HuARLT2 cells were seeded onto glass coverslips. For reactivation, cells were treated with 2μM sodium butyrate and 10% SF-9 supernatant containing RTA-expressing baculovirus [29, 32]. Cells were fixed with 4% PFA at various times post seeding, then processed essentially as for HeLa cells, with the addition of Alexa Fluor 647 conjugated secondary antibodies. 293T or 293T OASL-/- cells were seeded onto glass coverslips, then transfected with ORF20WT-myc with or without RIG-I N. Cells were fixed with 4% PFA, permeabilized with 0.1% Triton X-100, blocked in PBS containing 10% FCS and 1% BSA, then stained with anti-myc and/or anti-IRF3 antibodies. HeLa S3 cells were seeded onto glass coverslips. The following day, they were transfected with ORF20WT-myc or OASL-V5 individually, or co-transfected with ORF20WT-myc and OASL-V5. 24 h post transfection, HeLa S3 cells were fixed with methanol and PFA, blocked in PBS containing 10% FCS and 1% BSA, then stained with primary and secondary antibodies diluted in PBS containing 1% BSA. All nuclei were counterstained with Hoechst. Images were obtained using a Nikon ECLIPSE Ti-E inverted microscope equipped with a spinning disk device (Perkin Elmer Ultraview) and images were processed using Volocity (Improvision) and Adobe Photoshop.
q-AP-MS to analyze protein-protein interactions was performed [54]. HeLa S3 cells were metabolically labeled for 10 days in lysine- and arginine-free DMEM (Pierce) supplemented with dialyzed fetal bovine serum (Pierce), p/s (Invitrogen), 2 mM glutamine (Invitrogen), and either 0.22 mM 13C6 15N2 L-lysine-2HCl and 0.1385 mM 13C6 L-arginine-HCl (Pierce) for heavy-labeled cells or the corresponding unlabeled amino acids (Pierce) for light-labeled cells. At day 10, approximately 7x106 heavy- or light-labeled HeLa S3 cells were plated into two 14 cm dishes each. The following day, heavy and light HeLa S3 cells were transfected with 48.5 μg DNA and 100 μl Lipofectamine 2000 (Life Technologies) diluted in arginine- and lysine-free medium. For the forward experiment, heavy labeled HeLa S3 cells were transfected with ORF20WT or OASL-myc, while light labeled HeLa S3 cells were transfected with LacZ-myc. In the crossover experiment, the cell labels were inverted.
24–36 h post transfection, cells were carefully washed with ice-cold PBS and then lysed in 1 ml NP-40 lysis buffer (see recipe under Immunoprecipitations) containing 50U Emprove bio benzonase (Merck #1.01695.0001) for 1 hour at 4°C. Clarified supernatant was immunoprecipitated with 75 μl anti-c-myc microbeads (Miltenyi Biotec) for 1 h at 4°C on a rotating platform. Forward samples were combined or crossover samples were combined and then immediately applied to equilibrated M columns in a μMACS separator (Miltenyi Biotec). Beads were washed four times with 200 μl NP-40 lysis buffer, then twice with 200 μl Miltenyi wash buffer 2. Bound proteins were eluted by 3 × 10 min incubations with 100 μl of 100 mM glycine, pH 2.5; all fractions were combined. Eluted protein was precipitated by sequential addition of 70 μl 2.5M sodium acetate, pH 5.0, 40 μl 20 mM Tris-HCl, pH 8.2, 1 μl Glycoblue (Ambion), and 1600 μl 100% ethanol, followed by overnight incubation at 4°C on a rocking platform. Proteins were pelleted by centrifugation at 17,949 ×g for 60 min at 4°C, the supernatant was removed, and the protein pellet was air dried at room temperature.
Later, the protein pellet was rehydrated, disulfide bonds were reduced and cysteines blocked and alkylated, and an in-solution digest was performed using Lys-C and trypsin. Peptides were further processed and analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) using a Dionex UltiMate 3000 n-RSLC low flow liquid LC system (Thermo Scientific) connected to an LTQ Orbitrap VelosPro mass spectrometer (Thermo Scientific).
Peptides were identified using Proteome Discoverer 1.3.0.339 and a human protein database extracted from SwissProt on a Mascot server (V 2.4, Matrix Science) with ORF20 and LacZ manually added. The following search parameters were used: enzyme, trypsin; maximum missed cleavages, 1; fixed modification, methyl methane thiosulfonate (C); variable modification, oxidation (M); peptide tolerance, 5 ppm; MS/MS tolerance, 0.5 Da, Arg6 and Lys8 as variable modifications for quantification. Peptide filter parameters were as follows: maximum peptide rank of 1, peptide confidence of medium, and Mascot ion score of at least 25. The log2 fold change values of heavy/light values of each identified protein were calculated to facilitate graphical visualization. Highly confident interaction partners were based on log2 fold change values with an absolute value ≥1 in one experiment and ≥ 0.7 in the other experiment. Highly confident interaction partners for ORF20-myc and OASL-myc were entered into VennDis to identify specific and shared proteins and to create a Venn diagram.
293T cells were transfected with either ORF20WT, OASL-myc, or both combined. 24 h post transfection, sucrose gradient fractionation of ribosomes was performed essentially as described [55, 56]. Cell lysates (50 mM Tris-HCl, pH 7.6, 150 mM NaCl, 1 mM DTT, 1% NP-40 substitute, 100 μg/ml cycloheximide, DNase I, RNAse inhibitor, and protease inhibitor) were prepared in the presence of 20 mM EDTA to disrupt ribosomes and polysomes or in the presence of 10 mM MgCl2 to stabilize ribosomes and polysomes. Lysates were centrifuged over a 5–45% linear sucrose gradient at 160,000 ×g in a Beckman SW40Ti rotor for 3 h at 4°C. Fractions were collected from the top using a Biocomp fractionator (Biocomp, NB Canada) and absorbance at 254 nm was measured. RNA and total protein were extracted from 350 μl of each fraction by using Trizol reagent (ThermoFisher Scientific) and subjected to denaturing agarose gel electrophoresis or 10% SDS-PAGE and immunoblotting as appropriate.
293T cells were transfected with pcDNA4/myc-His B (EV), ORF20WT, ORF20FL, or ORF20B, and a small amount of either pcDNA3.1(+) (EV, control) or RIG-I N to induce expression of endogenous OASL. 24 h post transfection, newly synthesized proteins were labeled with puromycin by treating cells for 15 minutes with 5 μg/ml puromycin [34]. Cells were immediately lysed in 200 μl 1× NuPAGE LDS sample buffer supplemented with 3% beta-mercaptoethanol, 250 U benzonase, and 5 mM MgCl2. Lysates were incubated for 30 minutes at room temperature, denatured for 10 minutes at 70°C, separated on 4–12% Bolt Bis-Tris plus gradient gels (ThermoFisher Scientific) with NuPAGE MOPS SDS running buffer (50 mM MOPS, 50 mM Tris base, 0.1% SDS, 1 mM EDTA), and transferred to 0.2 μm PVDF membranes. Anti-puromycin, anti-OASL, anti-myc, and anti-actin immunoblotting were performed sequentially. Between antibody incubations, membranes were treated with Restore western blot stripping buffer (ThermoFisher) according to the manufacturer’s instructions.
293 cells in 12-well plates or 293T cells in 6-well plates were transfected with the indicated plasmids for 24–48 h. For 293 cells, 24 h post transfection, cells were treated with Lipofectamine or transfected with 5’pppRNA complexed with Lipofectamine for 24 h. RNA was prepared using commercially available kits. cDNA was synthesized using anchored-oligo(dT)18 and the Transcriptor first strand cDNA synthesis kit (Roche) or the iScript cDNA synthesis kit (Biorad). The quantity of GAPDH, OASL, IRF3, IFNAR, STAT1, ORF20, ORF16, ORF46, or K8.1 cDNA was determined using the LightCycler 480 Sybr Green I Master 2× mix (Roche), 125 nM of gene-specific forward and reverse oligonucleotides, and 1 μl cDNA, or the GoTaq 2x qPCR master mix, 200 nM of gene-specific forward and reverse oligonucleotides, and 1 μl cDNA, in a Roche LightCycler 480 instrument. mRNA levels were quantified relative to GAPDH and the 2-ΔΔCT method [57] was used to compare the amount of the indicated mRNA between samples. The forward and reverse oligonucleotide sequences were GAAGGTGAAGGTCGGAGTC and GAAGATGGTGATGGGATTTC for GAPDH, GCCATGTACTCCAGAACTCATC and GGCCTGGGATAACTCATTGTAA for OASL, AGCCTCGAGTTTGAGAGCTA and TGGTCCGGCCTACGATG for IRF3, CACCATTTCGCAAAGCTCAG and ACCATCCAAAGCCCACATAA for IFNAR, GCTGCAGAACTGGTTCACTAT and GGGTCATGTTCGTAGGTGTATTT for STAT1, CGATCTATGGCGGTTTCTAAGT and TTACGCAGTCGGCAATTCT for ORF20, AGATTTCACAGCACCACCGGTA and CCCCAGTTCATGTTTCCATCGC for ORF16, CACTGCTGCGATCCAGAGGATA and GAACCTGACATTGCGGATCCAC for ORF46, and TAAACGGGACCAGACTAGCAGC and GTTTTCTGCGACCGGTGATACG for K8.1. The oligonucleotide sequences for ORF16, ORF46, and K8.1 were previously published [58].
All siRNAs were purchased from Dharmacon/GE Life Sciences: ON-TARGETplus non-targeting pool (D-001810-10-20, control siRNA), ON-TARGETplus human IRF3 SMARTpool siRNA (L-006875-00-0010), ON-TARGETplus human STAT1 SMARTpool siRNA (L-003543-00-0005), and ON-TARGETplus human IFNAR1 SMARTpool siRNA (L-020209-00-0005). 293T cells were reverse transfected with siRNAs complexed with Lipofectamine 2000. Per well of a 6 well plate, 1 μl of 50μM siRNA stock was combined with 199 μl OptiMEM. Separately, 6 μl Lipofectamine 2000 was combined with 194 μl OptiMEM. Diluted Lipofectamine was added to diluted siRNA, then all 400 μl were added directly to the plate. 293T cells (450,000–600,000 cells) were resuspended in 1.6 ml/well, then added to the siRNA-Lipofectamine complexes. 2 days later cells were co-transfected with pcDNA4/myc-His B (EV) or ORF20WT, and either pcDNA3.1(+) (EV) or RIG-I N. 24 h later, RNA was prepared and q-RT-PCR was performed for OASL, GAPDH, and either IRF3, STAT1, or IFNAR as appropriate. In parallel, 293T cells were transfected with siRNAs for preparation of protein lysates and immunoblotting analysis.
150,000 293T OASL-/- cells were seeded per well in 24-well plates. The following day, replicate wells were transfected with either pcDNA3.1(+) (EV), mOASL1, or hOASL: 40μl of DNA mixes containing 748 ng DNA and 2.62μl Fugene HD in OptiMEM were added per well. Approximately 24 hours after transfection, one master dilution of the appropriate virus was prepared, then used to infect transfected cells with VSV-GFP for 1 hour, MHV68-GFP for 2 hours, or with KSHVLYT wildtype or KSHVLYT ORF20stop for 4 hours, at 37°C. Virus inoculum was removed, cells were washed once with medium, and fresh medium was added. The next day, 16 h, 20 h, or 24 h post infection for VSV-GFP, MHV68-GFP, or KSHVLYT, respectively, cells were detached using trypsin, fixed for 30 minutes with 4% PFA, and the number of green cells was determined by flow cytometry on an LSRII instrument. For growth curves with MHV68-GFP, at the indicated time points 10% of medium was harvested, frozen at -70°C, and replaced with fresh medium. After all samples were collected, the titers in supernatants were determined by TCID50 assay on M2-10B4 cells.
1 day after seeding in Cellovations endothelial cell medium lacking selection antibiotics, HuARLT2-rKSHV.219 cells were reactivated by the addition of 2 μM sodium butyrate and 10% SF-9 supernatant containing KSHV RTA-expressing baculovirus, kindly provided by Thomas F. Schulz [29, 32]. Cells were incubated for various times, then fixed for immunofluorescence or lysed for preparation of RNA.
Statistical analysis was performed using Graphpad Prism. Statistical significance was determined by 1way ANOVA followed by Tukey’s multiple comparison test (Fig 9B–9D, Fig 11, S6 Fig), 1way ANOVA followed by Dunnett’s multiple comparison test (Fig 10E), or by two-tailed unpaired t test (Fig 10B–10D, 10F and 10H). For all figures, ns indicates not significant, * indicates P<0.05, ** indicates P<0.01, and *** indicates P<0.001.
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10.1371/journal.pgen.1003461 | The Histone Demethylase Jarid1b Ensures Faithful Mouse Development by Protecting Developmental Genes from Aberrant H3K4me3 | Embryonic development is tightly regulated by transcription factors and chromatin-associated proteins. H3K4me3 is associated with active transcription and H3K27me3 with gene repression, while the combination of both keeps genes required for development in a plastic state. Here we show that deletion of the H3K4me2/3 histone demethylase Jarid1b (Kdm5b/Plu1) results in major neonatal lethality due to respiratory failure. Jarid1b knockout embryos have several neural defects including disorganized cranial nerves, defects in eye development, and increased incidences of exencephaly. Moreover, in line with an overlap of Jarid1b and Polycomb target genes, Jarid1b knockout embryos display homeotic skeletal transformations typical for Polycomb mutants, supporting a functional interplay between Polycomb proteins and Jarid1b. To understand how Jarid1b regulates mouse development, we performed a genome-wide analysis of histone modifications, which demonstrated that normally inactive genes encoding developmental regulators acquire aberrant H3K4me3 during early embryogenesis in Jarid1b knockout embryos. H3K4me3 accumulates as embryonic development proceeds, leading to increased expression of neural master regulators like Pax6 and Otx2 in Jarid1b knockout brains. Taken together, these results suggest that Jarid1b regulates mouse development by protecting developmental genes from inappropriate acquisition of active histone modifications.
| Histone modifications are involved in transcriptional regulation and thus affect cellular identity, differentiation, and development. We study the histone demethylase Jarid1b (Kdm5b/Plu1), as it has been reported to be highly expressed in several human cancers and therefore might present a novel target for anti-cancer therapies. To gain insights into the physiological role of Jarid1b, we have generated a Jarid1b knockout mouse. We show that loss of Jarid1b affects survival of newborn mice and that Jarid1b is required for the faithful development of several neural organs. To understand how Jarid1b regulates embryogenesis, we identified genes with increased H3K4me3 at a genome-wide scale as well as Jarid1b target genes during development. In Jarid1b knockout embryos, master regulators of neural development are expressed at higher levels, underscoring the importance of Jarid1b in transcriptional regulation. Furthermore, we extend previous reports of overlapping Jarid1b and Polycomb target genes to show the functional relevance of this observation. Our results provide the first detailed analysis of the role of Jarid1b in normal development and provide a basis for further studies evaluating the contribution of Jarid1b to tumorigenesis.
| Embryonic development is characterized by a coordinated program of proliferation and differentiation that is tightly regulated by transcription factors and chromatin-associated proteins. As embryonic cells differentiate, certain genes are activated while others are repressed, resulting in a unique pattern of gene expression in each cell type.
Histone H3 lysine 4 tri-methylation (H3K4me3) localizes to transcription start sites with high levels present at actively transcribed genes [1], [2], even though H3K4me3 at promoters is not a definite indication for transcriptional activity [3]. Methylation of H3K4 is catalyzed by a family of 10 histone methyltransferases in mammals [4]. Five of these are members of the Trithorax group of proteins that were first described in Drosophila to be required for maintenance of Hox gene expression by counteracting Polycomb-mediated repression. In Mll1 and Mll2 mutant mice, target genes are properly activated but expression fails to be maintained leading to embryonic lethality [5], [6]. In addition, H3K4 histone methyltransferases function in hematopoiesis [7], [8] and neurogenesis [9].
H3K4me3 is found in a constant balance with Polycomb-mediated repressive H3K27me3. Presence of both H3K4me3 and H3K27me3 at promoters is referred to as bivalency [10]. The category of bivalent genes is enriched in developmental regulators and is particularly abundant in embryonic stem cells (ESCs) that have the potential for several lineage choices [11]. Moreover, Polycomb proteins repress non-lineage specific gene expression, thereby ensuring developmental potency of embryonic and tissue stem cells during lineage specification, differentiation and development (reviewed in [12]). Polycomb proteins are classified into two separate complexes referred to as Polycomb repressive complex 2 (PRC2), which mediates H3K27me3, and PRC1, which catalyzes mono-ubiquitylation of H2A (H2AK119ub1) [13], [14]. Classical models propose a sequential mechanism in which H3K27me3 creates a binding site for PRC1 leading to further repression [14], [15], even though emerging studies suggest that Polycomb function is more complex [16]–[18].
While histone methylation was initially viewed as a stable modification, the discovery of histone demethylating enzymes has changed this paradigm [19]. Demethylation of H3K4me3 is catalyzed by the JARID1 (KDM5) family, which in mammals has four members: JARID1A, JARID1B, JARID1C and JARID1D [20]. The Drosophila JARID1 homologue LID (Little imaginal discs) is required for normal development [21], and the C. elegans homologue RBR-2 (retinoblastoma binding protein related 2) regulates vulva formation and lifespan [22], [23]. Mice mutant for Jarid1a are viable, displaying only mild phenotypes in hematopoiesis and behavior [24]. A recent report suggests that Jarid1b mutant mice are embryonic lethal between E4.5 and E7.5 [25]. The molecular mechanisms underlying this phenotype were not addressed. In contrast, others obtained viable Jarid1b mutant mice [26]. However, the requirement of Jarid1b for the differentiation of ESCs along the neural lineage [27], [28] suggests that Jarid1b may function in mouse development. In humans, JARID1B is highly expressed in several types of cancer, and it was shown to regulate proliferation of breast cancer cells and a slow cycling population of melanoma cells that promotes prolonged tumor growth (reviewed in [20]).
While the role of Jarid1b in mice remains controversial [25], [26], an understanding of its in vivo function is essential to direct future studies evaluating JARID1B as a potential drug target in cancer therapy. Jarid1b expression has been reported in various tissues during mouse embryogenesis whereas its expression becomes restricted in adults [29]. Here we report the first detailed analysis of the contribution of Jarid1b to mouse development. We show that Jarid1b is required for the proper development of several neural systems in the mouse and address the mechanisms underlying the observed defects.
To characterize the function of Jarid1b during mouse development, we generated constitutive Jarid1b knockout mice. Conditionally targeted Jarid1b mice containing a lacZ-Neo-reporter cassette flanked by FRT sites and in which Jarid1b exon 6 is flanked by loxP sites [28] were crossed with mice constitutively expressing Flp and Cre recombinase to obtain Jarid1b+/− mice. Jarid1b+/− mice were further intercrossed to generate Jarid1b−/− mice. Instead of the expected 25 percent of knockout mice, we only obtained 9.3 percent of adult Jarid1b knockouts (Figure 1A), suggesting that Jarid1b−/− mice are sub-viable. Analysis of early and late embryos from Jarid1b+/− intercrosses showed expected ratios while an increased number of Jarid1b knockouts was present among pups found dead during the first day after birth (Figure 1A), indicating that this might be the critical time for survival.
We have previously shown that conditional deletion of Jarid1b using this construct in vitro results in complete loss of Jarid1b protein and no generation of truncated or alternatively spliced variants [28]. Loss of Jarid1b in vivo was confirmed in all Jarid1b−/− embryos tested (see examples in Figure S1A and S1B), indicating that partial survival of Jarid1b knockouts in not due to incomplete deletion. Moreover, expression of other Jarid1 family members is unchanged both in vitro [28] and in vivo (Figure S1C).
To determine more precisely when Jarid1b−/− pups die, we performed caesarean deliveries and closely monitored the pups (Figure 1B). While approximately 95 percent of wild-type pups survive, we found that 50 percent of the knockouts die within the first two hours after delivery and another approximately 20 percent die after 14 to 24 hours (Figure 1C). All pups that survive the first day, develop normally until adulthood. Interestingly, survival of Jarid1b+/− pups is also slightly, even though not significantly, reduced during the first day. While most of the Jarid1b knockouts are grossly normal and not generally growth retarded (Figure 1D), we observed an increased incidence of developmental defects like exencephaly and eye defects among Jarid1b knockouts (Figure 1B, 1E and 1F). Taken together, loss of Jarid1b leads to major neonatal lethality of which only a small fraction can be explained by severe morphological abnormalities.
There is a large spectrum of physiological systems whose defects can challenge neonatal survival including those affecting parturition, breathing, suckling and neonatal homeostasis [30]. The first extrauterine challenge for neonates is breathing and since the majority of Jarid1b−/− pups die immediately after birth, we studied the respiratory system in more detail. Analysis of lungs from E18.5 fetuses revealed a normal size and weight (3.34±0.26 versus 3.45±0.44 percent body weight in heterozygotes versus knockouts, respectively) as well as a normal lobulation pattern (data not shown). Next, we isolated lungs from Jarid1b−/− newborns that had died within 2 hours after delivery and had either not shown any sign of breathing or exhibited gasping respiration (Figure 2A). While the wild-type lung showed saccular inflation, knockout lungs were compact and poorly inflated visible both from gross appearance and histology (Figure 2B and 2C), suggesting that Jarid1b−/− neonates die due to an inability to establish normal breathing. Moreover, preterm (E18.5) Jarid1b−/− lungs were abnormally compact compared to controls (Figure 2D), which might indicate a failure of prenatal breathing activity [31].
Respiratory failure might be caused by delayed lung maturation characterized by reduced surfactant expression [32]. Therefore, we analyzed expression of surfactant proteins (Sftpa1, Sftpb, Sftpc and Sftpd) in Jarid1b knockout mice (Figure S2A). None of the four surfactants was reduced in Jarid1b knockout lungs at E18.5, suggesting that respiratory failure is not due to pulmonary immaturity. In agreement with this, intrauterine administration of dexamethasone, a glucocorticoid that induces fetal lung maturation [33], did not improve survival of Jarid1b knockout pups (Figure S2B).
We also examined other physiological systems that are required for neonatal survival including the rib cage, diaphragm, craniofacial appearance and the palate as well as the cardiovascular system [30], but did not detect any abnormalities in the Jarid1b knockouts (Figure S3A–S3E). We conclude that while the lungs, skeletal and cardiovascular systems are properly developed, Jarid1b−/− neonates are unable to reliably establish respiratory function.
Immediate breathing after birth is also dependent on brainstem rhythmogenic and pattern forming neural circuits that develop before birth [34]. We therefore isolated brains from neonates after caesarean delivery, but found no gross abnormalities or differences in size of Jarid1b−/− brains compared to controls (Figure S3F and S3G). Essential rhythmogenic networks regulating breathing are located in the brainstem. Therefore, we recorded spontaneous C3–C5 nerve activity in an in vitro brainstem-spinal cord preparation from E18.5 embryos. Surprisingly, given the respiratory defects in newborn Jarid1b knockouts, central respiratory rhythmogenesis was unperturbed in Jarid1b−/− embryos (Figure S3H).
To monitor neurological reflexes of newborn Jarid1b−/− pups, we tested their response to pinching stimuli [35]. As opposed to control neonates, Jarid1b mutants only weakly reacted to a tail pinch (Figure S3I), suggesting that Jarid1b newborns show motosensory deficits characterized by hyporesponsiveness.
These results together with our previous in vitro data showing that Jarid1b is required for the differentiation of ESCs along the neural lineage [28] prompted us to analyze the development of neural systems in more detail in Jarid1b−/− embryos. As a first step we analyzed cranial nerves, a pair of 12 nerves that are essential for sensory and motor functions and reside in the mid- and hindbrain [36]. Defects in cranial nerve development may compromise neonatal survival. Cranial and spinal nerves can be visualized by whole-mount immunostaining at E10.5 using an anti-neurofilament antibody. Comparison of Jarid1b−/− embryos with controls revealed that while all nerve pairs are present, several cranial and spinal nerves are dysmorphic in the Jarid1b knockouts (Figure 3A). We used an arbitrary scoring system to quantify the differences between genotypes and found that Jarid1b knockouts are significantly affected while slight defects are already detectable in heterozygotes compared to wild-type (Figure 3B). Cranial nerves are involved in a diverse range of functions including movement of the eye, innervation of muscles of mastication, facial expression and tongue, and in transmitting information from chemoreceptors to the respiratory center [36], 37, and thus, defects in cranial nerve development may be relevant to reduced survival of Jarid1b knockouts. For example, the hypoglossal nerve (XII), which is dysmorphic in Jarid1b knockouts, innervates the muscles of the tongue, crucial for upper airway aperture during breathing.
Next, we analyzed Jarid1b expression during the time of mouse development when cranial nerves are specified. From embryonic day 8, the hindbrain becomes transiently partitioned along the anterior-posterior (AP) axis in a series of 8 rhombomeres that influence the spatial distribution of neuronal types [34]. Using the lacZ-Neo-reporter cassette present in the targeting construct [28], we observed high ubiquitous expression of Jarid1b in embryonic but not extraembryonic tissues at E8.5 (Figure S4). Moreover, in agreement with previous reports [38], at E12.5 and E14.5, Jarid1b expression was observed in several neural tissues including the fore- and hindbrain, neural retina, spinal cord and dorsal root ganglia as well as other tissues (Figures S5 and S6), indicating that Jarid1b could be involved in the development of several organs.
Cranial nerve development is imparted by genes involved in AP patterning and rhombomere specification, neuronal determination or survival and axonal migration [37]. Compartmentalization of the hindbrain, and in particular rhombomeres 3 and 4, have emerged as territories for the maintenance of breathing frequency after birth [34]. Rhombomeres are characterized by specific patterns of Hox gene, Krox20 (Egr2) and Kreisler (Mafb) expression, leading us to analyze expression of these genes by RNA in situ hybridization in Jarid1b−/− embryos. However, we did not observe any defects in the hindbrain patterning of E8.75 embryos (Figure 3C), suggesting that other mechanisms are responsible for spinal nerve abnormalities in Jarid1b−/− embryos.
In addition to sporadic cases of exencephaly, we frequently observed defects in eye development in Jarid1b−/− embryos and pups (Figure 4A–4D). In the most severe cases, eyes were completely absent (anophthalmia; Figure 4C). Other embryos exhibited microphthalmia (Figure 4B and 4D) or an incomplete closure of the optic fissure (Figure 4A and 4D). Moreover, after birth, the eyelid was often found open in Jarid1b−/− pups while it was closed in control mice at this time (Figure 4C). Altogether, externally visible eye defects were observed in approximately 22 percent of Jarid1b−/− embryos and pups (Figure 4E), but never in the Jarid1b knockouts that survive to adulthood. Histological analysis of two microphthalmic Jarid1b−/− eyes at E18.5 revealed a misfolding of the neural retina and a much smaller lense (Figure 4F and 4G). To test whether Jarid1b is expressed in the developing eye, we performed β-galactosidase stainings on sections of E12.5 and E14.5 eyes from targeted Jarid1b embryos (Figure 4H). At both stages, Jarid1b is specifically expressed in the inner layer of the neural retina, which contains retinal ganglion cells. Thus, Jarid1b seems required for the proper development of a mouse neurosensory organ, the eye.
We have previously shown that Jarid1b binds to the transcription start sites of many developmental regulators in mouse ESCs, many of which are also bound by Polycomb group proteins [28]. Therefore, we speculated that Jarid1b might also regulate Polycomb target genes in vivo. Hox genes represent classical Polycomb targets and their misexpression in Polycomb mouse mutants results in transformations of the axial skeleton [39], [40].
To investigate whether such transformations are also present in Jarid1b mutants, we stained skeletal preparations of E17.5 embryos to visualize cartilage and bone. While we did not observe any defects in the anterior region of the vertebral column (occipito-cervico-thoracic region), we found a transformation of the 26th vertebra, which is supposed to be the last lumbar vertebra (L6) into the first sacral vertebrae (S1) (Figure 5A and 5B). Moreover, we also observed a transformation of the 34th vertebra (Figure 5B and Figure S7). Thus, Jarid1b−/− embryos display posterior transformations of the skeleton, which similar to Polycomb mutants are not completely penetrant [39], [40].
To identify genes in addition to the Hox genes that might be misregulated in Jarid1b−/− embryos, we focused on an early embryonic stage (E8.5) where morphological defects were not yet observed. We expected that several of the phenotypes observed in the Jarid1b mutants arise from misspecification events early in development, as genes involved in eye specification, neural tube closure and hindbrain patterning start to be expressed from E8.0 [41], [42].
First, we performed chromatin immunoprecipitation (ChIP) followed by sequencing (seq) of head regions of E8.5 embryos (Figure S8A) for H3K4me3 and H3K27me3 to identify genes that change their chromatin state and thus might become misregulated in Jarid1b−/− embryos. By this analysis, we identified 492 peaks with increased H3K4me3 levels in Jarid1b knockouts versus heterozygotes, whereas only 27 peaks were detected in the reverse comparison (Figure S8B). Representative examples of loci with increased H3K4me3 in the knockouts as well as loci with unchanged chromatin states are shown in Figure 6A and 6B, respectively. The results were validated in an independent experiment by ChIP-qPCR showing that the differences in H3K4me3 are reproducible (Figure 6C). Comparison of genes with increased H3K4me3 in knockout embryos with all genes revealed an enrichment of repressed (H3K27me3 positive) and bivalent (H3K4me3/H3K27me3 positive) genes among genes with increased H3K4me3 (Figure 6D and Figure S8C), suggesting that aberrant active histone marks accumulate mainly at genes that are usually not actively transcribed. Gene ontology analysis of genes with increased H3K4me3 in the Jarid1b−/− embryos identified regulators of transcription and development including genes involved in ectoderm, nervous system and skeletal development as significantly overrepresented (Figure 6E and Figure S8D). To identify genes that are directly bound and regulated by Jarid1b, we also attempted ChIP experiments for Jarid1b in E8.5 embryos but unfortunately the results were of low quality due to very limited amounts of starting material. Instead, we compared genes with elevated H3K4me3 in Jarid1b−/− embryos with genes bound by Jarid1b in ESCs [28] and found that approximately one quarter was bound by Jarid1b in ESCs (Figure S8E). Thus, it is likely that some of the genes with increased H3K4me3 are also Jarid1b targets during early mouse development.
Next, we performed gene expression analysis of mRNA isolated from E8.5 Jarid1b heterozygotes and knockouts. Except for Jarid1b, we did not identify any genes that were more than 2-fold changed in the knockouts (Figure S8F). We validated a number of genes by RT-qPCR and confirmed that Jarid1b was not expressed in the knockouts, whereas Jarid1a and Jarid1c as well as L1cam and Pax2 remained unchanged (Figure S8G). Taken together, while we detected increased levels of H3K4me3 at a number of developmental regulators early in embryogenesis, these chromatin changes do not translate into detectable global transcriptional changes at this stage of development.
Deletion of Jarid1b in ESCs leads to a global increase in H3K4me3, while global H3K4me3 levels remain unchanged in Jarid1b depleted neural stem cells isolated from E12.5 embryos [28]. Likewise, depletion of JARID1B in MCF7 cells [43] or depletion of Jarid1a in mouse embryonic fibroblasts [24] did not result in a global elevation of H3K4me3. To analyze the effect of Jarid1b depletion in vivo, we prepared protein extracts from different stages of embryos. We confirmed lack of Jarid1b protein in all knockout embryos analyzed (Figure 7A). While we detected little change in H3K4me3 by immunoblotting in heads of E12.5 (data not shown) and E14.5 Jarid1b−/− embryos, global H3K4me3 levels were strongly increased in heads of late (E17.5) Jarid1b−/− embryos and in forebrains of Jarid1b−/− newborns (Figure 7A). These results suggest that H3K4me3 accumulates in Jarid1b knockouts as embryonic development proceeds, while H3K4me3 levels remain fairly constant during normal fetal development (Figure S9).
Next, we wanted to know at which classes of genes H3K4me3 accumulates in brains of newborn mice. Since the brain is a complex and heterogeneous organ, we first determined whether Jarid1b expression is limited to specific regions at this stage of brain development. However, β-galactosidase stainings on sections of brains from newborns revealed high overall expression of Jarid1b (Figure 7B). In addition, RT-qPCR analysis showed similar expression of Jarid1b in fore- and hindbrain, which is reduced in heterozygotes and lost in knockouts (Figure 7C). Thus, we divided the brain into forebrain and hindbrain for ChIP experiments and selected a number of genes that represent different chromatin states (Figure 7D–7G and S10). We observed increased H3K4me3 at repressed (H3K27me3-positive) genes, including Otx2, Pax9, HoxB5 and Hesx1, and at active (H3K4me3-positive) genes (Sema5b), but not at unmodified genes in P0 forebrains. Some bivalent genes, for example Pax6, showed increased H3K4me3 and slightly reduced H3K27me3, while others remained unchanged (e.g. Neurod2). Similar results were obtained in independent ChIP experiments using forebrain or hindbrain. These data suggest that H3K4me3 is increased at transcription start sites in late stages of brain development.
To test which genes are directly bound by Jarid1b, we also performed ChIP for Jarid1b (Figure 7D–7G and Figure S10). We detected Jarid1b binding at transcription start sites of bivalent (Pax6) and H3K4me3-positive (Sema5b) genes, which is in agreement with our previous findings in ESCs [28]. Moreover, we detected low levels of Jarid1b binding (2- to 4-fold above background) at several of the H3K27me3-positive loci with increased H3K4me3 in the Jarid1b knockouts (Otx2, Pax9, Hoxb5), suggesting that elevated levels of H3K4me3 at many of these loci are due to a loss of direct association of Jarid1b.
To determine whether changes in chromatin modifications are accompanied by differences in expression, we performed RT-qPCR in P0 brains of controls and Jarid1b knockouts (Figure 7D–7F and Figure S10). We detected increased levels of the transcription factor Otx2 in forebrains of Jarid1b−/− newborns. Furthermore, in line with a shifted balance of H3K4me3 versus H3K27me3, expression of the neural master regulator Pax6 was increased in Jarid1b−/− P0 brains. In contrast, expression of actively transcribed genes, like Sema5b, was unchanged despite higher levels of H3K4me3. We conclude that Jarid1b mutants accumulate higher levels of H3K4me3 and show increased expression of genes important for regulating embryonic development.
Next, we analyzed at which stage between E8.5 and P0 changes in gene expression arise in Jarid1b knockouts. While we did not detect transcriptional changes at E8.5, expression of Otx2, Pax6 and Sema5b was increased in heads of E12.5 Jarid1b knockout embryos compared to controls (Figure S11A). Since the transcription factor Pax6 controls the balance between neural stem cell (NSC) self-renewal and neurogenesis [44], we tested whether deletion of Jarid1b affected this balance. Sorting of NSCs and neuronal progenitor cells (NPs) from E12.5 brains (Figure S11B) revealed a slight (but not significant) increase in NSCs in Jarid1b knockouts and no change in NPs. Similarly, global levels of neuron and astrocyte markers remained unchanged in P0 brains (Figure S9B), which is in agreement with normal gross morphology of Jarid1b knockout brains (Figure S3F). Thus, the detectable changes in gene expression observed in Jarid1b knockout mice does not appear to be a result of abnormal numbers of NSCs or NPs.
Finally, we tested whether increased expression of Otx2 and Pax6 correlated with survival. However, as shown in Figure S11C, we did not detect a significant difference in expression of Otx2 and Pax6 in brains of newborns that were alive 2 hours after caesarean delivery versus newborns that died immediately. In contrast, the expression of Otx2 was significantly higher in the adult brain of surviving knockout animals as compared to wild type (Figure S11D), suggesting that transcriptional regulation by Jarid1b is not restricted to embryogenesis only, but affects selected genes rather than global transcription.
Embryonic development is regulated by transcription factors as well as chromatin-mediated processes resulting in tissue-specific gene expression. Here, we show that the histone demethylase Jarid1b is required for faithful mouse embryonic development (see model in Figure S12). Deletion of Jarid1b results in major neonatal lethality caused by an inability of the newborn mice to establish breathing. Jarid1b mutant embryos display a number of defects related to neural systems, including the misorganization of cranial and spinal nerves as well as increased incidence of exencephaly that might contribute to neonatal lethality. Respiratory rhythmogenic circuits in the brainstem of Jarid1b mutant embryos appear intact since a spontaneous motor output on cervical nerves was observed under in vitro conditions. In agreement, Krox20 and Kreisler, essential genes involved in specification of respiratory-related rhombomeres, are also not affected in mutant embryos. Thus, we speculate that the breathing problems of Jarid1b mutant neonates may stem from either compromised pattern forming circuits controlling airway patency, or an inability of the rhythmic motor output to reach respiratory muscles, caused by defects in cranial and spinal nerve development. Several other organs important after birth appeared undisturbed. However, the spectrum of physiological systems required for neonatal survival is large [30] and we cannot exclude that there are other subtle defects that manifest in secondary physiological problems interfering with survival of Jarid1b knockouts.
Previous in vitro studies of ESCs with either reduced [28] or increased [27] levels of Jarid1b have reported a role for Jarid1b during differentiation of ESCs into neurons. This raises the question of why Jarid1b is specifically required in neural systems. During embryogenesis, Jarid1b is expressed in several neural organs including the brain, spinal cord and eye, but also in a number of other systems (this study, [38]). Moreover, in ESCs, Jarid1b is targeted to transcription start sites of genes that regulate development, including genes involved in neurogenesis and ectoderm development [28]. Thus, a combination of tissue-specific expression and target gene selectivity might explain neural-specific phenotypes. It should be noted, however, that other systems are also affected by Jarid1b depletion, exemplified by homeotic transformation of the skeleton (this study) or slightly reduced expression of meso- and endodermal markers during embryoid body differentiation of ESCs [28]. Interestingly, knockdown of Jarid1b in the retina of newborn mice leads to abnormal morphology of rod photoreceptor cells and misregulation of rod-expressed genes [45], supporting a role for Jarid1b in neuronal cells of the eye. In addition, other Jarid1 family members have reported functions in behavior and/or neurulation, suggesting that these processes are susceptible to changes in H3K4 methylation. Jarid1a knockout mice display abnormal clasping of the hindlimbs [24], while mutations of human JARID1C occur in patients with X-linked mental retardation [46]. Knockout of Jarid1c in the mouse results in embryonic lethality due to defects in neurulation and cardiogenesis [47]. Taken together, while several Jarid1 family members are involved in the control of neural systems, they may regulate different aspects of development and cannot fully compensate for the absence of other Jarid1 members resulting in gene-specific phenotypes.
To determine how Jarid1b contributes to the regulation of mouse development, we analyzed global as well as gene-specific histone methylation levels in Jarid1b−/− embryos. Consistent with the previously reported catalytic activity of Jarid1b [23], [43], H3K4me3 was increased around transcription start sites of developmental regulators already early during mouse development, particularly at genes that are normally in a repressed or poised state. At this early stage of development, we could not detect any global changes in gene expression levels, but we cannot exclude that expression of some genes might be affected in a subset of cells as the E8.5 mouse embryo is composed of many distinct cell layers. For example, the development of the eye initiates at E8.0 with the evagination of the optic pit from a subset of cells in the diencephalon [41]. In analogy, increased H3K4me3 at transcription start sites may reflect small increases in many cells of the early embryo or result from large increases in a subset of cells. Increased H3K4me3 around transcription start sites may render the associated genes more susceptible to later activation, especially during developmental time windows or in specific cell types where additional signaling molecules create a competent transcriptional state.
As embryonic development proceeds, a global increase in H3K4me3 becomes detectable. Locally, similar to early embryos, increased H3K4me3 is present at the transcription start sites of genes in Jarid1b−/− brains. Even though altered H3K4me3 per se may not be sufficient to induce transcriptional changes or cell fate conversions [48], it may have functional consequences when prompt responses to signaling events are required or for the fine control of steady state transcript levels [49]. Indeed, we detected increased expression of Pax6 and Otx2, two master regulators of eye and neural development (reviewed in [50], [51]), in forebrains of Jarid1b−/− pups. For both Pax6 and Otx2, it was shown that not only deletion but also overexpression affect eye and neural lineage development [44], [52], [53].
Binding of Jarid1b itself was found at H3K4me3-positive genes (both active and bivalent), which is in agreement with previous ChIP-seq data [28], [54]. Interestingly, the overlap between Jarid1b and Polycomb target genes was functionally supported in this study by the observation that Jarid1b knockout embryos show homeotic transformations of the skeleton, which is a hallmark of Polycomb mutant mice. Moreover, the observation that Jarid1b is bound to several repressed genes that are marked by aberrant H3K4me3 in the knockouts, suggests that Jarid1b is directly required to prevent aberrant accumulation of active chromatin modifications at developmental regulators during embryogenesis.
Most of the phenotypes that we observed in Jarid1b knockouts occurred with incomplete penetrance. This is not uncommon and has been reported for other histone demethylases [55] but also transcription factors [56]. The penetrance is often affected by the genetic background of the mice [50], [55]. This might also explain the difference in survival of our knockout mice compared to a previous study [25]. To test this hypothesis, we crossed our Jarid1b mutant mice that were derived on a C57BL/6 background into a mixed C57BL/6/129 genetic background (Figure S13). On the mixed background, 40% of knockouts die after birth, compared to 70% on a C57BL/6 background. Besides, we observed a similar frequency of exencephaly and a reduced response of the newborns to pinching stimuli, while no eye phenotypes were detected on the mixed background. These results suggest that different genetic background of mice strains used, could partly explain the divergence of obtained results.
Moreover, many disease-causing mutations only have detrimental defects in a subset of individuals, and phenotypic discordance remains even in the absence of genetic and environmental variation. It was shown that feedback induction of genes with related functions differs across individuals leading to a buffering of stochastic developmental failure through redundancy [57]. In the Jarid1b mutants, we did not observe upregulation of other Jarid1 members at the transcript level. However, we cannot exclude that protein levels are increased, or that these proteins are preferentially recruited to Jarid1b target sites to compensate for lack of Jarid1b. In addition, systematic analysis of transcript levels and their correlation with phenotypes has shown that variability in gene expression underlies incomplete penetrance [58]. It was proposed that fluctuations in gene expression can be controlled by wild-type developmental networks. In contrast to transcription factors that can induce cell fate conversions, chromatin modification are thought to rather fine tune transcription. In Jarid1b mutant embryos, both repressed and bivalent genes acquire increased levels of H3K4me3, which might render these genes more susceptible to unscheduled activation. Indeed, we observed increased expression of Pax6 and Otx2 in newborn knockout brains. Raj et al. [58] propose a model in which expression must surpass a threshold during a window of development. The same might be true for histone modifications. In plants, progressive increase in H3K27me3 was shown to be capable of switching a bistable epigenetic state of an individual locus [59]. While global levels of H3K4me3 are unchanged during early embryogenesis, H3K4me3 accumulates in the Jarid1b knockouts as embryonic development proceeds. For some genes or in specific cell types, this might lead to a switch in the balance of active versus repressive histone modifications, and if this coincides with a developmental window of transcriptional potency, it might affect phenotypic outcomes. Moreover, since Jarid1b binds to a large number of target genes [28], it could be expected that a wide range of phenotypes with varying severity is observed in Jarid1b knockout embryos.
The functions of histone demethylases in vivo are starting to emerge, however, in many cases the mechanisms of their action remain to be elucidated. Here, we present a detailed analysis of the role of the H3K4me2/3 demethylase Jarid1b during mouse development. In the adult organism, Jarid1b expression is also observed but it becomes more restricted. Since high expression of Jarid1b is detected during meiosis and in adult testis [29] as well as in several types of cancer [26], Jarid1b has been proposed to belong to the family of testis-cancer antigens [60]. In future studies, it will be very interesting to characterize the function of Jarid1b in adult mice as Jarid1b presents a potential drug target for anti-cancer therapies and an understanding of its in vivo role will help to guide targeting efforts.
The derivation of targeted (Jarid1bNeo/+) and conditional (Jarid1bF/F) Jarid1b mice has been previously described [28]. Conditional Jarid1b mice were crossed with Cmv-cre transgenic mice [61] to obtain heterozygous mice, which were further inter-crossed to generate Jarid1b knockouts. Jarid1b mice were maintained on a C57BL/6 background, unless otherwise stated. All mouse work was approved by the Danish Animal Ethical Committee (“Dyreforsøgstilsynet”).
For cesarean deliveries, timed matings were setup using Jarid1b+/− mice to generate experimental pups. Jarid1b+/+ mice were used for foster mothers. Pregnant Jarid1b+/− females were injected subcutaneously with 100 µl of Promon (50 mg/ml, Boheringer Ingelheim) at E16.5 and E18.5 to prevent natural birth. Pups were delivered at E19.5 by caesarean, massaged gently to stimulate breathing and placed on a 37°C warm plate during initial examination. Pups were then placed with a foster mother and examined regularly during the first 24 hours after delivery. Dexamethasone (Sigma) or saline control was administered subcutaneous (0.4 mg/kg) to pregnant females at E17.5 and E18.5 [33].
Histological analysis was performed according to standard procedures. Briefly, embryos or tissues were fixed over night in 4% buffered formaldehyde and subsequently incubated in baths of buffered formaldehyde, 96% ethanol, 99% ethanol, xylene and paraffin. Paraffin blocks were cut into 8–10 µm sections on a microtome (Microm HM355S). Sections were deparaffinised, stained with hematoxylin and eosin, dehydrated and mounted using VectaMount (Vector).
The neuraxis in caesarean delivered E18.5 embryos was removed by dissection in an ice cold, oxygenated (95% O2, 5% CO2) solution containing 250 mM glycerol, 3 mM KCl, 5 mM KH2PO4, 36 mM NaHCO3, 10 mM D-(+)-glucose, 2 mM MgSO4 and 0.7 mM CaCl2. Brainstem-spinal cord preparations, which contained the entire brainstem and the cervical part of the spinal cord, were placed in a 2 ml recording chamber with a temperature of 29°C and was constantly superfused at a rate of 2 ml/min with preheated oxygenated (95% O2, 5% CO2) artificial cerebrospinal fluid solution (ACSF). The ACSF solution contained 130 mM NaCl, 5.4 mM KCl, 0.8 mM KH2PO4, 26 mM NaHCO3, 30 mM D-(+)-glucose, 1 mM MgCl2 and 0.8 mM CaCl2. Glass-pipette suction electrodes (tip-diameter of 40–160 µm, A-M Systems, Carlsborg, USA) were placed on C3, C4, or C5 rootlets to record spontaneous respiratory-related nerve-activity. Nerve potentials were amplified by a custom-built nerve amplifier (×50,000), filtered at DC-2 KHz, and digitized (2.5 KHz) by a PCI–6289, M Series A/D-board (National Instruments, Austin, USA) controlled by Igor Pro (Wavemetrics, Lake Oswego, USA) software.
For whole-mount beta-galactosidase stainings, embryos were fixed in PBS with 0.25% glutaraldehyde for 10–30 min, washed in PBS and stained with PBS containing 0.02% NP40/IGEPAL, 0.01% sodium deoxycholic acid, 2 mM MgCl2, 20 mM Tris-HCl pH 7.4, 5 mM Potassium ferrocyanide and 5 mM Potassium ferricyanide until desired colour intensity. After post-fixation in 4% paraformaldehyde over night at 4°C, embryos were passed through a glycerol gradient incubating several days at each concentration, and finally stored in 100% glycerol.
For beta-galactosidase stainings of cryo-sections, embryos were fixed in 0.2% paraformaldehyde over night at 4°C, incubated in PBS with 2 mM MgCl2 and 30% sucrose over night at 4°C, embedded in OCT (TissueTek, Sakura) and stored at −80°C. Samples were cut into 8 µm sections on a cryostat (Leica CM3050), post-fixed in 0.2% paraformaldehyde for 10 min on ice, washed in PBS with 2 mM MgCl2, incubated in PBS with 2 mM MgCl2, 0.01% deoxycholic acid and 0.02% NP40 for 20 min on ice, and stained as described above. Sections were washed, counter-stained with eosin, passed through an ethanol gradient, incubated in xylene and mounted in VectaMount (Vector).
Whole-mount in situ hybridization of mouse embryos was performed as previously described [62].
Embryos were isolated at E10.5, fixed in 4% paraformaldehyde for 2 hours and stored in 100% methanol at −20°C. After bleaching with methanol/H2O2, embryos were rehydrated, blocked in PBS with 2% milk and 0.1% triton (PBSMT) and stained over night at 4°C with anti-neurofilament antibody (Developmental Studies Hybridoma Bank, 2H3, 1∶50). Embryos were washed in PBSMT, and incubated over night at 4°C with peroxidase-conjugated goat anti-mouse IgG (Jackson ImmunoResearch Laboratories, 111-035-146, 1∶500). After several washes in PBSMT, embryos were incubated in 0.3 mg/ml DAB (Sigma) in 0.5% NiCl2 for 30 min, H2O2 was added to a concentration of 0.0003% and the embryos incubated until the desired colour intensity was obtained. Finally, embryos were dehydrated through a methanol gradient and cleared in 1∶2 benzyl alcohol∶benzyl benzoate in glass containers.
For skeletal preparation, embryos were isolated at E17.5, eviscerated and the skin removed. Embryos were fixed over night in 100% ethanol, rinsed in 95% ethanol and stained over night in 0.15 mg/ml Alcian Blue in 95% ethanol containing 20% glacial acidic acid. Embryos were washed in 95% ethanol, cleared in 1% KOH for 4 hours and stained with 50 mg/l Alizarin Red in 1% KOH over night at 4°C. Final clearing of embryos was performed in 1% KOH for 3 hours and through a gradient of 1% KOH/glycerol until final storage in 100% glycerol.
Antibodies used in this study include anti-Jarid1b (DAIN) [28], anti-H3K4me3 (Cell Signaling, C42D8), anti-H3K27me3 (Cell Signaling, D18C8), anti-H3 (Abcam, 1791), anti-H4 (Millipore, 05-858), anti-ß-III-tubulin (Sigma, T8660), anti-Glial Fibrillary Acidic Protein (GFAP) (DakoCytomation, Z0334), anti-Vinculin (Sigma, V9131) and anti-ß-tubulin (Santa Cruz, sc-9104).
Whole brain from E12.5 was dissociated, filtered, resuspended in PBS with 5% FBS and stained for 20 min on ice using the following antibodies: anti-Prominin-1-biotin (MACS Miltenyi Biotec, 130-092-441), anti-CD-15-FITC (BD Biosciences 332778), anti-A2B5-APC (MACS Miltenyi Biotec, 130-093-582) and anti-CD24 (BD Biosciences, 553262) [63]. Cells were washed, incubated with secondary fluorescent-conjugated antibodies for 20 min on ice, washed again and resuspended in buffer for viability dye staining containing 7AAD (BioLegend 420404). Cells were analyzed on a FACS Aria flow cytometer (BD Biosciences). Single viable cells were gated into the following populations: Neural progenitors: CD15 low, Prominin low, CD24 high; Neural stem cells: CD15 high, Prominin high, CD24 low.
Chromatin immunoprecipitation (ChIP) and ChIP-sequencing were performed as previously described [28]. MACS2 [64] was used to identify regions with increased histone methylation. For E8.5 embryos, five heads of embryos with 3–8 somites were pooled per ChIP, corresponding to approximately 100,000 cells in total. For ChIP and expression analysis of P0 brains, single brains were divided into fore- and hindbrain.
Base-calling and demultiplexing of raw sequencing data was performed using the standard Illumina pipeline (CASAVA, version 1.8.2) followed by alignment to the mouse genome (mm9 assembly) with bowtie (version 0.12.7) [65] using the following parameters: -S -m 1 mm9. Samtools (version 0.1.18) [66] and bedtools (version 0.1.18) [67] were applied for conversion of files between alignment formats. Furthermore, reads were extended to a total length of 250 bp (estimated DNA fragement size) in the 3′ direction. Various command-line utilities from UCSC (http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/) were used to generate normalized bigwig track files for viewing in the UCSC genome browser. The files were normalized to tags per million after removing duplicate reads. Peak calling was performed in MACS2 [64] using the following settings: –broad -f BAM -g mm.
Gene expression analysis was previously described [28]. For RNA isolation of E8.5 embryos, the RNAeasy Microkit (Qiagen) was used. For microarray analysis, four heads of E8.5 embryos were pooled per sample and three biological replicates analyzed. RNA was hybridized on mouse Gene 1.0 ST arrays by the RH Microarray Center at Rigshospitalet, Copenhagen, following Affymetrix procedures. Microarray data was analyzed using Gene Array Analyzer software [68] with default settings, P-value<0.05 and a log2 fold change of +/−1. The ChIP-seq and microarray data have been submitted to the Gene Expression Omnibus (GEO) database (GSE41174). Primer sequences are provided in Table S1.
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10.1371/journal.pcbi.1004974 | The Role of Adaptation in Bacterial Speed Races | Evolution of biological sensory systems is driven by the need for efficient responses to environmental stimuli. A paradigm among prokaryotes is the chemotaxis system, which allows bacteria to navigate gradients of chemoattractants by biasing their run-and-tumble motion. A notable feature of chemotaxis is adaptation: after the application of a step stimulus, the bacterial running time relaxes to its pre-stimulus level. The response to the amino acid aspartate is precisely adapted whilst the response to serine is not, in spite of the same pathway processing the signals preferentially sensed by the two receptors Tar and Tsr, respectively. While the chemotaxis pathway in E. coli is well characterized, the role of adaptation, its functional significance and the ecological conditions where chemotaxis is selected, are largely unknown. Here, we investigate the role of adaptation in the climbing of gradients by E. coli. We first present theoretical arguments that highlight the mechanisms that control the efficiency of the chemotactic up-gradient motion. We discuss then the limitations of linear response theory, which motivate our subsequent experimental investigation of E. coli speed races in gradients of aspartate, serine and combinations thereof. By using microfluidic techniques, we engineer controlled gradients and demonstrate that bacterial fronts progress faster in equal-magnitude gradients of serine than aspartate. The effect is observed over an extended range of concentrations and is not due to differences in swimming velocities. We then show that adding a constant background of serine to gradients of aspartate breaks the adaptation to aspartate, which results in a sped-up progression of the fronts and directly illustrate the role of adaptation in chemotactic gradient-climbing.
| Biological sensory pathways are presumed to evolve for the processing of environmental information, yet quantitative evidence is scant. Chemotaxis allows bacteria to sense chemical gradients but their ecological distribution, e.g. whether natural gradients sensed by E. coli change slowly or rapidly in space and time, is unknown. That distribution matters, as it controls constraints and selective pressure acting on the pathway. We used microfluidic devices to generate controlled chemoattractant gradients and measure the speed of bacterial climbing of those gradients. We could thereby assay the impact of adaptation properties of the chemotaxis pathway onto the progression of gradient climbing. We specifically show that loss of adaptation, induced by adding a background of serine to gradients of aspartate, leads to a faster progression of the bacteria along the chemoattractant gradient. We finally discuss why our experiments suggest that ecological conditions are likely to involve chemoattractant profiles more complex than constant gradients usually considered in the laboratory.
| A major example of adaptation in transduction pathways is bacterial chemotaxis, where saturating stimuli are followed by a precise recovery of the cell’s pre-stimulus tumbling frequency [1, 2]. The recovery is achieved by an integral feedback control [3], which is molecularly mediated by the methylation of chemoreceptor clusters [4–7]. More recently, the output of the chemotaxis pathway was also found to adapt via motor remodeling [8]. Time scales of adaptation are on the order of ten minutes for 1mM of L-aspartate and shorten as the amplitude of the stimulus reduces [9, 10]. In the linear regime, adaptation takes place over a few seconds, as illustrated by the chemotactic impulse responses measured either by tethering [11] or by trajectories of free bacteria [12].
The E. coli chemotaxis pathway has two major amplification steps: the first is at the level of the clusters of receptors localized at the cell poles [13–16]; the second is at the level of the motors controlling the rotation of flagella [17]. Their combination results in gains of several hundreds that allow the detection of tiny variations in the concentration of chemicals [5]. Maintaining that benefit over an extended dynamic range of concentrations is the oft-heard justification for adaptation.
Adaptation is not precise for all attractants and concentrations, though. A well-known example is the chemoattractant serine, preferentially bound by the Tsr receptor. In standard conditions of bacterial preparation and culture, the frequency of tumbling reduces as the concentration of serine increases [18]. Loss of precise adaptation is due to the reduced availability of occupation sites for (de-)methylation on the receptor clusters [4–7]. In the linear regime, adaptation is quantified by the integral of the impulse response and precise adaptation corresponds to a vanishing integral [11]. Both serine and aspartate feature a two-lobe response, yet for serine the areas of the positive and the negative lobes differ [12]. Lack of adaptation is also observed for the chemorepulsion to leucine [12]. Even for aspartate (or its non-metabolizable analogue alpha-methyl-DL-aspartate), the E. coli chemotaxis pathway shows imprecise adaptation at high concentrations [19, 20].
Is the lack of precise adaptation an imperfection (as its common designation “imperfect adaptation” tends to suggest) or does it actually have functional relevance? No major impairment of motility is observed for chemotaxis in serine [18, 21–24]. Furthermore, theoretical arguments suggest that lack of precise adaptation might actually bring some advantages. First, the chemotactic velocity in a static constant gradient is predicted to be larger if the chemotaxis response is not adapted [25]. Second, accumulation at peaks of concentration should be favored by breaking adaptation [26]. Finally, the optimal degree of adaptation should depend on the spatial and temporal profile of the chemoattractant fields [27].
Previous theoretical works all employ linear response theory, which disregards the variations of sensitivity, dynamic range and response as bacteria progress along the gradients. These effects are crucial as variations might reduce the bacterial response and overwhelm the aforementioned effects, which assume that the response remains fixed. In particular, this limitation makes the prediction [25] of a stronger chemotactic velocity in the absence of perfect adaptation not conclusive, and the issue remains moot.
Theoretical issues are reviewed and discussed in the first section below, which provides the motivation for the experiments that are reported in the rest of the paper. In order to conclusively resolve the issue, we developed microfluidic techniques and built a setup where bacteria climb two static concentration gradients of aspartate and serine, otherwise identical in shape and intensity. The rationale for our set-up is that bacteria come from the same population so as to reduce variability due to different cultures and/or conditions. Gradients of chemoattractants span an extended range of concentration, from micromolar values at the entry of the channel to mM’s in the reservoirs used to establish the gradients. We also engineered gradients of aspartate with a uniform background of serine. The integration of the two signals leads to loss of precise adaptation to aspartate, which can be used to directly assess the role of adaptation in the progression of bacteria along the gradients. We quantified the progression by measuring the cumulative distribution of bacteria into the channels and by tracking the position of the most advanced bacteria (the 10th, 20th and 40th) as a function of time. We could thereby compare the progression of the bacterial fronts for different chemoattractants and conditions, as we report below.
Linear response theory cannot be employed to conclusively analyze the progression of bacteria over an extended range of concentrations. Indeed, the amplitude and the form of the linear response kernel (the function K(t) defined below) change as bacteria climb the gradients. Furthermore, the state around which one should linearize is generally different from the resting state [28] and systematically drifts if adaptation is not precise. The scope of this section is to present a qualitative analysis based on linear response theory, which highlights the mechanisms involved in the dynamics and the uncertainties on quantitative numerical factors that are crucial for a conclusive answer. This sets the stage for the experiments presented in the following sections.
In the linear response regime, the chemotactic velocity along the concentration gradient is the product of the gradient and the chemotactic coefficient χ, which has the expression [27]:
χ = α u 2 3 σ 2 × ∫ 0 ∞ e - σ t K ( t ) d t . (1)
Here, the impulse response K(t) is the change in the probability of running (vs tumbling) at time t as a unit impulse in concentration is imparted at time 0. The time-integral ∫ 0 ∞ K ( t ) d t measures the derivative of the steady-state probability of running with respect to the concentration, i.e. a zero value corresponds to precise adaptation.
The other parameters in Eq (1) α = 1 - cos φ τ r , σ = 2 D r o t + α , (2)
involve the angle φ of scatter during tumblings (the experimental value is 〈cos φ〉 ≃ 0.3) and the rotational diffusivity is approximately Drot ≃ 0.1 rad2/s in standard laboratory conditions [5]. Note that Eq (1) differs from estimates based on a single run, e.g. those used in [25, 28]. In particular, the proper limit to times longer than the microscopic decorrelation times is taken, which ensures that time-correlations among successive runs are well captured [27]. Note also that, at variance with the prior prediction in Ref. [29], the expression (1) correctly captures the chemotactic velocity when adaptation is not precise (see SM for a comparison with numerical simulations).
As we mentioned above, the expression (1) has the limitation that it should only be understood as referring to local values. For instance, in the absence of adaptation, the running time τr varies with the position along the gradient whilst the running speed u ≃ 15μm/s is roughly constant in our conditions. The form and the amplitude of the linear response kernel (see Eq (3)) are expected to vary along the gradient. Their dependence on the position along the gradient constitutes the main factor of uncertainty as it directly controls the local value of the up-gradient velocity (see, e.g., Eq (4)).
The first term in Eq (1) is proportional to u2 τr for Drot τr small and to u 2 / ( D r o t 2 τ r ) in the opposite limit, i.e. it increases with τr if the running time is smaller than the correlation time set by the rotational diffusivity. The first behavior u2 τr is the only possible dimensional combination when Drot is neglected. The product u × uτr∇c is intuited as the velocity times the difference in concentration across a run, which is the signal driving the bias of the run along the concentration gradient. Conversely, extending runs beyond ∼1/Drot is not efficient. Indeed, trajectories of duration τr are then roughly composed of n = τr Drot stretches of length u/Drot and independent orientation. Only one of those n stretches is biased, though, so that the behavior above u2/Drot × 1/n is obtained. Note that Drot τr is small for E. coli (see data in the sequel).
The second integral term in Eq (1) is conveniently recast using specific expressions for experimental responses [11, 12]:
K ( t ) = K 0 λ e - λ t λ t - ( 1 - A ) 2 ( λ t ) 2 . (3)
Here, A controls the loss of precise adaptation, the timescale λ−1 controls the memory of the past concentration detections and the amplitude K0 reflects the sensitivity and the dynamic range of the response. Experiments for serine give A ≃ 0.03 in the range 5μM to 500μM and λ ≃ 1s−1 with a weak dependence on concentration [12]. The response to aspartate is also well described by Eq (3) with A = 0 (precise adaptation). Elementary integrals in Eq (1) for the form Eq (3) yield:
χ = K 0 u 2 α 3 σ 2 × λ 2 ( σ + A λ ) ( σ + λ ) 3 . (4)
The inset in Fig 1 shows that the term in square brackets grows for A = 0 up to τr ≲ 3s. If λ is allowed to vary, the square bracket in Eq (4) has a maximum for λ = 2σ/(1 − 3A). The resulting behavior with respect to τr reduces then to the first term in the square brackets discussed previously (see Fig 1). For positive A, there is an additional positive contribution to the velocity, which amounts to ≃7% for A ≃ 0.03. The inset in Fig 1 shows that extending the running time τr as well as having A positive, i.e. breaking perfect adaptation, can be advantageous for parameters that are comparable to those of wild-type E. coli.
As for the amplitude K0 in Eq (4), its dependence upon the levels of CheYp is much stronger than for all the other parameters, due to the steep dependence of the motor response shown in Fig 1. Furthermore, the dependence of K0 on details of the motor response is quite subtle, as discussed below. Linear response theory is again unable to provide a quantitative hold but it can be useful to identify the underlying factors at stake. The linear-regime expression for K0 reads [30]:
K 0 ∝ a ( 1 - a ) h ′ ( y ) ( 1 - y ) h ( y ) ( 1 - h ( y ) ) , (5)
where y is the fractional concentration of CheYp, a is the fractional concentration of CheAp and h(y) is the clockwise bias motor response in Fig 1.
The expression (5) is proportional to the slope of the curve, i.e. the absolute sensitivity, which is maximum at the inflection point of the motor response. However, the expression (5) contains additional dependencies that reflect the coupling of sensing and running involved in the chemotactic velocity [27]. The effect of those additional factors is that the optimization of the absolute sensitivity does not generally maximize the chemotactic performance [27], as it was also found in [28] by numerical simulations of chemotaxis models [31].
The expression (5) depends on details of the motor response and not just its qualitative sigmoidal shape. For instance, taking a Hill-shaped motor response h(y) = [1 + (y/y0)−H]−1 and using the quasi-steady state relation y = a/(a + K), Eq (5) gives K0 ∝ HK(1 − a), which shows that the amplitude is maximal at zero activity a = 0. This is intuitively understood by noting that the expression of K0/(1 − a) in the limit of small y’s becomes proportional to the relative sensitivity d log h/d log y, which is constant for a Hill function. However, an equally sensible allosteric-like shape h(y) = 1/[1 + C((1 + y/K1)/(1 + y/K2))n] with K1 > K2, which is suggested by the conformation spread discussed in [32], gives a maximum amplitude for 0 < a < 1/2. This is verified by calculating the derivate of K0 with respect to a at a = 0 (where it is positive) and a = 1/2 (where it is negative). The two alternatives above subtly differ in their behavior at y = 0: the Hill-shaped form vanishes whilst the allosteric one does not (for any finite value of the constant C). The non-vanishing is due to the basal finite rate of switching between the two conformations of the motor in allosteric models.
In summary, it is qualitatively clear that maximizing absolute sensitivity, i.e. having the motor set at the point of maximum slope, and perfect adaptation might a priori not maximize the chemotactic velocity. However, sharp statements require a detailed knowledge of the bacterial response and its variation along the gradients, which is currently not fully available. For instance, the conclusion [20] based on numerical simulations that imprecision of adaptation has little effect on the rate of chemotactic velocity differs from what will be presented below. Experimental data show that a moderate breaking of perfect adaptation has significant effects in our conditions.
We first verified that the adaptation to aspartate and serine for our bacterial strain RP437 (see Materials and Methods) behaves as expected. We measured the mean run times for bacteria in different background concentrations of chemoattractants by standard procedures described in S1 Text. We normalized the run times to the mean run time (∼1.15 s) in the absence of any chemoattractant and reported the normalized values in Fig 2. As expected, the values for aspartate did not change over more than three decades of concentration, whilst the normalized values for serine increased from 1.0 ± 0.1 at 1μM of serine to 2.1 ± 0.2 at 3mM. These behaviors are similar to those for the strain AW405 in Ref. [18].
To quantify the role of the running speeds, we tracked bacteria in homogeneous concentrations of serine or aspartate. We filtered tumbling periods and measured running speeds as described in S1 Text. Fig 3 shows the dependence on concentration: the measured mean running speeds in aspartate and serine are 14.5 ± 5.0 μm/s and 15.0 ± 6.0 μm/s, respectively (mean and standard deviation refer to the velocity distribution over the bacterial population), i.e. their values are within the respective error bars.
Our results are in agreement with previous experiments (see Fig. 3 of Ref. [24]) on our same strain RP437. At temperatures >25°C, a strong increase in the running velocity is observed when a background concentration ≳ 300μM of serine is added. However, at the temperature 18 ± 1°C of our experiments, the reported dependence of the running velocity on the concentration of serine is consistent with our results. A larger range of concentrations was explored in Ref. [23] for the E. coli strain MTCC 1302. The dependence of the running speed on serine concentration was found to be strong and non-monotonic yet at concentrations higher than those of our experiments; in the range up to mM, the dependence found in Ref. [23] is again consistent with our data. Note that the behavior of the RP437 strain differs from the strain AW405, where a 40% variation of the running speed was reported [18]. We chose the strain RP437 because subsequent analyses are simplified if the running velocity is constant.
We used the microfluidic device shown in Fig 4 (see Materials and Methods) to measure the progression of E. coli in equal-magnitude gradients of aspartate and serine. The main purpose is to show that loss of adaptation to serine does not hamper the climbing of serine gradients, which actually progresses faster than for aspartate. The faster progression reflects the combined effects of different properties of sensitivity and adaptation. The two contributions will be disentangled in the next section by comparing gradients of aspartate with and without a background of serine.
Specifically, the microfluidic device shown in Fig 4 features two different channels with linear gradients of aspartate and serine, respectively. Both gradients range from 0 to 1mM. On the high concentration side, the channels are connected to two reservoirs filled with the corresponding chemoattractant. The reservoirs are large enough that their concentration holds constant throughout the duration of the experiment. On the low concentration side, the channels are connected to the injection channel, where a given bacterial density and lower chemoattractant concentration (here zero) is maintained. Injected bacteria are transported by the hydrodynamic current; a fraction of them spreads into the lateral channels and creates two fronts that climb the corresponding gradients. The bacterial concentration in the injection channel is OD600 = 0.05, i.e. ∼4 × 107 bacteria per ml. That density empirically guarantees that the number of bacteria in the lateral channels is large enough for reliable statistics yet low enough for convenient imaging and to ensure that the distortion of the gradients due to the bacterial consumption of chemoattractants is negligible (see S1 Text). The reservoirs act as sinks (on the timescale of our experiments) for the bacteria arriving at the end of the channels.
To quantify the progression of bacteria in the lateral channels, we measured the number of bacteria in the channels as a function of time. In particular, we measured the “progression function”, i.e. the cumulative distribution of the number of bacteria summed from a given location to the end of the channel (on the reservoir’s side). Fig 5 shows that the progression function in the advanced part of the channel raises faster for serine than aspartate. This is also confirmed by extracting the bacterial positions at different times from the images and, for each time, ranking them in increasing order along the channel coordinate (see SI). In Fig 6 we show the position of the 10th, 20th and 40th most advanced bacteria. The progression is approximately linear for the first 20–30 minutes, followed by a decrease in slope and eventual saturation, which will be discussed below. For the 10th most advanced bacterium in the first 30 minutes, we measured a slope of 1.5 ± 0.2 μm/s for the serine gradient, whereas in the aspartate gradient the slope is 0.85 ± 0.14 μm/s. Similar results are found by considering the 20-th or 40-th bacterium (see Fig 6). Note that the linearity of the progression versus time is only approximate: our linear fit is meant to give an idea of the velocities and their differences and should not be taken as suggestive of a perfectly constant velocity along the gradients. Indeed, even for aspartate, the sensitivity of the response is expected to change along the linear gradient because of the Weber-type response. For serine, loss of adaptation will further lead to an increase of the running time along the gradient. The role of loss of adaptation in the faster progression will be directly assessed in the next section but can already be surmised from the fact that the curves for serine and aspartate start similarly (when the running times are comparable) and their difference gets more pronounced as time elapses (and the respective running times diverge).
The shape of the progression function in Fig 5 is compared to results of numerical simulations in the SI. We also verified that the progression of bacteria is genuinely due to chemotaxis, viz. the control experiment with a constant profile of chemoattractants without any gradients, features a much slower progression (see SI).
In conclusion, loss of precise adaptation does not impair the climbing of gradients of serine, which is actually faster compared to the climbing of aspartate gradients with the same slope and extension. Our E. coli strain has of course different sensitivities to aspartate and serine, which is the motivation for the experiments hereafter.
We tried inducing loss of precise adaptation to aspartate by adding a constant background of serine. The appealing feature is that we could then directly compare the response to the same chemoattractant with or without adaptation. If loss of precise adaptation indeed leads to a larger chemotactic velocity, we expect that the same aspartate gradient should lead to a stronger progression of bacteria when a serine background is present.
The rationale for expecting loss of precise adaptation to aspartate in the presence of a background of serine goes as follows. Methylation processes are responsible for the feedback that controls the adaptation of the chemoreceptors [4–7]. Loss of precise adaptation is due to the reduced availability of occupation sites for (de-)methylation on the receptor clusters. Serine (Tsr) and aspartate (Tar) preferential receptors jointly participate in the allosteric clusters and assist each other in methylation process [33–37]. Chemotactic responses should then be affected by the presence of multiple signals that are integrated by the chemotaxis pathway. Some experimental evidence supporting this hypothesis was previously reported in Ref. [19], viz. the kinase activity of CheA measured by FRET for different combinations of chemoattractants agrees with the predictions of the allosteric models. A prediction of those models is that the more abundant Tsr receptors assist Tar receptors in keeping their adapted state [38]. Therefore, we expect that the addition of serine increases the methylation level of receptor clusters and the ensuing reduction in available sites leads to loss of precise adaptation to aspartate.
We tested the previous prediction by adding a constant background of 30μM of serine and varying the concentration of aspartate within the same range as in Fig 2. We measured the run time of bacteria and found that the mean run time (normalized again to the value without any chemoattractants) increased from 1.7 ± 0.2 at 1μM to 2.1 ± 0.2 at 100μM, showing that adaptation to aspartate is indeed altered by a background of serine. We then verified that the running velocity is weakly affected by the presence of the serine background (see Fig 3). Finally, we performed the speed race assay in the aspartate channel with a background of 30μM serine (see Fig 6) to demonstrate the role of the loss of precise adaptation on the chemotactic velocity. The approximate slope of the bacterial progression indeed increased in comparison to the channel with aspartate only, e.g. the slope for the most advanced 10th bacteria increased from the value 0.85 ± 0.14 μm/s previously reported, to 1.31 ± 0.07 μm/s.
The progression of the 10th, 20th and 40th most advanced bacteria slows down at long times for all attractants, irrespective of adaptation, and eventually saturates (see Fig 6). Saturation sets in when the curves in Fig 5 approach a steady profile and saturation levels depend on the choice 10, 20, 40. These observations suggest the following specific mechanism, in addition to the general remark that the relative concentration gradient ∇c/c decreases along linear profiles.
When bacteria reach the end of the lateral channels and penetrate into the reservoirs, they disappear from our images. Due to the size of the reservoirs, this is equivalent to an absorbing boundary condition at the end of the channels. During the phase when all bacteria are advancing in the lateral channels, the frame that contains the most advanced individuals (10,20,40) systematically progresses with them. However, when advanced bacteria start to be absorbed in the reservoirs, the frame containing the most advanced individuals shifts backward to total again 10, 20 or 40. Bacteria in the channel still move forward yet the backward shifts of the frame and the inclusion of less advanced bacteria entail a slow down of the progression curve in Fig 6. When influx and outflux of bacteria eventually balance, the density in the lateral channels reaches a stationary profile and the progression function becomes constant.
The intuitive arguments above are supported by numerical simulations of the drift-diffusion equation derived in Ref. [27]. We take a diffusion constant consistent with the run time and the velocity in Figs 2 and 3 and parameters of the drift consistent with those measured in Ref. [39]. The resulting progressions, profiles and timescales of saturation are compatible with our experimental observations. Since the saturation effect is not directly related to the role of loss of adaptation, we refer to the S1 Text for details.
Our experiments show that a moderate loss of precise adaptation does not impair the climbing of serine gradients, which is actually faster than for equal-magnitude gradients of aspartate over the same, extended range of concentrations. The comparison between gradients of aspartate in the presence/absence of a background of serine directly demonstrates the role of the loss of precise adaptation. We showed that the sped-up progression in the channels largely results from the increase of the run time from its value ≃1.15s in the absence of any chemoattractant. Since the previous value of the run time is not peculiar to our E. coli strain RP437 and media, we expect that similar results hold for other conditions and strains.
What are the functional and evolutionary implications of our results? An important caveat is that quantitative assessments of the selective advantages brought by chemotaxis and its ecological conditions of selection are essentially unknown. The common sense in the field is that selective pressure on chemotaxis is important and the drive toward effective chemotactic performance is substantial, which is the point of view pursued hereafter. However, we stress that the level of differences in the chemotaxis performance that are significant for evolutionary selection is an important open issue.
Our results suggest that precise adaptation to aspartate is not due to the need of efficient climbing of static and extended gradients. If that were the main functional pressure, a moderate loss of precise adaptation would be preferable, as shown by our experiments adding a background of serine. Actually, there is no physiological support to consider the response to aspartate as paradigmatic and the response to serine as accidentally imperfect. For instance, ring-forming assays, which combine growth and motility, show that the serine-chasing ring of bacteria is the first to spread from an initial inoculum, later followed by the aspartate ring [21]. A similar observation is made in capillary assays [40]. Furthermore, E. coli chemotaxis toward amino acids correlates with their utilization [41]. Serine is consumed earlier than other amino acids in tryptone broth [42] and reduces growth at high concentrations [20, 43–45]. Based on these facts, it is unlikely that E. coli selective pressure on the response to aspartate is stronger than to serine.
More generally, we find it unlikely that chemotaxis is selected for climbing simple profiles, like constant linear and exponential gradients usually considered in the laboratory. In Ref. [27] we raised the possibility that physiological conditions might be complex, with gradients strongly varying and fluctuating. A reason mentioned in [27] is the uptake of chemoattractants by bacteria in the colonies that they form as they grow, coupled with the scarce levels of attractants in the conditions where chemotaxis is likely to be important. Strongly varying gradients were also recently inferred for E. coli in Ref. [46] and constitute the typical environment for marine bacteria [47]. The important point in fluctuating profiles is that chemotaxis does not involve climbing of gradients only, yet also maintaining contact with the peaks of the profile. As we have shown here, climbing of gradients is favored by a non-adapted response with a running time that increases with concentration. At the level of the impulse linear response, that corresponds to a positive lobe stronger than the negative one. The task of maintaining contact with peaks of the chemoattractant profiles was analyzed theoretically in Ref. [26] and numerically in Ref. [20]. They both show that the optimal linear response is not adapted and should feature an impulse response with a strong negative lobe. The optimal degree of adaptation in fluctuating profiles should then involve a trade-off between the positive and the negative lobes in the impulse response, which depends on the environmental conditions and might result in perfect adaptation if fluctuations are sufficiently strong [27]. Future experiments with controlled fluctuating environments will be needed to test those predictions.
A final conjecture is that the degree of adaptation to aspartate or serine might actually depend on environmental conditions. Allosteric models for chemotaxis predict that the degree of adaptation is controlled by the relative levels of Tar and Tsr receptors [33–38]. Furthermore, the levels of the two types of receptors change with the state of the bacterial colony [48, 49]. It is then likely that the relative predominance of Tsr over Tar receptors changes, e.g. with the conditions of culture and growth. The variations of Tar and Tsr expression levels with the environmental conditions might then provide informative clues on bacterial chemotaxis.
We conclude stressing that the function of biological systems is a notoriously tricky issue, yet it is essential to understand what molecular pathways are doing, what is evolutionarily shaping them and to go beyond the list of their parts. Chemotaxis has been thoroughly investigated and we have a unique knowledge of the pathway and its molecular components [5]. It is thanks to this knowledge that one can concretely ask functional questions and they seem to call for a stronger coupling with bacterial metabolism and physiology.
For the study reported here, we used the RP437 strain. We electroplated the pBRBRO plasmid, a colE1-based plasmid bearing the mOrange gene under the control of a leaky promoter (Tac) considering no tight repressor allele (lacIq) were present in neither the strain nor the plasmid. Single colonies were picked from a fresh plate and were grown overnight in Tryptone Broth (TB) supplemented with the appropriate antibiotics. The saturated culture was pelleted and resuspended in the same volume of TB. The washed culture was diluted to OD = 0.002 and allowed to grow up to OD = 0.2 − 0.3. Cells were harvested and washed 3 times in the motility medium [18] prior to injection in the microfluidic setup. They were then diluted to have an OD = 0.05, which corresponds to ∼4.107 bacteria/ml.
The channels were carved into a plastic piece (PMMA) using a micromilling machine (MiniMill/GX, Minitech Machinery) and appropriate carbide tools (NS tool). Inputs and outputs to the channels were pierced using a 600μm drill (Performance Micro Tool). The carved channels were closed with a glass coverslip using a UV glue (NBA107, Norlands). The assembled setup consists of two reservoirs (10 x 2 x 0.3 mm) connected to an injection channel (10 x 1 x 0.3 mm) through the lateral channels (4 x 0.725 x 0.3 mm) where bacteria climb the gradients. At the junctions between the channels, ridges of 250/300μm in height/length were added to reduce the extension of the flow from the injection channel.
The reservoirs were filled with a 1mM solution of the appropriate amino acid together with fluorescein that has roughly the same diffusion coefficient as the chemoattractants. The input and output of the reservoirs were sealed using adjusted metallic plugs. A flow of the same motility buffer without chemoattractant was then applied in the injection channel. A linear stable gradient took about 3 hours to form, as checked by imaging the fluorescein. The input of the injection channel was then switched to a solution of bacteria. The flow was maintained using a syringe pump at 20 μl min−1. Flow was interrupted during acquisition of images of the channels, which were taken every 5 minutes. Images of bacteria and their density in the channels were extracted using the automated image analysis program Fiji [50] (see S1 Text for further details).
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10.1371/journal.pcbi.0030106 | A Survey of Genomic Properties for the Detection of Regulatory Polymorphisms | Advances in the computational identification of functional noncoding polymorphisms will aid in cataloging novel determinants of health and identifying genetic variants that explain human evolution. To date, however, the development and evaluation of such techniques has been limited by the availability of known regulatory polymorphisms. We have attempted to address this by assembling, from the literature, a computationally tractable set of regulatory polymorphisms within the ORegAnno database (http://www.oreganno.org). We have further used 104 regulatory single-nucleotide polymorphisms from this set and 951 polymorphisms of unknown function, from 2-kb and 152-bp noncoding upstream regions of genes, to investigate the discriminatory potential of 23 properties related to gene regulation and population genetics. Among the most important properties detected in this region are distance to transcription start site, local repetitive content, sequence conservation, minor and derived allele frequencies, and presence of a CpG island. We further used the entire set of properties to evaluate their collective performance in detecting regulatory polymorphisms. Using a 10-fold cross-validation approach, we were able to achieve a sensitivity and specificity of 0.82 and 0.71, respectively, and we show that this performance is strongly influenced by the distance to the transcription start site.
| Computational techniques are used in biology to prioritize DNA sequence variants (or polymorphisms) that may be responsible for population diversity and the manifestation of species-specific traits. Predominantly, they have been used to predict the class of polymorphisms that alter protein function through allele-specific changes to amino acid composition. However, polymorphisms that alter gene expression have been increasingly implicated in manifestation of similar traits. Prioritization of these polymorphisms is challenged, though, by the lack of knowledge regarding the mechanisms of gene regulation and the paucity of characterized regulatory polymorphisms. Our work attempts to address this issue by assembling a collection of regulatory polymorphisms from the existing literature. Furthermore, we use this collection to investigate and prioritize various properties that may be important for identifying novel regulatory polymorphisms.
| Our ability to identify the molecular mechanisms responsible for specific genetic traits within our population will be enhanced by our imminent ability to decipher each individual's genome. This is evident from recent advances in sequencing and genotyping technologies, which allow an increasing number of variants to be sampled for association and linkage (reviewed in [1–3]) and contribute a growing number of sources of variation and their frequencies to public databases each year. As new variants are identified, each becomes a molecular window into our past, present, and future—each aids in tracing our genetic heritage and in charting the footsteps of our common evolution, and possesses the potential to predict disease or drug susceptibilities, ideally acting as an early-warning system in preventative medical practice (reviewed in [4,5]). However, our ability to catalog genotypes has far outstripped our ability to implicate them in phenotypes. Currently, more than 6 million unique single-nucleotide polymorphisms (SNPs) are included in version 126 of dbSNP [6]; of these SNPs, only a very small fraction have been associated with a phenotype using genetic association or linkage analysis. This is because association studies are costly, time-consuming, and dependent on the frequency of the genotype in the sampled population. Furthermore, many SNPs are not necessarily expected to have a function. To select candidates for functional validation, computational methods have been developed to identify SNPs that alter the protein-coding structure of genes [7–16]. These types of computational methods tend to prioritize putative functional SNPs by identifying those SNPs that alter a protein's amino acid sequence, are located within well-conserved regions or functional protein domains, and alter the biochemical structure of the protein. However, very few methods identify regulatory SNPs (rSNPs) that alter the expression of genes. Such rSNPs have been implicated in the etiology of several human diseases, including cancer [17,18], depression [19], systemic lupus erythematosus [20], perinatal HIV-1 transmission [21], and response to type 1 interferons [22]. This work aims to extend computer-based techniques to identify this particular class of functional variants within the core promoter regions of human genes.
Conventional computational approaches to rSNP classification have predominantly relied on allele-specific differences in the scoring of transcription factor weight matrices as supplied from databases such as TRANSFAC and Jaspar [15,16,23]. SNPs located within matrix positions possessing high information content are assumed more likely to be functional. Support for this hypothesis to date, however, has been restricted to single-case examples. Furthermore, a recent study has failed to detect significant weight matrix signals in 65% of regulatory polymorphisms (n = 40) [24]. However, the prevailing hypothesis in computational regulatory element prediction has been that the majority of predictions using unrestricted application of matrix-based approaches are false positives. By extending this technique and using phylogenetic footprinting between mouse and human, it was demonstrated that from ten SNPs that show significant allele-specific differences in Jaspar predictions, seven also demonstrated electrophoretic mobility shift differences [23]. However, only two of the seven had a marked effect in reporter gene assays. Conservation alone has also been demonstrated as a poor discriminant of function in a study of regulatory polymorphisms in Eukaryotic Promoter Database promoters, where zero of ten experimentally validated regulatory variants were in conserved binding sites [25].
A substantial challenge with developing strategies for identifying functional noncoding variants has been the shortage of characterized regulatory variants. Few studies have successfully identified the causative variant(s) after a susceptibility haplotype is identified. To address this problem, we have assembled the largest openly available collection of functional regulatory polymorphisms within the ORegAnno database (http://www.oreganno.org) [26]. From this dataset, we have examined several features of these SNPs as they relate to polymorphisms of unknown function (ufSNPs) within the promoter regions of associated genes (up to 2 kb). Our hypothesis is that using a combination of regulatory and population genetics properties, the discriminative efficacy of individual properties can be evaluated, and significant predictors of rSNP function can be chosen. Within our assayed set, we have found that the best discriminants are the distance to the transcription start site (TSS), local repetitive density and content, sequence conservation, minor allele frequency (MAF) and derived allele frequency, and CpG island presence. Notably, the unrestricted application of a matrix-based approach is demonstrated to be one of the least effective classifiers.
We have used this dataset of rSNPs and their properties to train a support vector machine (SVM) classifier. Two approaches were used to train the classifier: one in which the properties of all rSNPs were compared with that of all the ufSNPs, and one in which each property value of the positive SNPs and ufSNPs within an associated gene were compared with the average values for each property within that gene (referred to here as the “All” and “Group” approaches, respectively). The All approach is designed to determine if there are any properties that are important across the test set, while the Group approach is designed to determine if there are important directional shifts in values within a promoter that may discriminate functional SNPs from ufSNPs. In a 10-fold cross-validated test, the SVM achieves a receiver operating characteristic (ROC) value of 0.83 ± 0.05 for the All analysis (sensitivity, 0.82 ± 0.08; specificity, 0.71 ± 0.13) and 0.78 ± 0.04 for the Group analysis (sensitivity, 0.72 ± 0.19; specificity, 0.68 ± 0.07).
Literature describing noncoding polymorphisms responsible for allele-specific differences in gene expression was surveyed from PubMed [27]. From this literature, 160 regulatory polymorphisms were identified in 103 publications; each was selected based on experimental evidence that confirmed its direct role in altering gene expression. This selection criterion specifically excluded those polymorphisms in which the experimental evidence could only confirm that the reported polymorphism was in linkage disequilibrium with an rSNP. Each identified rSNP was manually curated in the ORegAnno database. Subsequently, 104 polymorphisms were selected based on the criteria that they were SNPs (excluding seven insertion–deletion polymorphisms), and were within 2 kb of the TSS of their associated gene (as annotated in version 37 of EnsEMBL [28]; Table 1). A 2-kb region was chosen to maximize the number of rSNPs included while minimizing the size of sequence investigated; at 2 kb, the addition of a single further rSNP would increase the surveyed region by 43%, whereas the previous addition resulted in an increase of 9%. At this window size, 39 rSNPs were excluded from analysis. An additional ten polymorphisms were excluded because of deprecated annotation of the gene or discordant genomic location with the associated gene. In total, the remaining 104-rSNP set contained polymorphisms involved in altering the expression of 78 different transcripts.
Using each of the 78 transcripts, SNPs within 2 kb of the TSS were extracted from version 37 of EnsEMBL (dbSNP version 125), producing exactly 951 ufSNPs. The ufSNP and rSNP genomic locations have been mapped (see Table S1).
A total of 23 different properties of relevance to assessing regulatory function were calculated for each SNP in both the 104-rSNP and ufSNP sets (Table 1). These properties were selected to represent a cross-section of well-documented methodologies for assessing the functional significance of both allele-specific changes and DNA sequences within noncoding regions.
Two types of analyses were conducted using the investigated properties. One was an all-versus-all approach, where the 104-rSNP and ufSNP sets were compared en masse. The other was a group analysis, where the average value of each property within each upstream noncoding region was first calculated, and then the individual SNP properties within that region were recalculated as the difference from this average. The All test data were designed to identify global characteristics of rSNPs, while the Group test data were designed to look for directional trends within the sampled region that might be indicative of SNP importance. For example, the All test is able to ask whether rSNPs have generic features that would distinguish them from any other promoter SNP; the Group test is designed to identify whether there are any features that distinguish rSNPs from other SNPs within the same upstream noncoding region.
The All and Group test data were input to the Gist SVM implementation [29]. We excluded the logarithmic distance to the TSS to prevent redundant classification with the raw distance to the TSS. Gist was run using the default parameters as described previously [30]. Of note, the Gist SVM requires that every value in the test and training parameter space is filled. To reflect the null hypothesis, that there are no differences between the ufSNPs and rSNPs, the All SVM was filled with promoter-specific average values wherever data could not be calculated. Likewise, the Group SVM was filled with zero values wherever data could not be calculated, indicating no divergence from average within the GROUP test set.
The individual importance of each property in discriminating regulatory polymorphisms was assessed in the All and Group test sets using a Wilcoxon rank sum test. Each value was corrected for multiple testing using the BioConductor MTP package (http://www.bioconductor.org) by controlling for the family-wise error rate (α = 0.05 and B = 10,000) [31,32].
The performance of the Gist SVM classifier was measured using a ROC curve. ROC scores of 1 indicate perfect discrimination, while those at 0.5 indicate random classification of the input SNPs. ROC performance measurements have been previously described in detail elsewhere [30].
A 10-fold cross-validation was performed to assess the overall performance of the SVM. The input data was randomly partitioned by transcript into ten sets. Data from one set were excluded, and the remaining nine sets were trained on for each fold validation. This analysis was performed for each set to cover the entire training site and to calculate an average ROC value for the SVM.
We were concerned that several properties may be indirect measurements of distance from the TSS, and that any discrimination strategy would be limited to characterizing this property alone. This concern is a particular challenge since distance ascertainment bias exists; most SNPs surveyed were within a few hundred base pairs of the TSS, which is much smaller when compared with our sampling distance of 2 kb. Furthermore, it has been well established in a previous study that distance to the TSS is correlated to detection of rSNPs (it is unknown if this is because they are more likely to affect essential transcription factor–binding sites, or because there is a higher density of transcription factor–binding sites in these regions) [24]. For this reason, the discrimination potential of distance to the TSS could not be ignored. To adjust for bias, however, we calculated the expectancy of observing a feature at a particular distance from the TSS for each individual chromosome (Figure 1; CpG islands are shown as an example of this trend). This expectancy value was used to normalize the observation values for several of the properties in this study (identified in Table 1). This was performed by subtracting the expectancy value from the observed value. The impact of this normalization is negligible when comparing normalized ROC values against unnormalized ROC values; using a 10-fold cross-validation, the unnormalized ROC values for the ALL test are 0.82 ± 0.05 (unnormalized) and 0.83 ± 0.05 (normalized), and values for the GROUP test are 0.79 ± 0.04 (unnormalized) compared with 0.78 ± 0.07 (normalized).
A total of 104 rSNPs and 951 ufSNPs in the upstream noncoding regions of 78 genes were compiled to test properties that discriminate polymorphisms with effects on gene expression. A multiple testing–corrected Wilcoxon rank sum test was used to analyze the All test data (Table 2). Analyzing the All test data identified several properties of significance in discriminating between rSNPs and ufSNPs (p < 0.05). The properties of significance in the All test data, in order of importance, were: 1) distance to the TSS (properties 13 and 14); 2) in a CpG island (property 19); 3) long repeat events (property 16); 4) local repetitive base percentage (property 13); 5) derived allele frequency (property 12); 6) minor allele frequency (MAF; property 11); 7) Regulatory Potential score (property 22); 8) in a repeat (property 14); and 9) ClustalW alignment distance (property 23).
However, a concern with the All analysis was that calculated property values for SNPs in individual upstream noncoding regions would not be comparable with those in other upstream noncoding regions due to differences in background property values. To address this, a multiple testing–corrected Wilcoxon rank sum test was also used to analyze the Group test data (Table 2). The properties of significance (p < 0.05) in the Group test data, in order of importance, were: 1) distance to the TSS (properties 13 and 14); 2) long repeat events (property 16); 3) in a CpG island (property 19); 4) MAF (property 11); 5) local repetitive base percentage (property 13); 6) ClustalW alignment distance (property 23); 7) derived allele frequency (property 12); 8) short repeat events (property 15); and 9) DNaseI hypersensitive site (property 20).
Both lists are highly concordant and demonstrate several properties that may be of utility when prioritizing SNPs for functional analysis either across the genome or within an individual upstream noncoding region. In both tests, distance to the TSS was found to be the most significant discriminant. While it is possible that ascertainment bias in the 104-rSNP set contributes to the strength of this discriminant in our study, this property has also been independently identified as an important discriminant in a previous study where, in 500-bp assayed regions, 50% of rSNPs identified through transfection experiments were within 100 bp of the TSS (n = 40) [24].
Furthermore, several other properties are consistently identified as being significant after normalization against distance to TSS. One property, ClustalW alignment distance, was identified in both the All and Group tests as being significant. The mean value of ClustalW alignment distance was slightly higher for the tested rSNPs compared with the ufSNPs, indicating that 1-kb multiple alignments centered on the tested rSNPs were more divergent than those centered on ufSNPs. This result is concordant with previous analyses of conservation around rSNPs (n = 10) [25]. However, trends in the other conservation scores used in this study, while nonsignificant in discriminating between the tested rSNP and ufSNPs, conversely suggest that the tested rSNPs are more conserved than ufSNPs. Since these metrics use tighter window sizes than those used for calculating the ClustalW alignment distance, this result suggests that increased mutation around an rSNP may be more informative than the conservation status of the rSNP itself.
Another property of significance was repetitive element content. Our results indicate that the tested rSNPs were less likely to be in or around repetitive elements. This suggests that regions that are likely to contain a transcription factor–binding site are less likely to accrue repetitive elements and be subject to dysregulation. We note, however, that ascertainment bias by which the 104-rSNPs set was surveyed in terms of repetitive elements is not known, and future collections of discovered rSNPs should address this issue.
Both MAF and derived allele frequency are also identified as significant discriminants. Unexpectedly, for genotyped SNPs, the MAF was higher in the 104-rSNP set than in the ufSNP set. Previous analyses of MAF have suggested that most functional SNPs are positioned around 6% [33] or possess no allele frequency bias [24]. In this study, the average MAF was approximately 22%. Since a subset of the 104-rSNP set has been derived from association studies, it is possible that ascertainment bias may explain part of this result as researchers may preferentially be choosing higher MAF SNPs because of their greater statistical power. Of further interest, the derived allele frequency was higher in the 104-rSNP set than in the ufSNP set. This could suggest that many of the derived alleles have been driven to higher frequencies due to new variants increasing in frequency in our population, through either population bottlenecks or positive selection. The former hypothesis is supported by the supplemental observation that when restricting populations to HapMap (http://www.hapmap.org) phase I populations only, the Asian and European populations mirror this result, while the African population has lower MAFs on average. The latter hypothesis, however, supports a model of evolution of genetic susceptibility to common diseases explained by ancient alleles recently becoming predisposed to disease due to changes in human lifestyle and life expectancy [34].
Another interesting result was that SNPs in the 104-rSNP set were less likely to be in CpG islands than were ufSNPs. Since CpG expectancy was normalized from average values at specific distances from the TSS of associated genes across individual chromosomes, an admixture of CpG and CpG-less promoters would drive the 104-rSNP set values lower than the ufSNP set values (Figure 1) [35,36]. However, without normalization, the significance of this value for the All and Group tests is similar (All, p = 3.78 × 10−5; Group, p = 1.96 × 10−3), suggesting that the rSNPs are in fact less likely to be in CpG islands.
Many tested properties fell below our significance threshold in these tests. Of interest, both weight matrix–based approaches did not discriminate well. In addition, our definition of coexpression was significantly broad as to allow multiple coexpressed partners for any given gene; this may have reduced the overall effectiveness of reducing transcription factor–binding profiles using this information. However, the performance of the coexpression-filtered approach was moderately better than the TRANSFAC approach alone. This suggests that targeted analysis of specific, biologically relevant transcription factors may further increase the discriminating ability of this approach. This should also act as a warning to those who have in the past applied the TRANSFAC approach to this problem indiscriminately. Furthermore, none of the DNA structural or stability analyses used were successfully discriminatory. This analysis could indicate that not only do these features have nongeneralizable effects using the data in this study, but since these analyses also measure local sequence composition, no particularly important effect is caused by specific base changes.
To evaluate whether the combination of the tested properties would enhance discrimination of rSNPs from ufSNPs, we trained a SVM for the ALL and GROUP test data. We tested the classification performance of SVMs by 10-fold cross-validation. For each SVM, the mean area under the ROC curve was 0.83 ± 0.05 and 0.78 ± 0.04, respectively. Both suggest good performance. It is significant, however, that when removing distance from the classification, the performance of each test drops to 0.52 ± 0.09 and 0.48 ± 0.07, respectively (Figure 2). This reduction in performance should not be taken to indicate that other properties identified in the multiple testing–corrected Wilcoxon rank sum test are not actually discriminatory since 10-fold cross-validation of All and Group test SVMs built with only the properties identified as significant using the multiple testing–corrected Wilcoxon rank sum test (p < 0.05) and excluding distance to the TSS achieved ROC values of 0.77 ± 0.08 and 0.75 ± 0.07, respectively. This result suggests that nonsignificant results may act to overparameterize the SVM model and mask subtle, true discriminatory signals.
To address the issue of distance bias further, we fortuitously identified that, across our dataset, in the 152 bp immediately upstream of the TSS, the average distance to the TSS for the ufSNPs was identical to that of the rSNPs. This 152-bp window therefore represented a region with no observable distance biases, albeit a greatly reduced subset in size; at this window size, only 16 rSNPs and 21 ufSNPs were available for analysis. When analyzed using a multiple testing–corrected Wilcoxon rank sum test for both All and Group test sets, only two properties were significant (p < 0.05): repetitive element density (property 13) and ClustalW alignment distance (property 23) (Table 2). We further tested window sizes of 500 bp, 1 kb, and 1.5 kb and noticed only a gradual reduction in performance of the tested properties for smaller window sizes (see Table S2).
We also examined the position of identified rSNPs to characterize possible bias. Our expectation was that well-established transcription factor–binding sites such as the TATA and CCAAT boxes may be overrepresented and contribute to lower distance values. A histogram of rSNPs for the first 300 bp of sequence from the TSS shows an expected increase around the 21–31 position where seven rSNPs are located, twice as many as average. However, it is apparent that these types of binding sites are only overrepresented slightly when compared with the distribution of rSNPs at other positions (Figure 3).
All pipeline software has been programmed in Perl and is available under the Lesser GNU Public Licence at http://www.bcgsc.ca/chum under the name CHuM (cis-acting human mutation modules). All data are available from this site.
This study introduces the largest publicly available collection of rSNPs—160 known rSNPs from literature. Using this collection, we investigate 104 rSNPs and 951 ufSNPs in human 2-kb upstream regions to identify properties that may discriminate functional from nonfunctional polymorphisms. We identify several properties that may be useful to researchers attempting to determine the functional status of upstream noncoding SNPs. The most important properties detected suggest that rSNPs are close to the TSS, are not within CpG islands, are isolated from repetitive elements, possess higher MAF and higher derived allele frequency, and are within comparatively more divergent regions. However, within a 152-bp window, where an equal distribution of rSNPs and ufSNPs from the TSS is obtained, the significant results suggest that only repetitive element content and local divergence remain important (we have included in Table S2 information on how property significance changes with window size). We further combined each of the properties identified in the 2-kb region to train an SVM to classify the functional status of the 104-rSNP set and 951-ufSNP set. We hypothesized that subtle differences in individual properties may be more important than any one property in detecting rSNPs. It is of note, despite mentioned ascertainment biases, that our sensitivity and specificity for the All test was 0.82 ± 0.08 and 0.71 ± 0.13, respectively, and for the Group test was 0.72 ± 0.19 and 0.68 ± 0.07, respectively. Also of note, the strength of the distance to the TSS as a discriminatory property was demonstrated in both tests when removal of the property significantly reduced the effectiveness of the classifier to near random performance. However, we observed that this reduction in performance was recovered in part when only the properties identified as significant through the multiple testing–corrected Wilcoxon rank sum test (p < 0.05) in the 2-kb All and Group tests were applied, and the distance to the TSS was excluded.
Through this work, several challenges are apparent with current predictive approaches to prioritize candidate rSNPs. Necessary to future analyses is a dataset of core promoter polymorphisms that are nonfunctional across a broad range of cell types; since our negative control set was a neutral set, it is assured that more accurate performance metrics can come from addition of a reliable negative control set. Furthermore, recent analysis of allelic expression difference has demonstrated that the effects of rSNPs may be highly context-specific such that function in one cell line may not imply function in others; to address this complication, future analysis may require expanded collections of cell line–specific positive and negative rSNPs [37]. Future studies of promoter polymorphisms will also need to take advantage of known transcription factor–binding sites. Such information will be invaluable in dissecting the causal nature of many of the properties.
In summary, this study introduces a new dataset for the investigation of rSNPs. We have also introduced one of the first gene regulation and population genetics–based approaches to classifying rSNPs in the core promoter regions of human genes. We identify the utility of different gene regulation and population genetics properties in discriminating literature-curated rSNPs. Such results are increasingly essential to researchers seeking criteria for prioritizing SNPs to test in association, binding, or expression assays. Furthermore, we provided evidence that popular methodological practices based on identification of allele-specific differences in position weight matrices through unrestricted application of the TRANSFAC database are poor criteria for SNP selection. However, we highlight the fact that because of the lack of extensive unbiased collections of rSNPs, it still remains challenging to dissect the existing effects of investigator or methodological biases in evaluating the importance of these properties. We hope that this work will stimulate active discussion and both the development of expanded collections of rSNPs and an improved class of bioinformatics tools for rSNP analysis that address these challenges. |
10.1371/journal.pgen.1002144 | Rare and Common Regulatory Variation in Population-Scale Sequenced Human Genomes | Population-scale genome sequencing allows the characterization of functional effects of a broad spectrum of genetic variants underlying human phenotypic variation. Here, we investigate the influence of rare and common genetic variants on gene expression patterns, using variants identified from sequencing data from the 1000 genomes project in an African and European population sample and gene expression data from lymphoblastoid cell lines. We detect comparable numbers of expression quantitative trait loci (eQTLs) when compared to genotypes obtained from HapMap 3, but as many as 80% of the top expression quantitative trait variants (eQTVs) discovered from 1000 genomes data are novel. The properties of the newly discovered variants suggest that mapping common causal regulatory variants is challenging even with full resequencing data; however, we observe significant enrichment of regulatory effects in splice-site and nonsense variants. Using RNA sequencing data, we show that 46.2% of nonsynonymous variants are differentially expressed in at least one individual in our sample, creating widespread potential for interactions between functional protein-coding and regulatory variants. We also use allele-specific expression to identify putative rare causal regulatory variants. Furthermore, we demonstrate that outlier expression values can be due to rare variant effects, and we approximate the number of such effects harboured in an individual by effect size. Our results demonstrate that integration of genomic and RNA sequencing analyses allows for the joint assessment of genome sequence and genome function.
| The recent availability of almost fully sequenced human genomes by the 1000 genomes project allows the direct study of genetic variants that influence levels of gene expression in the cell. In this study, we explore the effect of rare and common variants on levels of gene expression. We show that the availability of a more comprehensive list of variants brings us closer to the likely causal variants, and we discuss their genomic and evolutionary properties. We also demonstrate the effects of variants that change splicing patterns or length of the protein product, the putative joint impacts of variants that affect gene expression, and those that affect protein structure. Finally, we show the impact of rare regulatory variants that cannot be detected by the conventional methodologies of association and require the interrogation of full genome sequencing and full transcriptome sequencing. These approaches bring us closer to the implementation of these data and methodologies to a direct clinical application.
| Deeper characterization of genetic variation is becoming increasingly available with advances in DNA sequencing technology [1]–[5]. This improves our ability to pinpoint protein-coding variants which disrupt protein structure, and has already begun to provide insight into the genetic basis of disease with unknown etiology [6], [7]. In addition to protein-coding variation, access to a more complete spectrum of genetic data facilitates the discovery of regulatory variants. However, relative to protein coding variation, the information about the structure of gene regulatory architecture is incomplete and the existence of a regulatory variant is largely inferred through its association with gene expression. Such associations have previously been identified as exhibiting widespread and tissue-specific patterns [8]–[11]. They are also increasingly linked to the basis of human phenotypic diversity [11]. For instance, recent studies have implicated the role of regulatory variants in the etiology of diseases such as obesity [12], celiac disease [13] and migraine [14]. Now, the compendium of variants acquired from genome sequencing of population samples provides increased potential for uncovering new associations, many of which, given this enhanced resolution, are presumed to be causal. Furthermore, we are able to begin to analyse genome-wide signals of interactions between disruptive protein-coding variation and regulatory variation. We investigate the landscape of regulatory variation as surveyed by population-scale sequencing by using data acquired from the 1000 genomes project, together with gene expression data in 60 CEU individuals (CEU: Utah residents with ancestry from northern and western Europe) acquired using RNA sequencing (RNA-Seq) and 57 CEU and 56 YRI individuals (YRI: Yoruba in Ibadan, Nigeria) acquired using gene expression arrays [15], [16]. In this study, we demonstrate the value of almost complete information from the 1000 genomes project to reveal the fine structure of rare and common regulatory variation.
We assessed the number of expression quantitative trait loci within 1 Mb of annotated genes (cis-eQTLs), and compared the power of HapMap3 (HM3) against the much higher SNP density of the 1000 genomes project (1KG) genetic variants, using gene expression data from lymphoblastoid cells for matching individuals (see Materials and Methods). For both CEU and YRI, similar numbers of eQTLs were found between the two projects independent of FDR (estimated by permutations; Figure 1 and Figure S1). This suggests that, with given power, the majority of the common regulatory effects can be captured by genome-wide SNP arrays. Using RNA sequencing data, we were also able to survey the difference between 1KG and HM3 for regulatory variation detected through allelic imbalance of heterozygous coding sites. In 1KG, twice as many heterozygous sites (36015 versus 14281) had significant allele specific expression (ASE; reviewed [17]) effect (p≤0.05), corresponding to 4971 genes versus 3175 genes. The median log effect size for this imbalance was 1.39 (Figure S2). This increase provides more power to explore within individual regulatory variation.
Given that the 1KG data provides an almost complete ascertainment of common SNPs, we sought to assess whether we are more likely to detect potentially causal regulatory variants. We observed that nearly 80% of all eQTVs discovered with the 1KG were not discovered with the HM3 (Table S1). This indicates that if these new variants are bonafide causal variants, whole genome sequencing is uncovering a large number of previously unidentified variants. Conversely, however, for the eQTVs discovered with the HM3, up 65% would not have passed the discovery threshold in the 1KG due to the extra multiple testing correction implicit through having 5–7 times as many variants (Table S2). In order to investigate if the 1KG eQTLs exhibited characteristics indicative of being a functional variant, we fine mapped HM3-discovered eQTLs into the 1KG (see Materials and Methods). We observed that for both populations, independent of the gene expression platform, the majority of these HM3 eQTLs were found in the 1KG with the same or a different variant of higher significance, and infrequently (<16%) would the association be worse, likely due to genotyping errors in the 1KG (Figure 2). Next, we compared the properties of these eQTLs in HM3 and 1KG. Since previous analyses have identified a strong enrichment of eQTLs around the transcription start site [18]–[20], we investigated if the 1KG associated SNPs were more proximal to the transcription start site of their associated gene than the HM3 associated SNPs, but no significant trend was observed (Figures S3, S4 and S5). This supports recent observations that the strongest effects on gene expression are not exclusively defined through promoter variation [15], [21]. We next asked if the newly discovered variants were on more evolutionarily conserved sites, which would suggest that they are more likely to be causal variants [22]. In this analysis we had to account for the fact that the HM3 SNPs are more conserved overall (Student's T-test p<2e-16; MW p<2e-16). Thus, we compared the within platform difference between the best association and the second best-linked association, expecting that the increase in conservation between the two could be higher within 1KG due to the best association being more often the causal variant than in HM3. However, no significant difference was observed (Figure S6), which indicates, consistent with ENCODE findings, that many regulatory elements and thus also genetic variants in these elements are unconstrained [23], [24].
We investigated the allelic expression properties of transcript variants that have a putative impact on transcript structure. For splicing variants (MAF≥5%), we saw an enrichment in associations in RNA-Seq data for the respective donor and acceptor exon read count levels (the number of RNA sequencing reads which overlap the exon for an individual, see Materials and Methods) compared to a background derived from synonymous variants (Figure S7). This enrichment was observed independent of mapping quality filter confirming that it is not due to mapping biases (Figure S8). We also investigated gained-stop codon variants for signals of nonsense mediated decay, finding greater than 4-fold enrichment in exon read count level associations for overlapping exons when compared to synonymous and nonsynonymous variants (Figure S9). When assessing this enrichment separately through ASE signals in the RNA-Seq data, we found that 44% (66 of 150 testable heterozygotes at 32 sites) of stop gained variants where ASE can be detected are significant compared to only 18.8% of synonymous variants and 20.9% of nonsynonymous variants.
Regulatory variation can also modify the functional impact of protein-coding variation. We had previously reported that 18.2% of nonsynonymous variants were modulated by regulatory variation [25]. We now discovered that at least 20.9% of testable heterozygotes for nonsynonymous SNPs (n = 32859) had significantly different expression levels of the two nsSNP alleles (p≤0.05; this is 23.3% for n = 38645 when both known alleles are not required to be observed). This corresponds to 46.2% of nonsynonymous variants having an ASE effect in at least one individual (n = 5686). These results suggest that regulatory variation may have a fundamental role in explaining individual differences in penetrance of disease predisposing variation. Thus, surveys of coding variation through large-scale exome resequencing studies [7], [26]–[28] would benefit from complimentary information of regulatory variation e.g. from RNA sequencing of the same samples.
Given that regulatory modifiers of protein-coding variation were prevalent, we looked for population genetic signals of interaction between protein-coding and cis regulatory variation. Such co-evolution would imply a selective advantage of some regulatory and coding variant combinations over other haplotypes in that locus potentially increasing linkage disequilibrium (LD). In order to seek for such patterns from the ASE data, we calculated the proportion of heterozygous individuals that have significant ASE as a proxy for linkage disequilibrium between the coding ASE variant and the unknown regulatory variants. Furthermore, we used both HM3 and 1KG datasets to control for putative effects of genotyping error (Figure S10). We observed an increase signal for nonsynonymous compared to synonymous variants (p = 3.5e-4; Figure S11), which is suggestive coevolution of functional nonsynonymous and regulatory variants. The result is unlikely to be caused by nonsynonymous SNPs being causal regulatory variants more often than synonymous SNPs: the two types of variants have a nearly equal enrichment of exon associations (Figure S9), and while a change in protein structure might change the overall expression level of the gene itself through an autoregulatory mechanism, this is not expected to lead to allelic imbalance. When stratifying by the derived allele's expression, in the 1KG data we observed a large and statistically significant enrichment of the ASE proportion for rare nonsynonymous variants where the derived allele has lower expression than the ancestral (Figure S12). This suggests that some low-frequency deleterious coding variants may be tolerated in the population only because they lie on a lower expressed haplotypes and thus have reduced penetrance. This may be particularly important for understanding the phenotypic effects of loss-of-function variants – it has been estimated that each person carries 250 to 300 loss-of-function variants (50 to 100 of which are previously implicated in inherited disorders) [29], but sometimes their functional impact may be diminished or strengthened by their regulatory background.
Genome sequencing offers the ability to interrogate the functional impact of recent and rare regulatory variants in individuals [30]. We calculated whether individuals sharing an rare ASE effect are more likely to show increased haplotype sharing, measured as haplotype homozygosity, which would be a signal of the ASE effect being driven by a shared rare regulatory variant (as described in [15]). Concordant with previous results, we found an excess of haplotype sharing for rare ASE haplotypes (Figure S13). Next, we sought to identify the putative causal variants by investigating genetic variants which were perfectly concordant with this rare ASE effect (see Materials and Methods). For each such effect, we identified a median of 4 and a mean of 8.83 putative regulatory SNPs (prSNPs) within 100 kb of the TSS (compared to a median of 3 and mean of 7.64 putative regulatory SNPs under the null; Figure 3 and Figure S14). Altogether, we identified at least one putative regulatory SNP for 1711 of 2693 genes demonstrating a rare ASE effect (compared to 1517 under the null), totalling 23234 prSNPs for rare ASE effects (compared to 20393 under the null). Additionally, the prSNPs showed signs of increased functional potential compared to the null group: they were more likely to be distributed around the transcription start site and within the gene relative to control SNPs (χ2 p-value<2e-16; Figure S15). Furthermore, the prSNPs were more likely to have a lower derived allele frequency (p = 3.437e-12) and also trended to have higher evolutionary conservation, indicating that they are more likely to be functional and putatively slightly deleterious (p = 0.07 with PhyloP vertebrate conservation scores; Figure S16).
We then sought for a signal of rare regulatory variants underlying large changes in gene expression by calculating whether individuals with outlier array expression values are enriched for rare genetic variants. We found that individuals with gene expression Z-score ≥2 (a measurement of how far the observed value is from the mean of the sample – see Materials and Methods) have an excess of rare variants within 100 kb of the transcription start site, a signal that was statistically significant (outside the 95% CI) for rare variants landing in highly conserved sites derived from 17-way vertebrate alignments (Figure 4). The average log-2 difference in expression from the mean for these variants was 0.74±0.52 in CEU and 0.66±0.49 in YRI (Figure S17). Overall, there was an excess of 162 coincident singleton, conserved SNPs with expression outliers (Z≥2) in the CEU sample (one-sided p-value<0.05) and the same number, 162, in YRI (one-sided p-value<0.05). Divided by the number of studied individuals, this indicates that there are approximately 3 such effects per individual for this cell type. For other Z-score thresholds and for RNA-Seq data, we observed the same type of enrichment (Figures S18 and S19).
In this study, we have analyzed common and rare regulatory variation in the human genome using resequencing data, highlighting the many advantages of population-scale sequencing in understanding the spectrum of functional variation in the genome. The comparison of eQTL discoveries using 1000 genomes and HapMap 3 data indicated that while many novel associations are discovered with resequencing data, most of common effects are already captured with genotyping arrays. Even though mapping common causal regulatory variants remains a challenge, we observed a clear enrichment of regulatory effects in splice-site and nonsense SNPs. Furthermore, we showed that regulatory variation can putatively modify the effects of a large proportion of nonsynonymous coding variants, and present population genetic evidence suggestive of such interactions. The possibility to study rare variants has been one of the main motivations for large-scale resequencing experiments, and we presented several novel approaches to analyse rare regulatory variation from genomic as well as RNA sequencing data. For rare regulatory effects identified from RNA sequencing data, we were able to pinpoint a median of four putative regulatory variants per rare effect, one of which is expected to be causal – a number low enough for feasible experimental validation. Additionally, individuals with outlying expression values were shown to have an enrichment of rare conserved regulatory SNPs, with each individual carrying an estimation of approximately 3 rare regulatory variants that have a large effect (Z> = 2) on gene expression in the studied cell type. Across all the tissues and developmental stages, each individual is expected to have even hundreds of such rare, large effect regulatory variants. We have also demonstrated how studies integrating genomic or exome sequencing with RNA sequencing data from different tissues will also provide information of how the functional effects of protein-coding variation are modified by regulatory variation. Altogether, these approaches will bring us closer to a joint assessment of how genome sequence affects genome function, and how this relates to phenotypic diversity.
We used 1000 genomes polymorphisms from the March 2010 pilot 1 and 2 release (www.1000genomes.org; REL-1003) and HapMap 3 release 3 genotypes (www.hapmap.org). For association analysis, we used 5 404 174 common (MAF≥0.05) SNPs for 60 RNA-sequenced CEU individuals (Utah residents with ancestry from northern and western Europe) and 5 329 982 and 6 976 232 SNPs for 57 and 56 expression-arrayed CEU and Yoruban individuals (Yoruba in Ibadan, Nigeria), respectively. For two individuals which were parents in a CEU trio which had variants independently called in the 1000 genomes (pilot 2), we intersected their genotype calls with pilot 1 calls; in cases where no genotypes were reported in the trio individuals, we added the reference homozygote state. Between pilot 1 and pilot 2, 3 398 517 sites were concordant and had genotypes reported (for 950 sites the reference and alternative allele were different between the trio individuals and the 58 pilot 1 individuals and these sites were excluded from further processing). For indels, we used calls from the same release. In total, 592 081, 586 604 and 710 931 common indels were used in each population sample (60 CEU with RNA-Seq, 57 CEU with arrays and 56 YRI with arrays). For rare variant and ASE-based analyses we used only the pilot 1 1000 genomes genotype calls; this was to prevent biases due to the improved rare variant calling on the pilot 2 trio.
RNA-sequencing and expression arrays experiments were performed and quantified on RNA extracted from lymphoblastoid cell lines as previously reported [15]. We updated our annotation for RNA-Seq quantification to use the Gencode v3b annotation [31].
For RNA-Seq data, we calculated associations per exon by quantifying the number of reads overlapping known exon annotation for each individual and then performing Spearman rank correlation with respect to corresponding genotypes as previously reported [15].
Allele-specific expression was assessed by calculating the allelic imbalance of variants over heterozygote positions. Significance is assessed using the binomial probability distribution where the probability of success is weighted by that individual's/lane's reference allele to non-reference allele mapping bias. ASE variants used in this study were not monoallelic as we required both reported alleles to be observed at least once. We also conditioned on the ASE effect being present for reads quantified above MAQ10 mapping quality and Phred score of 10 but subsequently reinforced there was no threshold effect by requiring significance when there was no mapping or base quality filter.
The best association per gene (or in the case of RNA-Seq data per exon) at or below the 0.01 permutation threshold was fine-mapped from the HM3 into the 1KG data. Each of these eQTL variants from the HM3 was compared to D′ calculated by Haploview for all the 1KG variants with a Spearman association of p≤10e-3 with the same gene. The 1KG variants which were in LD (D′≥0.8) with the original HM3 variant were deemed to be underlying the same effect originally discovered in the HM3; the best association for that gene in the 1KG meeting this LD criterion was selected for comparison to the original HM3 variant. This methodology allowed us to survey new discoveries irrespective of whether they were the same variant, different variants at different frequencies or divergently-located with respect to the transcription start site.
Functional variation was determined using the EnsEMBL 54 pipeline [32]. Splicing variants were compared to the Gencode annotation and were deemed accurate for essential splicing variants if they were within 5 bp of an exon boundary and accurate for a general splicing variant if they were within 100 bp of an exon boundary. For testing exon association enrichment, we took the splicing variant associations for their respective donor (5′) and acceptor (3′) exons. To find a matching set to test for enrichment of association, we considered synonymous variants which were greater than 15 bp away from an exon boundary. We calculated enrichment by calculating the qvalue statistic1-π0 for acceptor and donor associations only when there were more than 30 associations; the log-ratio of this enrichment was reported [33]. This calculation was made across the range of mean read depths for exons from 1–1000 reads. Stop gain variants were tested against the exon they overlapped and were also compared in a similar way to synonymous and nonsynonymous mutations.
The PhyloP base-wise conservation scores were based on alignments of 46 vertebrate genomes, 33 mammalian genomes, and 10 primate genomes, and were downloaded from UCSC [34]. Ancestral alleles were obtained from the 1000 genomes pilot release.
Haplotype homozygosity indicates the relative age of a haplotype by assessing the incidence (or lack thereof) of recombination or other mutation. This is achieved by comparing the extent of homozygosity between haplotypes by calculating the length of sequence from a target marker until a mismatch occurs. Phased data is required in non-haploid species to assess and compare individual haplotypes from a target marker position. Furthermore, since haplotype homozygosity is being assessed from a heterozygous target marker (required for assessing ASE), a decision needs to be made about what allele should be taken to represent the reference haplotype and in what direction haplotype homozygosity should be assessed (5′ or 3′). Here, we used phasing data as provided by the 1000 genomes project pilot release and compared all haplotypes carrying each allele for the heterozygous marker and in both directions to select the allele and direction with the average longest tract of haplotype homozygosity. Then, given this direction and reference haplotype, we take as a criterion for comparison that there must be at least 6 individuals where between 2 and 4 have ASE significant haplotypes in the same direction and at least 2 are non-significant for ASE. To compare the extent of haplotype homozygosity given the reference haplotype and the ASE status of each haplotype we compare significant ASE to significant ASE haplotypes and significant to non-significant ASE haplotypes and compute the average length of haplotype homozygosity for all pairwise individual comparisons within these categories. We then stratify the results for each ASE marker based on the number of significant ASE haplotypes were original discovered.
To identify putative regulatory SNPs on rare regulatory haplotypes using ASE calculations we looked for all variants within 100 kb of the transcription start site which satisfied ASE sharing in 1, 2 or 3 individuals when at least 6 heterozygotes individuals could be tested for ASE. To satisfy sharing, the variant must be heterozygous with the same direction of effect (assessed through phasing) when an ASE effect is present in an individual and homozygous when the ASE effect is not present. To assess how well our putative causal regulatory variants discovery was performing we assessed the distribution of discoveries around the transcription start site by comparing counts of real predictions versus control predictions in 5 kb windows using the Fisher's exact test (Bonferroni-corrected for multiple testing). Control (null) predictions were obtained by matching each ASE test by reassigning significance in the opposite direction. For instance, if there were 6 heterozygotes, 2 of which show ASE, the control reassignment would assign ASE to the two least significant heterozygote individuals. To assign direction of effect, we matched the distribution of real directions determined with the phasing data to the control set.
We compared the co-occurrence of expression outliers with rare variants by recomputing our expression files as Z-scores and binning at each allele frequency the distribution of expression measurements that were incident with the non-reference variant. To assess significance of divergence in this distribution, individual labels were permuted 200 times and 90 and 95% CI were obtained.
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10.1371/journal.pgen.1004586 | The Vip1 Inositol Polyphosphate Kinase Family Regulates Polarized Growth and Modulates the Microtubule Cytoskeleton in Fungi | Microtubules (MTs) are pivotal for numerous eukaryotic processes ranging from cellular morphogenesis, chromosome segregation to intracellular transport. Execution of these tasks requires intricate regulation of MT dynamics. Here, we identify a new regulator of the Schizosaccharomyces pombe MT cytoskeleton: Asp1, a member of the highly conserved Vip1 inositol polyphosphate kinase family. Inositol pyrophosphates generated by Asp1 modulate MT dynamic parameters independent of the central +TIP EB1 and in a dose-dependent and cellular-context-dependent manner. Importantly, our analysis of the in vitro kinase activities of various S. pombe Asp1 variants demonstrated that the C-terminal phosphatase-like domain of the dual domain Vip1 protein negatively affects the inositol pyrophosphate output of the N-terminal kinase domain. These data suggest that the former domain has phosphatase activity. Remarkably, Vip1 regulation of the MT cytoskeleton is a conserved feature, as Vip1-like proteins of the filamentous ascomycete Aspergillus nidulans and the distantly related pathogenic basidiomycete Ustilago maydis also affect the MT cytoskeleton in these organisms. Consistent with the role of interphase MTs in growth zone selection/maintenance, all 3 fungal systems show aspects of aberrant cell morphogenesis. Thus, for the first time we have identified a conserved biological process for inositol pyrophosphates.
| Fungi are an extremely successful and diverse group of organisms ranging from the small single-celled yeasts to the indefinitely growing filamentous fungi. Polarized growth, where growth is restricted to defined regions, leads to the specific cell shape of yeast cells, as well as the very long hyphae of filamentous fungi. Fungal polar growth is controlled by an internal regulatory circuit of which the microtubule cytoskeleton comprises the transport road for numerous cargos needed for polarized growth. However, the microtubule cytoskeleton is not static, but a dynamic structure, which is modulated by microtubule-associated proteins and the interaction with other cellular structures. Our present analysis has identified a new regulator of the microtubule cytoskeleton in the fission yeast S. pombe: a member of the highly conserved Vip1 inositol polyphosphate kinase family. Vip1 proteins have a dual domain structure consisting of an N-terminal kinase domain which synthesizes inositol pyrophosphates and a C-terminal domain, which we show to negatively regulate the kinase output. Our results suggest that modulation of microtubule dynamics is correlated to Vip1 kinase activity. Importantly, polarized growth and microtubule dynamics were also modulated by Vip1 family members in A. nidulans and U. maydis thus uncovering a conserved biological role for inositol pyrophosphates.
| Cell polarization can be viewed as the generation and upkeep of a defined cellular organization. The readout of cell polarization in fungal systems is polarized growth resulting in a specific cell shape and size. This ranges from the 14 µm long cylindrical Schizosaccharomyces pombe fission yeast cell, which maintains its shape by restricting growth zones in a cell cycle dependent manner to the extremely polarized growth of filamentous fungi such as Aspergillus nidulans where hyphal extension can occur in a continuous and infinite manner [1]–[3]. Fungi are capable of morphological transitions in response to external signals and this represents an important virulence trait of pathogenic fungi such as the corn smut fungus Ustilago maydis. Here, the transition from a non-pathogenic haploid yeast-like form to a dikaryotic filament is required for the fungus to enter the host tissue [4]. Such an alteration in growth form is also present in non-pathogenic model yeasts such as S. cerevisiae and S. pombe where it acts as a foraging response [5]–[7]. Polarized growth in fungi depends on the interplay between the MT and actin cytoskeletons and in some systems septins [8]. In S. pombe, where growth occurs at the cell tips which contain oscillating Cdc42, actin cables are used for the transport of growth vesicles. On the other hand, MT plus-end dependent transport of the landmark complex Tea1-4 via the kinesin Tea2 is required for marking potential zones of growth [1], [9]–[13]. Correct delivery of Tea1-4 requires alignment of antiparallel interphase MTs along the long axis of the fission yeast cell. The dynamic MT plus-ends are oriented and polymerize towards the cell end; upon contact with the tip MT dynamics are modified, the landmark complex unloaded and anchored at the cell tip [14]–[16]. MT dynamics are regulated mainly by the diverse group of proteins at the MT plus-end. Here, the central component is the conserved EB1 family, which is essential for plus-end association of numerous +TIPs [17]. Interestingly, the Tea1-4 complex is also present in filamentous fungi where a recent publication has uncovered additional functions namely regulating MT dynamics and MT guidance at the hyphal tip. Loss of the S. pombe Tea1 homologue TeaA in A. nidulans results in an inability to maintain the direction of growth and thus results in meander-like growing hyphae [3], [18]. TeaA present at the hyphal tip is responsible for focusing of MTs at a single point and the regulation of MT plus-end dynamics via negative modulation of the XMAP214 family member AlpA [19]. If this negative regulatory function on MT dynamics is a common feature of Tea1-like proteins remains to be determined but the MT phenotype of S. pombe tea1Δ (deletion) cells supports such a scenario [14].
Although core mechanisms of growth zone definition and maintenance are conserved in fungi, the consistently growing hyphae of filamentous fungi require a much more sophisticated system of MT-based transport than is necessary for yeast cell growth [3], [20]–[27]. For example in U. maydis the MT cytoskeleton is required for long distance endosomal transport via plus- and minus-end -directed motor proteins such as kinesin and dynein, respectively [24], [28]–[32]. This transport process has been shown to be crucial for efficient secretion [33], [34]. Important molecular cargos of these endosomes are septins, mRNAs and ribosomes [35]–[37]. Interestingly, local translation of septin mRNA on shuttling endosomes loads these membranous carriers with newly synthesized septin protein for transport towards the hyphal tip [35].
In this work we describe a new core element of fungal growth zone selection and MT cytoskeleton regulation: the conserved Vip1 family which synthesizes diphospho-myo-inositol polyphosphates (inositol pyrophosphates). These high energy molecules are mainly made from inositol hexakiphosphate (IP6) and are generated by two classes of enzymes: IP6Ks and the PPIP5Ks (called Vip1 family throughout this work) (recently reviewed in: [38]–[41]. The Vip1 family, which was discovered in S. pombe and S. cerevisiae, was shown to have enzymatic activity by using an S. cerevisiae strain where the genes coding for the IP6K Kcs1 and the nudix hydrolase Ddp1 had been deleted [42], [43]. In humans two homologues exist named PPIP5K1 and PPIP5K2 [44], [45]. All members of this enzyme class have a dual domain structure consisting of an N-terminal “rimK”/ATP-grasp superfamily domain which phosphorylates position 1 on the fully phosphorylated inositol ring and a C-terminal domain with homology to histidine-acid-phosphatases [44]–[47]. The function of the latter domain remains a matter of debate. Key histidine residues are conserved in this domain, but unusually Vip1-like proteins do not have an aspartate residue next to the second histidine [45]. Furthermore detailed analysis of the human Vip1 phosphatase-like domain demonstrated that this domain is catalytically inactive [48]. However phenotypic analysis of S. pombe strains expressing Asp1 variants (fission yeast Vip1 member) with mutations of conserved C-terminal domain histidine residues suggested that these residues are required for negative regulation of the kinase activity [7]. In addition, truncated S. cerevisiae and human Vip1 variants which only contained the N-terminal kinase domain generated more inositol pyrophosphates than the full-length versions pointing to a kinase antagonizing activity of the C-terminal domain [45], [49]. Now, we provide evidence that Vip1-like proteins harbor two enzymatic activities.
Inositol pyrophosphates regulate cellular processes by two different modes of action: (i) modulation of protein function by reversible binding of these high energy molecules and (ii) protein pyrophosphorylation [50], [51]. An example for the first type of regulation is the Akt kinase which is involved in insulin signaling. Here, specific inositol pyrophosphates were shown to bind to the Akt PH domain thus blocking activation of this kinase [52]. An example for the second type of action is the regulation of the antiviral response via activation of the interferon transcription factor IRF3. In a cell free system IRF3 was phosphorylated by specific inositol pyrophosphates and this process required the transfer of the β-phosphate of the pyrophosphate group [49].
The cellular processes regulated by inositol pyrophosphates are wide-ranging and diverse. These include the phosphate availability response in S. cerevisiae, the chemotactic response in Dictyostelium, the antiviral response and insulin signaling in mammals and the dimorphic switch in S. pombe [7], [49], [50], [52], [53]. We have now extended our analysis of Vip1 biological function and have found that inositol pyrophosphates have a conserved role in fungal morphogenesis.
We had previously generated S. pombe strains that expressed the endogenous Asp1 variants Asp1D333A and Asp1H397A [7]. The former Asp1 variant has a single amino acid change at position 333, a key catalytic residue for kinase activity, while H397 is a highly conserved histidine residue of the C-terminal acid phosphatase-like domain (Figure 1A) [43]. Phenotypic analysis of these Asp1 variant expressing strains suggested that Asp1D333A and Asp1H397A have an altered enzymatic activity compared to the wild-type Asp1 protein [7]. We therefore assayed the ability of Asp1D333A and Asp1H397A to generate inositol pyrophosphates.
As it had not been demonstrated previously that the S. pombe Asp1 protein could generate inositol pyrophosphates, we first tested with an in vitro assay if this was the case. Asp1 was expressed in bacteria as a glutathione-S-transferase (GST) fusion protein and purified. Using a recently published method that allows analysis of inositol pyrophosphates by PAGE, we found that purified GST-Asp1 generated inositol pyrophosphates (from now on called IP7) in an ATP-dependent manner using IP6 as a substrate (Figure S1A, right panel) [54]. This activity was dose-dependent, as the amount of IP7 generated increased with increasing amounts of protein used (Figure S1B). We next tested if the GST-tagged Asp1D333A and Asp1H397A proteins (Figure 1B) also generated IP7 and found that Asp1D333A was unable to convert IP6 to IP7 (Figure 1C, lane 5). Interestingly, comparing equal amounts of protein, the Asp1H397A variant generated more IP7 than the wild-type Asp1 protein (Figure 1C, lane 6 and 4, respectively). Analysis of IP7 production by Asp1 and Asp1H397A proteins over a time period of 0 to 10 hrs revealed that IP7 production increased with time and that Asp1H397A could produce up to 100% more IP7 than the wild-type Asp1 protein (Figure 1D, left panel, lanes 7–12 and 1–6, respectively; quantification shown in 1E). Similar results were obtained when comparing IP7 production of the wild-type Asp1 and the Asp11-364 variant (contains only the kinase domain) (Figure S12). These data point to a negative role of the C-terminal acid phosphatase-like domain. To analyze this further we determined the Km and Vmax values for Asp1 and Asp1H397A (Figure 1F). The Km values for Asp1 and Asp1H397A were 58.18 µM and 57.32 µM respectively implying a similar affinity for the substrate. However the Vmax for Asp1H397A was 36% higher than that of Asp1 (Figure 1F).
To learn more about the negative impact of the Asp1 phosphatase-like domain on IP7 production, we (i) tested if addition of Asp1365-920 reduced the inositol pyrophosphate out-put in an Asp1 in vitro kinase assay and (ii) determined the in vivo read-out of Asp1 variants with mutations in conserved residues of the phosphatase-like domain (Figure 2A).
The presence of purified bacterially expressed GST-tagged Asp1365-920 in the IP7 in vitro assay together with full length Asp1 reduced the amount of IP7 in a dose-dependent manner (Figure 2B). IP6 amounts were unaffected by Asp1365-920 as shown by the incubation of only this Asp1 variant with IP6 in the in vitro assay (Figure S13). Thus, the negative effect was only seen for the IP7 output. We therefore propose that the Asp1 C-terminal phosphatase-like domain has phosphatase activity and its substrate is inositol pyrophosphate generated by the Asp1 N-terminal kinase domain (see discussion).
We had shown previously that the asp1H397A strain was more resistant to microtubule poisons such as thiabendazole (TBZ) while the asp1D333A strain was more sensitive to TBZ compared to the wild-type strain [7]. A deletion of asp1+ (asp1Δ strain) also led to TBZ hypersensitivity (Figure S2A). Furthermore a strain where the wild-type asp1+ had been replaced by the asp1D333A, H397A variant also showed the same increased TBZ sensitivity as the asp1D333A and asp1Δ strains (Figure S2A). These data strongly suggest that the TBZ resistance/sensitivity of these strains is solely dependent on the function of the Asp1 kinase. Absence of Asp1 kinase activity results in TBZ hypersensitivity (asp1Δ, asp1D333A and asp1D333A, H397A strains) while increased Asp1 kinase function (asp1H397A strain) results in TBZ resistance. The Asp1 C-terminal phosphatase-like-domain appears to modulate only the function of the Asp1 N-terminal kinase domain as the asp1D333A, H397A strain has the same TBZ phenotype as asp1Δ and asp1D333A strains (Figure S2A).
These results demonstrate that increased TBZ resistance can be used as an in vivo read-out for a non-functional Asp1 phosphatase domain. We expressed wild-type asp1+ and the mutant versions asp1H397A, asp1R396A, asp1H807A and asp11-794 on a plasmid from the thiamine-repressible nmt1+ promoter [55] in the asp1Δ strain. Western blot analysis revealed that expression of full length Asp1 variants was similar (Figure S14). Expression of these asp1 variants except asp1H397A did not affect cell growth (Figure 2C, growth on thiamine (nmt1+ promoter repressed) versus growth on thiamine-less (nmt1+ promoter de-repressed) plates. Plasmid-borne high expression of asp1H397A is lethal as has been shown previously [43].
As shown in Figure 2C plasmid-borne expression of full length asp1+ allowed partial growth of the asp1Δ strain on TBZ containing plates. However expression of Asp1R396A, Asp1H807A and Asp11-794 resulted in better growth of the asp1Δ strain on TBZ medium. We conclude that the conserved phosphatase signature motif is required for the function of the C-terminal domain.
To test if the Asp1 C-terminal domain is also able to regulate Asp1 kinase activity in trans in vivo, we constructed a plasmid, which expressed Asp11-364 and Asp1365-920 from two separate nmt1+ promoters on the same plasmid (Figure S3A). Expression of this plasmid in the asp1Δ strain resulted in a similar phenotype as expression of a plasmid expressing only Asp11-364 (Figure 2D, protein levels shown in Figure S3B–C). Thus in this in vivo situation, it appears that both Asp1 domains need to be on the same molecule for the negative impact of the C-terminal domain to be exerted.
Our in vitro kinase assay demonstrated that the Asp1D333A variant has no enzymatic activity, while that of Asp1H397A is higher than that of the wild-type Asp1 protein. Thus it is very likely that the resistance/sensitivity to microtubule poisons is a result of different intracellular inositol pyrophosphate levels.
We had previously identified a truncated Asp1 variant (Asp11-794) as a multi-copy suppressor of the TBZ-hypersensitivity of a mal3 mutant strain [7]. Mal3 is the fission yeast member of the EB1 family of MT associated proteins [56]. We therefore determined if Asp1 function modulated the MT cytoskeleton by analyzing the interphase MT cytoskeleton of the various asp1 strains via expression of GFP-α-tubulin (using the endogenous nmt81::gfp-atb2+ construct) [57]. asp1 variant strains with or without the presence of GFP-α-tubulin had a similar growth phenotype (Figure S4A).
In S. pombe, interphase MTs are polymerized in the vicinity of the nucleus, align along the long axis of the cell and grow with their plus-ends towards the cell end, where they pause prior to de-polymerization [58]. The organization of interphase MTs of the gfp-atb2 asp1H397A strain was comparable to the wild-type strain while those of the fainter fluorescent gfp-atb2 asp1D333A and gfp-atb2 asp1Δ MTs appeared to be more disorganized (Figure 3A). In particular, the number of interphase MTs that were not oriented along the long axis of the cell was increased in asp1D333A (Figure S15) and asp1Δ cells although this was not statistically significant. However the number of interphase MTs that depolymerized at the lateral cortex/cytoplasm and not at the cell tip was increased significantly in asp1D333A and asp1Δ cells compared to wild-type cells (Figure 3B). An example is shown for an asp1D333A MT that touched the lateral cortex and became depolymerized instead of being deflected as seen for such MTs in asp1+ cells (Figure S4B).
Measurement of MT parameters in the 3 asp1 variant strains revealed that MT dynamics were altered. asp1D333A cells showed an increased MT growth rate while the rate of MT shrinkage was decreased in asp1H397A cells (Table 1). Interphase MTs of asp1D333A cells had an increased number of catastrophe events while those of asp1H397A cells were reduced compared to wild-type cells (Table 1). The average MT length for both asp1 mutant strains was increased compared to the MTs of the wild-type strain. Thus, all measured MT parameters were affected by intracellular inositol pyrophosphate levels. asp1D333A MTs are more dynamic, whereas asp1H397A MTs have the opposite phenotype.
Interestingly, we found that the residence time of the MT plus-end at the cell tip was dependent on the asp1 variant expressed in the cell. Measurement of the time that a MT stays at the cell tip showed that the residence time of a MT plus-end at the cell tip is variable. Nevertheless, when we compared this MT parameter for wild-type and asp1D333A cells we found that the latter MTs had on average a significantly reduced pausing time at the cell tip before depolymerization (Table 1). For example, only 12% of asp1D333A MTs but 28% of wild-type MTs paused at the cell tip for more than 60 seconds (Figure 3C). In contrast, the residence time of asp1H397A MT plus-ends at the cell tip appeared to be increased compared to wild-type MTs but this was not statistically significant (Table 1). We therefore increased Asp1 generated inositol pyrophosphate levels even further by plasmid-borne expression of the Asp1 variant Asp11-364 (kinase only) in the asp1H397A strain. Under these conditions the average MT pausing time was increased by 30% in cells expressing Asp11-364 (asp1H397A strain plus vector: 42.2±36.8 seconds; n = 105; asp1H397A strain plus pasp11-364: 53.6±38.4 seconds; n = 110; p<0.025 (t-test)). A detailed depiction of MT pausing time in this assay is shown in Figure 3D and an example of the increased MT residence at the cell tip is shown in Figure 3E.
Thus, inositol pyrophosphate levels appear to regulate the residence time of a MT plus-end at the cell tip. Increasing the levels of Asp1 generated inositol pyrophosphates increases pausing at the tip prior to a catastrophe event, while lowering the amount of Asp1 generated inositol pyrophosphates has the opposite effect.
Proteins associated with MT plus-ends play a leading role in regulating MT dynamics [59]. Of particular importance is the EB1 family, which is central to the association of other +TIPs with the MT plus-end. To determine if Asp1 affects MT dynamics via the EB1 family member Mal3, double mutant strains between mal3Δ (mal3 deletion) and the asp1 alleles asp1H397A, asp1D333A and asp1Δ were constructed. The asp1H397A mal3Δ strain showed a reduced TBZ sensitivity compared to the single mutant mal3Δ strain, demonstrating that increased Asp1 kinase function rescues the mal3Δ mutant TBZ phenotype (Figure 4A). Loss of Asp1 kinase activity increased the TBZ hypersensitivity of mal3Δ strains as shown for the asp1D333A mal3Δ and asp1Δ mal3Δ strains (Figure 4A). Similar results were obtained when these strains grew on medium containing the MT drug methyl-benzimidazol-2-yl-carbamate (MBC) (Figure 4B).
We next assayed if plasmid borne overexpression of the Asp1 variant Asp11-364 (Asp1 kinase domain only), rescued the mal3Δ TBZ hypersensitivity, and found this to be the case (Figure 4C). Furthermore increasing intracellular IP7 levels by other means than asp1+ manipulation, namely by using a strain where the ORF coding for the nudix hydrolase Aps1 was deleted (aps1Δ), also decreased the TBZ hypersensitivity of the mal3Δ strain (Figure 4D). Nudix hydrolases degrade inositol pyrophosphates and disruption of the nudix hydrolase encoding gene increases the intracellular concentration of inositol pyrophosphates 3-fold [60], [61].
As Mal3 stabilizes MTs, mal3Δ cells do not have a normal interphase MT-cytoskeleton, where MTs are aligned in antiparallel bundles along the cell axis to the cell ends. Instead such interphase cells have very short MT stubs present around the nucleus as MT catastrophe events are increased (compare wild-type MTs to mal3Δ MTs) (Figure 4E) [56], [62], [63].
This short interphase MT phenotype was rescued partially in the mal3Δ asp1H397A strain (Figure 4E).We determined MT parameters in the mal3Δ and mal3Δ asp1H397A strains and observed no difference in the number of MTs/cell (Figure S16). An analysis of the interphase MTs of a mal3Δ asp1D333A strain was not possible as interphase MTs of this strain were extremely short and unstable.
Interestingly, MT length, MT growth time and the relative MT intensity were all increased significantly in the double mutant mal3Δ asp1H397A strain compared to the single mutant mal3Δ strain (Table 2 and Figure 4F). MTs grew longer before a catastrophe event in the mal3Δ asp1H397A strain compared to the mal3Δ strain and MT length was increased in the former compared to the latter strain (Table 2). Furthermore the relative MT fluorescence intensity was increased 1.25 fold in the mal3Δ asp1H397A strain compared to mal3Δ strain (Figure 4F). Thus MT dynamics regulation by inositol pyrophosphates does not require the EB1 protein.
Next, we analyzed Mal3-GFP particle movement in the various asp1 strains. The EB1 family decorates MTs and forms the comet-shaped structures at the MT plus-end characteristic of plus-end tracking proteins [59], [63]. The Mal3-GFP distribution on MTs was similar in all asp1 strains and was as described [63]. We determined the speed of the outmost outbound Mal3-GFP comets moving towards the cell end. As shown in Figure 4G movement of such Mal3-GFP particles in the wild-type and asp1H397A strain was similar, while asp1D333A Mal3-GFP comets were faster. The speed of movement of outmost outbound Mal3-GFP was directly correlated to the MT growth rate of the particular asp1 strain (Table 1 and Figure 4G). We also assayed movement of the kinesin Tea2-GFP in the 3 asp1 variant strains and found that the speed of Tea2-GFP signals at the end of MTs was comparable to Mal3-GFP comets (Figure S17).
Interphase MTs in S. pombe control proper polarized growth by delivering the Tea1-4 landmark protein complex to potential sites of growth at the cell tip [10], [12], [64], [65]. Consequently, an aberrant interphase MT cytoskeleton can result in an altered positioning of the growth zones and in cells with a branched or bent morphology.
In wild-type fission yeast cells growth at a specific cell end is cell cycle controlled. After cytokinesis, cells will first grow in a monopolar manner selecting the old end (the end present before the previous cell division) as the first growth zone. The attainment of a critical cell size and completion of S-phase allow a switch to bipolar growth (NETO transition) at both cell ends in the G2 cell cycle phase [66], [67]. We had shown previously that Asp1 kinase function is essential for NETO, as 84% of asp1D333A cells grow exclusively monopolar on an agar surface using the old end as the site of growth [7]. However, the cylindrical cell shape was maintained in most asp1D333A cells demonstrating that the growth zone was still at the cell end. Abnormal growth zone positioning i.e. the selection of a growth zone not at the cell tip was observed in less than 5% of asp1D333A cells [7].
Next we asked, if proper polarized growth could also be re-established in asp1 mutants that were re-entering the vegetative cell cycle after nutrient starvation. Re-entry of cells into the cell cycle from G0 requires a de novo definition of the growth zones. [13], [16]. We thus examined the morphology of asp1+ and asp1 mutant cells after exit from stationary phase: on agar 93% of asp1+, 100% of asp1H397A but only 73% of asp1D333A cells grew as normal cylindrically shaped cells (Figure 5A). The remaining 27% of growing cells had an abnormal morphology, indicating that proper polarized growth was not re-established (Figure 5A–B). Incubation of stationary asp1D333A cells into fresh liquid medium massively aggravated the ectopic growth phenotype: under these conditions 80% of the cells had an aberrant, branched or lemon-shaped appearance indicating that Asp1 kinase activity was required for polarized growth and growth zone selection under these circumstances (Figure 5C–D). We also determined if cells when exiting from G0 state on solid medium showed the monopolar to bipolar growth pattern of exponentially growing cells. However we found a wide variety of growth patterns even for the asp1+ cells indicating that cells need to undergo a number of cell divisions before the normal growth pattern is stably re-established. It was thus not possible to determine if asp1D333A cells deviate from the norm.
As the Vip1 family is conserved from yeast to man, we determined if Vip1 members also played a role in cell morphogenesis in other organisms. We therefore analyzed the function of Asp1-homologues in the filamentous ascomycete Aspergillus nidulans and the dimorphic basidiomycete Ustilago maydis. In both fungi, the importance of the MT cytoskeleton for fungal growth has been investigated extensively [18], [21]–[26], [31], [68], [69]. We decided to generate and characterize strains where the genes coding for the Asp1-homologues had been deleted as we have shown for S. pombe that the asp1Δ strain behaved identical to the asp1D333A strain under all conditions tested (Figure S2A–B; [7]).
The A. nidulans Asp1 orthologue AN5797.2 has the characteristic Vip1 family dual domain structure (Figure S5) and was named vlpA (Vip1-like protein). To test if VlpA generates inositol pyrophosphates, bacterially expressed and purified GST-VlpA1-574, which contains the putative kinase domain was used in the in vitro kinase assay [54]. VlpA1-574 generated IP7 in an ATP dependent manner using IP6 as a substrate (Figure 6A, lanes 6, 4 and 5, respectively). This activity increased with increasing amounts of VlpA1-574 (Figure 6B).
We next deleted the endogenous vlpA gene and found that the vlpA-deletion strain (ΔvlpA) showed a growth delay and smaller colonies than the wild-type strain (approximate 50% diameter of colony on glucose medium) (Figure 6C). The majority of hyphae in the ΔvlpA strain displayed a normal morphology however swelling of hyphae was observed in some instances (Figure 6D, left). This phenotype could be caused by mis-positioning of the growth zone.
We constructed a strain expressing N-terminally GFP-tagged VlpA fusion protein under the control of the inducible alcA promoter instead of native VlpA. Under repressed conditions with glucose as carbon source, the strain exhibited a growth delay (Figure 6C, bottom right most panel). Under de-repressed conditions with glycerol or induced conditions with threonine, the slow growth phenotype was alleviated implying that GFP-VlpA can complement the growth defect of the vlpA deletion. We constructed a strain expressing GFP-VlpA under the native promoter and found that GFP-VlpA fluorescence was observed predominantly in the cytoplasm (Figure 6E, left).
Interestingly, the A. nidulans VlpA is needed for correct growth zone selection as it is required for the correct positioning of the second germtube. Once the first hypha reaches a determinate length, a second germ tube appears on the spore after the first septum at the base of the first hypha was formed [70]. This second germination site normally lies opposite of the first hypha (Figure 6F). In A. nidulans, MTs are formed from spindle pole bodies (SPB) and from septum-associated MT-organizing centers (septal-MTOCs) [71], [72]. MTs emanating from the septum of the first hypha grow towards the first germtube as well as into the direction of the spore. The MTs from the septa towards the spore are required for the positioning of the second germtube [70]. In the vlpA deletion strain, 24% of the spores did not produce a second germtube from the spore but produced a second hypha by branching out of the first hypha situated between septum and spore (Figure 6F). This aberrant phenotype was rescued by expressing a VlpA variant GFP-VlpA1-574 (contains the kinase domain) from the alcA promoter in the vlpA deletion strain (Figure 6F). Interestingly, expression of this VlpA variant in the wild-type strain altered growth zone selection (Figure 6F). These results demonstrate that (i) VlpA kinase activity is required for growth zone selection and (ii) physiological levels of VlpA kinase are required for proper growth zone selection. Thus, the Vip1-like proteins from A. nidulans and S. pombe are both required for growth zone selection.
A comparable phenotype of aberrant growth zone selection had been observed previously for the apsB6 mutant (Figure 6F) [70]. The apsB gene was identified by mutant screening. Anucleate primary sterigmata (aps) mutants are partially blocked in conidiation due to failure of the organized migration of nuclei into the conidiophore metulae. The mutants also show irregular distribution of nuclei in vegetative hyphae [73]. ApsB is a MTOC component that interacts with gamma-tubulin [74]. The apsB6 mutant shows an altered MT organization as it forms fewer MTs out of SPBs, compared to the wild-type and substantially fewer MTs from septa [72]. We therefore analyzed such parameters in the vlpA-deletion strain.
GFP tagged KipA, which is a kinesin localizing at growing MT plus-end, was used as plus-end marker to determine MT parameters [71]. Comparing wild-type to the vlpA-deletion strain during a five minute time period, we observed a reduction of newly emanating GFP-KipA signals in the vlpA-deletion strain at SPBs (27%) and at septal-MTOC (33%) (Figure 6G, Figure S6, Movie S1 and S2). The growth rate of the MT plus-ends was slightly reduced in the vlpA-deletion strain (21%) (Figure 6H). Pausing of MT plus-ends at hyphal tips was analyzed by using GFP-α-tubulin. Since the pausing time at hyphal tips was too short to determine if differences existed between the wild-type and the vlpA deletion strains, we scored the number of MT plus-ends reaching hyphal tips during a 1 minute time period. We counted fewer MT plus-ends in the vlpA deletion strain compared to the wild-type strain indicating that MT dynamics at the hyphal tip was altered in the absence of VlpA (Figure 6I).
Finally, we studied the function of an Asp1 homologue in a distantly related fungus, the basidiomycete U. maydis. Sequence comparison revealed a protein designated UmAsp1(um06407 in MUMDB; MIPS Ustilago maydis database [75], with 922 amino acids and 49% sequence identity to S. pombe Asp1 over its entire length (Figure S5). To study its function we generated deletion strains in laboratory strain AB33. This strain is a derivative of wild-type strain FB2 that contains an active bW2/bE1 heterodimeric transcription factor under control of the nitrate-inducible promoter Pnar1. Thereby, b-dependent filamentation can be elicited by changing the nitrogen source in the medium [76]. We observed that a corresponding deletion strain of Umasp1Δ exhibited reduced proliferation during yeast-like growth in comparison to wild-type (Figure S7). Assaying TBZ sensitivity revealed that Umasp1Δ strains were hypersensitive to this MT inhibitor (Figure 7A; Figure S7B–C). For microscopic analysis we compared wild-type and Umasp1Δ strains expressing GFP-Tub1 (GFP fused to α-tubulin). The Umasp1Δ strain showed an increased number of cells that were clearly different from the cigar-shaped wild-type cells. Cells exhibited an increased diameter in the central region and/or were rounded-up at the poles (Figure 7B; Figure S8). Such cells were classified as having a disturbed shape and quantification revealed that about 40% of Umasp1Δ cells had an abnormal cell morphology (Figure 7B). Analysis of the MT cytoskeleton showed specific deviations from wild-type MTs. In wild-type cells 4 to 5 microtubular bundles are observed that are facing with their plus ends towards the poles [23], [25], [77]. We observed that the MT organization was altered in Umasp1Δ cells: a conservative quantification scoring only cells with drastic changes revealed that in comparison to the wild-type MT organization was altered (Figure S9). The most profound differences observed were (i) Umasp1Δ cells with large buds exhibited depolymerized MTs (Figure S9B, bottom panels); a phenotype rarely observed for wild-type cells. (ii) Umasp1Δ cells mostly with no bud or a small bud (early G2 phase) [23] had significantly more MT bundles. Instead of the 4 to 5 bundles present in wild-type cells, we observed 6 to 8 (Figure 7C–D; Movies S3 and S4). The fluorescence intensity of the GFP-Tub1 signal was drastically reduced in these bundles (Figure 7C, E) suggesting that loss of UmAsp1 results in an increased number of MT bundles with fewer MTs within such a bundle.
Studying the subset of intact MTs indicated that MT growth rate, which was analyzed by determining the velocity of the GFP-tagged U. maydis EB1 protein Pep1 [77], was not significantly different compared to wild-type (Figure 7F), but the residence time of MTs pausing at the cell end was significantly reduced (Figure 7G). In summary, UmAsp1 is needed for correct morphology and MT organization during proliferation of yeast-like cells.
To investigate the function of UmAsp1 during hyphal growth, AB33 filamentation was induced on plates and in liquid medium. Wild-type forms a fuzzy colony indicative for efficient hyphal growth (Figure 8A, top, left panel). This was disturbed in Umasp1Δ strains (Figure 8A, top, right panel). Filaments were shorter, often bipolar and the amount of abnormal filaments was clearly increased in Umasp1Δ strains (Figure 8B–C, Figure S10). Thus, as in hyphae of A. nidulans, UmAsp1 is important for filamentous growth.
To study the subcellular localization we generated strains expressing UmAsp1-GFP (C-terminal fusion to GFP). The resulting strain was phenotypically indistinguishable from wild-type (Figures 7A, 8A–C) demonstrating that the fusion protein is fully functional. Studying the subcellular localization in yeast or hyphal cells did not reveal any pronounced subcellular accumulation of the protein as has been shown for other Vip1-like proteins (Figure 8D). However, UmAsp1-GFP fluorescence was reduced in hyphae, suggesting that the protein amount decreases after filament induction (Figure 8E, Figure S11). Indeed, western blot analysis demonstrated that UmAsp1-GFP protein amounts decreased over time (Figure 8F). Thus, UmAsp1 protein amounts decrease and hence presumably intracellular inositol pyrophosphate levels appear to be down-regulated during the switch to hyphal growth.
In this work we have defined the function of the C-terminal domain of the Vip1 family member Asp1 from S. pombe and have identified a new role for inositol pyrophosphates in fungal polarized growth and the modulation of MTs. In all three fungal model systems analyzed transport-based processes along the MT cytoskeleton are essential for proper polarized growth. However the long hyphal compartments of the filamentous fungi require a more sophisticated system of localized delivery [3], [18], [24]. Thus although Vip1-like proteins play a role in polarized growth in S. pombe, A. nidulans and U. maydis their specific roles are not expected to be identical.
All Vip1 family members have a dual domain structure consisting of an N-terminal kinase domain and a C-terminal histidine acid phosphatase-like domain. Generation of inositol pyrophosphates has been shown for the budding yeast and human Vip1 family members [43]–[45], [48]. In this work we have extended the analysis to two further fungal Vip1-like proteins: the S. pombe Asp1 and the A. nidulans VlpA. Both proteins generated inositol pyrophosphates in vitro. The use of Asp1 and VlpA N-terminal- only-variants mapped the kinase activity to the N-terminal part of the respective protein.
The precise function of the C-terminal phosphatase-like domain of Vip1-like proteins has been elusive. The histidine acid phosphatase signature motif is in principle present in Vip1-like proteins but the conserved “HD” motif has been replaced by H(I,V,A) [45]. A recent publication has shown that the phosphatase-like domains of the human Vip1 members are catalytically inactive. Instead the authors show that this domain plays a role in inositol lipid binding [48]. On the other hand, a comparison of the amounts of inositol pyrophosphates generated by human and the S. cerevisiae full-length Vip1 proteins versus N-terminal kinase-domain-only-variants, showed that the latter variants exhibited more specific activity [43], [45]. This implied a negative impact of the phosphatase-like domain on inositol pyrophosphate production. However it was unclear, if this effect was due to the large size differences between the full length and the kinase-domain-only-variants [45]. In this paper we demonstrate that the phosphatase-like domain has a regulatory function: (i) the Asp1H397A variant generated significantly more inositol pyrophosphates in vitro than the equally sized wild-type Asp1 protein (Figure 9A). The Km values for these two proteins were similar, but Vmax for the mutant Asp1 variant Asp1 H397A was higher. (ii) Addition of the phosphatase-only variant Asp1365-920 to an Asp1 protein containing in vitro kinase assay massively reduced the IP7 output (Figure 9A). However the presence of a mutated phosphatase variant, Asp1365-920 H397A in the assay did not have this effect (Figure S18). Thus our results suggest that the C-terminal phosphatase-like domain of Asp1 has enzymatic activity and its substrates are the inositol pyrophosphates produced by the N-terminal kinase domain of the protein (Figure 9B, model II). However as we have not formally proven that the C-terminal domain has phosphatase activity other modes of regulation are possible as shown in model I (Figure 9B).
We have shown previously that specific extrinsic signals appear to up-regulate Asp1 kinase activity via the cAMP PKA pathway [7]. We speculate that such an up-regulation might occur by modification and result in down-regulation of the Asp1 C-terminal domain function. Such a scenario could also be envisaged for other external signal induced processes regulated by Vip1 family members, such as the antiviral response [49].
The present work has defined a new role for inositol pyrophosphates generated by the Vip1 family: the modulation of fungal growth and the MT cytoskeleton. In S. pombe, interphase MT organization and MT dynamics were strongly altered in the asp1 mutant strains. In A. nidulans MT arrays from the SPB and the septal MTOC were affected in the vlpA deletion strain while in U. maydis loss of the Vip1-like protein resulted in increased TBZ sensitivity and an increase of cells with aberrant MT organization.
How then do inositol pyrophosphates modulate the MT cytoskeleton? In all systems tested to date and shown for U. maydis and A. nidulans in this work, Vip1 proteins are predominantly cytoplasmic without a specific subcellular localization [42], [48]. However as inositol pyrophosphates appear to modulate processes by binding to proteins or by pyrophosphorylation of proteins, direct association of Vip1 proteins with their targets might not be necessary. Our analysis in S. pombe demonstrated that in the absence of the +TIPs EB1 family member Mal3 the MT cytoskeleton can still be modulated by inositol pyrophosphates. Furthermore MT localization of EB1 proteins appeared unaffected in the S. pombe asp1D333A and the Umasp1Δ strain. As the EB1 protein family is at the center of the +TIP network of MT plus-ends and required for the recruitment of the majority of +TIPs [17], [59], we reason that such MT proteins are unlikely targets of inositol pyrophosphates. We have started to search for MT relevant inositol pyrophosphate targets by expressing either asp11-364 (kinase domain only) or asp1365-920 (phosphatase domain only) in various S. pombe mutants with an altered MT cytoskeleton. Our rationale is that the mutant phenotype of a strain with a deletion of a direct Vip1 target should not be affected by varying inositol pyrophosphate levels. We found that inositol pyrophosphates show a “genetic interaction” with the MT plus-end components that can associate with MTs independently of EB1 (our unpublished observations). However other MT structures might also be modulated by inositol pyrophosphates: MTs emanating from SPBs and septal MTOCs are reduced in the A. nidulans vlpA-deletion strain as has been shown for the apsB mutant strain [72]. ApsB is a conserved MTOC associated protein that interacts with γ-tubulin [74].
Of particular interest is the observed direct correlation between intracellular inositol pyrophosphate levels and the time that S. pombe MT plus-ends stay at the cell tip before a catastrophe event. Components of a fungal growth zone can regulate MT plus-end dynamics as has been shown for A. nidulans Tea1 family member TeaA, which negatively regulates the activity of the XMAP215 protein AlpA [19]. Thus it is feasible that Asp1 enzymatic activity regulates MT dynamics at the cell tip. Although immunofluorescence analysis of S. pombe Asp1-GFP did not show a specific cytoplasmic localization [42], localization of the human Vip1 member PPIP5K1 was slightly enhanced at the plasma membrane [48]. Plasma membrane targeting of PPIP5K1 in NIH3T3 cells was increased dramatically following PtdIns3 kinase activation [48].
In fission yeast the switch from mono- to bipolar growth (NETO) is a complicated process that is regulated by a number of interwoven processes [10], [12]. These range from the correct positioning of landmark proteins by the MT cytoskeleton to the successful completion of S-phase and cytokinesis [64]–[66], [78]. We have shown previously that asp1D333A cells are able to correctly initiate growth at the old end after cytokinesis but cannot undergo NETO [7]. Correct selection of the first growth zone was also observed for the positioning of the first germtube of spores of an A. nidulans vlpA-deletion strain. However, similar to the S. pombe NETO event the positioning of the second growth zone (second germtube) was aberrant. Interestingly, plasmid-borne expression of A. nidulans VlpA1-574 (kinase domain) in a wild-type background also led to an alteration in the positioning of the second germ tube. We presume that VlpA1-574 expression increases intracellular inositol pyrophosphate levels and thus propose that fine-tuning of inositol pyrophosphate levels is required for the correct positioning of the second germ tube in A. nidulans.
In accordance with this hypothesis we observed that hyphal growth was also disturbed in U. maydis. Loss of UmAsp1 caused reduced and aberrant filamentous growth, including an increase of bipolar filaments. This is reminiscent of strains treated with MT-inhibitors or carrying mutations in MT-dependent motors such as kinesin-3 type Kin3, dynein Dyn1/2 or missing the RNA-binding protein Rrm4 involved in endosomal mRNA transport [35]–[37].
Interestingly, UmAsp1 levels decreased after the switch to hyphal growth indicating that alternative growth forms require a modulation of intracellular inositol pyrophosphate levels. Noteworthy, these filaments are arrested in the G2 cell cycle [79] suggesting a connection to cell cycle control.
A change in inositol pyrophosphate levels also regulates the environmentally controlled switch to an alternative growth form of S. pombe namely pseudohyphal invasive growth [7]. Here, Asp1 generated inositol pyrophosphates were essential for the switch to occur and increasing intracellular levels of these high energy molecules increased the cellular response. A similar scenario has been described recently for the regulation of the antiviral response by human Vip1 generated inositol pyrophosphates [49]. Ectopic expression of human Vip1 family members strongly increased the interferon response. Thus, modulation of the kinase activity of Vip1-like proteins might be a general mechanism of eukaryotic cells to react to extrinsic signals.
PCR-generated DNA fragments containing the S. pombe asp1+, asp1D333A, asp1H397A asp1364-920 ORFs, the S. cerevisiae VIP1-535 and the A. nidulans vlpA1-574 were cloned into E. coli expression vector pKM36 (a gift from Dr. K. Mölleken, Heinrich-Heine-Universität, Düsseldorf, Germany) to generate GST-tagged proteins. These proteins were expressed and purified from E.coli strain Rosetta (DEB) according to protocol (Sigma Aldrich). Protein concentration was determined using Bradford. Defined quantities of the Vip1-like proteins were used in an enzymatic reaction followed by PAGE analysis [54]. Intensity of IP7 bands was determined with ImageJ 1.44 (NIH). Determination of Km and Vmax: Enzymatic reactions with 2 µg of protein were carried out for 6 hrs using 0–300 µM IP6 substrate. The amount of IP7 generated per reaction was determined by quantifying the relevant IP7 band and converting this number using an IP6 calibration curve. IP6 was obtained from Sigma-Aldrich. Michaelis-Menten enzyme kinetics were calculated with GraphPad Prism6 (GraphPad Software, Inc.).
All strains used are listed in Table 3. S. pombe strains were grown and new strains were obtained as described [7]. A. nidulans was grown in supplemented minimal medium including 2% glucose, 2% glycerol or 2% threonine [80]. A. nidulans strain constructions were as described [81].To generate a N-terminal GFP fusion construct of VlpA a 900 bp fragment of vlpA (starting from ATG) was amplified from genomic A. nidulans DNA with appropriate primers. This AscI-PacI-digested PCR fragment was cloned into the corresponding sites of pCMB17apx (for N-terminal GFP fusion proteins of interest expressed under the control of alcA promoter, containing Neurospora crassa pyr4 as a selective marker) [82], generating pCoS105. The 1.5-kb promoter of vlpA was amplified from genomic DNA with appropriate primers and cloned into the corresponding sites of pCoS105, generating pCoS228. They were transformed into wild-type strain TN02A3. To express VlpA variant GFP-VlpA1-574 (contains the kinase domain) from the alcA promoter, the fragment of vlpA was amplified from genomic A. nidulans DNA with appropriate primers. This AscI-PacI-digested PCR fragment was cloned into the corresponding sites of pCMB17-pyroA (pyr-4 was replaced with pyroA in pCMB17apx), generating pCoS197, which was transformed into the wild-type strain TN02A3 and vlpA-deletion strain. Integration event was confirmed by PCR. vlpA was deleted via transformation of a deletion cassette (Program Project grant GM068087) into TN02A3 and the deletion confirmed by southern blotting. U. maydis strain constructions and growth of yeast like cells was performed according to published protocols [76]. Filamentous growth of AB33 and variants was induced by shifting 20 or 50 ml of exponentially growing cells (OD600 = 0.4–0.5) from complete medium (CM) to nitrate minimal medium each supplemented with 1% glucose. Cells were incubated at 28°C shaking at 200 rpm for 4 to 8 h prior to microscopy. For serial dilution patch tests, cells were pre-grown to OD600 = 0.5 before plating. For quantitative inhibition studies, cells were grown to OD600 = 0.5 and 300 µl were streaked out on a CM-plate. The filter paper present at the plate centre contained either 10 µl DMSO (solvent control) or 10 µl TBZ (10 mg/ml). After three days of growth at 28°C the radius of growth inhibition was measured.
asp1+, asp11-364 (plasmid p672), asp1H397A plasmids are derivatives of pJR2-3XL and have been described previously [7]. For the asp11-364+asp1365-920 containing plasmid, p672 was cut with SapI and a PCR generated DNA fragment containing the nmt1+ promoter followed by the DNA sequence encoding asp1365-920 inserted via homologous recombination in S. cerevisiae [83]. asp1R397A and asp1H807 were generated by directed mutagenesis using the QuikChangeII Site-Directed Mutagenesis Kit (Stragene) and after verification of sequence by sequence analysis cloned into pJR-3XL via S. cerevisiae homologous recombination. To determine expression of plasmid-borne asp1 variants, the appropriate asp1 containing DNA sequences were fused to gfp and expression of the fusion protein was determined by western blot analysis as has been described [7]. U. maydis Vlp1G expression was determined via western blot analysis as has been described [36].
For imaging of living S. pombe cells, cells were pre-grown in minimal medium at 25°C or 30°C and slides were prepared by mounting cells on agarose pads as described in [84]. Images were obtained at room temperature using a Zeiss Spinning Disc confocal microscope, equipped with a Yokogawa CSU-X1 unit and a MRm Camera. Slides were imaged using AxioVision software. Images shown are maximum intensity projections of 10–25 z-slices of 0.24–0.5 µm distance. For measurement of MT dynamics, strains expressing GFP-Atb2 [57] under control of the nmt81 promoter were pre-grown under promoter-derepressing conditions for at least 48 hrs. For technical reasons, we used the nmt81::gfp-atb2+ construct, as this facilitated the measurement of the sometimes faint MTs of the asp1D333A strain. Time-lapse images were acquired in 5–10 sec intervals.
For live-cell imaging of A.nidulans germlings and young hyphae, cells were grown on coverslips in 0.5 ml of Supplemented minimal media with 2% glycerol (de-repression of the alcA promoter, moderate induction). Cells were incubated at 30°C overnight/1 day. Coverslips were mounted on slide glass. Tempcontrol mini (Pepcon) was used for a constant temperature of the slide glass during microscopy. Images were captured using an Axiophot microscope using a Planapochromatic 63 times oil immersion objective lens, the Zeiss AxioCam MRM camera and the HBO103 mercury arc lamp (Osram) or HXP 120 (Zeiss, Jena, Germany). Images were collected and analyzed with the AxioVision system (Zeiss). Signal intensity was quantified with ImageJ software.
Live cell imaging of U. maydis was performed according to published protocols [36]. Microscope and camera were controlled by MetaMorph (Version 7.7.0.0, Molecular Devices, Seattle, IL, USA). The same software was used for measurements and image processing including the adjustment of brightness and contrast. MT bundles were visualized with a 63× Planapochromat (NA 1.4, Zeiss) or 100× α-Planapochromat (NA 1.46, Zeiss) in combination with a HXP lamp or laser illumination (488 nm), respectively. Z- stacks were composed of 38 planes with 270 nm spacing (63×) and 66 planes with 240 nm spacing (100×). Exposure time was 100 ms. Deconvolution was performed with Fiji. A theoretical PSF was determined with the diffraction PSF 3D plugin and images were generated using the Deconvolve 3D plugin [85], [86]. 3D movies were generated with MetaMorph. To determine the number of MT bundles z-stacks were collapsed to a maximum projection and after cytoplasmic background subtraction the number of bundles was determined. For determination of MT bundle intensity the maximum values of a longitudinal line scan (Fig. 7C) were plotted over distance. Each value from the x-axes was included in a whisker diagram (Fig. 7E) showing the median and range of fluorescent MT bundles (n = 10 cells for wild-type and Umasp1Δ, respectively). Fluorescence micrographs of Umasp1-GFP were acquired with 500 ms exposure time in a single plane. Before determining average cytoplasmic fluorescence images were background subtracted. For measurement of MT growth (Fig. 7F) strains expressing GFP-Tub1 were used. Z- stacks were composed three planes with 1 µm spacing (100× objective). Exposure time was 100 ms. For measurement of MT residence time (Fig. 7G) strains expressing Peb1-GFP were used. Z- stacks were composed of 5 planes with 800 nm spacing (100× objective). Exposure time was 100 ms. Statistical analysis was done with Prism5 (Graphpad).
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10.1371/journal.pbio.2005258 | The brown algal mode of tip growth: Keeping stress under control | Tip growth has been studied in pollen tubes, root hairs, and fungal and oomycete hyphae and is the most widely distributed unidirectional growth process on the planet. It ensures spatial colonization, nutrient predation, fertilization, and symbiosis with growth speeds of up to 800 μm h−1. Although turgor-driven growth is intuitively conceivable, a closer examination of the physical processes at work in tip growth raises a paradox: growth occurs where biophysical forces are low, because of the increase in curvature in the tip. All tip-growing cells studied so far rely on the modulation of cell wall extensibility via the polarized excretion of cell wall–loosening compounds at the tip. Here, we used a series of quantitative measurements at the cellular level and a biophysical simulation approach to show that the brown alga Ectocarpus has an original tip-growth mechanism. In this alga, the establishment of a steep gradient in cell wall thickness can compensate for the variation in tip curvature, thereby modulating wall stress within the tip cell. Bootstrap analyses support the robustness of the process, and experiments with fluorescence recovery after photobleaching (FRAP) confirmed the active vesicle trafficking in the shanks of the apical cell, as inferred from the model. In response to auxin, biophysical measurements change in agreement with the model. Although we cannot strictly exclude the involvement of a gradient in mechanical properties in Ectocarpus morphogenesis, the viscoplastic model of cell wall mechanics strongly suggests that brown algae have evolved an alternative strategy of tip growth. This strategy is largely based on the control of cell wall thickness rather than fluctuations in cell wall mechanical properties.
| Tip growth is known in organisms with filament-like structures, such as fungi (e.g., hyphae), plants (e.g., root hairs, moss protonemata), and algae (e.g., filamentous thalli). The driving force for growth in these organisms is the difference in osmotic pressure (turgor) between the inside of the cell and the external medium, a force contained by the cell wall. Physical laws imply that the higher the curvature of the cell, the lower the pressure (stress) perceived by the cell wall. Paradoxically, growth takes place at the dome-shaped cell apex, which has high curvature. Tip-growing cells studied so far (mainly plants) compensate the low wall stress in the apex by chemically loosening their cell wall. We studied Ectocarpus, which is a representative of brown algae, a eukaryotic branch very divergent from land plants, fungi, and green algae. We carried out a series of quantitative measurements at the cellular level and showed that the cell wall is thinner at the tip (36 nm) than on the shanks (170 to 500 nm). Using a viscoplastic model of cell wall expansion, we showed that the cell wall thickness gradient, together with dome curvature, generates sufficient wall stress to account for the observed growth pattern.
| The prostrate filaments of the alga Ectocarpus develop via tip growth [28]. Prior to identifying the underlying mechanisms, a series of additional biophysical information was collected. The cell wall dye calcofluor-white was used in pulse-chase experiments to measure the growing region more precisely. The stain was localized in the first 3 μm distal from the tip of the cell, corresponding to roughly half of the dome (Fig 2A). To assess the direction of growth at the local level, 0.2 μm–diameter fluorescent beads (microspheres) were loaded on the surface of the cell, and their displacement during growth was followed using time-lapse microscopy. This method was initially developed for other plant cell types [30] and recently optimized for Ectocarpus [31]. Bead trajectories were drawn, and the angles these trajectories made with the cell contour were calculated. Statistical analyses of the distribution of these angles showed a moderate deviation (relative mean difference < 10%) between the fluorescent marker trajectory and an orthogonal displacement pattern. Moreover, linear regression exhibited no systematic dependence of the angle on meridional abscissa (Pearson correlation coefficient r = −0.03), indicating that growth can be considered orthogonal to the cell surface in the dome independently of the position along the meridional abscissa (Fig 2B and S1 Fig).
In plants, turgor is the essential force for growth, whatever the mechanical properties of the wall. Although it exerts the same pressure throughout the cell wall, the resulting wall stress σe perceived locally in the cell wall varies because of fluctuation in local measurements (see below). The calculation of wall stress is independent of the mechanical features of the cell wall (e.g., elastic, viscoplastic, plastic) and therefore independent of the biophysical model used subsequently. In addition to turgor, wall stress depends on the curvature of cell κ and on the thickness of the cell wall δ at each position of the cell surface (Fig 3A). Wall stress is partitioned into three directions: meridional (s), circumferential (θ), and normal (n) (see equation S2 in S1 Text). Because the cell wall is thin compared to cell size, the normal component of the wall stress is considered negligible compared to the two others [32].
To calculate the wall stress at many different points in an Ectocarpus apical cell, we obtained quantitative data for three components: (1) turgor (P), (2) curvature (κ) of the cell surface, and (3) cell wall thickness (δ). First, turgor in the apical cells was measured using the nonintrusive technique of incipient plasmolysis [34] on >100 cells for each of the 10 solutions of different osmolarities used in the experiment (Fig 4A). The value was subsequently corrected to take into account cell shrinking according to the protocol described in [34] (S2 Data). The calculated apical cell turgor was 0.495 MPa, which is about 5 times the atmospheric pressure and is on the same order of magnitude as tip-growing cells from other eukaryotic groups, including the pollen tube (0.1–0.4 MPa, average at 0.2 MPa; [5]).
The second component of wall stress is the curvature of the cell surface (κ). To measure κ, the contour of Ectocarpus apical cells was first drawn manually. Then from 17 individual cell contours (S2 Fig), both the meridional and the circumferential curvatures as well as an average cell contour were calculated (Fig 4B). The same procedure was used for the tobacco pollen-tube contour. Compared with the pollen tube, the Ectocarpus apical cell displayed a sharper tip and a higher circumferential curvature on the shanks because of its smaller radius.
The third component of wall stress is cell wall thickness (δ). Staining of Ectocarpus filaments with calcofluor-white, which labels cellulose (1–4) and callose (1–3)-beta-D-glucans [35], displayed a very clear gradient in thickness from the tip to the shanks of the apical cell (Fig 5A, also visible in 3D reconstruction from confocal microscopy). However, cellulose microfibrils are only a minor component of the brown algal cell wall (8% maximum dry weight), because they are immersed in a more abundant matrix of polysaccharides (45% dry weight) made of alginates (linear polymers of β-[1→ 4]-D-mannuronate and α-[1→ 4]-L-guluronate) and fucans (α-L-fucosyl residues) [23,36]. Therefore, we prepared longitudinal sections of apical cells. First, 300 nm–thick serial sections showed a gradient in thickness increasing from the tip to the shanks in the most meridional sections (Fig 5B, middle section), whereas the thickness appeared even throughout the cell in the most tangential sections (Fig 5B, top and bottom sections). Measurements of cell wall thickness across 70 nm–thick serial sections observed with transmission electron microscopy (TEM) further supported the presence of a cell wall thickness gradient (Fig 5C). Overall, 2,500 measurements were corrected (see Materials and methods) and plotted as a function of s. The distribution depicted a gradient that could be modeled as a Pearson-like function characterized by the lowest value δmin = 36.2 nm at the tip (s = 0), the asymptotic maximum value δmax = 591 nm, and a midpoint at s1/2 = 16.8 μm (Fig 5C). Cell wall thickness at the distal part of the dome (s = 8 μm) was 169 nm, i.e., 4.7 times the thickness at the tip (Fig 5C, close-up).
The establishment of a cell wall thickness gradient contrasts with most tip-growing cells from other eukaryote groups [13,38], in which cell wall thickness is either constant (e.g., 250 nm in pollen tube [39]) or higher at the tip (e.g., oscillating growth in the pollen tube [40,41]).
Fig 6A, 6B and 6C show a diagram of these biophysical factors in both Ectocarpus and pollen tube apices.
Using this set of biological data, wall stress was calculated in both the meridional (σs) and the circumferential (σθ) directions, which ultimately allowed calculating the overall wall stress σe (Fig 3A; equation S3). Although σe fluctuates between 2.5 and 3.5 MPa (with the lowest value in the dome) in the pollen tube, it reaches a maximum of 25.6 MPa in the Ectocarpus tip and decreases distally, reaching values similar to that in the pollen tube 70 μm away from the tip (Figs 6D and 7A). This stress value in the dome of Ectocarpus apical cells is remarkably high compared to other tip-growing cells, which, moreover, show the opposite stress gradient, increasing from tip to shank.
Using the model, we tested the impact of the cell wall thickness gradient on tip shapes and growth rates. Steeper or gentler cell wall thickness gradients were sufficient to substantially alter the typical Ectocarpus cell shape and growth rate, suggesting that the cell wall thickness gradient must be tightly regulated in vivo (S6 Fig, central column; S4 Movie). However, cells display some significant variation in cell wall thickness (Fig 5C). In vivo observation of Ectocarpus tip growth also showed variability in growth rate and in cell shape (e.g., displayed in S2 Fig), which may be due to transitory variation in cell wall thickness. The extremely low growth rate of this species can easily allow the activation of regulatory mechanisms that adjust the cell wall thickness gradient through modifications in cell wall biosynthesis.
We performed the same experiments on cells with three different initial cell shapes (flat, typical Ectocarpus-like, and sharp). Using the Ectocarpus cell wall thickness gradient “Normal” resulted in convergence of the resulting shapes to the typical Ectocarpus shape (S6 Fig, middle row; S5 Movie). Therefore, the cell wall thickness gradient may also govern the tip resilience to deformation so that initial cell shape can be recovered after transient deformation (e.g., due to an accident during growth). When simulations used a modified cell wall thickness gradient (“Steep” or “Gentle”) on these different initial cell shapes, all cells grew and converged to the same final shape specific to the given thickness gradient (S6 Fig, top and bottom rows; S6 Movie). These simulations supplement those by Dumais and colleagues [33], who explored various gradients in Φ and σy in a context where cell wall thickness was constant.
The preponderant role of the cell wall thickness gradient in the control of tip growth raises the question of how this gradient is established and maintained. Calculations considering the cell wall extension rate and the maintenance of the cell wall thickness gradient during growth allowed inference of the level of cell wall material delivery and/or biosynthesis along the cell. According to this calculation, the overall delivery rate of cell wall material and/or synthesis in the pollen tube is much higher than in Ectocarpus (note the different scales of the x-axis in Fig 9A, left, top versus bottom). The maximum culminates 3.0 μm away from the most distal position and drops to nil in the tube shanks (Fig 9A, top left). This calculation is in agreement with former in situ observations using FM4-64 that labels both endocytic and exocytic vesicles [52–54] (Fig 9A, top middle). This pattern is also in agreement with TEM observations in pollen tubes [55] and in other tip-growing walled cells where vesicle trafficking is concentrated in the most distal part of the tip (root hairs and green algae reviewed in [56], ascomycetes [13]).
This mechanism contrasts with Ectocarpus, in which the cell wall flux is predicted to be significant in the shanks of the cell (Fig 9A, bottom left). How the cell wall is constructed in brown algae is still largely unknown. Cellulose may be synthesized from cytosolic uridine diphosphate (UDP) glucose via linear complexes of cellulose synthases localized in the plasma membrane, where they elongate cellulose microfibrils into the cell wall [57]. It is still unknown how the other main cell wall components (alginates and fucans) reach the cell wall at the tip of the Ectocarpus apical cell, but the current delivery mode is thought to be through Golgi-derived vesicles [58]. Therefore, we used FM4-64 to investigate the pattern of vesicle trafficking in Ectocarpus. FM4-64 displayed an homogeneous spatial pattern all along the cell, with no specific vesicle localization (Fig 9A, bottom middle). TEM observations provide further support because vesicles were never concentrated in any of the meridional sections of the dome of an apical cell (bottom right). Instead, chloroplasts and chloroplastic endoplasmic reticulum (CER) involved in the production of photosynthates [14] were observed in the dome as well as in the shanks of the cell (Fig 9A, bottom right). Therefore, observations of biological activity are compatible with the establishment and maintenance of a cell wall thickness gradient at an extremely slow rate, where CER can deliver the main components of the cell wall all throughout the cell with nevertheless the highest rate in the dome. To confirm this initial observation, we performed fluorescence recovery after photobleaching (FRAP) assays on Ectocarpus apical cells. We compared the fluorescence signal recovery dynamics in 5 different zones in the dome and shanks of the cell (Fig 9B, left). Considering the increase in the fluorescence signal over time, we used the normalized slope at t = 0 as a proxy for the intensity of membrane replacement by exocytosis, potentially reflecting cell wall–building activity (S7 Fig). The results showed that the exocytosis rate (Fig 9B right) reflects the cell wall flux inferred from the model (Fig 9A, bottom left). Our FRAP experiments support the prediction of the highest exocytosis activity at the base of the dome (zone C at s ≈ 5 μm) and significant activity in the shanks (zone E ≈ 10 μm from the dome end). Altogether, FRAP and TEM observations are compatible with the calculation of the cell wall flux inferred from the model.
Using a combination of serial longitudinal sections observed by TEM and optical microscopy, we first showed that Ectocarpus displays a gradient of cell wall thickness in its apical cells. Reports indicate that cells from other organisms display a cell wall thickness gradient. However, in most cases, accurate measurements could not be obtained from the methods employed such as epifluorescence microscopy of plant trichomes stained with propidium iodide [59] or bright-field microscopy of entire ghost cell walls of healing tips of the green alga Acetabularia [60]). Recently, Davì and colleagues [61] developed a technique on fission yeast enabling a resolution of 30 nm in living cells. However, this resolution is in the lower limit of Ectocarpus cell wall thickness, and TEM therefore appeared to be the most reliable technique. The accuracy of the TEM technique revealed a very steep thickness gradient ranging from 36 nm at the very tip to 169 nm at the base of the dome, corresponding to an average slope of 1.6%. No such gradient has been reported in the growth zone of other organisms. In the diffusely growing trichome of Arabidopsis, the cell wall thickness increases in the cell with a slope of 0.3% [59]. In the apical cell of Neurospora, cell wall thickness is constant in the dome and gradually increases along the shanks [62], a pattern similar to that observed in fission yeast [61].
In terms of biophysics, this gradient in cell wall thickness resulted de facto in a decrease in the stress from the shanks to the tip. The biological measurements specific to the Ectocarpus apical cell (turgor, dome geometry, and cell wall thickness) were integrated in the viscoplastic model initially proposed by Lockhart and further developed for tip growth by Dumais and colleagues [33]. The observed cell wall thickness gradient quantitatively compensated for the reduction of stress with an increase in curvature from the shanks to the tip. After adjusting the plasticity values, the model was able to achieve self-similar growth at the speed observed in vivo. Regarding the cell wall mechanical properties, the model inferred two main differences with the pollen tube. First, the extensibility Φ and the yield threshold σy remained constant throughout the Ectocarpus cell, in contrast to the pollen tube models in which the constant thickness of the cell wall necessarily requires modification of the cell wall mechanical properties to allow growth [63]. Plotting σy and Φ together with cell wall thickness δ clearly illustrates the different strategies developed by Ectocarpus and the pollen tube (Fig 7D): in Ectocarpus, δ is the only varying factor, but in the pollen tube, both σy and Φ vary, whereas δ remains constant. Using a Lab-on-a-Chip platform, Shamsudhin and colleagues [64] confirmed that the pollen tube displays an apparently increasing elastic modulus from the tip to the shanks, which is correlated with the presence of methyl-esterified pectins [65].
Secondly, compared with the pollen tube, the overall value of strain rate is approximately 100 times lower, but stress is approximately 10 times higher in Ectocarpus (Fig 6), suggesting that the Ectocarpus cell wall is generally more resilient to yielding during growth. Experimental work is clearly still needed to refine the values of the yield threshold and extensibility, but our calculation of wall stress offers a solid basis on which their order of magnitude can be inferred. Nano-indentation of Ectocarpus cell wall produced values of elastic modulus much lower (approximately 1–4 MPa, [27]) than those reported in the pollen tube (approximately 20–400 MPa, [64]). However, the different nano-indentation experimental procedures used in these studies (depth of indentation, shape of the indenter, osmotic conditions, physical model) make comparisons questionable [47]. Nevertheless, the elastic modulus assessed using nano-indentation and the cell wall mechanical properties inferred from the growth model together suggest that Ectocarpus is more elastic but less prone to expansion during growth than the pollen tube. The distinction between cell wall elasticity and growth has already been made in the green alga Chara [66] and has since been reported in other plant cells (reviewed in [67]). The inverse relationship observed in Ectocarpus is fairly compatible with the dual role of the cell wall in brown algae, i.e., coping with frequent environmental changes in osmotic pressure (tides), which requires a high elasticity, and resistance to yielding in the face of high wall stress due to the thin cell wall and high turgor. The lack of a functional relationship between intrinsic elasticity and cell wall extensibility has already been reported in land plants [67]. Likewise, the presence of stiff or soft polysaccharides—as assessed in vitro—does not correlate with the expansion of the cell wall during plant growth (e.g., [68,69] and reviewed extensively in [67]), nor apparently during growth in brown algae [70].
Another puzzling question is how Ectocarpus controls the cell wall thickness gradient necessary to ensure the maintenance of cell shape. It is unknown whether cell wall thickness fluctuates during growth, as recently reported in the fission yeast [61], but this fluctuation may account for the variation in cell shape and growth rate observed in living organisms. Nevertheless, the gradient in thickness requires regulation of cell wall biosynthesis, which in brown algae like in land plants involves both in muro cellulose synthesis and the delivery of other components (fucans and alginates in brown algae) through vesicle trafficking (Golgi and flat cisternae respectively in Fucales; [58]). FRAP data showed that the highest exocytosis activity was localized in the basal region of the dome, just before the cell adopts its cylindrical shape. This coincides with the highest cell wall flux computed from the model and with the pattern described in the pollen tube [71,72]. How exocytosis vesicles are targeted to these positions is unknown. In yeast and land plants, mechanosensors localized in muro control cell wall biosynthesis enzymes to modulate cell wall thickness and respond to cell wall damage [60,73]. The Ectocarpus genome codes for several mechanosensor proteins (integrins, transmembrane proteins containing the WSC carbohydrate-binding domain [25,74]), and these proteins may well be key regulatory factors in this process.
The palette of tip-growing strategies among species is not restricted to the control of cell wall thickness and of cell wall mechanical properties through pectin methyl-esterification. Other molecular mechanisms, including pectate distortion cycle in Chara [75], secretion of glucanases and chitinases in fungi [13], and intussusception in prokaryotes [76], have been proposed to account for the differential cell wall mechanics along the cell. Therefore, distinct key cell wall biophysical factors, and potentially a combination of them [61], appear to have been selected during evolution to achieve cell wall growth. The evolutionary history of brown algae is short (approximately 250 My) and distinct from that of land plants. The marine environment characterized by high physical pressure and ionic concentrations, low gravitational forces, and high drag forces due to tremendous sea currents may have promoted the development of specific, singular strategies in these peculiar organisms. In the case of tip growth, although we cannot formally exclude the possibility that a gradient of wall mechanical properties exists and contributes to morphogenesis in Ectocarpus, our results suggest that this organism has favored a singular approach based on cell wall thickness and hence on control of wall stress. The question remains whether the particular features of this organism, including its slow growth, make the control of cell wall thickness more efficient than the control of cell wall mechanical properties.
Parthenosporophyte filaments of Ectocarpus sp. (CCAP accession 1310⁄4) were routinely cultivated in natural seawater (NSW) as described in [77]. For microscopic observations and time-lapse experiments, early parthenosporophytes were obtained from gamete germination on sterile coverslips or glass-bottomed petri dishes.
Ectocarpus prostrate filaments were treated with 1, 10, and 50 μM IAA (Sigma-Aldrich I3750) prepared in 2, 20, and 100 μM NaOH, respectively (final concentration). Growth rates were measured for each concentration 24 h post treatment (n = 10), using NSW supplemented with 2 μM NaOH as a control. Turgor was measured in 1 μM IAA using 2 μM NaOH as the control (see Measurement of turgor in the apical cell and correction for details).
Ectocarpus filaments were immersed for 1 min in a range of sucrose concentrations (diluted in NSW), and the proportion of plasmolyzed apical cells was measured by counting apical cells (n > 100) under an optical microscope. The rate of plasmolysis was plotted against external osmolarity (ce). The limit plasmolysis (cpl) corresponds to the value of ce at which 50% of apical cells were plasmolyzed. The mean cpl value was calculated from 3 independent experiments. Solution osmolarities were measured with an osmometer (Osmometer Automatic, Löser, Germany). Because the cell wall of Ectocarpus is partly elastic, plasmolyzed cells have a reduced volume that must be taken into account to calculate the real internal osmolarity (ci) and thus the real internal turgor (P). To do so, the coefficient of apical cell volume shrinking (x, equal to the ratio of the cell volume upon plasmolysis to the cell volume in normal growth conditions) was measured on apical cells (n = 9), and the corrected internal osmolarity was calculated as ci = x. Cpl. The difference between internal and external osmolarities is Δc = ci − 1,100 with the seawater osmolarity = 1,100 mOsm L−1, and the turgor is P=ci–ce410, in MPa.
Apical cell contours were drawn manually from confocal images of meridional plans of apical cells immersed in NSW. Similar procedure was followed for tobacco pollen tubes from photos given by Gleb Grebnev (B. Kost’s lab, Erlangen Univ., Germany). We devised a Python 3 script to compute the average contour for a series of images and used it on Ectocarpus (n = 17; S2 Fig) and tobacco pollen tubes. The program starts with a hand-drawn contour for each cell, from which it computes a smoothed cubic spline curve. A set of equidistant points (we used a point-to-point distance of 50 nm) are extracted from the spline, and the meridional curvature κs is computed at each point (S2 Fig). To obtain average symmetrical curvatures, a pair of windows starting from the tip point and sliding in both directions was used (window width = 200 nm, sliding step = 50 nm). The discrete values of the κs = f(s) function were used to iteratively compute the position of cell wall point coordinates as values of x (the axial abscissa) and r (the distance to the axis), together with the meridional abscissa s, the curvatures κs and κθ, and φ the angle between the axis and the normal to the cell wall. In particular, the circular symmetry of the dome imposes at the tip (where s = 0), that κθ = κs and thus σθ = σs, whereas in the cylindrical part of the cell κs = 0 and thus σθ = 2σs.
Ectocarpus filaments were prepared for TEM. Filaments grown on sterile glass slides were fixed with 4% glutaraldehyde and 0.25 M sucrose at room temperature and washed with 0.2 M sodium cacodylate buffer containing graded concentrations of sucrose. The samples were post-fixed in 1.5% osmium tetroxide, dehydrated with a gradient of ethanol concentrations, and embedded in Epon-filled BEEM capsules placed on the top of the algal culture. Polymerization was performed first overnight at 37 °C and then left for 2 d at 60 °C. Ultrathin serial sections were cut tangentially to the surface of the capsule with a diamond knife (ultramicrotome) and were mounted on copper grids or glass slides. Two types of sections were produced. Serial sections (300 nm thick) were stained with toluidine blue to show the main cellular structures, including the cell wall, and mounted on glass slides. Sections (70 nm thick) were stained with 2% uranyl acetate for 10 min and 2% lead citrate for 3 min, mounted on copper grids (Formvar 400 mesh; Electron Microscopy Science), and examined with a Jeol 1400 transmission electron microscope. A compilation of the sections for the 15 cells is shown in S3 Fig. Original photos are available at https://www.ebi.ac.uk/biostudies/studies/S-BSST215.
From TEM pictures obtained on fixed Ectocarpus apical cells, only longitudinal sections with the thinnest walls were considered to avoid bias due to misaligned sections (all images are shown in S3 Fig). Measurements were carried out every 386 nm along 15 different cells, at the meridional abscissa from the tip (s = 0) up to s = ±70 μm using Fiji image analysis software. Altogether, 2,500 measured values of apparent thickness w were corrected, making the assumption that actual cell radius was R = 3.27 μm (but was seen as apparent radius a) and applying the following formula: δ=R-a2+R2-(a+w)2 (S4 Data). As askew sectioning results in cell walls looking thicker, the only remaining bias is expected to cause overestimation of the thickness at the tip.
Corrected values δ for cell wall thickness were plotted as a function of the position s along the cell. As the relationship δ = f(s) displayed the aspect of an inverted bell, we designed 3 functions with this shape, derived from classical functions, to match them with the experimental values—(1) “Gauss”: δ=δmax-(δmax-δmin)exp(-(s/s1/2)2log(2)); (2) “Lorentz”: δ=δmax-(δmax-δmin)(1+(s/s1/2)2)-1; and (3) “Pearson”: δ=δmax-(δmax-δmin)(1+3(s/s1/2)2)-1/2. The values δmin, δmax, and s1/2 were adjusted for each of these functions, with a respective residual standard error of 0.08, 0.05, and 0.04. Therefore, we used the Pearson model with its optimized values δmin = 36.2 nm, δmax = 591 nm, and s1/2 = 16.81 μm for further modeling (Fig 5C).
Ectocarpus cells were boiled twice in 1% SDS, 0.1 M EDTA and then treated with a solution of 0.5 M KOH at 100 °C. Pellet was rinsed extensively with MilliQ water and dried on a glass slide. Imaging was performed on dried samples. A Veeco Bioscope catalyst atomic force microscope coupled with a Zeiss inverted fluorescent microscope was used for imaging. RTESP probes (Bruker) were used in Scanasyst mode.
The protocol was adapted from [30] and is described in detail in [31]. Young sporophyte filaments grown in glass-bottom petri dishes were covered with sonicated 0.1% (w:v NSW) of FluoSpheres amine, 0.2 μm, red (F8763, Molecular Probes), washed with NSW and mounted under a TCS SP5 AOBS inverted confocal microscope (Leica) controlled by the LASAF v2.2.1 software (Leica). The growth of 25 apical cells growing parallel to the glass surface was monitored, and bright-field and fluorescent pictures of median planes for each apical cell were acquired at several time points. Cell wall contours were hand-drawn on time-lapse images using GIMP, together with their respective indicator points. The position of the extreme tip (s = 0) was fixed for each meridional contour, and the drawing of cell contours and microsphere positions were aligned during the time course by using steady microspheres attached on fixed positions. A spline was adjusted on each contour and on each series of indicator points. The angle at each possible intersection between these trajectories and the cell contour splines were computed, making use of their first derivatives. Further analysis performed using R [78] consisted of (1) determining the distribution of angles, their mean, and standard deviation and (2) testing the hypothesis of dependence between the angle and the meridional abscissa. From the 156 measured angles between the tangent to cell wall and the trajectory, we computed the mean value m = 1.71 = π/1.83 radian (or π/2 − 9.16%) and the standard deviation s = 0.52 = π/6.09 radian. To test independence between the angle and the position in the dome, we computed the Pearson correlation coefficient between the angle and the absolute value of the meridional abscissa. It was r = −0.031.
Staining of Ectocarpus filaments with calcofluor-white was carried out as described in [28].
FM4-64FX (F34653, Invitrogen) stock solution was diluted to 385 μM in DMSO and then diluted to 7.7 μM in NSW. Coverslips with Ectocarpus filaments were immersed in 50 μL of 7.7 μM cold FM4-64FX on ice and immediately mounted on a confocal microscope. Endocytosis and further trafficking of the fluorochrome was followed for 1 h at room temperature. The fluorochrome was excited with a 561 nm neon laser, and emission was observed with a 580–630 nm PMT.
For the FRAP assay, filaments were stained with 100 μM FM4-64FX for 10 min at 4 °C and rinsed 4 times with cold, fresh seawater. Photobleaching was performed on about 25 μm (s) along the cell from the tip, and recovery was monitored using an inverted Nikon Ti Eclipse Eclipse-E microscope coupled with a Spinning Disk (Yokogawa, CSU-X1-A1) and a FRAP module (Roper Scientifics, ILAS). Images were captured with a 100x APO TIRF objective (Nikon, NA 1.49) and an sCMOS camera (Photometrics, Prime 95B). For the detection of the FM4-64FX stained samples, we used a 488 nm laser (Vortran, 150 mW) for the excitation and the bleaching steps and collected the fluorescence through a 607/36 bandpass filter (Semrock). Image acquisition using the MetaMorph software 7.7 (Molecular Devices) was as follows: 1 image/s, displaying 6 images before bleaching, 1 image at the precise time of bleaching, 50 images during the recovery phase, for a total of 57 images by cell.
Images for one given cell were processed as a stack using Fiji [79] and R [78]. For each time point t (with bleaching occurring at t = 0), the background signal Z(t) was averaged from 4 separate square regions of approximately 1 μm2; the spontaneous fluorescence decrease was estimated by monitoring the signal U(t) in an unbleached region; the local signal was recorded in regions A–E as defined in Fig 9B. Note that all zones, including E, are sufficiently far from the edge of the photobleached zone to be unaffected by homogenization due to membrane lateral flux in the considered timescale. Following [80], the corrected signal for region A (and similarly for regions B–E) was computed as:
Ac(t)=(A(t)-Z(t)-(A(0)-Z(0)))U(0)-Z(0)U(t)-Z(t).
The recovery activity was estimated by matching the measured Ac(t) values to the function Y(t) = Y(0) + α(1 − exp(−t/τ)), where Y(0) and α and τ are free parameters. We computed the normalized slope at t = 0 as (1/α)(dAc/dt)(0) = 1/τ, for 9 observations in each of the 5 (A–E) zones selected (see S7 Fig).
The meridional contour of 6 tobacco pollen tube apices were traced from photos given by Gleb Grebnev (B. Kost’s group, Erlangen University, Germany), and the curvature was computed as described for Ectocarpus cells. Turgor and cell wall thickness were obtained from the literature [38]. In the absence of precise determination of their respective values, we derived a working hypothesis from previous literature reports showing that variations of Φ and σy occur simultaneously in opposite directions [49–51]. This intuitive relationship is consistent with molecular models of the cell wall [50]. Given that our model can derive the value of the expected strain rate ε˙* from other values (S1 Text), we propose to partition this product equally between its two members. Thus, we computed Φ=ε˙* and (σe-σy)=ε˙*, leading to σy=σe-ε˙*. These arbitrary values were useful for giving an example of what could be a possible state (Fig 6F and 6G; Fig 7B right) and performing simulations.
Programs developed as part of this work were written in Python 3.6 [81], making use of NumPy [82] and Matplotlib [83] libraries, in a GNOME-Ubuntu environment (laptop and workstation). The source code is available at https://github.com/BernardBilloud/TipGrowth.
Modeling is described in S1 Text. The simulation program performed a simple simulation with graphic output or an array of simulations within a range of Φ and σy values. The input was a list of cell wall point coordinates and values from, for instance, computations of average contours (ad hoc generated data were also used for simulations starting with geometrically designed profiles). For each point, the stress was computed from turgor, curvature, and cell wall thickness values. Then, using Φ and σy, the strain rate and the normal velocity were computed. The velocity and displacement direction (normal to the cell wall) gave the new position of the point, calibrated for a tip growth of 1 nm at each step. After computing new positions for all points, the program designed a cubic spline (without smoothing) from which a new sample of points was extracted, thus keeping a constant distance between points throughout the simulation. Accuracy of the simulation was evaluated by averaging point-to-point distances between the simulated profile and the initial profile translated at the expected speed. Values of Φ and σy were progressively optimized using a steepest-descent approach. As starting values, we used the coefficients of the linear model derived from the points (σe,Φ(σe − σy)) for which Φ(σe − σy)) > 1: Φ = 2.5 × 10−3 min−1 MPa−1 and σy = 11 MPa. These values were used to simulate growth up to 25 μm, and divergence with the expected behavior was evaluated by comparing them to the initial points translated by 25 μm in the axial direction. As a numerical value, we took the logarithm of rD (residual distance), which was the weighted average point-to-point distance, where the weight was exp(s2log(2)), to maintain the dome shape. Optimized values Φ = 2.51 × 10−3 min−1 MPa−1 and σy = 11.18 MPa gave a simulation with a log(rD) of −3.0. As a comparison, the mean log(rD) between the initial average contour and the 17 experimental contours used to build it was −4.41, with a standard deviation of 0.35.
To assess the robustness of the results, we performed a bootstrap analysis. Three thousand samples were constructed by drawing with replacement 17 cell contours and 15 cell wall TEM images out of their respective datasets. For each sample, the average contour and the cell wall gradient were computed as explained above. The stress σe and expected strain rate ε˙* were computed as functions of the meridional abscissa s. To test consistency with the model, the (σe;ε˙*) points were fitted a Lockhart equation by adjusting values Φ and σy and computing the Pearson correlation coefficient (r2) for the increasing part of the function, i.e., σe > σy;ε˙*>0.
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10.1371/journal.pcbi.1005938 | Across-subjects classification of stimulus modality from human MEG high frequency activity | Single-trial analyses have the potential to uncover meaningful brain dynamics that are obscured when averaging across trials. However, low signal-to-noise ratio (SNR) can impede the use of single-trial analyses and decoding methods. In this study, we investigate the applicability of a single-trial approach to decode stimulus modality from magnetoencephalographic (MEG) high frequency activity. In order to classify the auditory versus visual presentation of words, we combine beamformer source reconstruction with the random forest classification method. To enable group level inference, the classification is embedded in an across-subjects framework. We show that single-trial gamma SNR allows for good classification performance (accuracy across subjects: 66.44%). This implies that the characteristics of high frequency activity have a high consistency across trials and subjects. The random forest classifier assigned informational value to activity in both auditory and visual cortex with high spatial specificity. Across time, gamma power was most informative during stimulus presentation. Among all frequency bands, the 75 Hz to 95 Hz band was the most informative frequency band in visual as well as in auditory areas. Especially in visual areas, a broad range of gamma frequencies (55 Hz to 125 Hz) contributed to the successful classification. Thus, we demonstrate the feasibility of single-trial approaches for decoding the stimulus modality across subjects from high frequency activity and describe the discriminative gamma activity in time, frequency, and space.
| Averaging brain activity across trials is a powerful way to increase signal-to-noise ratio in MEG data. This approach, however, potentially obscures meaningful brain dynamics that unfold on the single-trial level. Single-trial analyses have been successfully applied to time domain or low frequency oscillatory activity; its application to MEG high frequency activity is hindered by the low amplitude of these signals. In the present study, we show that stimulus modality (visual versus auditory presentation of words) can successfully be decoded from single-trial MEG high frequency activity by combining source reconstruction with a random forest classification algorithm. This approach reveals patterns of activity above 75 Hz in both visual and auditory cortex, highlighting the importance of high frequency activity for the processing of domain-specific stimuli. Thereby, our results extend prior findings by revealing high-frequency activity in auditory cortex related to auditory word stimuli in MEG data. The adopted across-subjects framework furthermore suggests a high inter-individual consistency in the high frequency activity patterns.
| Since the first reports of cortical gamma band activity [1, 2], these high frequency responses have been linked to a plethora of brain processes and mental tasks, for example visual perception and processing [3–6], auditory perception [7, 8] or memory [9–12]. Although numerous theories about the origin and function of these high frequency oscillations and their relation with lower frequencies like theta and alpha have been proposed (e.g., [13–15]), there is an ongoing debate about whether gamma band responses reflect narrowband oscillations or broadband power increases, possibly echoing an increase in spiking activity [6, 16, 17]. One obstacle in this quest is the 1/f characteristic of the brain’s frequency power spectrum and a low signal-to-noise ratio (SNR) gamma band activity in magnetoencephalography (MEG) or electroencephalography (EEG) recordings. To increase SNR, trial averaging is a frequently used tool to cancel out random variance. However, this approach can potentially obscure or cancel meaningful brain activity [18]. Indeed, local field potentials and electrocorticographic data from monkeys revealed systematic trial-to-trial variations in gamma power and frequency in a visual [19] and a memory task [20]. Importantly, the averages across trials in these studies displayed the classic sustained gamma effect, indicating that single-trial responses are crucial to understand the brain’s dynamics [18]. One powerful approach to assess single-trial information are multivariate decoding techniques. Whether such methods are applicable to low SNR gamma band MEG data, however, remains unclear. In the present paper, we investigate the predictive value of single-trial gamma power regarding the modality of stimulus presentation (auditory or visual presentation of words) in human MEG data. While comparable contrasts have been used to test classifier performance or as example datasets (e.g., [21, 22]), our aim was to unravel single-trial high frequency patterns in human MEG data. To decode information about stimulus-modality from the time-frequency data, we used a combination of beamforming [23] and random forest classification [24]. This approach was embedded into an across-subjects cross-validation framework, where the classifier was tested on single trials of unseen subjects to assess the generality of the spatial time-frequency pattern. Our results confirm that gamma SNR in single trials is high enough to achieve stable classification accuracy significantly above chance. Interestingly, the classification model yields high informational value to a broad bandwidth in the gamma range. Furthermore, we show that the characteristics of the gamma activity are similar enough across trials and even subjects to yield reliable classification performance.
The study was approved by the Institutional Review Board of the University of Konstanz and in accordance with the Declaration of Helsinki.
A total of 24 participants (17 female; mean age = 22 years, range = 19–26 years; 21 right-handed) took part in this MEG experiment. Three participants were excluded due to technical problems, one due to excessive environmental noise. The data from the remaining 20 participants are presented here. All of the participants gave written informed consent prior to the experiment and received course credits or nominal financial compensation for participation. All participants were German native speakers and reported normal or corrected-to-normal vision, and no history of neurological disease.
Parts of this data have been published in [12], with respect to independent research questions and analyses.
The experiment consisted of a study phase and a subsequent recognition test. Only data from the study phase are reported here. In the study phase, participants were presented with words either visually (projected centrally on a screen) or auditorily (via nonferromagnetic tubes to both ears). The duration of the visual word presentation was determined by the duration of the respective audio file, i.e., the time to pronounce the word (mean duration = 697 ms, s.d. = 119 ms). Each word was followed by a fixation cross. The duration of the word and fixation cross together added up to 2000 ms. Participants were instructed to count the syllables of the word and indicate via button press whether the word had two syllables. A question mark (max. duration of 1500 ms) prompted the subject’s response. The button press ended the presentation of the question mark. A fixation cross with variable duration (1000 ms to 1500 ms) was presented before each item. After the encoding phase, participants performed a distractor task and a surprise recognition test phase.
The stimuli consisted of 420 unrelated German nouns, grouped into three lists with 140 words. Half of each list’s words had two syllables, the other half had one, three or four syllables. Two lists were presented during the study phase and one list during the test phase. The assignment of the lists to study or test phase was counterbalanced across participants. Items were presented in random order, with the constraint that not more than 5 words of the same modality and not more than 5 words from the same condition were presented sequentially.
MEG data was recorded with a 148-channel magnetometer (MAGNES 2500 WH, 4D Neuroimaging, San Diego, USA) in a supine position inside a magnetically shielded room. Data was continuously recorded at a sampling rate of 678.17 Hz and bandwidth of 0.1 Hz to 200 Hz, and later downsampled to 300 Hz to reduce computational load. All data processing prior to classification was done using FieldTrip [25], an open-source MATLAB toolbox for MEEG data analysis. Data was epoched into single trials, with epochs ranging from 1500 ms before item presentation to 4000 ms after item presentation. Trials were visually inspected for artifacts, contaminated trials were rejected. Thereafter, trials were corrected for blinks, eye movements, and cardiac artifacts using independent component analysis (ICA).
For coregistration with the individual structural magnetic resonance image (available for 17 out of 20 participants; for the remaining three participants we used an affine transformation of an MNI-template brain; Montreal Neurological Institute, Montreal, Canada), the shape of the participant’s head as well as three markers (nasion, left and right ear canal) and the location of the head position indicator (HPI) coils were digitized prior to the experiment using a Fastrak Polhemus 3D scanner (Polhemus, Colchester, VT, USA).
Single-trial source space activity was reconstructed using a linearly constrained minimum variance (LCMV) beamformer [23] with weight normalization (neural activity index; [23, 26]). First, the spatial filter was computed adopting a realistic single shell head model [27] based on the individual structural magnetic resonance image (MRI) and a source model with grid points covering the whole brain volume (resolution: 15 mm). The data covariance matrix was computed for −500 ms to 1000 ms relative to stimulus presentation. To account for the rank-deficiency of the data that was introduced by the application of the ICA, the covariance matrix was regularized by loading its diagonal with 5% of the sensor power. Subsequently, the spatial filter was applied to the single trials to obtain virtual electrodes at all grid point locations.
For the classification of the oscillatory activity, single-trial time frequency representations were calculated at every virtual electrode applying a Fast Fourier Transform. Gamma band activity was estimated using frequency smoothing (Slepian sequence multi taper approach), yielding 20 Hz-wide frequency bands centered at 35 Hz, 65 Hz, 85 Hz, 115 Hz and 135 Hz. The power was calculated separately for 250 ms long time windows from −500 ms to 1000 ms and the post-stimulus activity was then expressed as relative change to baseline power, as using relative change helps to overcome issues arising from the 1/f shape of MEG data.
The random forest algorithm [24], an ensemble method, aggregates the results of several classifiers. These so-called base learners are classification and regression trees [28], which partition the data by adopting binary splits. The aim of this partitioning process is to reduce the impurity regarding the class labels in the daughter nodes that result from this split: preferably, all observations from one class should arrive in the same node. In every split, the tree algorithm searches first for the predictor that maximizes the purity of the daughter nodes and then for the best split point within that predictor. Random forests now grow numerous trees; each of these trees, however, is built on a bootstrap sample of the original data and in every split only a random subsample of all predictors is searched. The variance introduced by this randomness leads to a robust prediction by the aggregated model. This approach furthermore enables random forest to cope particularly well with highly correlated predictor variables [29], which is of special interest when working with MEEG data. Additionally, data with more predictors than observations (small n large p problems) are also handled effectively since the predictor variables are searched successively [30], which makes this approach particularly interesting when dealing with high-dimensional source-space MEEG data. For every predictor, the algorithm returns an estimate of how important this variable was for the model’s prediction. The version used here is based on the impurity reduction introduced by a predictor variable across all trees, which is measured by the Gini index [28, 29, 31].
Random forest classification was performed using the scikit-learn module for Python [32]. The aim of the decoding was to classify trials regarding their stimulus modality: visual or auditory. The predictors were [voxel, time point, frequency band]-triplets, providing 16 624 predictors, overall. For every subject, the more prevalent class (auditory or visual stimulation) was downsampled such that every dataset contained equal trial numbers for both cases. The total trial number across all subjects was 4270 trials.
The classification was embedded in a cross-validation framework across subjects: the classifier was trained on the data from all but one subject and then tested on the data of this left-out subject. This procedure was repeated for all 20 subjects, such that every dataset was used as test set once. This approach ensures that the classifier is never tested on data it was trained on and thus controls for possible overfitting of the classifier. Moreover, it allows the assessment of across-subjects predictability of the data regarding the response variable.
Each of the 20 cross-validation models aggregated the results of 15 000 classification trees, where every tree was built on a bootstrap sample of all observations in the trainings set. To ensure that the model incorporated a sufficient number of trees, classification performance was assessed with 25 000 trees for two folds [33], yielding comparable results as the sparser model. At each binary split, the algorithm considered N f e a t u r e s predictors to find the best split. The accuracies on the test datasets as well as the variable importances were merged across the cross-validation folds. The performance of the classifier was then tested against 50% chance level using a binomial test [34], since a permutation based test was computationally not feasible.
The performance of the random forest algorithm was compared to the accuracies obtained by fitting two support vector machine (SVM) models [35, 36] on the data. The general procedure was as described above, instead of the random forest model, however, either a linear SVM or a non-linear SVM with a radial basis function (RBF) kernel was fitted using the scikit-learn module in Python. The penalty parameter was set to 1.0 in both models and a kernel coefficient of 1/Nfeatures for the RBF kernel.
The performance of the random forest model was subsequently tested against the two SVM models by applying a Fisher’s exact test to the obtained classification accuracies.
To assess the predictive value of single-trial gamma power towards stimulus modality, we used MEG data from 20 subjects and adopted an across-subjects classification scheme. Data was first source reconstructed with a linearly constrained minimum variance (LCMV) beamformer, subsequently, we used the random forest algorithm to classify the modality of stimulus presentation (auditory or visual).
The random forest model classified auditory versus visual trials with 66.44% accuracy, which is significantly better than chance (binomial test, ntrials = 4270, p < 0.001). As the confusion matrix in Fig 1A shows, the accuracy was slightly better for auditory trials (69.60%) than for visual trials (63.19%). In the adopted 20-fold cross-validation scheme, every fold corresponded to the data of one subject, hence, the classifier was always tested on data of one subject which was not included in building the model. The classifier accuracy on the 20 cross-validation folds is depicted in Fig 1B. The performance on the different folds is diverse, ranging from 50.98% to 84.86%, however, the accuracy for all but three folds is above 60% (note that the folds, since they are part of the whole classifier model, are not tested for significance). The good classifier performance indicates that the gamma power patterns are remarkably stable across trials and even subjects. For a comparison of this across-subjects approach to within-subject analyses, see supplementary Figure in the S1 Appendix.
The random forest classifier provides the variable importance as an importance estimate for every predictor in the model. This measure indicates the informational value of a given predictor towards the discrimination of the two classes, auditory and visual modality. Fig 2 reports the highest 2% of variable importance values, i.e., those [voxel, time point, frequency band]-triplets that were most informative for partitioning the data. This cutoff was chosen because cutoffs of higher values (> 2%) would have included features with variable importances equal to zero, i.e., variables that did not contribute information to the classifier. Not only visual, but also auditory regions contributed to the model, even though visual areas yielded more information than the auditory cortex. Interestingly, the lower frequency bands of 25 Hz to 45 Hz and 55 Hz to 75 Hz did not rank as important as the 75 Hz to 95 Hz band. Even frequencies above 100 Hz contributed to the model in both visual and right auditory cortex. Gamma power beyond 125 Hz, however, did not add substantially to the classification model.
All time windows but the last one (750 ms to 1000 ms) supplied information to the classifier, in higher frequencies, the earlier time windows seemed to play a more pronounced role compared to the lower gamma frequencies. Fig 3 shows the time-frequency representations of variable importance for the visual and auditory peak voxels: the visual peak voxel (MNI coordinates: [−4 −100 12]) falls into left calcarine sulcus, the auditory peak voxel (MNI coordinates: [68 −20 10]) into right superior temporal gyrus (labels determined with the Automated Anatomical Labeling (AAL) atlas [37]). The time-frequency representations for those two peak voxels (Fig 3) confirm the pattern evident across all voxels (Fig 2). Thus, the 75 Hz to 95 Hz band yielded a characteristic and stable activity pattern in both the auditory and visual cortex. The visual response was specifically characterized by a broadband gamma increase in the range of 55 Hz to 125 Hz. The auditory response yielded informational value in an overlapping but narrower frequency range (75 Hz to 125 Hz).
To investigate the underlying gamma power changes, the variable importance rankings were compared to the power differences between auditory and visual trials. To this end, auditory and visual power changes relative to baseline were averaged across trials and subjects, and the difference between the visual and the auditory condition was computed. These differences are depicted in Fig 4A: the spatial pattern of power is shown for the 250 ms to 500 ms time window and two frequency bands (75 Hz to 95 Hz, top, and 105 Hz to 125 Hz, bottom in Fig 4A). Red colors refer to higher gamma power in the visual condition and blue colors to higher power in the auditory condition. The black lines encircle those voxels which were among the 2% most informative predictors for the classifier. Fig 4B shows the underlying gamma power relations for the same peak voxels as presented in Fig 3. Interestingly, the classifier analysis based on single trials also rated predictors as highly informative where a difference in the averages is small, as is most evident for the time-frequency representation of the auditory condition (75 Hz to 95 Hz, 500 ms to 750 ms).
To get a general assessment of the relative performance of random forests on MEG data, we compared this approach to the outcome of support vector machines (SVM), a widely used method in multivariate data analysis. We applied two SVM types, a linear SVM and a non-linear SVM with a radial basis function (RBF) kernel. The results of this comparison are shown in Fig 5. The linear SVM yielded an accuracy of 63.07% and performed significantly worse than the random forest model (two-sided Fisher’s exact test, odds ratio = 86.79%, p = 0.002, 0.004 corrected). The RBF SVM performed slightly better than the random forest model with an accuracy of 68.34% (two-sided Fisher’s exact test, odds ratio = 91.1%, p = 0.047, 0.094 corrected). Note that the comparison between the non-linear SVM and the random forest model does not survive a correction for multiple comparisons (Bonferroni-correction for two Fisher’s exact tests).
In the present work, we investigated the predictive value of single-trial gamma power to classify the stimuli’s modality. This was done in an across-subjects cross-validation framework which allowed us to estimate not only the gamma pattern stability across trials but also across subjects.
The decoding of MEEG high frequency activity on a single-trial basis can be challenging due to the low SNR: while intracranially recorded high frequency activity up to 180 Hz has been used to decode movements [38, 39], comparable approaches with MEEG data were not successful [40, 41]. Some studies could show a contribution of high gamma power (along with lower oscillatory activity) to the overall classifier performance [42, 43]. In this study, we successfully decoded stimulus modality exclusively from high frequency activity: the classifier model was able to correctly classify 66.44% of the trials based on their source reconstructed gamma activity pattern, reliably distinguishing visual from auditory word presentation. Thus, the SNR of single-trial gamma power in source-level MEG data was high enough to successfully apply single-trial multivariate analyses. Interestingly, more auditory (69.60%) than visual trials (63.19%) were classified correctly, although visual areas yielded more information to the classifier. One possible explanation for this could be that the classifier-inherent cutoff values for gamma power in the visual voxels were rather conservative and therefore missed small gamma increases in visual cortex in visual trials, but still reliably detected the absence of visual activity in auditory trials.
The classification model was built across subjects, adopting a 20-fold across-subjects cross-validation, where the classifier was trained on 19 subjects and then tested on the data of the left-out 20th subject. Hence, the trials of any given subject were classified by a model which was built on the data from different subjects. Using this approach, we assessed the common patterns across trials and subjects. The accuracy pattern across the different folds was higher than 60% for all but three subjects. Low accuracies indicate either higher noise levels in these participants or activity patterns which deviate from the across-subjects consensus as uncovered by the random forest model. The overall classification accuracy of 66.44% is comparable to previous reports of across-subjects MEG data classification (e.g., [44]).
The variable importance indicates which predictors were used by the model to yield the classification performance, by providing the common pattern across trials and subjects that differentiated between the two conditions. Clearly, gamma band activity from both visual and auditory areas was exploited by the model, although the visual cortex was more important than the auditory cortices, expressed by higher ranking variable importances. Overall, a broad range of frequencies and a time span of 750 ms included gamma band activation relevant to the random forest model.
In this study, we show the feasibility of applying the random forest algorithm [24] to single-trial source-localized time-frequency data. With its non-parametric, non-linear approach and its capability to handle high dimensional datasets with highly correlated predictors, this method is well suited for MEG data (also see [45–48]) and can detect subtle differences concealed in the averaged data. In contrast to approaches comparing averaged time courses, the multivariate analysis presented here furthermore solves the multiple comparison problem that is inherent to the application of univariate tests for time points or similarly high-dimensional data.
Another advantage of this method is the possibility to directly compare predictors (e.g., frequency bands) to each other regarding their importance in the model: for example, we are able to state that the 75 Hz to 95 Hz frequency band is the most important frequency band, and that the visual cortex has higher informational value for the classification than the auditory cortex.
Comparing the random forest algorithm to SVM methods shows that it clearly outperforms a linear SVM model in this dataset. The SVM with the non-linear kernel showed a comparable performance to the random forest, however, using non-linear SVMs comes at the expense of interpretability and usability. While it is possible to assess the relative importance of the different predictors with random forests, this is hindered with non-linear SVMs, since the classification is not computed in the original feature space. Furthermore, while SVMs often require an extensive search for optimal parameter settings, the application of the random forest algorithm is less complex and needs less fine-tuning.
In our data, the left primary visual cortex was most informative for the classification among all brain regions. Additionally, also higher visual areas ranked as highly informative, which is concordant with the localization of visual gamma band responses in intracranial electroencephalography (iEEG) and MEG studies (e.g., [5, 49–51]). The classification further identified auditory regions as informative. Although iEEG reliably shows high gamma responses to auditory stimuli [52–55], auditory high frequency activity above 75 Hz has only rarely been shown in MEG studies: examples include high gamma responses to sound and pitch perception [56, 57]. Within the auditory regions, the most important voxel in our data was located in the right superior temporal gyrus, which is in line with iEEG studies investigating phoneme and word processing [7, 55] and the above-mentioned MEG studies. Further important regions included Heschl’s gyrus and the planum temporale. Interestingly, the right auditory cortex showed higher importance with more voxels involved compared to the left auditory cortex, although the stimuli were words and should typically evoke language-related activity localized to the left hemisphere [54, 55]. This may be explained by the fact that both conditions used words as stimuli and thus, left-hemispheric language related activity is not able to distinguish between auditory and visual trials.
The time windows most important to the classification covered 0 ms to 750 ms after stimulus onset, while the last time window (750 ms to 1000 ms) did not show any high ranking variable importance values, implying that gamma activity was most informative to the classifier during presentation of a word (mean = 700 ms).
Informative predictors in auditory areas, however, were only found between 250 ms and 750 ms, although previous studies reported early auditory (high) gamma responses following phoneme or word stimuli (e.g., [7, 55, 58, 59]).
In both, visual and auditory brain areas, the most important frequency band was the 75 Hz to 95 Hz band. Especially in the 250 ms to 500 ms window, this frequency band exhibited exceeding informative value for the classification. Yet, the visual areas overall provided informative predictors across a broad frequency range (55 Hz to 125 Hz). This points to underlying broadband gamma activity in single trials rather than a narrowband response, which is typically elicited by high contrast stimuli such as gratings, (e.g., [5, 51]). The high frequency activity beneficial for classification is similar to visually induced broadband gamma activity reported in iEEG and MEG studies [6, 49, 60–62]. Vidal et al. [61], for example, describe a lower frequency band of 45 Hz to 65 Hz and high gamma activity of 70 Hz to 120 Hz in their MEG study on visual grouping. Related to reading, broadband high frequency activity above 50 Hz has been reported in iEEG studies [63–66]. Furthermore, compared to the narrowband responses elicited by high contrast stimuli such as gratings, which are typically centered at lower frequencies, (e.g., 50 Hz [5] or 60 Hz [51]), our results yielded the 75 Hz to 95 Hz frequency band as most informative.
In the auditory areas, the most important variables were concentrated in the 75 Hz to 95 Hz and 105 Hz to 125 Hz frequency bands. This is in line with iEEG studies on syllable and word processing, which report gamma responses from 80 Hz up to 200 Hz [54, 67]. Thus, our results might reflect the lower end of the high gamma response described in these studies, potentially cropped by low SNR above 125 Hz.
To summarize, we have shown that single-trial gamma activity can be successfully used to classify stimulus modality. Importantly, the successful across-subjects classification suggested that single-trial gamma-band activity contains high inter-individual consistency. Future studies investigating discriminative rather than consistent activity across trials should explore the possibilities provided by the present approach. The classifier identified both visual and auditory areas as informative with high spatial specificity. Our results furthermore suggest that single-trial high frequency activity after visual word presentation is characterized by a broadband rather than a narrowband response.
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10.1371/journal.pcbi.1003079 | A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex | Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations—local oriented filters and divisive normalization—whereas the second stage involves novel computations—compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.
| Much has been learned about how stimuli are represented in the visual system from measuring responses to carefully designed stimuli. Typically, different studies focus on different types of stimuli. Making sense of the large array of findings requires integrated models that explain responses to a wide range of stimuli. In this study, we measure functional magnetic resonance imaging (fMRI) responses in early visual cortex to a wide range of band-pass filtered images, and construct a computational model that takes the stimuli as input and predicts the fMRI responses as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. A novel component of the model is a nonlinear operation that generates selectivity for second-order contrast, that is, variations in contrast-energy across the visual field. We find that this nonlinearity is stronger in extrastriate areas V2 and V3 than in primary visual cortex V1. Our results provide insight into how stimuli are encoded and transformed in the visual system.
| Studies of visual cortex typically measure responses to a narrow set of stimuli designed to investigate a particular phenomenon. For example, a study might use sinusoidal gratings varying in contrast to study contrast response functions [1], [2], another study might use silhouettes to study shape tuning [3], [4], and yet another study might use arrays of line segments to study texture representation [5], [6]. This approach provides valuable insights, but different effects are studied in isolation and different models (e.g., linear filtering, static nonlinearities, divisive normalization, MAX) are proposed for different effects. To advance our understanding, we seek to develop an integrated model that explains responses to a wide range of stimuli (Figure 1).
In this study, we measure functional magnetic resonance imaging (fMRI) responses in early visual cortex to a wide range of band-pass grayscale images, and we develop a model that starts with images and predicts these responses. The model has a cascade architecture and comprises four main components. The first component is a set of V1-like Gabor filters that are applied to the image. These filters are adapted from our previous work on modeling fMRI responses [7]. The second component is a divisive normalization operation that is applied to filter outputs. Divisive normalization is a well-established computation that accounts for several nonlinear response properties of V1 neurons [8]–[10]. The third component is a compressive static nonlinearity that is applied after summation of contrast-energy across the visual field. We recently found that this nonlinearity is important for accurately predicting responses to stimuli varying in position and size [11]. The fourth component is a variance-like nonlinearity that is used in the summation of contrast-energy. This nonlinearity generates selectivity for second-order contrast and shares some similarities with filter-rectify-filter models that have been proposed for texture perception [12], [13].
We provide software code that implements the complete model along with example datasets at http://kendrickkay.net/socmodel/. This is useful for the goal of reproducible research [14] and provides the opportunity for others to improve upon our work. We welcome efforts to consider potential alternative models—including models developed in psychophysics, computer vision, and the theoretical literature, as well as models that posit specific circuit-level mechanisms—and to determine whether these models better account for the experimental measurements we have made. We hope the open exchange of data and code will spur further modeling efforts.
This paper is structured as follows: We start by motivating each component of our model through targeted examples of stimuli and responses. We then use cross-validation to show that the full model does not overfit the data but in fact improves prediction accuracy. Finally, we examine the parameters of the model and inspect the effect of the parameters on the behavior of the model. This examination reveals that compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast.
We measured blood oxygenation level dependent (BOLD) responses in visual field maps V1, V2, V3, and hV4 while subjects viewed a large number of stimuli. In the main experiment, a total of 156 distinct stimuli were presented in random order 3–6 times each. The BOLD response amplitude of each voxel to each stimulus was estimated from the time-series data using a GLM (see Methods).
The model we developed for predicting the BOLD response consists of a sequence of operations (Figure 2A). The BOLD response is predicted by applying V1-like Gabor filters to the luminance image (V1 energy), normalizing the filter outputs by local population activity (Divisive normalization), summing contrast-energy across a specific region of the visual field (Spatial summation) using a variance-like nonlinearity (Second-order contrast), and applying a compressive static nonlinearity (Compressive nonlinearity). The key novel component of the model is the computation of second-order contrast (Figure 2B), hence the name of the model. The model has eight free parameters (Figure 2A, bracketed variables) and is fit to the response amplitudes of each voxel.
To motivate and explain the second-order contrast (SOC) model, we start with simpler versions of the model and incrementally build up to the full model (Figure 2C). At each step of the process, we assess how well a simple model explains responses to a range of stimuli and improve performance by adding a new component to the model. A caveat to this approach is that increasingly complex models may provide better fits, but these improvements may simply reflect overfitting to the noise in the data. In a later section, we use cross-validation to obtain unbiased estimates of model accuracy and verify that the more complex models are indeed more accurate than the simpler models.
The simplest model is the complex-cell energy (CC) model, which involves computing V1 energy and summing across the visual field. Previous studies indicate that the CC model is a reasonable starting point: the CC model accounts for substantial variance in BOLD responses in early visual areas to grayscale natural images [7] and a closely related model accurately characterizes BOLD responses to a checkerboard pattern positioned at different visual field locations [15]. For the purposes of this project, the summation weights in the CC model were constrained to be Gaussian across space and equal for different orientations; this is a reasonable approximation for voxel responses [7]. We assessed how well the CC model accounts for responses to a set of stimuli that included oriented gratings and mixtures of oriented gratings presented at different contrast levels (henceforth referred to as grating stimuli). Results for an example voxel in V1 are shown (Figure 3).
Responses increase with contrast and with number of orientations, consistent with recent fMRI measurements [16], [17]. This pattern of results is qualitatively reproduced by the CC model (Figure 3, red curve). However, the CC model fails quantitatively: it does not account for the fact that responses tend to saturate at low contrasts. To improve performance, we augmented the CC model with divisive normalization, a computational mechanism that explains a variety of nonlinear behaviors of V1 neurons including contrast saturation [8]–[10]. The divisive normalization (DN) model fits the data accurately (Figure 3, orange curve).
To test the DN model on a wider range of stimuli, we measured responses to noise patterns covering different portions of the visual field (henceforth referred to as spatial stimuli). Results for an example voxel in V2 are shown (Figure 4). The DN model does a reasonable job capturing the pattern of responses to the stimuli (Figure 4, orange curve). However, the model underestimates responses to stimuli covering a small portion of the receptive field and overestimates responses to stimuli covering a large portion of the receptive field. This can be seen most clearly by inspecting responses to the stimuli labeled ‘Bottom to top’.
We observed this pattern of underestimation and overestimation of spatial responses in a previous study [11] and resolved the issue by applying a compressive static nonlinearity after spatial summation. Intuitively, the compressive nonlinearity boosts responses to stimuli that only partially overlap the receptive field, and can be interpreted as providing tolerance for changes in stimulus position and size [11]. We attempted to improve the performance of the DN model by incorporating, in an analogous fashion, a compressive nonlinearity after spatial summation. We find that the compressive spatial summation (CSS) model better fits the data (Figure 4, blue curve).
The CSS model accurately fits responses to the spatial stimuli; and since the CSS model is a more general case of the DN model, the CSS model accurately fits responses to the grating stimuli. However, the CSS model fails to fit responses to the two sets of stimuli simultaneously. For example, if the CSS model is fit to the spatial stimuli, the predicted responses to the grating stimuli substantially overestimate the actual responses (Figure 5A, blue curve). This failure suggests that the CSS model is incomplete and must be modified to account for the full range of responses.
Under the CSS model, the predicted response co-varies with the total amount of contrast-energy within a certain region of the visual field (subject to a compressive nonlinearity). This explains why the model predicts large responses to the grating stimuli, as these stimuli contain contrast-energy throughout the spatial extent of the stimulus. Suppose, however, that BOLD responses are not driven by contrast-energy per se, but by variations in contrast-energy. This might explain why the grating stimuli elicit relatively weak BOLD responses.
To improve the performance of the CSS model, we incorporated a variance-like nonlinearity into the spatial summation stage of the model. This nonlinearity suppresses responses to stimuli with spatially homogeneous distributions of contrast-energy and enhances responses to stimuli with spatially heterogeneous contrast-energy distributions. We find that the new model, which is the full second-order contrast (SOC) model, simultaneously fits both the spatial stimuli and the grating stimuli (Figure 5A, green curve).
The noise patterns used for the spatial stimuli consist of contours that are spatially separated from one another; this spatial separation gives rise to variation in contrast-energy and generates large responses from the SOC model. We hypothesized that reducing the spatial separation of the contours would reduce variation in contrast-energy and lead to reduced BOLD responses. To test this hypothesis we measured responses to noise patterns with different levels of contour separation (Figure 5B). As expected, we find that the response is lowest at the smallest separation and increases at larger separations. This pattern of results is accurately predicted by the SOC model (Figure 5B, green curve) but not the CSS model (Figure 5B, blue curve).
To systematically evaluate the merit of the SOC model, we fit that model and each of the simpler models (CC, DN, CSS) independently to the data using five-fold cross-validation. Cross-validation produces a prediction of each data point based on a model that is not fit to that data point. Models are evaluated by how well model predictions match the data.
Because the SOC model subsumes the simpler models, it is guaranteed to produce the best fits for a given set of data. However, there is no guarantee that the SOC model will cross-validate well, i.e. generalize to unseen data. The SOC model will cross-validate well only if the effects described by the model are sufficiently large and there are sufficient data to estimate model parameters accurately. Cross-validation controls for model complexity since overly complex models will tend to fit noise in the data and, as a result, generalize poorly. Alternative methods for model selection include AIC and BIC, and these methods produce similar results (see Supporting Figure S1).
In all visual field maps, we find that the SOC model has the highest cross-validation accuracy (Figure 6). The accuracy of the SOC model is slightly lower than the noise ceiling, i.e., the maximum performance that can be expected given the noise in the data. Using the metric of explainable variance which takes into account the noise ceiling (see Methods), we find that on average, the SOC model accounts for 88%, 92%, 89%, and 84% of the explainable variance in V1, V2, V3, and hV4, respectively (median across voxels in each map). These values indicate the high predictive power of the SOC model.
Metrics like variance explained are convenient for summarizing model accuracy, but it is important to examine the specific aspects of the data that drive these metrics. To visualize results from a large number of voxels on a single plot, we adopt the strategy of averaging data across voxels and averaging the predictions of each model across voxels. Note that this averaging is only for sake of visualization; cross-validation accuracy is computed on a voxel-by-voxel basis and does not involve averaging data.
Examining the data and model predictions for a representative visual field map, we see that the SOC model clearly outperforms the other models (Figure 7). In interpreting this plot, keep in mind that the predictions of a model may depend on the specific stimuli to which the model is fit. For example, when fit to a wide range of stimuli, the DN model fails to predict responses to the grating stimuli (Figure 7, orange curve), despite the fact that the DN model succeeds when the model is fit only to the grating stimuli (see Figure 3). As another example, the CC model performs quite poorly for the stimuli tested in this study (Figure 7, red curve), which may seem surprising given previous reports that the CC model (or variants thereof) can characterize responses to grayscale natural images [7] and retinotopic mapping stimuli [15]. However, the results are not inconsistent. The key realization is that the CC model may perform well if fit and tested on stimuli that probe a limited range of stimulus dimensions (e.g. a limited range of contrasts). With a wide range of stimuli, failures of the CC model become evident, and more complex models are necessary to explain the data.
We developed the SOC model using carefully controlled stimuli and have demonstrated that the model accurately characterizes responses to these stimuli. A major advantage of controlled stimuli is ease of interpretation: with controlled stimuli, it is relatively easy to identify the stimulus properties that drive effects in the data [18]. However, a stimulus set composed of controlled stimuli is inherently biased towards certain stimulus types at the exclusion of others, leaving open the question of how well the model characterizes responses to stimuli in general.
To estimate general accuracy, in a separate experiment we measured responses to 35 objects and quantified how well the SOC model—with parameters derived from the controlled stimuli—predicts the responses. On average, the SOC model accounts for 65%, 72%, 69%, and 59% of the explainable variance in V1, V2, V3, and hV4, respectively (median across voxels in each map). These values are lower than the corresponding values obtained for the controlled stimuli, underscoring the fact that summary metrics of model performance are highly dependent on the type of stimuli used. Nevertheless, the values are encouragingly high and confirm that the SOC model has predictive power for ecologically relevant stimuli [19]. One interpretation of the reduced performance on object stimuli is that such stimuli contain higher-order features that are not accurately represented by the SOC model; investigating these features can be the focus of future studies.
In the divisive normalization stage of the SOC model, the population activity used to normalize filter outputs consists of the sum of the outputs of filters at the same position but different orientations (see Methods). The reason we assumed the population has the same spatial extent as the filter outputs is simplicity: by making that assumption, the space of model parameters is vastly reduced and the interpretation of the divisive normalization stage is simplified. However, divisive normalization models of V1 neurons often consist of a central excitatory region that is normalized by a larger surround region [20], [21], and such models are used to account for surround suppression, a phenomenon that is closely related to second-order contrast (see Discussion). Thus, one might speculate that if the spatial extent of the population were enlarged, the resulting model might be sufficient to account for our data.
To address this issue, we tested a version of the DN model in which the spatial scale over which normalization occurs is flexible and fit to the data. The hypothesis is that this model might account for the data as well as (or better than) the more complex SOC model. We find that the DN model with flexible normalization (Figure 8B, yellow bar) outperforms the original DN model (Figure 8B, orange bar) but does not achieve the same accuracy as the SOC model (Figure 8B, green bar). This indicates that simply enlarging the normalization pool is not sufficient and that the additional computations in the SOC model are necessary to account for the data. We also tested several other control models, including a model that demonstrates that the squaring operation in the computation of second-order contrast is critical (Figure 8B, cyan bar).
One of the control models (RM2) omits the Divisive normalization component of the SOC model, and performs about as well as the full SOC model. This can be attributed to the fact that the effect of Divisive normalization on the overall response of the model can be approximated, through suitable choice of parameters, by the other components of the model, most notably the Compressive nonlinearity component. For a simple example of this phenomenon, suppose we have a cascade of two power-law nonlinearities, each with exponent 0.5. If the first nonlinearity is omitted, the overall input-output relationship can still be preserved if the exponent of the second nonlinearity is set to 0.25. While a compressive nonlinearity is not an exact substitute for divisive normalization, it approximates many of the same effects within our measurements. We have chosen to include the Divisive normalization component in the SOC model for two reasons. One is to maintain historical continuity, as previous studies have incorporated divisive normalization immediately following a linear filtering stage [e.g. 9]. The second reason is that even though normalization (immediately after the linear filtering stage) is not essential for the current set of data, it is likely that normalization will prove essential at finer scales of measurement (sub-millimeter voxels). For example, a major effect explained by normalization is cross-orientation suppression at the level of single neurons in V1 [10]; this effect is largely obscured at the current scale of measurement (2.5-mm voxels). This observation highlights the fact that the model inferences we make are limited by the resolution of our BOLD measurements and that there is value in developing models at finer scales of measurement.
We now turn to examining the parameters of the SOC model. There are three parameters of interest, σ, n, and c. The σ parameter controls the size of the 2D Gaussian over which contrast-energy is summed, the n parameter controls the strength of the compressive nonlinearity, and the c parameter controls the strength of the variance-like nonlinearity that generates selectivity for second-order contrast (see Methods for details). To summarize the n and c parameters, we calculate the median parameter value across voxels in each map. To summarize the σ parameter, we fit a line relating receptive field eccentricity and σ and extract the σ value at 2° eccentricity.
For each parameter of interest, we plot the summary value observed in each visual field map (Figure 9, top). Because raw parameter values are difficult to interpret, we also perform simulations that clarify the effect of the parameter values on the overall stimulus-response relationship (Figure 9, bottom). In these simulations, we calculate the response of the SOC model using the typical parameter values found in each visual field map (thus, four instances of the SOC model were simulated). These simulations directly reflect the behavior of the SOC model as fitted to each map and do not incorporate any assumptions beyond what is determined from the data and the model.
Inspecting the variation in parameter values, we find that the σ parameter increases from V1 to V2 to V3 to hV4, reflecting an increase in receptive field size (Figure 9A). We find that the n parameter decreases from V1 to V2 to V3 to hV4, reflecting an increase in normalization (Figure 9B). Finally, we find that the c parameter is higher in V2 and V3 than it is in V1 and hV4, reflecting increased selectivity for second-order contrast (Figure 9C). All pairwise differences between visual field maps are statistically significant (p<0.05, two-tailed randomization test) with the exception of n in V3 vs. hV4 and c in V2 vs. V3.
We describe a computational model, termed the second-order contrast (SOC) model, that predicts BOLD responses in early visual cortex to grayscale band-pass filtered images. The model builds on earlier modeling work [7], [10], [15] and introduces a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model vary systematically across visual field maps, reflecting differences in receptive field size, differences in the strength of normalization, and differences in selectivity for second-order contrast.
We have developed a model that predicts BOLD responses to a wide range of stimuli. Stimulus-driven BOLD responses arise principally from metabolic demands of peri-synaptic neural activity [22], [23]. Hence, BOLD is one of the many ways that neural activity can be measured, and our model of BOLD responses is a model of neural population responses. However, the spatial resolution of our BOLD measurements (2.5-mm voxels) is lower than the resolution required to analyze and dissect neural circuits, and this may lead some to conclude that our model of BOLD responses does not actually provide much insight into neural computation. We believe this view to be in error.
To explain our position, it is useful to highlight the distinction between functional models and circuit models. Functional models are stimulus-referred (i.e. start with the stimulus) and specify what aspects of the stimulus drive responses in a given area. Building functional models has a long history in electrophysiology [for review], , where researchers explain the spiking activity of neurons in terms of relatively simple computations applied to the stimulus. Circuit models go further than functional models by identifying the specific neural circuitry that gives rise to the observed responses. Hence, functional models may be simpler than circuit models and multiple competing circuit models may be consistent with a given functional model. There is value in functional characterizations of neural responses, especially if one seeks to link neural circuits to perceptual judgments and behavior [26].
To illustrate the distinction between functional models and circuit models, consider a model that explains the spiking activity of a V1 simple cell by the application of an oriented linear filter to the stimulus, followed by a rectification nonlinearity. This model, known as an LN or linear-nonlinear model [24], is a functional but not a circuit model—it describes how stimuli relate to responses, but does not characterize the many stages of processing performed by the visual system before V1 (e.g. retina, LGN) nor the specific neural circuit by which orientation tuning arises [e.g. feedforward computation on LGN afferents or intracortical processing within V1—see 27]. Nevertheless, the model is useful for understanding how stimuli are represented in the visual system.
The SOC model developed in this paper is a functional model—it characterizes the relationship between visual stimuli and measured BOLD responses. Like functional models of neuronal responses, the SOC model does not propose specific neural circuits. Rather, the SOC model provides insight at the functional level, that is, in identifying the aspects of the stimulus that drive responses in different visual field maps. For example, the model indicates that second-order contrast is an important factor that drives population responses in V2 and V3, and we can reasonably infer that this same stimulus property drives responses of individual neurons in these maps. To test and expand upon this hypothesis, one could adapt the stimuli and model used in this study to single-unit electrophysiology and assess how well neuronal responses are accounted for. In doing so, we may find it necessary to extend the model to account for response properties that are evident at the level of individual neurons but which are not readily observed at the population level.
Neural activity is coupled to the BOLD response through a complex set of neurovascular mechanisms [23], [28]. Thus, physiological responses measured using BOLD fMRI reflect both neural activity and these coupling mechanisms. Since the coupling mechanisms are not explicitly modeled in the present work, an implicit assumption in the interpretation of our results is that the BOLD response provides a linear (or approximately linear) measure of some aggregated neural activity. Under this assumption, we attribute the various nonlinear operations in the SOC model to nonlinearities arising in neural processing. However, there may be nonlinearities in neurovascular coupling, and this possibility limits the inferences we can make from our BOLD measurements. For example, if there is a nonlinearity in the relationship between the total amount of neural activity in a voxel and the strength of the BOLD response measured from that voxel, then the level of compression estimated by the Compressive nonlinearity component of the SOC model may differ from the level of compression associated with the underlying neural activity. Going forward, we believe that developing a better understanding of the different types of neural activity (e.g. synaptic activity, spiking activity) and the mechanisms that couple these various types of neural activity to the BOLD response is of high importance.
The SOC model has a cascade architecture, consisting of a series of computations that are applied to the stimulus. The success of the SOC model is consistent with the long-standing hypothesis that the visual system can be characterized as a cascade of operations [29]–[34]. However, cascade models come in a variety of different forms and vary in essential characteristics such as the number of stages in the model and the computations that are applied at each stage. Our work contributes to the field by proposing a specific model and showing that this model quantitatively accounts for a sizable range of experimental measurements in the living human brain.
The SOC model is most similar to the cascade model that is being developed by Heeger, Landy, and colleagues [34], [35]. These authors propose that the stimulus is transformed through two or more stages of canonical operations, each stage consisting of filtering, which is a linear operation (L); rectification, which is a nonlinear operation (N); and normalization, which is a nonlinear operation (N). Mapping these operations onto the SOC model, we see that the SOC model is a two-stage cascade model with an overall form of LNNLNN (Figure 2A).
There are differences between the SOC model and the Heeger-Landy model. First, the SOC model is fully computable, starting with images and predicting physiological responses. Second, the filtering operation in the second stage of the SOC model is generic: variance in contrast-energy drives responses irrespective of how contrast-energy is arranged in the stimulus. In contrast, the Heeger-Landy model uses oriented second-order filters. Third, the normalization operation in the second stage of the SOC model is implemented as a compressive nonlinearity. This is reasonable because under certain conditions, the effects of divisive normalization can be approximated with a compressive nonlinearity [11].
It is common in cascade models to designate different stages as corresponding to different visual areas. Thus, it is tempting to view the first stage of operations in the SOC model (the first LNN) as corresponding to primary visual cortex (V1) and the second stage of operations (the second LNN) as corresponding to extrastriate areas. However, this interpretation is complicated by the fact that the full two-stage SOC model predicts V1 responses more accurately than the one-stage DN model (see Figure 6). To reconcile this finding, we hypothesize that the computation of first-order contrast (the first LNN) occurs in V1 (or is inherited from earlier processing), the computation of second-order contrast (the second LNN) occurs downstream from V1, and feedback introduces second-order effects into V1 responses. Some support for this circuit-level hypothesis comes from studies reporting that surround suppression—which, as we later explain, is intimately related to second-order contrast—is mediated by feedback from extrastriate areas to V1 [36], [37].
A key component of the SOC model is a nonlinearity that computes variance in contrast-energy within a specific region of the visual field. This nonlinearity enhances responses to stimuli that have heterogeneous distribution of contrast-energy and suppresses responses to stimuli that have homogeneous distribution of contrast-energy. We find that the nonlinearity is substantially stronger in extrastriate areas V2 and V3 compared to V1, suggesting that selectivity for second-order contrast is mainly a feature of extrastriate cortex. We do find, however, that the strength of the nonlinearity in hV4 is comparable to that in V1, indicating that in hV4 first-order contrast is relatively effective at driving responses.
The concept of second-order contrast—or, more generally, second-order stimuli—has a long history in visual psychophysics [for review], [ see 12,13] and other sensory modalities [38]. Second-order stimuli involve modulation of a stimulus property (e.g. contrast) across space or time in such a way that the modulation cannot be detected by a first-order filter. For example, consider a sinusoidal grating whose amplitude is modulated by a sinusoidal grating of lower spatial frequency. Such a stimulus varies in contrast across space, but this variation cannot be detected by a first-order luminance filter since average luminance remains constant throughout the extent of the stimulus. To explain the perception of second-order stimuli, researchers have proposed filter-rectify-filter (FRF) models in which first-order filters are applied to the stimulus, the outputs of these filters are rectified, and second-order filters are applied to the rectified outputs.
Extending results from animal models [e.g. 39], [40], [41], several fMRI studies have found evidence of second-order processing in human visual cortex [35], [42]. These studies used adaptation techniques to infer selectivity for second-order modulation of contrast [42] and orientation [35], [42], and proposed a variant of the FRF model to account for their results [35]. Our results are consistent with these adaptation studies in finding that second-order effects exist in many visual field maps including V1. We extend these studies by executing a different experimental and modeling approach: We demonstrate second-order effects directly in visually evoked responses. Moreover, we develop a model that operates on images and quantitatively predicts responses at the level of single voxels.
Our finding that selectivity for second-order contrast is particularly strong in extrastriate areas is consistent with the fact that sparsely distributed contours strongly activate such areas [43]. This is because sparsely distributed contours give rise to large amounts of contrast variation. Our results are also consistent with the results of a study that developed and compared models of neural responses in V1 and V2 [44]. In that study, neural responses were characterized using a model in which V1-like filters are applied to the stimulus, the outputs of the filters are rectified, and then a flexible set of weights on the rectified filter outputs is used to predict responses. Importantly, fitted weights tended to be more negative in V2 than in V1. This suppression may serve to reduce responses to stimuli that are spatially homogeneous in contrast-energy, similar to the variance-like nonlinearity we propose in the SOC model. A quantitative comparison of these models is an important future direction.
Second-order contrast is a key feature of the SOC model, and it is useful to clarify the connection between second-order contrast and phenomena that have been extensively studied in the visual system. One such phenomenon is surround suppression, which has been studied both psychophysically and physiologically and is thought to underlie perceptual processes such as scene segmentation [45], perceptual constancies [46], and enhancement of salience differences [47]. A basic form of surround suppression is size tuning, whereby the response of a neuron is highest for a grating of a certain size and is suppressed if the grating is enlarged [20], [48]. The SOC model was not specifically designed to account for size tuning, but a simulation demonstrates that the SOC model does in fact exhibit size tuning (Figure 10). Intuitively, response suppression for large gratings stems from the absence of variation in contrast-energy; conversely, response enhancement for small gratings stems from the presence of variation in contrast-energy. This simulation demonstrates the close relationship between second-order contrast and surround suppression.
The SOC model's explanation of surround suppression differs from that provided by traditional models of surround suppression. In such models, a central excitatory region is divisively normalized by a larger surround region, and response suppression for large gratings stems from increased stimulation of the surround [20], [21]. The fact that surround suppression might have different computational explanations—either divisive normalization over a large spatial extent or second-order mechanisms—has been previously recognized [35]. We find that divisive normalization by itself does not fully account for our data, even if the spatial extent of normalization is enlarged (see Figure 8B, yellow bar). Thus, our results suggest that second-order mechanisms play an essential role in producing surround suppression effects. The ability to tease apart computational explanations such as these is made possible by our approach of measuring responses to a wide range of stimuli and testing general models that operate on arbitrary stimuli.
Second-order contrast also has an interesting connection to the statistics of natural images. The distribution of local contrast in a natural image tends to be sparse, with local contrast often near zero [50]–[53]. We reasoned that because of this sparseness, the amount of second-order contrast in natural images should be relatively high. To verify this hypothesis, we constructed a collection of natural image patches and quantified the amount of second-order contrast in each image by computing the response of the SOC model to the image. For comparison we also computed responses of the SOC model after scrambling the phase spectrum of each patch.
The responses of the SOC model are, on average, higher for the natural image patches (Figure 11A). Reduced responses to the phase-scrambled patches can be attributed to the fact that phase-scrambling takes localized structures (which induce high variation in contrast-energy) and disperses them throughout the image (Figure 11B). The fact that natural stimuli have relatively high amounts of second-order contrast is consistent with previous analyses of natural image statistics [54]. We suggest that selectivity for second-order contrast can be interpreted as an efficient coding strategy in which the visual system is tuned to the statistical features of natural scenes [49]. Stated simply, the idea is that the visual system is tuned in such a way that commonly experienced stimuli (e.g. stimuli with second-order contrast) evoke stronger responses than less commonly experienced stimuli (e.g. stimuli without second-order contrast).
Our simulations show that scrambling the phase spectra of natural image patches reduces variation in contrast-energy and leads to reduced responses from the SOC model. In general, reduction in contrast-energy variation may explain why phase scrambling tends to reduce activation levels in the visual system. For example, phase-scrambling line and edge stimuli reduces variation in contrast-energy and, as expected, reduces BOLD responses in early visual areas [55]. Of course, the phase spectrum consists of other stimulus characteristics besides variation in contrast-energy, and the visual system might also be sensitive to these characteristics. One example is alignment of phases across spatial frequencies, which occurs at edges in natural images [56].
The SOC model has high accuracy but is not perfect, especially when tested on naturalistic object stimuli (see Results). To improve performance, future work could continue the approach taken in the present study of designing controlled stimuli, assessing model predictions, and introducing new model components as necessary. It may be productive to consider how well the SOC model predicts responses to simple icons and shapes as such stimuli have been previously used to study the tuning properties of extrastriate areas [57]–[59].
Future work could also be directed towards expanding the range of stimuli for which the SOC model operates. For tractability we restricted the stimuli in this study to a band-pass range of spatial frequencies. A natural step would be to extend the SOC model to operate on stimuli with arbitrary spatial frequency content. This could be done, for example, by replicating the model architecture at multiple spatial scales and allowing the predicted response to be a weighted sum across scales. Ultimately, additional stimulus properties such as color, motion, and depth will need to be considered.
Three experienced fMRI subjects (three males; age range 29–39; mean age 33) participated in this study. All subjects had normal or corrected-to-normal visual acuity. Informed written consent was obtained from all subjects, and the experimental protocol was approved by the Stanford University Institutional Review Board. One subject (JW) was an author. Subjects participated in 1–2 scan sessions for the main experiment, and one subject participated in an additional scan session for the object experiment. Subjects also participated in 1–4 separate scan sessions to identify visual field maps [details in 60].
We used a randomized event-related design to minimize anticipatory and attentional effects. Stimuli were presented in 8-s trials, one stimulus per trial. During the first 3 s of a trial, the nine images comprising a given stimulus were presented in random order at a rate of 3 images per second (duty cycle: 167-ms ON/167-ms OFF). Then for the next 5 s, no stimulus was presented.
For the main experiment, the 156 stimuli were randomly divided into four groups. In each run, the stimuli from one of the groups were presented once and in random order. To establish the baseline signal level, each run also included null trials in which no stimuli were presented (“blank” stimuli). Two null trials were inserted at the beginning and end of each run, and one null trial was inserted after every five stimulus trials. Each run lasted 6.7 minutes. Each scan session consisted of three sets of four runs (thus, each stimulus was presented three times over the course of the session). For the object experiment, the 35 stimuli were presented once and in random order in each run. Null trials were included to establish the baseline signal level as in the main experiment. Each run lasted 6.0 minutes, and each scan session consisted of ten runs.
To improve signal-to-noise ratio for the main experiment in subjects 1 and 2, two independent scan sessions were conducted. The stimulus ordering in the second session was matched to that in the first session, and the data from the two sessions were directly averaged together (after data pre-processing).
Functional MRI data were collected at the Stanford Center for Cognitive and Neurobiological Imaging using a 3T GE Signa MR750 scanner and a Nova 32-channel RF head coil. In each scan session, 22 slices roughly parallel to the parieto-occipital sulcus were defined: slice thickness 2.5 mm, slice gap 0 mm, field-of-view 160 mm×160 mm, phase-encode direction anterior-posterior. A T2*-weighted, single-shot, gradient-echo EPI pulse sequence was used: matrix size 64×64, TR 1.337702 s, TE 28 ms, flip angle 68°, nominal spatial resolution 2.5×2.5×2.5 mm3. The TR was matched to the refresh rate of the display such that there were exactly 6 TRs for each 8-s trial.
For post-hoc correction of EPI spatial distortion, measurements of the B0 magnetic field were performed. Field maps were collected in the same slices as the functional data using a 16-shot, gradient-echo spiral-trajectory pulse sequence. Two volumes were successively acquired, one with TE set to 9.091 ms and one with TE increased by 2.272 ms, and the phase difference between the volumes was used as an estimate of the magnetic field. To track slow drifts in the magnetic field (e.g. due to gradient heating), field maps were collected before and after the functional runs as well as periodically between functional runs.
Voxels in each visual field map were pooled across subjects. Unless otherwise indicated, error bars represent ±1 standard error (68% confidence intervals) across voxels and were obtained using bootstrapping.
We fit the SOC model to each voxel using response amplitudes to the SPACE, ORIENTATION, GRATING, PLAID, CIRCULAR, and CONTRAST stimuli. Model fitting was performed using nonlinear optimization (MATLAB Optimization Toolbox) with the objective of minimizing squared error. The predicted response to a given stimulus was obtained by computing the response of the model to each of the nine images comprising the stimulus and then taking the average across these responses.
Fitting all of the parameters in the SOC model (r, s, x, y, σ, c, n, g) simultaneously is computationally prohibitive. To reduce computational requirements, we determined a single set of canonical values for the r and s parameters before fitting the remaining parameters (detailed below). This strategy has the additional benefit of simplifying the interpretation of the model; for example, voxel-to-voxel differences in the overall strength of normalization can be solely attributed to differences in the n parameter and not differences in the r and s parameters (see Figure 9B).
Our fitting approach was as follows. To determine a single set of canonical values for the r and s parameters, we selected from each subject the ten voxels in V1 with the highest GLM cross-validation accuracy and exhaustively evaluated each combination of r and s, where r is chosen from {.01 .05 .1 .2 .3 .4 .5 .6 .7 1 1.5 2} and s is chosen from {.002 .005 .01 .02 .05 .1 .2 .5 1 2 4 8}. For each combination of r and s, we optimized x, y, σ, and g with c fixed to 0.9 and n fixed to 0.5, and then optimized all of these parameters simultaneously. On average across voxels, the values that produced the best fits were r = 1 and s = 0.5. We then fixed the r and s parameters to these values and fit the remaining parameters of the model for every voxel. To guard against local minima, we used a variety of initial seeds for the c and n parameters. For every combination of c and n, where c is chosen from {.1 .4 .7 .8 .85 .9 .95 .975 .99 .995} and n is chosen from {.05 .1 .2 .3 .4 .5 .6 .7 1}, we optimized x, y, σ, and g with c and n fixed, and then optimized all of these parameters simultaneously.
The SOC model was fit using two different resampling schemes. In the full fit scheme, we fit the model to the entire set of responses. This was used to derive best estimates of the parameters of the SOC model. In the cross-validation scheme, we fit the model using five-fold cross-validation (random selection of folds). This was used to obtain unbiased estimates of the accuracy of the SOC model.
Accuracy was quantified as the percentage of variance explained (R2) in the measured response amplitudes by the cross-validated predictions of the response amplitudes:where di indicates the ith measured response amplitude and mi indicates the ith predicted response amplitude. The R2 value indicates the percentage of variance relative to 0 that is predicted by the model. Note that defining R2 with respect to deviations from 0 as opposed to deviations from the mean (which is the typical statistical formulation) avoids the arbitrariness of the mean, which varies depending on the specific data points under consideration.
Model accuracy was compared to the noise ceiling, defined as the maximum accuracy that a model can be expected to achieve given the level of noise in the data [70], [71]. Noise ceiling estimates were obtained using Monte Carlo simulations in which a known signal and noisy measurements of the signal are generated and the expected R2 between the signal and the measurements is calculated. In these simulations, the signal and noise are assumed to be Gaussian-distributed with parameters matched to the response amplitudes and associated error bars obtained from each voxel [see 11 for additional details]. Model accuracy was also compared to a flat response model that simply predicts the mean response for every stimulus.
To obtain a metric of model accuracy that is adjusted for the noise ceiling and the flat response model, we define percent explainable variance aswhere R2 indicates the raw performance of the model, FR indicates the performance achieved by the flat response model, and NC indicates the noise ceiling. For example, 50% explainable variance means that the amount of variance predicted by a model is halfway between the amount of variance predicted by the flat response model and the maximum amount of variance that can be predicted given the noise in the data.
As an additional assessment of model accuracy, we took the fits of the SOC model from the main experiment (full fit scheme) and predicted the response amplitudes in the object experiment. To compensate for instability in the gain of response amplitudes across scan sessions (e.g. due to imperfections in co-registration), we allowed a non-negative scale factor to be applied to the predicted response amplitudes before computing R2 values. For fair comparison, the simulations used to estimate the noise ceiling for the object predictions also included the scale adjustment.
Example datasets and code implementing the SOC model are provided at http://kendrickkay.net/socmodel/.
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10.1371/journal.ppat.1004496 | The Plasmodesmal Protein PDLP1 Localises to Haustoria-Associated Membranes during Downy Mildew Infection and Regulates Callose Deposition | The downy mildew pathogen Hyaloperonospora arabidopsidis (Hpa) is a filamentous oomycete that invades plant cells via sophisticated but poorly understood structures called haustoria. Haustoria are separated from the host cell cytoplasm and surrounded by an extrahaustorial membrane (EHM) of unknown origin. In some interactions, including Hpa-Arabidopsis, haustoria are progressively encased by host-derived, callose-rich materials but the molecular mechanisms by which callose accumulates around haustoria remain unclear. Here, we report that PLASMODESMATA-LOCATED PROTEIN 1 (PDLP1) is expressed at high levels in Hpa infected cells. Unlike other plasma membrane proteins, which are often excluded from the EHM, PDLP1 is located at the EHM in Hpa-infected cells prior to encasement. The transmembrane domain and cytoplasmic tail of PDLP1 are sufficient to convey this localization. PDLP1 also associates with the developing encasement but this association is lost when encasements are fully mature. We found that the pdlp1,2,3 triple mutant is more susceptible to Hpa while overexpression of PDLP1 enhances plant resistance, suggesting that PDLPs enhance basal immunity against Hpa. Haustorial encasements are depleted in callose in pdlp1,2,3 mutant plants whereas PDLP1 over-expression elevates callose deposition around haustoria and across the cell surface. These data indicate that PDLPs contribute to callose encasement of Hpa haustoria and suggests that the deposition of callose at haustoria may involve similar mechanisms to callose deposition at plasmodesmata.
| Haustoria are specialised invasive structures that project from fungal or oomycete hyphae into host plant cells during infection, acting as sites for molecular exchange between host and pathogen. Haustoria are targets of plant defence responses, including the deposition of membranes and polysaccharides in an encasement structure that surrounds the haustorium. It is assumed that the encasement physically seals the haustorium off from the host cell. Here we have used cell biological and genetic approaches to reveal that the plasmodesmata-associated receptor-like protein PDLP1 plays a role in infection success of the Arabidopsis downy mildew pathogen, specifically in the development of the encasement. Using live cell imaging, we observed that PDLP1 relocates to the extra-haustorial membrane, and this is required for deposition of the polysaccharide callose in the encasement. This directly correlates pathogen success with the structure of the encasement, verifying the significance of the encasement in host defence. Further, our data pose the possibility that callose deposition at plasmodesmata and the haustorial encasement exploit similar mechanisms. Our findings shed light on plant defences at haustoria and how they inhibit pathogen success.
| Eukaryotic filamentous pathogens such as rusts, powdery mildew fungi, and oomycetes including Arabidopsis downy mildew Hyaloperanospora arabidopsidis (Hpa) and Phytophthora spp., form specialized feeding structures in host cells called haustoria. Haustoria are unicellular protrusions from hyphae and function as the site of molecular exchange of nutrients and effectors between host and pathogen [1]. In the model interaction between the biotrophic pathogen Hpa and its natural host Arabidopsis, this invasive process induces subcellular rearrangements in host cells, particularly in the membranes surrounding the invasive structure [2]–[4]. For fungi and oomycetes, haustoria present a host-pathogen interface in which the pathogen is separated from the host cytoplasm by different layers: the extrahaustorial matrix (EHMx) which contains cell wall material derived from the pathogen and the plant, and the host extrahaustorial membrane (EHM) [5]–[7]. The EHM is continuous with the host plasma membrane (PM) but differs in protein composition [8]–[10] and appearance [6], [11], [12] suggesting functional specialisation of this membrane domain. During fungal infection, the EHM and PM at the site of invasion may be constricted by one or more neck bands [5], [13], [14] physically sealing the EHMx off from the host cell wall. Analogous but less densely stained structures have been observed in oomycete – plant interactions [15].
After successful entry in the host tissue, plant pathogens often encounter post-invasive defence barriers, such as depositions of host-derived material at haustoria. These materials include membranes, callose, cellulose, pectin, silicon, phenolic compounds, antimicrobial peptides, toxic secondary metabolites and reactive oxygen species [16]–[23], and following initial deposition at the neck of haustoria progressively encase the entire structure [2], [24]. Callose deposition is considered as a hallmark of plant defence responses [25] but the direct role of callose deposition in defence against an oomycete pathogen has not yet been determined.
Phospholipid membranes define cellular and subcellular structures. In eukaryotic cells, the PM is the outermost of the cellular membranes, encasing the cytoplasm and cellular organelles. The PM is not uniform in composition but contains specialised domains that may perform different functions. Indeed, it has recently been shown in plant cells that the protein composition of membrane domains changes following elicitation with pathogen-associated molecular patterns (PAMPs) [26], and that different receptor complexes form in different membrane domains [27] suggesting that protein activation can be confined to specific membrane domains. Plasmodesmata (PD) are PM lined channels that bridge plant cell walls, creating membrane and cytoplasmic continuity between adjacent cells. The PM that lines these pores is proposed to be a specialised PM domain [28] and this membrane has been found to contain functionally specialised receptors [27], [29], Remorin [30] (specific to lipid rafts) and TETRASPANIN3 [31] (associated with tetraspanin enriched microdomains). The identity of proteins that are present and function at PD is poorly characterised [32] and while the functional significance of the proteinaceous composition of the membranes within PD is not fully understood, membrane specialisation is assumed to relate to the regulation of molecular flux between cells [32]. Recently, a number of membrane proteins have been identified as PD-located but it is unclear how these proteins are specifically recruited to this membrane domain.
The PD LOCATED PROTEIN (PDLP) family is composed of eight receptor-like proteins which contain a cytoplasmic domain, a single transmembrane domain and two extracellular Domains of Unknown Function 26 (DUF26) [33]. PDLPs are recruited to PD membranes via their transmembrane domain [33], where they are exploited as a scaffold or receptor for viral movement proteins for the assembly of viral tubules through PD [34]. It has been noted that PDLPs exhibit functional redundancy, as might be expected for members of a gene family with overlapping patterns of expression [35]. PDLP5 was recently identified as a mediator of salicylic acid (SA) induced PD closure, a process required for resistance against the bacterial pathogen Pseudomonas syringae pv. maculicola [36]. PDLP5 activity is correlated with callose deposition at PD [36], which induces PD closure [37]. PDLPs have also been associated with the transmission of herbivory responses [38]. However, despite these clues to their functional context, the molecular function of PDLPs has still not been identified.
In this study we found that in addition to its PD-associated function, PDLP1 mediates callose deposition around Hpa haustoria and that this activity is required for plant immunity. PDLP1 expression is specifically upregulated in mesophyll cells harbouring Hpa haustoria and PDLP1-GFP localises at the EHM of developing haustoria prior to encasement where, when overexpressed, it promotes EHM membrane proliferation. PDLPs are required for callose encasement of the haustoria and this is negatively correlated with infection success. These data suggest that PDLPs are involved in callose deposition at multiple cellular locations that include PD and haustoria.
PDLP5 transcriptionally responds to SA and has a role in defence against hemibiotrophic bacteria [36]. To assess if other members of the PDLP family are expressed in response to pathogen inoculation, we first checked the expression pattern of the eight PDLP genes during a time course of Hpa Waco9 infection in Arabidopsis Col-0. Using recently available transcriptomic data [39], we observed that both PDLP1 and PDLP5 expression was increased 5 days post inoculation (DPI) when compared to 3 DPI (Figure S1). To determine if PDLP1 plays a role in defence we examined expression of both PDLP1 and PDLP5 at the cellular level during Hpa infection. Plants stably expressing promoter::GUS fusions were generated for PDLP1 and PDLP5 and examined for GUS expression 5 DPI. As negative controls, we compared PDLP1 and PDLP5 expression to PDLP2 and PDLP3 using plants expressing PDLP2pro:GUS and PDLP3pro:GUS [35]. While some low level expression was evident for PDLP2 and PDLP5, neither the PDLP2, 3 nor 5 promoters showed GUS expression that was associated specifically with Hpa infection (Figure 1). By contrast, GUS staining for PDLP1pro:GUS was visible in cells harbouring haustoria along Hpa hyphae (Figure 1). This result indicates that in contrast to PDLP5, the PDLP1 promoter is upregulated specifically at the site of Hpa cellular invasion.
Given that PDLP1 is specifically expressed in haustoria-containing cells we examined the subcellular location of PDLP1-GFP after Hpa infection to determine if this increase in expression is likely to affect PD function. Plants that constitutively express PDLP1-GFP under the 35S promoter (PDLP1 OE, [33]) were imaged at 3–6 DPI to observe haustoria at various stages of encasement (Figure 2). In uninfected leaves, PDLP1-GFP localises to PD (white arrows, Figure 3A, [33]). Following inoculation with Hpa PDLP1-GFP was visible in the PD and surrounding unencased haustoria (Figure 2A, 3A). Infiltration of infected tissue with aniline blue stained any developing, callose-filled encasements. Haustoria with developing encasements showed aniline blue staining at the neck of the haustorium while PDLP1-GFP completely surrounded the structure (Figure 2A), illustrating that PDLP1-GFP associates with haustoria prior to development of the encasement. PDLP1-GFP remained associated with the haustorium as the encasement developed (Figure 2B) but was not associated with fully encased haustoria at a late stage of development (Figure 2C). During the encasement process, small PDLP1-GFP containing bodies could be seen peripheral to the haustorium (Figure 2E). These bodies are possibly secretory vesicles depositing encasement material at the developing structure. The localisation of the PDLP1-GFP fusion during infection was also imaged when expressed from its native promoter. In plants stably expressing PDLP1pro::PDLP1-GFP [33], PDLP1-GFP was observed surrounding haustoria (Figure S2A). Like in PDLP1 OE plants, PDLP1-GFP was also observed in the developing encasement, but sometimes this association with the encasement could be resolved into two layers that suggest PDLP1-GFP is concentrated in membranes surrounding the encasement (Figure S2B). Many PM proteins are not present in the EHM but are associated with the haustorial encasement, i.e. they are observed at the neck of haustoria early in encasement development and completely surrounding the haustorium when the encasement is fully developed [3]. Localisation of PDLP1 at haustorial membranes prior to encasement suggests it is differentially incorporated into the EHM relative to other PM proteins. Significantly, this localisation also indicates that PDLP1 has a non-PD associated function.
In order to establish whether or not PDLP localisation at haustoria is characteristic of PD proteins, we next examined the localisation of fusions to the PD-associated membrane proteins MOVEMENT PROTEIN-17 (MP17, Figure S3, [40]), TETRASPANIN3 (TET3; Figure S3 [31]) and the PD CALLOSE BINDING PROTEIN 1 (PDCB1, Figure S3, [41]) in unencased haustoria (Figure 3B). Each fusion was expressed from the 35S promoter. As observed for other PM-localised proteins, PDCB1-mCit and TET3-YFP were both visible in the developing encasement (Figure 3B) while MP17-GFP showed no association with haustoria (Figure 3B). Since PD-associated proteins did not localise at the EHM during Hpa infection, we concluded that the haustorial association is specific to PDLPs. Indeed, similar to PDLP1-GFP, PDLP2-GFP and PDLP3-GFP, from 35S promoter expression, were also observed surrounding unencased haustoria (Figure 3C, S3). Qualitative assessment of these images indicates that fluorescence associated with haustoria is fainter for these marker proteins, and when combined with the observation that PDLP3-GFP was not always visible at the haustorial periphery, raises the possibility these PDLPs have a weaker association with haustorial membranes. Irrespective, this observation indicates that, while not expressed at high levels in haustoria-containing cells, other PDLP family members carry targeting information for haustorial structures.
PDLPs have two extracellular DUF26 domains, a transmembrane (TM) domain and a short cytoplasmic tail (CT) (Figure 3D, [33]). A construct that fuses the fluorescent protein mCitrine (mCit) between the signal peptide and C-terminus (including the transmembrane domain and cytoplasmic tail) of PDLP1 (mCit-TMCT, Figure 3E) targets mCitrine to PD [33]. To determine if haustorial targeting information is also contained within the C-terminal domains of the protein, we examined the localisation of mCit-TMCT during Hpa infection. As found for PDLP1-GFP, mCit-TMCT is located surrounding unencased haustoria (Figure 3F). Thus, PDLP targeting to haustoria is conferred by the PDLP1 C-terminal tail and/or the transmembrane domain.
To test whether PDLPs play a role in defence against Hpa, we assessed Hpa susceptibility in transgenic and mutant lines. Expression of 35S::PDLP1-GFP (PDLP1 OE) significantly impairs molecular flux between leaf epidermal cells but a pdlp1 knockout mutant showed no alterations in molecular flux compared with Col-0 [33]. However, double knockout mutants for pdlp1,2 and pdlp2,3 showed increased molecular flux suggesting functional redundancy within the protein family [33]. For this reason, the triple knockout mutant pdlp1,2,3 [38] was used in all mutant assays. Following spray inoculation with the compatible isolate Hpa Waco9, Hpa sporulation 6 DPI was reduced in PDLP1 OE relative to wild-type Col-0 plants while Hpa sporulation was increased in pdlp1,2,3 mutant plants (Figure 4). These results indicate that PDLP1 is a positive regulator of plant immunity against Hpa.
While Arabidopsis Col-0 ecotype exhibits a compatible interaction with Hpa Noco2, Hpa isolate Emoy2 is recognised by the Resistance (R)-protein RPP4 in Col-0 [42]. To determine whether PDLPs play a role in RPP4-mediated resistance, we assayed the pdlp1,2,3 mutant for changes in susceptibility toward Hpa Emoy2. A small but significant increase in the number of conidiophores on pdlp1,2,3 mutants relative to Col-0 was observed suggesting that PDLPs also positively regulate immunity in response to Emoy2 (Figure S4). pdlp1,2,3 mutants exhibit a two-fold increase in conidiophore development relative to Col-0 while rpp4 mutants exhibit a 35-fold increase in conidiophore development [43]. Given the haustorial location of PDLP1 it seems unlikely that it would act downstream of cytoplasmic RPP4, and more likely that PDLPs positively regulate a basal defence response.
To identify other resident proteins of PDLP1-containing membranes, we immuno-purified (IP) PDLP1-GFP from both infected and uninfected tissues. Proteins that co-immunoprecipitate with PDLP1 (Table 1) were classified as proteins identified in PDLP1 OE samples only, i.e. absent from control samples, or those for which the ratio of spectrum counts for PDLP1 OE (infected or non-infected): control was greater than or equal to 4. Further, candidates were restricted to those that are located in cellular membranes (based on GO Cellular Component terms; PM, endosomes, vesicles, tonoplast), or are associated with compartments known to be subcellular locations of PDLP1 (ER, Golgi, PD [33]). Tandem mass spectrometry identified an almost identical subset of proteins in both infected and uninfected tissue samples (Table 1, Table S1). Several candidates have been implicated in plant defence, notably PEN3 [44], PEN1 [45], WAK2 [46], AHA1 [47] and VAMP721 [48], [49]. VAMP721 is implicated in delivery of the resistance protein RPW8 to the EHM during Golovinomyces orontii infection of Arabidopsis [48]. Others have functions associated with lipid modification, such as the phosphatidylinositol interactor PCAP1 [50], the phosphatidate phosphatase PAP1 [51], and the SEC14 domain protein PATL1 [52]. PATL1 [52] and VAMP721 [53], [54] are found at the cell plate which, like PD and haustoria, is another location at which callose is deposited.
Given that PDLP1-GFP is located at haustoria and PD, and that these membrane domains are both sites of callose deposition, we investigated whether PDLP1 plays a role in callose encasement of Hpa haustoria. Aniline blue staining of callose in infected leaves was used to assess callose deposition in encasements [21]. Staining of wild-type, PDLP1 OE and pdlp1,2,3 leaves 4–5 DPI revealed differences in the frequency of haustorial encasements (Figure 5A). 4–5 DPI Col-0 and PDLP1 OE plants exhibited many haustoria fully encased in a callose-rich material (Figure 5A). By contrast, Hpa infected leaves of pdlp1,2,3 plants showed few aniline-blue stained haustoria, similar to the callose synthase mutant pmr4 (Figure 5A) [55]. We used automated callose detection [56] to quantify the number of aniline blue stained encasements in infected leaves. PDLP1 OE plants produced significantly more callose encasements per image area whereas pdlp1,2,3 mutants produced fewer callose-encased haustoria compared with wild type (Figure 5B). To determine if this difference was due to a reduced number of haustoria produced by Hpa on pdlp1,2,3 mutants we co-stained infected tissue with trypan blue and aniline blue. Counts of aniline blue stained haustoria and trypan blue stained haustoria in a single image indicate that relative to Col-0, pdlp1,2,3 mutants have a reduced proportion of encased haustoria (Figure S5). We also performed haustorial counts on the pdlp1 mutant and double mutants pdlp1,2, pdlp2,3 and pdlp3,1. None of these lines showed a significant difference in the proportion of encased haustoria relative to Col-0 (Figure S5), indicating that no single mutation present in these lines is responsible for the phenotype observed in the pdlp1,2,3 mutants.
At higher magnification, the aniline blue stained encasement layer that surrounded haustoria in PDLP1 OE leaves appears thicker than that observed around haustoria in wild-type leaves (Figure 5C). A thin encasement is visible in pdlp1,2,3 mutant leaves but they do not stain with aniline blue, suggesting a decrease or absence in callose accumulation around Hpa haustoria in the absence of PDLPs (Figure 5C). Thus, PDLPs are positive regulators of callose deposition during the encasement of Hpa haustoria.
To further examine structural differences in encased haustoria in wild-type and PDLP1 OE plants, we next observed haustoria by transmission electron microscopy. In both encased and unencased haustoria, the EHMx appeared to consist of two layers that differ in electron density: an electron dense layer adjacent to the EHM and an electron translucent layer adjacent to the haustorial membrane (Figure 6). The translucent layer of the EHMx did not appear different in thickness or quality between wild-type and PDLP1 OE cells (Figure 6A–F) and may correspond with the haustorial wall [12]. However, while in wild-type plants the electron dense layer of the EHMx stained similarly to the plant cell wall, and may represent the true EHMx [12], this layer was frequently more densely stained relative to the host cell wall in PDLP1 OE plants (Figure 6B). At higher magnification, this increased staining density in the EHMx correlates with the presence of membrane invaginations at the boundary between the electron dense layer of the EHMx and the host cell (arrows, Figure 6C–G). When the haustorium is fully or partially encased, the model for haustorium formation would suggest that an additional membrane layer would be present here. In our images, each time the haustorium was encased (Figure 6C, F, G) we did not see clear evidence of an additional membrane layer but this may be due to the increased membrane convolution in these regions, or alternately poor membrane preservation. In the PDLP1 OE line, membrane invaginations are uniform in diameter (approximately 25 nm) and in an oblique section could be measured to be greater than 450 nm long (Figure 6G). Invaginations, or convolution of the EHM, were also observed in wild-type cells but when compared with haustoria in PDLP1 OE plants were less frequent and shorter in length (Figure 6C–E, Table S2).
Callose deposition is not always observed during infection of haustorium-forming pathogens. We tested whether PDLP1 plays a role during infection of Albugo laibachii, an Arabidopsis oomycete pathogen which forms haustoria in Arabidopsis mesophyll cells, but does not trigger callose deposition [57]. No PDLP1 signal at the EHM of A. laibachii haustoria could be observed (Figure S6) suggesting that PDLP1 localisation is specific to an Hpa response and/or callose deposition.
It has been established that during plant development, callose is deposited at PD where it regulates cell-to-cell communication [58], [59]. PDLP1 OE plants show reduced molecular flux via PD [33] and PDLP5 overexpression increases callose deposition at PD [36], so we asked whether PDLP1 also promotes callose deposition at PD. Qualitative assessment of aniline blue staining of PDLP1 OE plants showed that callose deposition was increased relative to wild-type plants but that this increase is not limited to PD – callose is deposited across the cell (Figure S7). mCherry-TMCT plants exhibited increased callose deposition by aniline blue staining but this callose appeared to be located in discrete membrane domains, likely PD (Figure S7). mCherry-TMCT plants also showed reduced intercellular flux via PD (Figure S7). Thus, as for PDLP5, it is likely that PDLP1 acts on the PD flux via callose deposition and that for PDLP1 this is mediated by the C-terminal domains of the protein.
Haustoria are the primary interface for molecular exchange between pathogen and host, for pathogen nutrient uptake [60]–[64], effector delivery [65]–[68] and targeted defence responses from the host [9], [69]. The encasement of haustoria by host cells has been observed in both compatible and incompatible interactions and can allow the host to suppress the growth of the pathogen [2], [9], [70], [71]. A variety of materials are deposited in haustorial encasements, including polysaccharides, proteins and membranous material, forming a barrier that is presumed to inhibit the loss of nutrients from the host and effector delivery from the pathogen. The beta-1,3-glucan callose is an abundant component of haustorial encasements but is not essential for their formation [72]. In this study we show that in the Hpa-Arabidopsis interaction, the pdlp1,2,3 mutant has reduced callose content in encasements and increased susceptibility to Hpa. This is in contrast to the pmr4 mutants, which similarly have reduced callose encasement of haustoria (Figure 5) but increased resistance to Hpa [55]. pmr4 mutants exhibit enhanced SA-dependent defence responses [73] which offers an explanation for enhanced resistance in the absence of callose. The opposite effect on susceptibility evident in two mutants depleted in callose suggests that callose regulation of SA-triggered responses is dependent upon callose synthase, or non-haustorial callose. Further, the pdlp1,2,3 mutant demonstrates that a callose-depleted encasement is less effective at impeding the pathogen and that callose is a critical component of targeted defence at haustoria in the Hpa-Arabidopsis interaction.
PDLPs were originally identified as a family of proteins that localise specifically at PD. They are a protein family of unknown function but have been associated with the regulation of molecular flux between cells via PD [33], virus tubule assembly at PD [34] and responses to both herbivores [38] and bacterial pathogens [36]. During Hpa infection, PDLP1 expression is increased in infected cells, but expression of PDLP2, PDLP3 or PDLP5 is not. PDLP5 expression is upregulated by SA [36]. It was recently demonstrated that Hpa effectors suppress the induction of a number of defence-responsive genes, including the SA responsive gene PATHOGENESIS-RELATED GENE 1 [74], [75]. It is possible that effectors delivered from haustoria also block SA induction of PDLP5 in infected cells.
We observed that while PDLP1-GFP is located at PD under normal conditions, upon infection with Hpa PDLP1-GFP is located at the EHM (Figure 2, 3 and S2). This association with the EHM was observed early in the infection, prior to haustorial encasement. PDLP1-GFP fluorescence was also associated with the encasement as it developed, and protein produced from native promoter expression could be resolved in two layers at the boundary of the encasement. No PDLP1-GFP could be seen at the haustorium in mature encasements (Figure 2). The EHM and PD are both specialised membrane domains that are continuous with the PM. Our data shows that while both membrane domains contain PDLPs, other proteins located in the plasmodesmal PM are not located in the EHM, indicating they differ in protein content. While no immediate similarity in the function of these membranes is apparent, this raises the possibility that PDLPs perform similar functions at PD and haustorial membranes.
While several contexts have been identified for PDLP function, we do not know the mode of activity for this family of proteins. Immunoprecipitation of PDLP1-GFP in infected and non-infected tissue did not identify any proteins that associate with PDLP1 specifically in infected tissue. This may be because cells harbouring haustoria are rare in comparison with the surrounding non-infected cells which might dilute the signal, or alternatively indicate that PDLP1 targeting in infected cells is a result of redirection of an endogenous pathway. VAMP721 was identified in PDLP1 containing membranes (Table 1) and has recently been found to be required for RPW8 targeting to the EHM of G. orontii haustoria. This allows the possibility that PDLP1 and RPW8 exploit the same trafficking pathway for delivery to the EHM, and that this pathway is required for defence during different host-pathogen interactions. VAMP721 is also required for cell plate formation, and in both samples PDLP1 immuno-purified with the cell-plate marker PATELLIN1. The related protein PATELLIN2 was also identified in the PD proteome [31], allowing the hypothesis that there is functional similarity between the membrane domains of haustoria, PD and the cell plate.
Callose deposition occurs at haustoria and PD, both membrane domains at which PDLP1 is observed. Here we have shown that PDLPs contribute to callose deposition in the encasement of Hpa haustoria. When we examined the callose content in haustorial encasements in the pdlp1,2,3 mutant, we observed that they were thinner and contained less callose compared with wild-type plants (Figure 5), further confirming the correlation between PDLP activity and callose deposition. The specific role of PDLPs as relates to callose deposition is unclear. PDLP1 localisation at the EHM precedes callose deposition and then follows the encasement as it develops. The localisation of PDLP1-GFP at the EHM raises questions relating to the spatio-temporal role of PDLP1. It is clear that PDLP1 present in membranes of the developing encasement could directly regulate callose filling of the encasement. However, we saw no evidence of callose deposition at the EHM prior to encasement development and so the significance of this localisation remains undefined.
PDLP2 and PDLP3 (the genes for which are not significantly expressed in haustoria-containing cells), as well as the synthetic protein mCit-TMCT, also localise to the EHM suggesting that, as for PD targeting, haustorial targeting information is located within the transmembrane region and/or cytoplasmic tail of PDLPs. The observation that overexpression of the TMCT also increases PD associated callose and reduces intercellular flux indicates that the C-terminal domains of PDLPs are also sufficient to promote callose deposition.
Transmission electron microscopy of haustoria that form in PDLP1 overexpressors showed a proliferation of membrane as tubules or invaginations at the host interface. A convoluted or invaginated EHM has previously been observed in the Hpa-Arabidopsis interaction [15] as well as in the Peronospora sp-cabbage interaction [11], the Albugo candida-Arabis alpina interaction [57], the G. orontii-Arabidopsis interaction [24] and the Puccinia coronata-Avena sativa interaction [76] but to our knowledge, no molecular players involved in the genesis of these convolutions have been described so far. Our data suggest that overexpression of PDLP1 promotes the formation, stability and/or modification of the EHM such that a much greater surface area of host membrane is present around the haustoria. How this relates to callose filling of the encasement remains to be determined.
This study has identified that PDLP1 contributes to callose deposition at Hpa haustorial encasements and PDLPs are required for full defence against this pathogen. We have demonstrated that in the Arabidopsis-Hpa interaction callose deposition in the haustorial encasement is a key defence response and that PDLP function extends beyond the regulation of intercellular flux. It is not clear how PDLP activity regulates callose deposition but this study has identified the possibility that this process is common to different subcellular locations and stimuli.
PDLP1 and PDLP5 regulatory sequences were amplified from 1.5 kbp upstream of the ATG and cloned via Gateway Technology (Invitrogen) into the plant expression vector pKGWFS7 [77]. These constructs were used to generate stably expressing Arabidopsis by floral dipping [78]. The synthetic construct SP-mCherry-TMCT, which produces the protein mCherry-TMCT (mCh-TMCT) was made as described [33].
Hpa isolates Noco2, Waco9 and Emoy2 were used in this study. For infection, 10 day old plants were spray-inoculated to saturation with a spore suspension of 5×104 spores/ml. Plants were kept in a growth cabinet at 16°C for 3 to 6 days with a 10 h photoperiod. To evaluate conidiospore production, 10 pools of 2 plants were harvested in 1 mL of water for each line. After vortexing, the amount of liberated spores was determined with a haemocytometer as described by [79]. Statistical analyses have been performed in three independent experiments, using ANOVA. To evaluate conidiophore development, Hpa infection structures were stained by boiling for 2 min in lactophenol trypan blue (10% phenol, 10% glycerol, 0.01% trypan blue and 10% lactic acid). Samples were cleared in 15 M chloral hydrate and mounted in 60% glycerol. For Hpa Emoy2 infection in Col-0 and the transgenic lines, the number of conidiophores per cotyledons was scored by manually scanning the abaxial and adaxial surfaces of each cotyledon of 50 plants per transgenic line. Two biological replicates were performed. For the imaging of Hpa Emoy2 development in the cotyledons, the samples were observed with a Leica DM R microscope. Pictures were taken with a Leica DFC 300 FX Digital Camera.
GUS activity was assayed histochemically with 5-bromo-4-chloro-3-indolyl-β-d-glucuronic acid (1 mg/ml) in a buffer containing 100 mM sodium phosphate pH 7, 0.5 mM potassium ferrocyanide, 0.5 mM potassium ferricyanide, 10 mM EDTA, 0.1% Triton. Arabidopsis leaves were vacuum-infiltrated with staining solution and then incubated overnight at 37°C in the dark. Destaining was performed in 100% ethanol followed by incubation in chloral hydrate solution. Sections were observed with a Zeiss Axioplan 2 microscope (Jena, Germany).
For callose staining of live infected tissue, 0.1% aniline blue [80] was pressure infiltrated into aerial tissues. For haustorial encasement quantification, infected leaves were stained with aniline blue as described [21] and stained encasements were quantified using CalloseMeasurer [56]. For in vivo localisation of fluorescent-tagged proteins in Arabidopsis, 10 day old infected seedlings were mounted in water and analysed on a Leica DM6000B/TCS SP5 (Leica Microsystems) or Zeiss LSM780 (Zeiss) confocal microscope. GFP was excited at 488 nm and collected at 515–525 nm; mCitrine and YFP were excited at 514 nm and collected at 525–540 nm; the aniline blue fluorochrome was excited with a 405 nm laser and collected at 440–490 nm.
Infected leaf samples were cut into 1 mm3 pieces and immediately placed in 2.5% (v/v) glutaraldehyde in 0.05 M sodium cacodylate, pH 7.3, with vacuum infiltration and then left overnight at room temperature to fix the tissue. Samples were rinsed in buffer, placed in 30% (v/v) ethanol on ice then transferred into flow-through capsules for further processing in a Leica AFS2 (Leica Microsystems) following a PLT protocol (progressive lowering of temperature) based on that described by [81]. This procedure was followed except for the following modifications; after dehydration through an ethanol series, infiltration steps were performed at −20°C with LR White resin plus 0.5% (w/v) benzoin methyl ether and polymerization was in Beem capsules, with indirect UV irradiation for 24 h at −20°C followed by 16 h at room temperature. The material was sectioned with a diamond knife using a Leica UC6 ultramicrotome (Leica Microsystems). Ultrathin sections of approximately 90 nm were picked up on 200 mesh copper grids which had been pyroxylin- and carbon-coated. The sections were stained with 2% (w/v) uranyl acetate for 1 h and 1% (w/v), lead citrate for 1 min and then washed in water and air dried. Grids were viewed in a FEI Tecnai 20 transmission electron microscope (FEI) at 200 kV and imaged using an AMT XR60 digital camera (Deben) to record TIF files.
For co-immunoprecipitation, 3–6 g of leaf material was ground to a fine powder in liquid nitrogen and resuspended in extraction buffer [50 mM Tris-HCl pH 7.5, 150 mM NaCl, 10% glycerol, 1 mM EDTA, 5 mM DTT, 1% IGEPAL CA630 (Sigma), 1× protease inhibitor cocktail for plant cell extracts (Sigma)] at a ratio of 2 mL buffer/1 g leaf tissue. The homogenized extract was centrifuged at 4°C/20000×g/20 min and the supernatant was passed through a double layer of Miracloth. GFP-tagged proteins were immunoprecipitated by adding 25 µL of GFP-Trap beads (Chromotek) followed by incubation on a rolling wheel at 4°C for 2 h. The beads were collected by centrifugation at 4°C/1000×g/2 min, resuspended in 1 mL extraction buffer and transferred to 1.5 mL tubes. The beads were washed by 3 further rounds of centrifugation (4°C/1000×g/1 min) followed by resuspension in 1 ml extraction buffer. To extract proteins, the beads were boiled for 5 min in 45 µL of 1× SDS sample buffer.
Samples for LC MS analysis were prepared by excising bands from one dimensional SDS-PAGE gels stained with colloidal Coomassie Brilliant Blue (Simply Blue Safe stain, Invitrogen). The gel slices were destained in 50% acetonitrile, and cysteine residues modified by 30 min reduction in 10 mM DTT followed by 20 min alkylation with 55 mM choroacetamide. After extensive washing with destaining solvent and 100% acetonintrile, gel pieces were incubated with trypsin (Promega) in 100 mM ammonium bicarbonate and 5% acetonitrile in water at 37°C overnight. Generated peptides were extracted with solution of 5% formic acid and 50% acetonitrile, evaporated to dryness, and dissolved in 2% acetonitrile, 0.1% trifluoroacetic acid prior LC-MS/MS analysis.
LC-MS/MS analysis was performed using a hybrid mass spectrometer LTQ-Orbitrap XL (ThermoFisher Scientific) and a nanoflow UHPLC system (nanoAcquity, Waters Corp.) The peptides were applied to a reverse phase trap column (Symmetry C18, 5 µm, 180 µm ×20 mm, Waters Corp.) connected to an analytical column (BEH 130 C18, 1.7 µm, 75 µm ×250 mm, Waters Corp.) in vented configuration using nano-T coupling union. Peptides were eluted in a gradient of 3–40% acetonitrile in 0.1% formic (solvent B) acid over 50 min followed by a gradient of 40–60% B over 3 min at a flow rate of 250 nL min−1 at 40°C. The mass spectrometer was operated in positive ion mode with nano-electrospray ion source with ID 20 µm fused silica emitter (New Objective). Voltage +2 kV was applied via platinum wire held in PEEK T-shaped coupling union. Transfer capillary temperature was set to 200°C, no sheath gas, and the focusing voltages in factory default setting were used. In the Orbitrap, MS scan resolution of 60,000 at 400 m/z, range m/z 300 to 2000, automatic gain control (AGC) target 1000000 counts, and maximum inject time to 1000 ms were set. In the linear ion trap (LTQ) the normal scan rate and normal range, AGC accumulation target 30,000 counts, and maximum inject time to 150 ms were used. A data dependent algorithm was used to trigger and measure up to 5 tandem MS spectra in the ion trap from all precursor ions detected in master scan in the Orbitrap. Following function and detailed settings were used: Orbitrap pre-scan function, isolation width 2 m/z and collision energy set to 35%, and precursor ion collision threshold 1000 counts. The selected ions were fragmented in the ion trap using collision induced dissociation (CID). Dynamic exclusion was enabled allowing for 1 repeat, with a 60 sec exclusion time, and maximal size of dynamic exclusion list 500 items. Chromatography function to trigger MS/MS event close to the peak summit was used with correlation set to 0.9, and expected peak width 7 s. Charge state screening enabled allowed only higher than 2+ charge states to be selected for MS/MS fragmentation.
Peak lists in format of Mascot generic files (.mgf files) were prepared from raw data using Proteome Discoverer v1.2 (ThermoFisher Scientific) and concatenated using in house developed Perl script. Peak picking settings were as follows: range m/z 300–5000, minimum number of peaks in a spectrum was set to 1, S/N threshold for Orbitrap spectra set to 1.5, and automatic treatment of unrecognized charge states was used. Peak lists were searched on Mascot server v.2.4.1 (Matrix Science) against TAIR (version 10) database with added constructs that were used throughout the experiments. Tryptic peptides only, up to 2 possible miscleavages and charge states +2, +3, +4 were allowed in the search. The following modifications were included in the search: oxidized methionine (variable), carbamidomethylated cysteine (static). Data were searched with a monoisotopic precursor and fragment ions mass tolerance 10 ppm and 0.8 Da respectively. Mascot results were combined in Scaffold v. 4 (Proteome Software, [82]) and exported in Excel (Microsoft Office). Peptide identifications were accepted if they could be established at greater than 95.0% probability by the Peptide Prophet algorithm [83] with Scaffold delta-mass correction. Protein identifications were accepted if they could be established at greater than 99.0% probability and contained at least 2 identified unique peptides. Protein probabilities were assigned by the Protein Prophet algorithm [84]. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.
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10.1371/journal.pcbi.1004698 | Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network | Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a "Mexican hat" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena.
| The recurrent interactions among cortical neurons shape the representation of incoming information but the principles governing their evolution are yet unclear. We investigate the computational role of recurrent connections in the context of sensory processing. Specifically, we study a neuronal network model in which the recurrent connections evolve to optimize the information representation of the network. Interestingly, these networks tend to operate near a "critical" point in their dynamics, namely close to a phase of "hallucinations", in which non-trivial spontaneous patterns of activity evolve even without structured input. We provide insights into this behavior by applying the framework to a network of orientation selective neurons, modeling a processing unit in the primary visual cortex. Various scenarios, such as attenuation of the external inputs or increased plasticity, can lead such networks to cross the border into the supercritical phase, which may manifest as neurological and neuropsychiatric phenomena.
| The anatomical abundance of lateral interactions [1, 2] between neurons of the local cerebral circuit (referred in this text as recurrent connections) suggest they play a fundamental role in cortical function. Indirect physiological evidence of their involvement in memory [3, 4], sensory processing [5] and in other brain functions [6, 7] reinforces this notion. Various models have been put forward in an attempt to explain the role of these lateral connections, however, an agreed framework is still missing and the topic is still far from being concluded. In the narrower scope of early visual cortex, some studies have related the role of recurrent connectivity to orientation tuning and contrast invariance [8–10]. Others have suggested a role in generating the accurate firing rates common to spontaneous activity [11].
An additional aspect of recurrently connected networks (relative to networks connected by feedforward links only) involves their dynamic properties. Networks with recurrent connections have been shown to form associative-memory related attractor states[12, 13], exhibit self-organization leading to “neuronal avalanches” [14, 15], and in general, have the potential to exhibit critical dynamics [16–18]. The idea that brain areas may operate near criticality was proposed on theoretical grounds by several authors in the past [18–22]. There is also a growing bulk of recent experimental evidence supporting it [14, 15, 23–26] (for reviews on near criticality in the brain see [16, 27]). Beggs and Plenz [14, 15] demonstrated that neural activity in acute slices and in slice cultures is organized in neural avalanches, whose size obeys a power law distribution. They interpreted their results in terms of critical branching processes [28]. Further work [23] showed that neuronal avalanches also appear in the spontaneous cortical activity of awake monkeys and in large scale human brain activity (e.g. [29, 30]). It was also demonstrated in slice cultures that the dynamical range of the network is maximized near the critical point [24]. Although these dynamic properties have by now been well established, only few papers in the neuroscience literature have so far attempted to link them to concrete brain functions, such as the function of the visual system.
A central question regarding recurrent interactions, which has not yet been properly addressed, is how they evolve to facilitate the network’s computational capacity and what principles govern this evolution. Their optimal pattern within the network also remains unknown. In this work, we address these issues using a first-principle information theoretic approach, namely using the principle of maximum information preservation (also known as ‘infomax’ [31]). This principle has been successfully implemented in a variety of computational neuroscience studies. Bell & Sejnowski [32] extended it to nonlinear output neurons implementing ICA (Independent Component Analysis) to achieve blind source separation. Later, they showed that the independent components of natural scenes are Gabor-like edge filters [33].
Tanaka et al [34] have demonstrated that the characteristics of orientation selectivity in V1 can be acquired by self-organization of recurrent neural networks according to Infomax learning. This work was recently extended by Hayakawa et al [35] to reveal a biologically plausible infomax learning algorithm.
The present work can be seen as a further extension of these earlier efforts, studying how the gradual development of a network’s recurrent interactions may optimize the representation of input stimuli. Unsupervised learning is applied in training networks to maximize mutual information between the input layer and an overcomplete recurrently connected output layer. The evolving pattern of recurrent interactions is investigated in a model of a hypercolumn in primary visual cortex, considered the base functional unit of V1, receiving input from both eyes, in a full representation of all possible orientations. Various constellations of input stimuli and network connectivity are examined, in aim of studying their relationship with different network measures. Methods to evaluate the optimal pattern of recurrent interactions in a neural network model and its dependence on the statistics of the external inputs were extended from Shriki et al. [36]. We first provide an analytical and numerically simulated account of a toy hypercolumn network model. Subsequently, a more ecological network is studied, in which natural scenes are used as input for training the network. These models allow us to compare the emerging network’s properties with those arising from earlier empirical and theoretical work.
The general scheme and many methods applied in this study can be viewed as a direct evolution of the earlier work reported in [36]. Below, we highlight the main extensions of the current models relative to the one presented in this former work in regards to the network structure, learning algorithm and other significant model ingredients.
The basic network model consists of two layers of neurons, N neurons at the input layer and M neurons at the output layer (Fig 1A), where M ≥ N. Thus, the network deterministically maps a low dimensional input space into a manifold in a higher-dimensional output space. Such a representation, which contains more output components than input components, is termed overcomplete. The feedforward interactions are described by the M × N matrix W and the recurrent interactions by the M × M matrix K.
During the presentation of each input sample, the input components xi are fixed. The dynamics of the output neurons are given by
τdsidt=−si+g(Σj=1NWijxj+Σk=1MKikSk), i=1,…,M
(1)
Where g is some nonlinear squashing function and τ is a characteristic time scale (here we set τ = 1 and g was taken to be the logistic function, g(x) = 1/(1+e−x)). We assume that the activities of the output neurons reach equilibrium after some time and define the output as the steady-state pattern of activity. For the cases we studied, numerical simulations of the network dynamics indeed stabilized and proved this assumption to be consistent. The steady-state responses are given by
si=g(Σj=1NWijxj+Σk=1MKikSk), i=1,…,M
(2)
To evaluate the neuronal representation of the external inputs we used the mutual information between the input and output of the network [37]. More specifically, the mutual information between the input vector, x, and the output vector, s, can be expressed as the difference between the entropy of the output and the conditional entropy of the output given the input. The conditional entropy can also be viewed as the entropy of the output noise. Here, the network response is a deterministic function of the input, and thus the mutual information depends only on the entropy of the outputs. As shown in [36], maximizing the output entropy (and therefore the mutual information) is equivalent to minimizing the following objective function:
ε=−12〈ln det(χTχ)〉x=−12Tr〈ln(χTχ)〉x
(3)
where xij=∂si∂xj is the Jacobian matrix of the transformation and reflects the sensitivity of the output units to changes in the input units. We also refer to this matrix as the susceptibility matrix as it is analogous to the susceptibility of physical systems to external fields.
The adaptive parameters of the algorithm are the sets of feedforward and recurrent interactions, Wij and Kij. The learning rules for these parameters are derived from this objective function using the gradient decent method, as shown in [36]. Here we focus only on the recurrent interactions. The gradient descent learning rule for the recurrent interactions is:
ΔK=−η∂ε∂K=η〈(χΓ)T+ϕTasT〉
(4)
Where η is the learning rate, the matrix ϕ is given by ϕ = (G−1−K)−1 and satisfies χ = ϕW, the matrix G is defined as g'k → g'i, the matrix Γ is defined as Γ = (χTχ)−1χTϕ and the components of the vector a are given by ak=[χΓ]kkgk″(gk′)3. The triangular brackets denote averaging over the input samples.
We defined several measures to characterize the behavior of the network and gain further insight into its dynamics. As described in the Results section, after the learning process converges, the networks tend to operate near a critical point. Thus, it is helpful to define metrics that may behave differently when the networks approach that critical point. One such measure is the time it takes the recurrent network dynamics to reach steady-state—the convergence time. Many dynamical systems exhibit a slow-down of the dynamics near critical points, often termed critical slowing down [38]. Thus, a substantial increase in the convergence time may indicate that the system is close to a critical point.
To gain insight in the present context, we note that near a steady state, the linearized dynamics (in vector notation) are given by τd(δs)dt=−[I−GK]δs. The inverse of the matrix [I−GK] appears also in the expression for the Jacobian matrix, which determines the objective function. Optimizing the objective function leads to very large eigenvalues in the Jacobian matrix (high-susceptibility), and therefore, the eigenvalues that dominate the dynamics become very small, which manifests as slowing down.
To estimate the convergence time, we defined a criterion for stability of the neuronal activities and measured the time it takes the network to satisfy this criterion. This stability criterion means that for each neuron in the network, the difference in its activity between the current time step and the previous time step is smaller than a predefined small number.
When the network becomes supercritical, it converges onto attractor states, which reflect the underlying connectivity. In the context of orientation tuning, which we study here, a natural measure to quantify this behavior is the population vector [39]. Each neuron is associated with a complex number. The magnitude of the number is the activity of this neuron and the phase is set according to the preferred angle or orientation of the neuron (in the case of preferred orientation, the orientation is multiplied by 2, to span the range from 0° to 360°). Given a pattern of activity in the network, these complex numbers are summed to yield a resultant complex number, termed the population vector. When the network response is uniform, the magnitude of the population vector is 0. When the network response peaks at some orientation, the magnitude of the population vector is finite.
Similar to previous papers concerning training of networks over natural scenes [33], we used photos involving forest scenes or single trees and leaves. The photos were converted to grayscale byte value of 0 to 255 and then”cut” into patches of 25-by-25 pixels. Each patch was represented as a vector with 625 components. Using PCA (Principal Component Analysis), the dimensionality of the images was reduced from 625 to 100. The inputs were also whitened by dividing each eigenvector by the square root of the corresponding eigenvalue. These whitened 100-dimensional inputs were used to train a network with 380 output neurons. The results were robust to different manipulations of the inputs. For example, we obtained qualitatively similar results even without dimensionality reduction or whitening, using smaller image patches.
The feed-forward filters were set to be Gabor filters with the same center in the visual field and the same spatial frequency. The size of each Gabor filter was 25-by-25 pixels. The full feed-forward matrix was a product of two matrices: A 380-by-625 matrix containing a Gabor filter in each row, which was multiplied from the right by a 625-by-100 matrix representing the reconstruction after the dimensionality reduction.
Close to the critical point, accurate simulation of the network dynamics requires a long time due to the phenomenon of critical slowing down. To explore the characteristics and dynamics of the network as it approached the critical point, we allowed simulations to run for very long periods. Thus, simulations could take up to weeks to complete based on network size and the value of the learning rate.
When the evolving networks approached a critical point, the objective function tended to be very sensitive to changes in the pattern of interactions. In some cases, the objective function could even increase rather than decrease, implying that the learning rate was not small enough. To overcome this problem, we calculated the expected value of the objective function before actually updating the interactions. When an upcoming increase was identified, the learning rate was reduced by a factor of one-half and the process was repeated again.
To establish the credibility of our model, we first identified conditions under which a comparison between analytical and numerical results could be facilitated. This was achieved via a toy model of a visual hypercolumn, which is amenable to analytical solution in the limit of very low contrast. An important insight from this toy model is that in the low contrast limit, optimal information representation is obtained at a critical point of the network dynamics. These results are then verified using numerical simulations of this simple model. Using similar simulation approach, we next show that critical behavior also arises in a more complex setting, when natural images are used as inputs in the training phase.
The architecture of the network model is presented in Fig 1B. Each input sample is a point on the plane, with an angle, θ0, representing the orientation of a visual stimulus and amplitude (its distance from the origin), r, representing the timulus contrast (Fig 1B). Each point can be represented as (x1,x2) = r(cosθ0,cosθ0). For clarity, we consider periodicity of 360° rather than 180°, which is the relevant symmetry when considering orientations. The angles θ0 are distributed uniformly between 0 and 2π. The amplitudes r are distributed according to a Gaussian distribution with a positive mean 〈r〉, representing the mean contrast. By varying the mean value of r we study the effect of stimulus statistics on the optimal network connections.
The network represents this two-dimensional input by M sigmoidal neurons (M≫1) interconnected with recurrent interactions (Kij, i,j = 1,…,M). The feedforward connections (rows of) are chosen to be unit vectors, uniformly distributed over all possible directions, i.e. (Wi1,Wi2) = r(cosϕi,cosϕi) where ϕi = 2πi/M, i = 1,…,M. Thus, the input to the i’th neuron has a cosine tuning function peaked at ϕi and the network has a ring architecture (Fig 1B). The feedforward connections are fixed throughout the learning. Our goal is to evaluate the matrix of recurrent connections K that maximizes the mutual information between the steady state responses of the output neurons and the stimulus. For a given input and connection matrix, the steady-state responses are given by
si=g(ΣjKijsj+x1cosθi+x2sin θi)
(5)
where g is the logistic function (see Methods).
The sensitivity matrix, χ, is an M×2 matrix given by:
χi1=∂si∂x1=gi′⋅[ΣlKilχl1+cosθi]
(6)
χi1=∂si∂x2=gi′⋅[ΣlKilχl2+sinθi]
(7)
Where gi'=g'(ΣjKijsj+x1cosθi+x2sin θi) is the derivative function of the neuronal transfer function and we have used the expression for si given in Eq (5).
To investigate analytically the optimal pattern of recurrent interactions when the typical input contrast is low, namely when 〈r〉→0, we assume that the interaction Kij between the i’th and j’th neurons is an even function of the distance between the neurons on the ring,
Kij=K(θi−θj)
(8)
When 〈r〉 approaches zero, the total external input to each neuron approaches zero. We denote the value of g’ at zero input by γ0 = g’(0). In the case of the logistic function, γ0 = 1/4. Since the number of output neurons, M, is large, we can take the continuum limit and transform the summations over angles to integrals. For instance, the equation for χi1 can be written as
χ1(θ)=γ0[M2π∫−ππdθ′K(θ−θ′)χ1(θ′)+cosθ]
(9)
and similarly for χi2. We define the Fourier series of K and χ1
K(θ)=1MΣn=0∞kncos(nθ)
(10)
χ1(θ)=Σn=0∞[ancos(nθ)+bnsin(nθ)]
(11)
Fourier transforming Eq (9) yields an=γ0δn1/(1−12γ0k1) and bn = 0, where k1 is the first cosine harmonic of the interaction profile, Eq (10). Thus,
χi1=γ0cosθi1−12γ0k1
(12)
and similarly
χi2=γ0sinθi1−12γ0k1
(13)
The 2 X 2 matrix χTχ is a diagonal matrix with elements
(χTχ)11=(χTχ)22=Mγ02(1−12γ0k1)2
(14)
Substituting these expressions in Eq (3), yields
ε=log(2Mγ02)+2log(1−12γ0k1)
(15)
Eq (15) implies that as k1 approaches the critical value k1c= 2/γ0 the objective function diverges to −∞. This means that the optimal pattern of recurrent interactions has the form
K(θi−θj)=2Mγ0cos(θi−θj)
(16)
The divergence of the objective function, that is of the sensitivity (or susceptibility) at k1c reflects the fact that at this point the network undergoes a phase-transition into a state of spontaneous symmetry breaking [9]. Formally, this can be illustrated by adding a uniform random component to the input that each neuron receives and examining the network response. As shown in [9], the network response is very different below and above the transition point. For k1c<2/γ0, the network settles into a homogeneous state with si = g(0). However, for k1c>2/γ0, the network dynamics evolve into an inhomogeneous solution with a typical ''hill'' shape [9], which is determined by the recurrent connections and can be interpreted as a "hallucination" of an oriented stimulus. Neurons, which are slightly more active due to the random noise, enhance the activity of neurons with similar preferred orientations, which in turn enhance the activity of the initial neurons through feedback. The winning neurons inhibit neurons with more distant preferred orientations, thus creating a "hill"-shaped profile. The location of the peak of this hill is arbitrary and depends on the specific realization of the noise in the input pattern and on the initial conditions of the neuronal activities. This dramatic change in the network behavior implies that near k1c the network is extremely sensitive to small changes in the input. This enhanced sensitivity increases the mutual information between the network response and the stimulus.
In the limit of 〈r〉→0 the objective function depends solely on the first harmonics of the interaction profile, leaving open the question of whether the higher order corrections in r predict large values of the higher harmonics of the interaction profile. Furthermore, in the analytic derivation we have assumed translational invariance of K, which raises the question of whether there are better solutions which break this symmetry of K. To address these questions, we simulated the gradient based learning algorithm for the evolution of the interaction matrix (Methods; [36]), with no restrictions on the form of the matrix. The network consisted of 2 input neurons and 141 output neurons. The nonlinear squashing function was the logistic function. The feedforward connections to each output neuron were unit vectors uniformly distributed between 0° and 360°, and were fixed throughout the learning. The initial recurrent interaction matrix was set to zero. The angle of each input was drawn from a uniform distribution, while the magnitude was drawn from a Gaussian distribution around a characteristic radius r with a standard deviation of 0.1 times the mean.
Fig 2 shows the results from a simulation with 〈r〉 = 0.1, namely when the inputs are relatively weak. As can be seen, the interaction pattern (Fig 2A) is translation invariant; i.e., each neuron has the same pattern of pre and postsynaptic interactions. It is important to note that we do not impose any symmetry on the connections. The resulting translation invariance is a natural result of the statistical symmetry of the inputs to the network. Fig 2B shows one row of the interaction matrix (representing the presynaptic connections into a single output neuron). For clarity, the values are multiplied by the number of neurons, M. This result is highly congruent with the analytical derivation presented above, Eq (16), that predicts a pure cosine profile with an amplitude of 8 for the logistic function. Fig 2C shows the response of the network as a function of the preferred orientation (PO) of the neurons (solid line) to a vertical input at the typical contrast (r = 0.1). The amplification in comparison to the network response without recurrent interactions (dashed line) is clearly seen. Responses to different contrasts are shown in Fig 2D.
We next investigated a more complex network model of a visual hypercolumn (Fig 1C). In this setting, gray-level image patches from natural scenery (see Methods) were used as inputs to train the network [40]. The network consisted in this case of 100 input neurons and 380 output neurons. To study the pattern of recurrent interactions systematically, we manually set the feed-forward filters to be Gabor filters with the same center in the visual field and the same spatial frequency, spanning all orientations. It is worth noting that this overcomplete network can also be used to learn the feed-forward connections themselves [36], and indeed, as we established thorough numerical simulations, when trained using natural scenes, the feed-forward filters turn out to be Gabor-like filters. This result is related to the fact that the algorithm for the feed-forward connections is a simple generalization of the infomax ICA algorithm [32] from complete to overcomplete representations. Training the infomax ICA algorithm using natural scenes is known to result in Gabor-like filters [33].
Fig 4A depicts the full matrix of recurrent connections. As can be seen, the matrix is symmetric and the interaction between two neurons depends only on the distance between their preferred orientations. This finding is in line with the behavior of the simple toy model. Again, it is important to note that the interaction matrix was not constrained to be symmetric. Rather, this is a natural outcome of the learning process, reflecting the symmetry in the pattern of feedforward interactions. Fig 4B plots the interaction strength as a function of the distance between the preferred orientations of the pre- and post-synaptic neurons. The emerging profile has a "Mexican hat" shape, with short-range excitation, longer-range inhibition and an oscillatory decay as the distance in preferred orientation increases.
To characterize the network behavior after training it with natural images we examined its response to simple oriented stimuli. Fig 4C depicts the steady-state profile of activity in response to a vertically oriented Gabor stimulus (solid line). The spatial frequency of the Gabor stimulus and the width of the Gaussian envelope were identical to those of the Gabor filters in the feedforward connections and the contrast was set to the mean contrast of the training stimuli. For comparison, the dashed line shows the response of the network without recurrent interactions. Clearly, the evolved recurrent interactions amplify and sharpen the response compared to the response without recurrent interactions. Fig 4D shows the network response to the same vertical stimulus for various contrast levels. Notably, the width of the profile is approximately independent of the contrast, and the effect of changing the contrast is mainly multiplicative.
Fig 4E–4G show the dependence of various measures for the network behavior (see Methods) on the scaling factor. Fig 4E shows that even small changes to the scale factor can significantly increase the objective function, resulting in poor information representation. Decreasing the scale factor reduces the amplification provided by the recurrent interactions and consequently reduces the sensitivity of the network to external inputs. Conversely, increasing the scale factor to values above 1 causes the recurrent interactions to become too dominant, and pushes the network into a pattern formation regime. In this regime, the network is again less sensitive to external inputs, but this time it is due to the attractor dynamics that govern its behavior. Fig 4F shows the convergence time of the network dynamics. At the optimal point, the convergence time starts to increase to very high values, reflecting critical-slowing down at the transition into attractor-dominated dynamics. The magnitude of the population vector also rises sharply near the optimal point (Fig 4G). Overall, the behavior of the convergence time and the population vector shows that indeed close to the optimal scaling factor from the learning process, the network experiences a phase transition. The behavior of these metrics also resembles their behavior in the low contrast case in the toy model (Fig 2F–2G).
We studied the long-term evolution of recurrent interactions in a model of a sensory neural network and their dependence on the input statistics. We found that under very general conditions, optimal information representation is achieved when the network operates near a critical point in its dynamics.
The study focused on a simplified model of visual hypercolumn, a local processing unit in the visual cortex. The feedforward interactions from the input layer to the output layer were manually set such that each neuron in the output layer had a certain preferred orientation. The recurrent interactions among these neurons evolved according to learning rules that maximize the mutual information between the external input to the network and the network's steady-state output. When the inputs to the network during learning were natural images, the evolved profile of interactions had a Mexican-hat shape. The idea that neurons with similar preferred orientations should effectively excite each other and that neurons with distant preferred orientations should effectively inhibit each other has been suggested in the past based on empirical findings, e.g. [9, 41, 42], but here it was derived using a first-principle computational approach. This pattern of interactions helps in amplifying the external inputs and in achieving a relatively constant width for the orientation tuning curves, which is consistent with experimental findings on primary visual cortical neurons [43, 44].
A learning algorithm for information maximization in recurrent neural networks was also derived in [34]. The major difference from the current work is that here the information is maximized between the external input and the steady-state output, whereas in [34] the input and output refer to the patterns of activity in the recurrent network at two consecutive time steps. The approach in [34] is aimed at maximizing information retention in the recurrent network, whereas here the focus is on sensory processing and on the representation of the external input. In addition, the neurons in [34] are stochastic binary neurons, whereas the neurons here are deterministic and have a smooth nonlinearity. The network model in [34] was also trained using natural images as external inputs, leading to Gabor-like feed-forward connections, consistent with the findings in [33]. However, the authors do not discuss the structure of the connections among the output neurons, so this important aspect cannot be compared with the present work, which focused on recurrent connectivity.
The present model is clearly overly simplified in many aspects as a model of the primary visual cortex. For example, the gradient-based learning rules employed here are likely to be very different from the plasticity mechanisms in the biological system, but the assumption is that they reflect the long-term evolution of the relevant neural system and converge to a similar functional behavior. Despite its simplicity, the model provides a concrete setting for examining the role of recurrent interactions in the context of sensory processing. This leads to general insights that go beyond the context of early visual processing, as we discuss below.
The dynamics of recurrent networks, like the one studied here, can allow the network to hold persistent activity even when the external drive is weak or absent. The network is then said to display attractor dynamics. In the context of memory systems, attractors are used to model associative memory [45, 46]. Different attractors correspond to different memory states, and the activity patterns that form the basin of attraction of each attractor correspond to various associations of this memory. In the context of early sensory networks, however, the persistent activity at an attractor may correspond to a hallucination. In addition, the flow from different initial patterns to the attractor implies loss of information and insensitivity to changes in the external inputs, and thus may be undesired in the context of sensory processing. An important result of this study is that the evolved networks naturally tend to operate near a critical point, which can be thought of as the border between normal amplification of inputs and hallucinations. In [9], a model of a visual hypercolumn, which is similar to our toy model, was studied analytically. There, the pattern of interactions was assumed to have a cosine profile and it was shown that when the amplitude of the cosine crosses a critical value, the network transitions into an attractor regime. In this regime, the network dynamics evolve into an inhomogeneous solution with a typical ''hill'' shape, which represents a hallucination of an oriented stimulus. Here, the learning algorithm leads the network to operate close to that critical point. Scaling up the resulting pattern of synaptic interactions by a small factor pushes the network into the undesired regime of attractors, namely into hallucinations [47, 48].
This tendency to operate near a critical point can be explained intuitively. The task of the network is to maximize the mutual information between input and output, which amounts to maximizing its sensitivity to changes in the external inputs. The network uses the recurrent interactions to amplify the external inputs, but too strong amplification may generate hallucinations. Thus, the learning process should settle at an optimal point, which reflects a compromise between these two factors. An interesting insight comes from comparing the network to physical systems that may experience phase-transitions in their behavior. A universal property of these systems is that their sensitivity to external influences, or in physical terminology their susceptibility, is maximized at the transition point [49]. Our adaptive sensory recurrent networks evolve to operate near a critical point in order to achieve maximal susceptibility and represent information optimally. It is important to note that neural systems respond to a wide range of inputs and that the target of the learning is to find the pattern of interactions that is optimal on average. Under certain conditions, the recurrent interactions may not contribute much to the representation. However, in many cases, especially if the typical inputs have a narrow distribution or tend to be weak, the optimal pattern of recurrent interactions is expected to be near critical. The dominance of low contrasts in natural images is therefore an important factor in driving the pattern of recurrent interactions to be near critical.
There are several important distinctions to be made when comparing previous research [14, 15, 24, 27, 50, 51] on critical brain dynamics with the present study. First, the present work addresses mainly the issues of long-term plasticity and the effect of input statistics, whereas previous modeling works consider mostly networks with random connectivity, which do not adapt to input statistics. Here we demonstrated that near-criticality emerges as a result of directly optimizing a well-defined measure for network performance using a concrete learning algorithm. In addition, an important role is played by the input statistics, and depending on these statistics the network may or may not approach criticality. Moreover, the resulting connectivity matrices are not random and the specific pattern that emerges is crucial for the network performance. We note that in [34] the network can adapt to the statistics of external inputs, but there criticality was demonstrated only when the network evolved without external input. Other studies, such as [52], model plasticity in recurrent neuronal networks, but not in an ecological sensory context.
Second, here the critical point relates to the transition from normal amplification of external inputs to an attractor regime. At the supercritical regime, the network may present inhomogeneous activity patterns but it is not necessarily driven to saturation. In other words, the supercritical regime does not necessarily correspond to an explosive growth of the activity or to epileptic seizures. In the subcritical regime, the representation is faithful to the input and cannot generate hallucinations, but the activity does not necessarily die out. This should be compared with models based on branching processes, in which the supercritical regime generally refers to runaway activity and the subcritical regime refers to premature termination of activity. In the present model, the network may have a branching parameter of 1 in both the subcritical and supercritical regimes. In this sense, the type of criticality presented by this model can be thought of as a subspace within the space of all networks with branching parameter equal to 1. Furthermore, in contrast to [21] and [18], the supercritical regime in the present model does not correspond to chaotic behavior.
The issues raised above call for future experimental and theoretical work aimed at elucidating the effect of input statistics on the approach to criticality and at characterizing the type of criticality that emerges. In particular, future modeling work should consider learning algorithms that optimize information representation in spiking and conductance-based neural networks, which have richer dynamics. An interesting approach to take spike times into account is proposed in [53] but the proposed algorithm is limited to one-layer feed-forward networks. Incorporating short-term plasticity in these models would also be valuable, because networks with short-term plasticity were demonstrated to exhibit robust critical dynamics [22, 45, 46].
An interesting universal phenomenon that occurs when networks approach the critical point is a change in the effective integration times. As demonstrated here, close to the critical point the time it takes the network to settle after the presentation of an input is considerably longer. This phenomenon is termed critical slowing down, [38, 54] and it may serve as a probe to characterize near-critical networks both in models and in experiments (e.g., by examining the power spectrum or by measuring the decay time after a perturbation). It should be pointed out that there is a trade-off between the information representation and the integration time. Near criticality, the output of the recurrent network is more sensitive to change in the inputs, but it takes more processing time. It is reasonable to assume that the brain also takes the processing time into account and not only the quality of the representation. This factor should drive networks in the brain to operate slightly below the critical point, i.e. in the subcritical regime, than would be predicted based on information representation alone.
Clearly, because the neurons in our network are characterized by their firing rates, the network dynamics are not rich enough to display spatiotemporal patterns of activity like neuronal avalanches, synchronized firing or chaotic behavior. Nevertheless, the rate models can often be translated to more realistic conductance-based neuronal networks, which display similar dynamics [55]. In particular, the conductance-based model of a hypercolumn that is investigated in [55] exhibits a critical point similar to the one described here, and the network state is neither synchronized nor chaotic in either side of the critical point.
In real-life biological settings, the pattern of recurrent interactions in a network can be driven into the supercritical 'pattern formation' regime as a result of several possible mechanisms. One possibility is via direct application of certain drugs that increase the effective synaptic efficacy. Bressloff et al. [47, 48] studied the dynamics of a network model of the primary visual cortex. They show that when the network's resting state becomes unstable, the various patterns of activity that spontaneously emerge correspond to known geometric visual hallucinations seen by many observers after taking hallucinogens. They propose that hallucinogens act by scaling the synaptic interactions until instabilities in the network dynamics begin to arise. Our work suggests that due to the network operating not far from the critical point, even a relatively small increase in the scale of the connections may drive it into the supercritical domain.
Another plausible scenario for approaching criticality is through a high degree of plasticity. In numerical simulations of the learning algorithm, an important parameter is the learning rate that controls the step size of the learning dynamics and can be biophysically interpreted as the degree of plasticity [56]. Interestingly, in simulations in which the learning rate was too high, the network did not stabilize at the optimal point near the phase transition but instead crossed it due to the large step size, resulting in poor information representation and hallucinatory behavior. This behavior suggests a potential causal relationship between abnormal neural plasticity and neurological or neuropsychiatric phenomena involving hallucinations, such as schizophrenia.
A third route to criticality is through attenuation of the external inputs. When the external inputs to the network are very weak the recurrent interactions at the output layer compensate by further approaching the critical point. This process increases the effective gain of the network but may lead to instabilities in the network dynamics and to false percepts. For instance, such a mechanism may play a role in the generation of hallucinations as a result of sensory deprivation. An interesting example in this context is tinnitus, a persistent and debilitating ringing in the ears [57]. Tinnitus often appears after damage to the hair cells in the ear, mostly by acoustic trauma or by pharmacological agents, such as Salicylate. It was also proposed that plasticity of the central nervous system may play a role in the etiology of Tinnitus [58]. Our model suggests that recurrent networks further along the auditory pathway may try to compensate for the attenuated signals by setting their interactions closer to the critical point. Operating too close to this instability may result in spontaneous activity that is manifested as persistent illusory sounds. The idea that sensory deprivation leads to criticality may also be related to the observation of criticality in slices and cultures [2]. A prediction of the present work would be that highly variable external stimulation will result in networks that are non-critical.
It is also interesting to discuss how a network that became supercritical can return to the normal subcritical regime. In principle, the gradient descent learning algorithm should drive the network to the optimal point even when it is supercritical. However, the learning is based on certain continuity assumptions regarding the mapping of input patterns to output patterns, which may be violated in the supercritical attractor regime. In particular, we assume that there is an invertible continuous mapping between input and output with a well-defined Jacobian matrix. Topologically, the output space may become disconnected with different islands corresponding to different attractor states, making the mapping non-invertible and dis-continuous. Under these conditions, the learning algorithm may not be able to optimize information representation and bring the network back to subcritical dynamics. A similar phenomenon might happen in real brains, preventing the intrinsic learning rules from getting the network back to normal healthy dynamics.
Our findings suggest that optimal information representation in recurrent networks is often obtained when the network operates near criticality. This is consistent with a growing body of theoretical and experimental literature relating to near criticality in the brain [2, 14, 15, 23, 27, 50, 59, 60]. The uniqueness of the present study is in the rigorous approach to the role of long-term plasticity in approaching criticality and we believe that further research should be dedicated to this issue.
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10.1371/journal.pgen.1007003 | Genomics-enabled analysis of the emergent disease cotton bacterial blight | Cotton bacterial blight (CBB), an important disease of (Gossypium hirsutum) in the early 20th century, had been controlled by resistant germplasm for over half a century. Recently, CBB re-emerged as an agronomic problem in the United States. Here, we report analysis of cotton variety planting statistics that indicate a steady increase in the percentage of susceptible cotton varieties grown each year since 2009. Phylogenetic analysis revealed that strains from the current outbreak cluster with race 18 Xanthomonas citri pv. malvacearum (Xcm) strains. Illumina based draft genomes were generated for thirteen Xcm isolates and analyzed along with 4 previously published Xcm genomes. These genomes encode 24 conserved and nine variable type three effectors. Strains in the race 18 clade contain 3 to 5 more effectors than other Xcm strains. SMRT sequencing of two geographically and temporally diverse strains of Xcm yielded circular chromosomes and accompanying plasmids. These genomes encode eight and thirteen distinct transcription activator-like effector genes. RNA-sequencing revealed 52 genes induced within two cotton cultivars by both tested Xcm strains. This gene list includes a homeologous pair of genes, with homology to the known susceptibility gene, MLO. In contrast, the two strains of Xcm induce different clade III SWEET sugar transporters. Subsequent genome wide analysis revealed patterns in the overall expression of homeologous gene pairs in cotton after inoculation by Xcm. These data reveal important insights into the Xcm-G. hirsutum disease complex and strategies for future development of resistant cultivars.
| Cotton bacterial blight (CBB), caused by Xanthomonas citri pv. malvacearum (Xcm), significantly limited cotton yields in the early 20th century but has been controlled by classical resistance genes for more than 50 years. In 2011, the pathogen re-emerged with a vengeance. In this study, we compare diverse pathogen isolates and cotton varieties to further understand the virulence mechanisms employed by Xcm and to identify promising resistance strategies. We generate fully contiguous genome assemblies for two diverse Xcm strains and identify pathogen proteins used to modulate host transcription and promote susceptibility. RNA-Sequencing of infected cotton reveals novel putative gene targets for the development of durable Xcm resistance. Together, the data presented reveal contributing factors for CBB re-emergence in the U.S. and highlight several promising routes towards the development of durable resistance including classical resistance genes and potential manipulation of susceptibility targets.
| Upland cotton (Gossypium hirsutum L.) is the world’s leading natural fiber crop. Cotton is commercially grown in over 84 countries, and in the United States, is responsible for $74 billion annually [1, 2]. Numerous foliar diseases affect cotton throughout the world’s cotton growing regions. Historically, one of the most significant foliar diseases has been bacterial blight, caused by Xanthomonas citri pv. malvacearum. Cotton bacterial blight significantly limited cotton yield in the late 20th century. In the 1940’s and 1950’s, breeders identified and introgressed multiple resistance loci into elite germplasm [3–5]. This strategy proved durable for over half a century. In 2011, cotton bacterial blight (CBB) returned and caused significant losses to farmers in the southern United States, including in Arkansas and Mississippi. Nonetheless, CBB has received little research focus during the last several decades because, prior to 2011, losses from this disease were not substantial. Modern molecular and genomic technologies can now be employed expeditiously to deduce the underlying cause of the disease re-emergence and pinpoint optimized routes towards the development of durable resistance.
CBB is caused by X. citri pv. malvacearum (Xcm); however, the pathogen has previously been placed within other species groupings [6–9]. The Xcm pathovar can be further divided into at least 19 races according to virulence phenotypes on a panel of historical cotton cultivars: Acala-44, Stoneville 2B-S9, Stoneville 20, Mebane B-1, 1-10B, 20–3, and 101-102.B [10, 11]. Historically, the most common race observed in the U.S. has been race 18, which was first isolated in 1973 [12]. This race is highly virulent, causing disease on all cultivars in the panel except for 101-102.B. However, this diagnostic panel of cotton varieties used to race type strains is no longer available from the USDA/ARS, Germplasm Resources Information Network (GRIN).
CBB can occur at any stage in the plant’s life cycle and on any aerial organ. Typical symptoms include seedling blight as either pre- or post-emergent damping-off, black arm on petioles and stems, water-soaked spots on leaves and bracts, and most importantly boll rot [10]. The most commonly observed symptoms are the angular-shaped lesions on leaves that can coalesce and result in a systemic infection. Disease at each of these stages can cause yield losses either by injury to the plant or direct damage to the boll. No effective chemical treatments for the disease have been released to date. Methods to reduce yield loss as a result of CBB include acid de-linting cotton seed prior to planting, field cultivation practices to reduce sources of overwintering inoculum and planting cultivars with known sources of resistance [3, 4, 8, 13, 14].
Xanthomonads assemble the type three secretion system (T3SS), a needle-like structure, to inject diverse type three effectors (T3Es) into the plant cell to suppress immunity and promote disease [15–19]. For example, transcription activator-like (TAL) effectors influence the expression levels of host genes by binding directly to promoters in a sequence-specific way [20]. Up-regulated host genes that contribute to pathogen virulence are termed susceptibility genes and may be modified through genome editing for the development of resistant crop varieties [21].
Plants have specialized immune receptors, collectively known as nucleotide-binding leucine rich repeat receptors that recognize, either directly or indirectly, the pathogen effector molecules [22, 23]. Historically, this host-pathogen interaction has been termed the ‘gene-for-gene’ model of immunity, wherein a single gene from the host and a single gene from the pathogen are responsible for recognition [24]. Recognition triggers a strong immune response that often includes a localized hypersensitive response (HR) in which programmed cell death occurs around the infection site [25]. Nineteen CBB resistance loci have been reported in Gossypium hirsutum breeding programs; however, none have been molecularly identified [8, 13].
Here we combine comparative genomics of the pathogen Xcm with transcriptomics of the host to identify molecular determinants of Cotton Bacterial Blight. This will inform the development of durable resistance strategies.
In 2011, farmers, extension specialists, and certified crop advisers in Missouri, Mississippi, and Arkansas observed cotton plants exhibiting symptoms of CBB. Widespread infected plant material was observed throughout much of the production area, but appeared to be centered around Clarksdale, Mississippi. In Fig 1, we collate reports from this outbreak and overlay these data with US cotton planting statistics to reveal that this disease has spread through much of the cotton belt in the southern U.S. (Figs 1 and S1, S1 Table). Since 2016, CBB has been reported from at least eight out of the sixteen states that grow cotton (Fig 1). In 2014, we collected diseased cotton leaves from two sites across Mississippi and confirmed pathogen causality following Koch’s postulates [26]. In addition, PCR amplification of the 16S rRNA gene confirmed that the causal agent was a member of the Xanthomonas genus. Multi locus sequence type (MLST) analysis and maximum-likelihood analysis were performed using concatenated sections of the gltA, lepA, lacF, gyrB, fusA and gap-1 loci for increased phylogenetic resolution (Fig 2A). The newly sequenced strains were named MS14002 and MS14003 and were compared to four previously published Xcm genomes and thirty-six additional Xanthomonas genomes representing thirteen species (Tables 1 and S2). MS14002 and MS14003 grouped with the previously published Xcm strains as a single unresolved clade, further confirming that the current disease outbreak is CBB and is caused by Xcm. The species designation reported here is consistent with previous reports [6, 7].
Race groups have been described for Xcm strains by analyzing compatible (susceptible) and incompatible (resistant) interactions on a panel of seven cotton cultivars. Different geographies often harbor different pathogen races [7]. Consequently, one possible explanation for the recent outbreak of CBB would be the introduction of a new race of Xcm capable of overcoming existing genetic resistance. Only 2 varieties of the original cotton panel plus three related cultivars, were available and these cultivars were not sufficient to determine whether a new race had established within the U.S. Thirteen Xcm strains were sequenced using Illumina technology to determine the phylogenetic relationship between recent isolates of Xcm and historical isolates. Isolates designated as race 1, race 2, race 3, race 12 and race 18 have been maintained at Mississippi State University with these designations. Additional isolates were obtained from the Collection Française de Bactéries associées aux Plantes (CFBP) culture collection. Together, these isolates include nine strains from the US, three from Africa, and one from South America and span collection dates ranging from 1958 through 2014 (Fig 1, Table 1). Illumina reads were mapped to the Xanthomonas citri subsp. citri strain Aw12879 (Genbank assembly accession: GCA_000349225.1) using Bowtie2 and single nucleotide polymorphisms (SNPs) were identified using Samtools [27, 28]. Only regions of the genome with at least 10x coverage for all genomes were considered. This approach identified 17,853 sites that were polymorphic in at least one genome. Nucleotides were concatenated and used to build a neighbor-joining tree (Fig 2B). This analysis revealed that recent U.S. Xcm isolates grouped with the race 18 clade. Notably, the race 18 clade is phylogenetically distant from the other Xcm isolates.
Xanthomonads deploy many classes of virulence factors to promote disease. Type three effectors (T3E) are of particular interest for their role in determining race designations. T3E profiles from sixteen Xcm isolates were compared to determine whether a change in the virulence protein arsenal of the newly isolated strains could explain the re-emergence of CBB. Genomes from 13 Xcm isolates were de novo assembled with SPAdes and annotated with Prokka based on annotations from the X. euvesicatoria (aka. X. campestris pv. vesicatoria) 85–10 genome (NCBI accession: NC_007508.1). T3Es pose a particular challenge for reference based annotation as no bacterial genome contains all effectors. Consequently, an additional protein file containing known T3Es from our previous work was included within the Prokka annotation pipeline [15, 29]. This analysis revealed 24 conserved and 9 variable Xcm T3Es (Fig 3A). Race 18 clade isolates contain more effectors than other isolates that were sequenced. The recent Xcm isolates (MS14002 and MS14003) were not distinguishable from the historical race 18 isolate, with the exception of XcmNI86 isolated from Nicaragua in 1986, which contains mutations in XopE2 and XopP.
Analysis of the genomic sequence of T3Es revealed presence/absence differences, frameshifts and premature stop codons. However, this analysis does not preclude potential allelic or expression differences among the virulence proteins that could be contributing factors to the re-emergence of CBB. Therefore, newly isolated strains may harbor subtle genomic changes that have allowed them to overcome existing resistance phenotypes. Many commercial cultivars of cotton are reported to be resistant to CBB [30–32]. Based on these previous reports, we selected commercial cultivars resistant and susceptible (6 of each) to CBB. In addition, we included 5 available varieties that are related to the historical panel as well as 2 parents from a nested association mapping (NAM) population currently under development [33]. All varieties inoculated with the newly isolated Xcm strains exhibited inoculation phenotypes consistent with previous reports (Fig 3B and 3C). In these assays, bright field and near infrared (NIR) imaging were used to distinguish water-soaked disease symptoms from rapid cell death (HR) that is indicative of an immune response. These data confirm that existing resistance genes present within cotton germplasm are able to recognize the newly isolated Xcm strains and trigger a hypersensitive response. Together, the phylogenetic analysis, effector profile conservation and cotton inoculation phenotypes, confirm that the recent outbreak of Xcm in the US represents a re-emergence of a race 18 clade Xcm and is not the result of a dramatic shift in the pathogen.
The USDA Agricultural Marketing Service (AMS) releases reports on the percentage of upland cotton cultivars planted in the U.S. each year (www.ams.usda.gov/mnreports/cnavar.pdf). Most of these varieties are screened for resistance or susceptibility to multiple strains of Xcm by extension scientists and published in news bulletins [30, 31, 34–38]. These distinct datasets were cross referenced to reveal that only 25% of the total cotton acreage was planted with resistant cultivars in 2016 (Fig 3D, S3 Table). This is part of a larger downward trend in which the acreage of resistant cultivars has fallen each year since at least 2009 when the percentage of acreage planted with resistant varieties was at 75%.
Differences in virulence were observed among Xcm strains at the molecular and phenotypic level. In order to gain insight into these differences, we selected two strains from our collection that differed in T3E content, virulence level, geography of origin and isolation date. AR81009 was isolated in Argentina in 1981 and is one of the most virulent strains investigated in this study; MS14003 was isolated in Mississippi in 2014 and is a representative strain of the race 18 clade (S2 Fig). The latter strain causes comparatively slower and diminished leaf symptoms; however, both strains are able to multiply and cause disease on susceptible varieties of cotton (S3 Fig). Full genome sequences were generated with Single Molecule Real-Time (SMRT) sequencing. Genomes were assembled using the PacBio Falcon assembler which yielded circular 5Mb genomes and associated plasmids. Genic synteny between the two strains was observed with the exception of two 1.05 Mb inversions (Fig 4). Regions of high and low GC content, indicative of horizontal gene transfer, were identified in both genomes. In particular, a 120kb insertion with low GC content was observed in AR81009. This region contains one T3E as well as two annotated type four secretion system-related genes, two conjugal transfer proteins, and two multi drug resistant genes (Fig 4 insert). MS14003 contains three plasmids (52.4, 47.4, and 15.3kb) while AR81009 contains two plasmids (92.6 and 22.9kb). Analysis of homologous regions among the plasmids was performed using progressiveMauve [39]. This identified four homologous regions greater than 1kb that were shared among multiple plasmids (Fig 4).
Both strains express TAL effector proteins as demonstrated through western blot analysis using a TAL effector specific polyclonal antibody (Fig 5) [40]. However, the complexity of TAL effector repertoires within these strains prevented complete resolution of each individual TAL effector using Illumina sequencing. In contrast, the long reads obtained from SMRT sequencing are able to span whole TAL effectors, allowing for full assemblies of the TAL effectors in each strain. The AR81009 genome encodes twelve TAL effectors that range in size from twelve to twenty three repeat lengths, six of which reside on plasmids. The MS14003 genome encodes eight TAL effectors that range in size from fourteen to twenty eight repeat lengths, seven of which reside on plasmids (Fig 5). Three partial TAL effector-like coding sequences were also identified within these genomes and are presumed to be non-functional. A 1-repeat gene with reduced 5’ and 3’ regions was identified in both strains directly upstream of a complete TAL effector. In addition, a large 4kb TAL effector was identified in AR81009 with a 1.5 kb insertion and 10 complete repeat sequences. The tool AnnoTALE was used to annotate and group TAL effectors based on the identities of the repeat variable diresidues (RVDs) in each gene [41]. Little homology was identified among TAL RVD sequences within and between strains; only two TAL effectors were determined to be within the same TAL class between strains (TAL19b of AR81009 and TAL19 of MS14003) and two within strain MS14003 (TAL14b and TAL16).
An RNA-sequencing experiment was designed to determine whether AR81009 and MS14003 incite different host responses during infection (Fig 6A and 6B). Isolates were inoculated into the phylogenetically diverse G. hirsutum cultivars Acala Maxxa and DES 56 [33]. Infected and mock-treated tissue were collected at 24 and 48 hours post inoculation. First, we considered global transcriptome patterns of gene expression. Fifty-two genes were determined to be induced in all Xcm-G. hirsutum interactions at 48 hours (Fig 6C, S4 Table). Of note among this list is a homeologous pair of genes with homology to the known susceptibility target MLO [42–45]. Gene induction by a single strain was also observed; AR81009 and MS14003 uniquely induced 127 and 16 G. hirsutum genes, respectively (Fig 6C). In contrast, the average magnitude of gene induction between the two strains was not significantly different (S4 Fig). Both Xcm strains caused more genes to be differentially expressed in DES 56 than in Acala Maxxa. Among the 52 genes significantly induced by both strains, sixteen conserved targets are homeologous pairs, whereas seventeen and fifteen genes are encoded by the A and D sub-genomes, respectively (Tables 2 and S4). It has been previously reported that homeologous genes encoded on the G. hirsutum A and D sub-genomes are differentially regulated during abiotic stress [46]. A set of approximately 10,000 homeologous gene pairs were selected and differential gene expression was assessed (Fig 7). For each pairwise comparison of Xcm strain and G. hirsutum cultivar, a similar number of genes were differentially expressed in each of the A and D subgenomes. However, some homeologous pairs were up- or down-regulated differentially in response to disease, indicating a level of sub-genome specific responses to disease. For example, SWEET sugar transporter gene Gh_D12G1898 in the D genome is induced over fourfold during infection with Xcm strain AR81009, while the homeolog Gh_A12G1747 in the A genome is induced to a much smaller extent.
SWEET sugar transporter genes have been reported to be targets of and upregulated by Xanthomonas TAL effectors in Manihot esculenta, Oryza sativa, and Citrus sinensis [21, 40, 47, 48]. In rice and cassava, the SWEET genes are confirmed susceptibility genes that contribute to disease symptoms. The previously reported susceptibility genes and the SWEETs identified here, are clade III sugar transporters (S5 Fig). The NBI Gossypium hirsutum genome encodes 54 putative SWEET sugar transporter genes. Of these 54 genes, three were upregulated greater than fourfold in response to inoculation by one of the two Xcm strains (Fig 8). Predicted TAL effector binding sites were identified using the program TALEnt [49]. MS14003 significantly induces the homeologs Gh_A04G0861 and Gh_D04G1360 and contains the TAL effectors M14b, M28a, and M28b, which are predicted to bind within the 300bp promoter sequences of at least one of these genes. Of note is TAL M28a, which is predicted to bind both homeologs (S6A Fig). In contrast, AR81009 induces Gh_D12G1898 to a greater extent than its homeolog Gh_A12G1747. TAL effectors A14c and A16b from AR81009 are predicted to bind to the Gh_D12G1898 and Gh_A12G1747 promoters; however, TAL A14a is predicted to bind only the Gh_D12G1898 promoter (S6B Fig). We note that while Gh_A12G1747 did not pass the fourfold cut off for gene induction, this gene is slightly induced compared to mock inoculation.
Cotton Bacterial Blight was considered controlled in the U.S. until an outbreak was observed during the 2011 growing season in Missouri, Mississippi and Arkansas [50]. Until 2011, seed sterilization, breeding for resistant varieties, and farming techniques such as crop rotation and sterilizing equipment prevented the disease from becoming an economic concern [51]. The number of counties reporting incidence of CBB has increased from 17 counties in 2011 to 77 counties in 2015 [38, 52, 53]. This paper investigates the root of the re-emergence and identifies several routes towards control of the disease.
When CBB was first recognized as re-emerging, several possible explanations were proposed including: (1) A highly virulent race of the pathogen that had been introduced to the U.S.; (2) Historical strains of Xcm that had evolved to overcome existing resistance (e.g. an effector gene change or host shift); and (3) Environmental conditions over the last several years that had been particularly conducive to the disease. Here, we present evidence that the re-emergence of CBB is not due to a large genetic change or race shift in the pathogen. Rather, the re-emergence of the disease is likely due to agricultural factors such as large areas of susceptible cultivars being planted. The presented data do not rule out potential environmental conditions that may also have contributed to the re-emergence. In this context, environmental conditions include disease conducive temperature and humidity as well as potentially contaminated seed or other agronomic practices that may have perpetuated spread of the disease outbreaks. Importantly, the presented data confirm that the presence of resistance loci could be deployed to prevent further spread of this disease. However, since many of the most popular farmer preferred varieties lack these resistance traits, additional breeding or biotechnology strategies will be needed to maximize utility. Notably, the current Xcm isolates characterized in this study all originate from Mississippi cotton fields in 2014. During the 2015 and 2016 growing seasons, resistant cotton cultivars were observed in Texas with symptoms indicative of bacterial infection distinct from CBB. Additional work is underway to identify and characterize the causal agent(s) of these disease symptoms.
Recent work on CBB in the US has focused on the most prevalent US Xcm race: race 18. However, races are not necessarily phylogenetically distinct clades. Race 18 isolates have been reported overseas, indicating that there may be independent origins of the race or cross-continent movement of this pathogen. Phenotypic race delineations were created before modern genetic and phylogenetic techniques were developed. However, modern genetics presents the opportunity to begin classifying strains based upon phylogenetic and effector profiles rather than phenotypes on a limited range of host varieties. Here, we identify all known and putative race 18 isolates as phylogenetically grouped into a single clade and distinct from other Xcm isolates. Future efforts can further explore phylogenetic relatedness among diverse isolates.
While resistant cotton cultivars were identified for all strains in this study, variability in symptom severity was observed for different strains when inoculated into susceptible cultivars. Two strains in particular, MS14003 and AR81009, have different effector profiles as well as different disease phenotypes. Comparative genomic analysis of the two pathogens revealed many differences that may contribute to the relative disease severity phenotypes. Similarly, transcriptomic analysis of two cultivars of G. hirsutum inoculated with these strains confirm that the genomic differences between the two strains result in a divergence in their molecular targets in the host.
Over the past decade, susceptibility genes have become targets for developing disease tolerant plants [54, 55]. These genes are typically highly induced during infection [56]. Therefore, RNA-Seq of infected plants has become a preferred way to identify candidate susceptibility genes. Once identified, genome editing can be used to block induction of these genes [57]. We report a homeologous pair of genes that are homologs of the MLO gene as targeted by both Xcm strains in both cotton cultivars. These genes are excellent candidates for future biotechnology efforts. Because the potential importance of these genes in cotton biology is unknown, their role in cotton physiology must first be explored. Knock-out mutations of MLO genes in other systems has led to durable resistance against powdery mildew as well as oomycetes and bacteria such as Xanthomonas [42, 45]. The dual purpose of host susceptibility genes has been observed previously. For example, the rice Xa13 (aka. Os8N3 and OsSWEET11) gene is required for pollen development but also targeted by a rice pathogen during infection [58]. Xa13 is a member of the clade III SWEET sugar transporters implicated in many pathosystems. In this case, the induction of Xa13 for pathogen susceptibility is mediated by a TAL effector. Of the 54 SWEET genes in the G. hirsutum genome, at least three are significantly upregulated during Xcm infection. In contrast to MLO, no single SWEET gene was induced by both pathogen strains in both hosts.
Analysis of SWEET gene expression after inoculation revealed a context for polyploidy in the G. hirsutum-Xcm pathosystem. This relatively unexplored area of plant-microbe interactions arose from our observation of a potential difference in induction magnitude between the homeologous Gh_A12G1747 and Gh_D12G1898 SWEET genes. Further analysis revealed many examples of preferentially induced or down-regulated homeologs in response to Xcm infection. Characterization of sub-genome specialization may lead to new insights regarding durability of resistance and susceptibility loci in polyploid crops. Future research may investigate the diploid ancestors of tetraploid cotton to further explore the evolution of host and pathogen in the context of ploidy events [59].
Multiple putative TAL effector binding sites were identified within each up-regulated SWEET promoter. These observations suggest that TAL M28a from MS14003 may induce the homeologs Gh_A04G0861 and Gh_D04G1360. Further, TAL effector A14a from AR81009 is likely responsible for the upregulation of Gh_D12G1898. Whether additional TAL effectors are involved in these responses is not clear. Genome organization in the host, such as histone modifications or other epigenetic regulations may also be affecting these interactions. Future research will investigate these mechanisms further.
Collectively, the data presented here suggest that the wide-spread planting of CBB-susceptible cultivars has contributed to the re-emergence of CBB in the southern U.S. It is possible that a reservoir of race 18 Xcm was maintained in cotton fields below the level of detection due to resistant cultivars planted in the 1990s and early 2000s. Alternatively, the pathogen may have persisted on an alternate host or was re-introduced by contaminated seed [9, 10]. Regardless of the cause of the re-emergence, the genomic comparisons among pathogen races and host cultivars has identified several possible routes towards resistance. These include the use of existing effective resistance loci as well as the potential disruption of the induction of susceptibility genes through genome editing. The latter is an attractive strategy in part because of recent progress in genome editing [60, 61]. In summary, within a relatively short time frame, through the deployment of modern molecular and genomic techniques, we were able to identify factors that likely contribute to the re-emergence of cotton bacterial blight and generate data that can now be rapidly translated to effective disease control strategies.
New Xcm strains were isolated from infected cotton leaves by grinding tissue in 10mM MgCl2 and culturing bacteria on NYGA media. The most abundant colony type was selected, single colony purified and then 16S sequencing was used to confirm the bacterial genus as previously described [62]. In addition, single colony purified strains were re-inoculated into cotton leaves and the appearance of water soaked symptoms indicative of CBB infection was confirmed. Both newly isolated strains as well as strains received from collaborators were used to generate a rifampicin resistance version of each strain. Wildtype strains were grown on NYGA, then transferred to NYGA containing 100μg/ml rifampicin. After approximately 4–5 days, single colonies emerged. These were single colony purified and stored at -80C. The rifampicin resistant version of each Xcm strain was used in all subsequent experiments reported in this manuscript unless otherwise noted.
Cotton varieties from the original cotton panel for determining Xcm race designations were obtained from the USDA/ARS, Germplasm Resources Information Network (GRIN). Varieties included in the G. hirsutum NAM population were provided by Vasu Kuraparthy [33]. Other commercial varieties were obtained from Terry Wheeler and Tom Allen. Disease assays were conducted in a growth chamber set at 30°C and 80% humidity. Xcm strains were grown on NYGA plates containing 100μg/ml rifampicin at 30°C for two days before inoculations were performed. Inoculations were conducted by infiltrating a fully expanded leaf with a bacterial solution in 10mM MgCl2 (OD600 specified within each assay).
The field tests were conducted as follows: Cotton cultivars are planted in two row plots (10–11 m in length, 1 m row spacing), in a randomized complete block design with four replications. Approximately 60 to 80 days after planting, Xcm was applied to the test area similar to that described in Wheeler et al. (2007) [37]. Briefly, Xcm is grown in trypticase soy broth (30 g/L) for 1 ½ days and then 19 L of the concentrated bacterial solution (108 cfu/ml) are diluted into 189 L of water (resulting in 106 cfu/ml). The surfactant Silwet L-77 (polyalkyleneoxide modified heptamethyltrisiloxane, Loveland Industries, Greely, CO) is added at 0.2% v/v. The suspension of bacteria are sprayed over the top of the cotton at a pressure of 83 kpa and rate of 470 L/ha. The nozzles used were TeeJet 8008. Symptoms were typically visible 14 days after application and plots were rated for incidence of symptoms 17–21 days after application [34–37].
Area of cotton planted per county in the United States in 2015 was obtained from the USDA National Agricultural Statistics Service: www.nass.usda.gov/Statistics_by_Subject/result.php?7061F36A-A4C6-3C65-BD7F-129B702CFBA2§or=CROPS&group=FIELD%20CROPS&comm=COTTONUSDA. Estimated percentage of upland cotton planted for each variety was obtained from the Agricultural Marketing Service (AMS): www.ams.usda.gov/mnreports/canvar.pdf.
Illumina based genomic datasets were generated as previously described [29]. Paired-end Illumina reads were trimmed using Trimmomatic v0.32 (ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36) [63]. Genome assemblies were generated using the SPAdes de novo genome assembler [64]. Strain information is reported in Supplemental Table 1. Similar to our previously published methods [29], the program Prokka was used in conjunction with a T3E database to identify type three effector repertoires for each of the 12 Xcm isolates as well as four Xcm genomes previously deposited on NCBI (S2 Table) [65].
Multi-locus sequence analysis was conducted by concatenating sequences of the gltA, lepA, lacF, gyrB, fusA and gap-1 loci obtained from the Plant-Associated Microbes Database (PAMDB) for each strain as previously described [66]. A maximum-likelihood tree using these concatenated sequences was generated using CLC Genomics 7.5.
A variant based dendrogram was created by comparing 12 Illumina sequenced Xcm genomes to the complete Xanthomonas citri subsp. citri strain Aw12879 reference genome (Genbank assembly accession: GCA_000349225.1) on NCBI. Read pairs were aligned to the reference genome using Bowtie2 v2.2.9 with default alignment parameters [27]. From these alignments, single nucleotide polymorphisms (SNPs) were identified using samtools mpileup v1.3 and the bcftools call v1.3.1 multi-allelic caller [28]. Using Python v2.7, the output from samtools mpileup was used to identify loci in the X. citri subsp. citri reference genome with a minimum coverage of 10 reads in each Xcm genome used Python version 2.7 available at http://www.python.org. Vcftools v0.1.14 and bedtools v2.25.0 were used in combination to remove sites marked as insertions or deletions, low quality, or heterozygous in any of the genomes [67, 68]. Remaining loci were concatenated to create a FASTA alignment of confident loci. Reference loci were used where SNP's were not detected in a genome. The resulting FASTA alignment contained 17853 loci per strain. This alignment was loaded into the online Simple Phylogeny Tool from the ClustalW2 package to create a neighbor joining tree of the assessed strains [69, 70]. Trees were visualized using FigTree v1.4.2.
Single Molecule, Real Time (SMRT) sequencing of Xcm strains MS14003 and AR81009 was obtained from DNA prepped using a standard CTAB DNA preparation. Blue Pippin size selection and library preparation was done at the University of Deleware Sequencin Facility. The genomes were assembled using FALCON-Integrate (https://github.com/PacificBiosciences/FALCON-integrate/commit/cd9e93) [71]. The following parameters were used: Assembly parameters for MS14003: length_cutoff = 7000; length_cutoff_pr = 7000; pa_HPCdaligner_option = -v -dal8 -t16 -e.70 -l2000 -s240 -M10; ovlp_HPCdaligner_option = -v -dal8 -t32 -h60 -e.96 -l2000 -s240 -M10; falcon_sense_option = —output_multi—min_idt 0.70—min_cov 5—local_match_count_threshold 2—max_n_read 300—n_core 6; overlap_filtering_setting = —max_diff 80—max_cov 160—min_cov 5—bestn 10; Assembly parameters for AR81009: length_cutoff = 8000; length_cutoff_pr = 8000; pa_HPCdaligner_option = -v -dal8 -t16 -e.72 -l2000 -s240 -M10; ovlp_HPCdaligner_option = -v -dal8 -t32 -h60 -e.96 -l2000 -s240 -M10; falcon_sense_option = —output_multi—min_idt 0.72—min_cov 4—local_match_count_threshold 2—max_n_read 320—n_core 6; overlap_filtering_setting = —max_diff 90—max_cov 300—min_cov 10—bestn 10. Assemblies were polished using iterations of pbalign and quiver, which can be found at https://github.com/PacificBiosciences/pbalign/commit/cda7abb and https://github.com/PacificBiosciences/GenomicConsensus/commit/43775fa. Two iterations were run for Xcm strain MS14003 and 3 iterations for AR81009. Chromosomes were then reoriented to the DnaA gene and plasmids were reoriented to ParA. The assemblies were checked for overlap using BLAST, and trimmed to circularize the sequences [72]. TAL effectors were annotated and grouped by RVD sequences using AnnoTALE [41]. Homologous regions among plasmids that are greater than 1 kb were determined using progressiveMauve [39]. Genomic comparisons between the MS14003 and AR81009 chromosomes were visualized using Circos [73]. Single-copy genes on each of the chromosomes were identified and joined using their annotated gene IDs. Lines connecting the two chromosomes represent these common genes and their respective positions in each genome. A sliding window of 1KB was used to determine the average GC content. Methylation was determined using the Base Modification and Motif Analysis workflow from pbsmrtpipe v0.42.0 at https://github.com/PacificBiosciences/pbsmrtpipe.
Western Blot analysis of Transcription Activator-Like (TAL) effectors was performed using a polyclonal TAL specific antibody [40]. Briefly, bacteria were suspended in 5.4 pH minimal media for 4.5 hours to induce effector production and secretion. Bacteria were pelleted and then suspended in laemmli buffer and incubated at 95 degrees Celsius for three minutes to lyse the cells. Freshly boiled samples were loaded onto a 4–6% gradient gel and run for several hours to ensure sufficient separation of the different sized TAL effectors.
Susceptible cotton were inoculated with Xcm using a needleless syringe at an OD600 of 0.5. Infected and mock-treated tissue were collected and flash frozen at 24 and 48 hours post inoculation. RNA was extracted using the Sigma tRNA kit. RNA-sequencing libraries were generated as previously described [74].
Raw reads were trimmed using Trimmomatic [63]. The Tuxedo Suite was used for mapping reads to the TM-1 NBI Gossypium hirsutum genome [75], assembling transcripts, and quantifying differential expression [27].
Read mapping identified several mis-annotated SWEET genes that skewed differential expression results. The annotations of SWEET genes Gh_A12G1747, Gh_D07G0487, and Gh_D12G1898 were shortened to exclude 20-30kb introns. Two exons were added to Gh_D05G1488. The 2.7kb scaffold named Scaffold013374 was also removed from analysis because its gene Gh_Sca013374G01 has exact sequence homology to Gh_A12G1747 and created multi-mapped reads that interfered with expression analysis.
Homeologous pairs were identified based on syntenic regions with MCScan [76]. A syntenic region was defined as a region with a minimum of five genes with an average intergenic distance of two and within extended distance of 40. All other values were set to the default. Comparisons between homeologs was performed by examining cuffdiff differential expression and classifying them according to the sub-genome expression pattern. Genes considered up or down regulated meet both differential expression from mock significance of q-value < 0.05 and the absolute value of the log2 fold change is greater than 2.
Bioinformatic prediction of TAL effector binding sites on the G. hirsutum promoterome was performed using the TAL Effector-Nucleotide Targeter (TALEnt) [50]. In short, the regions of the genome that were within 300 basepairs of annotated genes were queried with the RVD’s of MS14003 and AR81009 using a cutoff score of 4. Promiscuously binding TALs 16 from MS14003 and 16a from AR81009 were removed from analysis.
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10.1371/journal.ppat.0040022 | Inhibition of IκB Kinase by Vaccinia Virus Virulence Factor B14 | The IκB kinase (IKK) complex is a key regulator of signal transduction pathways leading to the induction of NF-κB-dependent gene expression and production of pro-inflammatory cytokines. It therefore represents a major target for the development of anti-inflammatory therapeutic drugs and may be targeted by pathogens seeking to diminish the host response to infection. Previously, the vaccinia virus (VACV) strain Western Reserve B14 protein was characterised as an intracellular virulence factor that alters the inflammatory response to infection by an unknown mechanism. Here we demonstrate that ectopic expression of B14 inhibited NF-κB activation in response to TNFα, IL-1β, poly(I:C), and PMA. In cells infected with VACV lacking gene B14R (vΔB14) there was a higher level of phosphorylated IκBα but a similar level of IκBα compared to cells infected with control viruses expressing B14, suggesting B14 affects IKK activity. Direct evidence for this was obtained by showing that B14 co-purified and co-precipitated with the endogenous IKK complex from human and mouse cells and inhibited IKK complex enzymatic activity. Notably, the interaction between B14 and the IKK complex required IKKβ but not IKKα, suggesting the interaction occurs via IKKβ. B14 inhibited NF-κB activation induced by overexpression of IKKα, IKKβ, and a constitutively active mutant of IKKα, S176/180E, but did not inhibit a comparable mutant of IKKβ, S177/181E. This suggested that phosphorylation of these serine residues in the activation loop of IKKβ is targeted by B14, and this was confirmed using Ab specific for phospho-IKKβ.
| Vaccinia virus (VACV) is the live vaccine used to eradicate smallpox and is also the most intensively studied poxvirus. Like many poxviruses, VACV produces a wide variety of proteins that inhibit parts of the host response to infection. Consequently, the virus can escape destruction by the immune system and be passed on to additional hosts. Here we report a new VACV immune evasion mechanism mediated by protein B14, a protein that contributes to virus virulence. B14 functions by interacting with a cellular protein called IKKβ, which is critical for mounting an innate immune response to infection, and also plays important roles in cancer and cell death. B14 prevents IKKβ being activated and consequently the cellular signaling pathway leading to activation of nuclear factor kappa B (NF-κB) is not induced. Without activation of NF-κB the host cell cannot produce other molecules that amplify the innate immune response to infection. This mechanism of action of B14 fits nicely with the observed increase in the host response to infection by a VACV strain lacking the B14R gene. Lastly, an increased understanding of how B14 inhibits IKKβ function may lead to development of novel drugs against this important cellular enzyme.
| Nuclear factor-κB (NF-κB) is critical for the innate and adaptive immune responses to infection. Various stimuli, such as the pro-inflammatory cytokines interleukin (IL)-1 and tumour necrosis factor (TNF), activate signaling pathways leading to NF-κB-dependent gene expression [1,2]. Several of these signaling pathways converge on the IKK complex [3–5], and this complex is therefore a prime target for anti-inflammatory drugs. It is also a logical target for pathogens aiming to minimize the host response to infection. The IKK complex, or signalosome, comprises a heterodimer of IKKα and IKKβ in association with NF-κB essential modifier (NEMO also called IKKγ) [6,7] and is critical for NF-κB activation induced by pro-inflammatory cytokines [8–10]. The IKK complex is activated by upstream kinases, such as transforming growth factor-β (TGFβ)-activated kinase-1 (TAK1), which phosphorylates IKKβ at Ser177 and Ser181 located in the activation loop [2,4,5]. Once activated, IKKβ phosphorylates the inhibitor of NF-κB (IκBα) [11] to initiate IκBα degradation. Phosphorylated IκBα (phospho-IκBα) is recognized by an F-box/WD protein, β-transducin repeats-containing proteins (β-TrCP), which functions as a receptor subunit of the SCF family ubiquitin ligase complex, and binds to the phosphorylated E3 recognition sequence on IκBα [12–15]. This poly-ubiquitinated IκBα remains associated with NF-κB but is degraded selectively via the 26S proteasome [16]. After IκBα degradation, NF-κB is translocated into the nucleus to induce transcription of responsive genes [17].
Poxviruses have developed strategies to modulate important cellular signaling pathways to evade host responses [18–20]. These viruses target many of the primary mediators of immune system including IL-1, IL-18, interferons (IFNs), TNF, complement, and chemokines [20–23]. Many of the genes encoding vaccinia virus (VACV) immunomodulators show amino acid similarity to host proteins that function in the immune system. However, others lack such similarity; for instance, the intracellular virulence factor N1 [24], anti-apoptotic protein F1 [25,26], and secreted chemokine binding protein [27].
VACV and other poxviruses interrupt the activity of NF-κB in several ways [21,28]. One strategy is to secrete proteins from the infected cell to bind cytokines, chemokines, or IFNs and prevent these reaching their receptors on cells. Another strategy is to express intracellular factors to regulate signaling pathways leading to NF-κB activation. Among these intracellular inhibitors, VACV proteins A52 and A46 antagonize IL-1R and toll like receptor (TLR) signaling [29–31] and N1 is a virulence factor [24] that is reported to interfere with NF-κB and IRF3 activity [32]. In addition, the crystal structure of N1 reveals it is a Bcl-2-like protein and N1 was shown to protect cells from apoptosis [33]. VACV protein K1 also inhibits NF-κB activation during infection [34]. Lastly, protein M2 downregulates ERK-mediated NF-κB induction in virus-infected cells [35].
B14R is one of the few VACV genes that are located in the terminal region of the virus genome and yet is conserved in many orthopoxviruses [36], suggesting an important function. However, B14 lacks sequence identity with proteins from outside poxviruses. An initial characterization of B14 showed it is an intracellular virulence factor that is expressed early during infection and affects the inflammatory response to infection in a murine model by an unknown mechanism [37]. In this study, the mechanism of action of B14 has been investigated. Data presented show that B14 associates with and inhibits the activity of the IKK complex and thereby inhibits NF-κB activation from multiple signaling pathways. This mechanism of action is consistent with the in vivo phenotype of a virus lacking the B14R gene [37].
Bioinformatic analyses indicated that B14 is a member of a family of poxvirus proteins that include B14, K7, C6, and A52 [38]. Subsequently, the A46 protein was shown to be related to A52 and was added to this family [29]. Given that proteins A46 and A52 are intracellular inhibitors of TLR signaling pathways [29–31], the presence of B14 in the same family suggested that B14 might also act to regulate signaling pathways leading to NF-κB activation.
To investigate the effect of B14 on NF-κB activation, a plasmid containing a luciferase reporter gene linked to a NF-κB-dependent promoter was transfected into HeLa cells and these cells were stimulated with IL-1β (Figure 1A), TNFα (Figure 1A), or PMA (Figure 1B). Luciferase activity was increased greatly by addition of each stimulant but the level reached was reduced in a dose-dependent manner in the presence of B14. Similar findings were observed using HEK 293 cells (unpublished data). Moreover, B14 decreased poly (I:C)-induced NF-κB dramatically (p-value = 0.0006; 95% decrease) (Figure 1E). In contrast, B14 did not reduce luciferase activity from ISRE (Figure 1C) and AP-1 (Figure 1D) reporter genes induced by IFNα and PMA, respectively. Notably, B14 increased PMA-induced AP-1 activity slightly (p = 0.02; 1.5-fold increase; Figure 1D). We also observed a small but significant reduction in poly (I:C)-induced ISRE activity in the presence of B14 (p-value = 0.01; 29% decrease; Figure 1E). However, it is uncertain if these relatively small changes seen with these reporter assays are relevant biologically. As a control we also expressed A20, a de-ubiquitinating enzyme that downregulates NF-κB and IRF3 [39–41] and observed strong inhibition of both pathways (Figure 1E).
Therefore, B14 is a specific downregulator of NF-κB but did not inhibit AP-1 or IRF responsive gene expression. The fact that B14 inhibits multiple pathways leading to NF-κB activation suggests that B14 might act at a position at or downstream of the site at which these pathways converge, namely the IKK complex.
To examine how B14 downregulates NF-κB activation, we searched for interactions between B14 and potential ligands using a luminescence-based mammalian interactome mapping (LUMIER) assay [42]. Components of the IKK complex were included in the assay because several pathways leading to NF-κB activation converge on this complex. HA-tagged B14 and A20 were transfected into cells together with different proteins fused with luciferase, and cell extracts were immunoprecipitated with anti-HA mAb. The immunoprecipitates were then tested for luciferase activity (Figure 2A). B14 interacted with IKKα, IKKβ, and NEMO but not with TBK1, IKKɛ, p65, and A20. As expected, A20 showed no interaction with any proteins screened in the assay except itself [43]. These observations indicated that B14 interacts with the IKK complex. Previously, VACV protein N1 was reported to interact with and inhibit the IKK complex [32] and therefore FLAG-tagged N1 was also included in this assay. Surprisingly, no interaction between N1 and IKKα, IKKβ, or NEMO was observed (Figure 2B), although FLAG-B14 and IKKα each co-precipitated with the IKKs. FLAG-GFP was included as a negative control and did not bind to the IKKs. Collectively, these data show that B14, but not N1, associates with the IKK signalosome.
To investigate these protein interactions further, we fractionated VACV-infected cell extracts by size exclusion chromatography (SEC) and blotted the fractions with antibody to B14. B14 eluted in two peaks corresponding to approximately 160 kDa and 700–900 kDa, despite having a monomeric size of 17 kDa. Given that the IKK complex also has a mass of between 700–900 kDa [1], we immunoblotted the column fractions with antibodies to IKK components and found that the IKK complex co-purified with the first B14 peak (Figure 2C). The column fractions were also blotted with Ab to N1 and this showed that N1 eluted with a mass of approximately 60–70 kDa (Figure 2C), quite distinct from the IKK complex and also distinct from the expected position of the 28-kDa N1 homo-dimer [24]. Therefore, B14, but not N1, co-purified with the IKK complex in the VACV-infected cell lysates.
The possible interaction between B14 and the IKK complex was investigated further by immunoprecipitation. HeLa cells were infected with a VACV strain expressing an HA-tagged version of B14 (vB14-HA) or VACV lacking gene B14R (vΔB14) [37], and cytoplasmic extracts were prepared. B14-HA was immunoprecipitated with anti-HA mAb and immunoprecipitates were analysed by immunoblotting with Abs against IKKα and IKKβ, NEMO, or HA (Figure 2D). The anti-HA mAb precipitated B14-HA together with IKKα, IKKβ, and NEMO from the vB14-HA infected cell lysates (Figure 2D, lane 4). The interaction between B14 and the IKK complex was also seen in the reciprocal immunoprecipitation using antibody to NEMO (Figure 2D, lanes 5 and 6) and anti-IKKα/β (unpublished data). In contrast, B14 and the IKK complex were not co-immunoprecipitated with a control mAb against glycogen synthase kinase (GSK)-3β (Figure 2D, lanes 7 and 8). In summary, B14 and the IKK complex co-purified and co-precipitated when each component was expressed at natural levels.
To identify which of the IKK components interacts with B14, mouse embryo fibroblasts (MEFs) lacking IKKα or IKKβ were analysed as above for HeLa cells (Figure 3). In vB14-HA-infected wild type MEFs B14 co-precipitated with the IKK complex (Figure 3A, lane 4). In the absence of IKKα or IKKβ, the anti-NEMO mAb still precipitated a complex of IKKβ-NEMO and IKKα-NEMO, respectively (lanes 5 and 6). However, B14 was co-precipitated from IKKα but not IKKβ null MEFs, indicating that B14 was incorporated in the IKKβ-NEMO complex (lane 5) and that IKKβ was needed for B14 to be part of the IKK complex. As a control, an anti-FLAG mAb did not immunoprecipitate any proteins (Figure 3A, lanes 7–9). The interaction between B14 and IKKβ was also investigated by SEC of extracts from wild type, IKKα, or IKKβ null MEFs (Figure 3B). B14 only co-purified with the IKK complex of 700–900 kDa when IKKβ was present, but was present in the second peak of 160 kDa in all samples. So, IKKβ is necessary for B14 to co-purify or co-precipitate with the IKK complex.
Upon stimulation, the IKK complex phosphorylates IκBα and this is then removed quickly via the proteasome system. Therefore, we examined the level of IκBα in cells stimulated with TNFα in the presence and absence of B14 (Figure 4). The amount of IκBα was reduced dramatically at 20 min after TNF treatment but had recovered to the original level by 50 min. However, in the presence of B14 the level of IκBα was greater at 20 min post-stimulation with TNF. Thereafter, the level of IκBα recovered to that before stimulation. Equal loading of samples was demonstrated by blotting for α-tubulin. Therefore, B14 increased IκBα stability after TNF stimulation, implying a negative effect on IKK activity.
The above experiment was performed in cells expressing B14 after transfection. To investigate whether the endogenous levels of B14 could affect IKK activity during virus infection, the phosphorylation status of IκBα was investigated in cells infected with VACV strains that do or do not express B14. Cells were infected with wild type (vB14), deletion mutant (vΔB14), or revertant (vB14-rev) viruses [37] at 2 p.f.u./cell and at 2 and 4 h p.i., cytoplasmic fractions were prepared and analysed by immunoblotting (Figure 5). The level of IκBα was indistinguishable in infected or uninfected cells, and similarly there was no difference following infection with viruses that did or did not express B14. However, following infection by all viruses, the level of phospho-IκBα was increased, but the increase was noticeably higher in cells infected with vΔB14, compared to vB14 and vB14-rev. To show that each virus caused equivalent infection, cell extracts were immunoblotted with antibody to the VACV intracellular protein N1 [24], and N1 was detected at similar levels in each sample at 2 h p.i. and at slightly higher levels in each sample later during infection (4 h) (Figure 5, bottom panel, lanes 2–4 and 6–8). In contrast, B14 was present in vB14- and vB14-rev-infected cells only. As expected, each VACV protein was absent in mock-infected cells. This suggested that B14 reduces IKK activity during VACV infection.
The effect of B14 on IKK activity in the absence of other VACV-encoded NF-κB inhibitors was investigated next using an in vitro kinase assay. Plasmids expressing TRAF2 or HA-tagged IKKβ were co-transfected with or without pCI-B14. TRAF2 acts as an intracellular stimulator of IKK activity. Extracts from transfected cells were immunoprecipitated with anti-HA mAb. The activity of the immunoprecipitated IKK complex was studied using a synthetic IκBα peptide substrate and 32P-γ-ATP followed by SDS-PAGE and autoradiography. Notably, the level of the phospho-IκBα peptide was reduced in the presence of B14, indicating B14 inhibited IKK activity (Figure 6). Coomassie blue staining of the SDS-polyacrylamide gel indicated that similar amount of the immunoprecipitated HA-IKKβ and substrate peptides were applied in the assay (Figure 6, lower panels).
To study the effect of B14 on IKK activity further, the IKK complex was activated by overexpression of either IKKα or IKKβ (Figure 7A), and B14 was found to inhibit this activation significantly and in a dose-dependent manner (Figure 7A). This indicated that B14 acts at, or downstream of, the IKK signalosome. The site of action was investigated further using IKK constitutively active mutants, IKKα SS/EE and IKKβ SS/EE that contain mutations in the activation loop [2] (Figure 7B). B14 inhibited IKKα SS/EE significantly and in a dose-dependent manner. In contrast, there was only a small (15%) reduction of IKKβ SS/EE-induced NF-κB activation in the presence of the highest amount of B14. These findings imply that once IKKβ is activated, B14 can no longer prevent NF-κB activation and also suggest a model in which B14 inhibits activation of the IKK complex by preventing phosphorylation of IKKβ in the activation loop.
This hypothesis was tested directly by using Ab to detect IKKβ that has been phosphorylated in the activation loop at serine 177 and 181 (Figure 8). HA-tagged IKKβ was transfected into 293 T cells either alone or together with increasing concentrations of B14. In the absence of transfected HA-IKKβ no phospho-IKKβ was detected, but after addition of HA-IKKβ, phospho-IKKβ was observed easily and was reduced in a dose-dependent manner as the concentration of B14 increased. Notably, while the amount of phospho-IKKβ decreased in the presence of B14, the amount of total HA-IKKβ remained fairly constant and blotting for tubulin confirmed equal loading of samples. Therefore, B14 inhibits NF-κB activation by preventing phosphorylation of IKKβ in the activation loop.
In this study, VACV protein B14 is shown to inhibit the IKK complex and to downregulate NF-κB-dependent gene expression, which is crucial for the innate and adaptive immune response to infection [3,4]. Our previous in vivo study, using recombinant VACVs that do or do not express B14, demonstrated B14 is an intracellular virulence factor that modulates the inflammatory response in vivo [37]. The activity of B14 described here is consistent with this phenotype: downregulation of NF-κB-dependent expression of pro-inflammatory cytokines will alter recruitment of inflammatory cells to sites of infection and so diminish the ability of the host to fight infection. Notably, a virus lacking the B14R gene was attenuated compared to parental virus [37].
The IKK complex is critical for activation of NF-κB [3,44–46] and therefore is a logical target for modulation by pathogens [4,47]. B14 is one of several VACV proteins that inhibit signaling pathways leading to NF-κB activation, but these proteins all have non-redundant functions because when the gene encoding each inhibitor is deleted individually, the deletion mutant displays an in vivo phenotype [24,29–31,37]. Therefore, these proteins must each have distinct functions. In this regard, B14 differs from A46 and A52 in that it targets a broader array of immune signaling pathways; for instance, A46 and A52 inhibit IL-1 but not TNF-induced signaling, whereas B14 inhibits both (Figure 1A). Also A46 and A52 target the signaling pathways upstream of the IKK complex [29–31], whereas B14 targets the activity of the IKK complex. B14 also differs from N1 in that N1 was reported to inhibit signaling pathways leading to NF-κB activation [32] and to IFN responses via TBK1 [32], whereas B14 did not inhibit IFN responses induced by either IFNα or poly (I:C) (Figure 1E). N1 was reported to target to the IKK signalosome by binding to the kinase complex when both components were overexpressed [32]. However, three independent experiments shown here contradict this: first, N1 did not bind to IKK components in the LUMIER assay (Figure 2B); second, N1 did not co-purify with IKK during biochemical fractionation of infected cells (Figure 2C); and third, N1 did not co-precipitate with IKK components using the anti-NEMO mAb (unpublished data). In addition, we showed previously that under the conditions tested N1 did not affect NF-κB activation in VACV-infected cells [33]. Therefore, B14, but not N1, associates with the IKK complex and thereby inhibits NF-κB responsive gene expression.
Concerning the site of action of B14, it is clear that B14 shuts down expression of reporter genes with NF-κB-responsive promoters in response to multiple stimuli (Figure 1) and that within infected cells the overall level of IκBα is not altered by virus infection (Figure 5) or by the expression of B14 in resting cells (Figure 4). However, in the presence of B14 there is a reduced level of phospho-IκBα in the infected cell lysates (Figure 5) and a reduced degradation of IκBα in TNFα-stimulated cells. These findings suggest a possible effect of B14 on the IKK activity. Direct evidence for the reduced phosphorylation of IκBα by IKK in the presence of B14 was provided by an in vitro kinase assay using a synthetic IκBα peptide substrate and IKK that had been immunoprecipitated from cells (Figure 6). Therefore, the mechanism of action of B14 lies upstream of IκBα phosphorylation. Consistent with this, B14 was found to co-purify with the IKK complex from infected cells and to co-precipitate with the IKK complex using specific antibodies either against tagged B14, NEMO (Figures 2 and 3), or against IKKα/β (unpublished data). Notably, the assembly of the IKK complex was not interrupted by B14. Furthermore, use of IKK null MEFs revealed that IKKβ is the target of B14 in the complex and B14 did not bind to or disrupt the IKKα-NEMO complex (Figure 3B). These findings indicate that the inhibitory effect of B14 on the activity of the IKK complex is not due to disassembly of the IKK complex.
B14 inhibited NF-κB activation driven by overexpression of either IKKα or IKKβ (Figure 7A) or by expression of the constitutively active IKKα SS/EE mutant in which the ser176 and ser180 in the activation loop were mutated to glutamic acid (Figure 7B). In contrast, B14 was unable to inhibit NF-κB gene expression by a similar constitutively active IKKβ SS/EE mutant (Figure 7B), indicating IKKβ but not IKKα is the target for B14. Furthermore, B14 associated with the IKK complex via IKKβ (Figure 3) and inhibited phosphorylation of IKKβ in the activation loop (Figure 8), thereby downregulating the activity of the IKK complex. However, once the IKKβ subunit is activated, B14 may not be inhibitory.
B14 co-purified with the IKK complex but was also present in a 160-kDa complex, much larger than the mass of monomeric B14 (17.3 kDa). Consistent with these findings, recombinant B14 made in Escherichia coli was oligomeric (unpublished data). Whether B14 is the only protein in the 160-kDa complex or whether it is complexed with other unidentified cellular or viral protein(s) is unknown. However, its presence in this complex suggests B14 might have function(s) additional to that described here. For instance, the slight increase of PMA-induced AP-1 activity in the presence of B14 (Figure 1D) may result from interaction of B14 with an unidentified protein(s). Alternatively, this may be a consequence of the downregulation of NF-κB responsive genes that negatively regulate AP-1 activity. There is ample precedent for small VACV proteins having more than one immunomodulatory activity. For instance, protein A52 is both a TLR inhibitor and an activator of p38 kinase to modulate IL-10 [48].
In summary, VACV virulence factor B14 inhibits the IKK signalosome by preventing phosphorylation of IKKβ in the activation loop, resulting in inhibition of NF-κB-dependent gene expression. This mechanism of action fits with the observed increased inflammatory response in vivo to infection with a virus lacking gene B14R [37]. Overall our findings reveal a novel strategy used by VACV to modulate cellular signaling pathways to aid viral immune evasion. The B14 may be an interesting target to develop anti-inflammatory therapeutics directed against the IKK complex.
Human embryonic kidney (HEK) 293 cells (a gift from Dr. Paul Farrell, Imperial College London), wild type, IKKα null, and IKKβ null MEF cells (provided by Dr. Michael Karin, UCSD) were cultured in Dulbecco's modified Eagle's medium (DMEM, Gibco BRL) supplemented with 10% heat-treated foetal bovine serum (FBS, heat-treated at 56 °C for 30 min, Harlan Sera-Lab), 50 IU/ml penicillin and 50 μg/ml streptomycin (Gibco BRL) and 2 mM L-glutamine (Gibco BRL). HeLa cells were maintained in Minimum Essential Medium (MEM Gibco BRL) supplemented with 1 x non-essential amino acid solution (Sigma) and identical chemicals as DMEM. The cells were incubated in a humidified incubator (Heraeus) with 5% CO2.
Expression vectors, VACV strains that do or do not express B14, and rabbit anti-serum against B14 have been described previously [37]. Plasmids expressing IKKs and IKK constitutively active mutants were kindly provided by Dr. Alain Chariot (University of Liège) and Dr. Richard Gaynor (Lilly Corporate Center), respectively. Reporter and TRAF2 plasmids were gifts from Dr. Andrew Bowie (Trinity College Dublin). Anti-IKKγ (NEMO) (BD Biosciences), anti-HA (Cambridge Biosciences), anti-GSK3β (BD Biosciences) mAbs were used for immunoprecipitation or immunoblotting. For immunoblotting, rabbit polyclonal anti-IKKα (Cell Signaling), anti-IKKα/β (Santa Cruz), NEMO (Cell Signaling), and IκBα (Santa Cruz) were used. In addition, murine mAb anti-P-IκBα (Cell Signaling), α-tubulin, IKKα and IKKβ (Upstate) were used. The anti-N1 polyclonal Ab was described previously [24]. Lastly, rabbit mAb against phospho-IKKα/β (16A6, Cell Signaling) was used to detect IKKβ that is phosphorylated at Ser177/181.
HeLa cells (8 × 104 per well) were seeded and then transfected with 100 ng of reporter plasmids, 50 ng of pSV-β-galactosidase (Promega), and the indicated amount of expression vectors with FuGENE 6 (Roche). The total amount of DNA (400 ng) was kept constant by supplementation with pCI (Promega). After overnight incubation, the transfected cells were simulated with 100 ng/ml of IL-1β, TNFα (Peprotech), or 50 ng/ml of PMA (Sigma) for 8 h. Cells were harvested in passive lysis buffer (Promega), and the relative stimulation of NF-κB activity was calculated by normalizing luciferase activity with β-galactosidase activity.
HEK 293 cells (6 × 104 per well) were seeded into 24-well tissue culture plates overnight before transfection. Reporter plasmids (90 ng), 10 ng of pTK-Renilla luciferase (pRL-TK, a gift from Dr. Andrew Bowie), and the indicated amount of expression vectors were delivered into cells with FuGENE 6. The total amount of DNA (500 ng) was kept constant by supplementation with pCI (Promega). After 24 h, cells were harvested in passive lysis buffer (Promega), and the relative stimulation of NF-κB-dependent gene expression was calculated by normalizing luciferase activity with Renilla luciferase intensity. In case of stimulations, the cells were incubated with stimuli described previously and 5 μg/ml of poly (I:C) (Invitrogen) for 12 h before lysis.
In all cases, data shown are from one of three to five independent experiments with similar qualitative results. Data from experiments performed in triplicate are expressed as means ± SD.
HeLa cells in 10-cm dishes were infected by vB14-HA or vΔB14 (10 p.f.u. per cell). At 4 h p.i., the infected cells were washed once with ice-cold PBS and lysed with IP buffer (50 mM HEPES [pH 7.5], 100 mM NaCl, 1 mM EDTA, 10% [v/v] glycerol, 0.5%[v/v] Nonidet P-40 containing 1 mM phenylmethylsulphonyl fluoride, 0.01% [v/v] aprotinin, and 1 mM sodium orthovanadate). For immunoprecipitation, the indicated antibodies were pre-incubated with protein G-Sepharose (Amersham) at 4 °C for 1 h. Then equal amounts of the beads were added and incubated with the cell lysates overnight at 4 °C. The immune complexes were washed, boiled with 30 μl of 5 x sample buffer, and analysed by immunoblotting.
Proteins were resolved and transferred to nitrocellulose membranes (Hybond ECL, Amersham). After transfer, the membranes were rinsed once in PBS and then incubated with blocking buffer (PBS containing 5% Marvel milk powder) for 30 min at RT. The primary Ab was added to the blocking buffer and incubated for 1 h on a rocking platform. The membranes were washed five times, for 6 min, with PBS, and then HRP-conjugated secondary Ab (Sigma) was added in blocking buffer. After 45-min incubation, the membranes were washed as above and then incubated with chemiluminescence reagent (ECL, Amersham) for signal detection. The membranes were wrapped in Saran wrap and exposed to X-ray film (Kodak).
At 4 h p.i., the infected cells were washed once with ice-cold PBS and lysed with CE buffer (10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), 10 mM KCl, 0.1 mM EDTA (pH 8.0), 0.1 mM EGTA, 1 mM DTT, 0.05 % NP-40, 20 mM β-glycerophosphate, 1 mM Na3VO4, 1 x protease inhibitor cocktail II (CalBio) for 30 min at 4 °C. The extract was centrifuged at 10,000 x g for 1 h at 4 °C. Five hundred μl of the supernatant was loaded onto a Superose 6 gel filtration column (Amersham) that had been equilibrated in gel filtration (GF) buffer (25 mM Tris-HCl [pH 7.6], 150 mM NaCl, 0.2% NP-40, 1 mM DTT). Approximately 10%–15% of each fraction was analysed by SDS-PAGE followed by immunoblotting with the indicated Ab. The column was calibrated in the GF buffer using protein standards kits from Amersham.
HEK 293 cells were transfected using the indicated vectors overnight and lysed in lysis buffer (20 mM Tris [pH 7.4], 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% Triton X-100, 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na3VO4, 1 μg/ml leupeptin, and 1 mM phenylmethylsulfonyl fluoride). The HA-tagged IKK proteins in the cell lysate were immunoprecipitated using anti-HA mAb. The precipitate was washed three times in lysis buffer and twice in kinase buffer (20 mM Hepes/KOH [pH 7,4], 25 mM β-glycerophosphate, 2 mM dithiothreitol, 20 mM MgCl2). The kinase assay was performed in a final volume of 20 μl of kinase buffer containing 10 μM ATP, 5 μCi of [γ-32P] ATP and 1 μg of IKK substrate peptide (Upstate) derived from IκBα sequence (KKKKERLLDDRHDSGLDSMKDEE). After incubation for 10 min at 30 °C, the reaction was stopped by the addition of 5× SDS sample buffer. Proteins were separated by SDS-PAGE and stained by Coomasie blue. 32P-labelled proteins were visualized by autoradiography.
For LUMIER assays [42], 293 ET cells were transfected with a pair of putative interactors fused to Renilla luciferase or HA/FLAG antibody tags. Post-nuclear supernatants from cells lysed in IP buffer (10% glycerol, 150 mM NaCl, 20 mM Tris-HCl [pH 7.4], 0.1 % Triton-X100, and inhibitors) were incubated with HA or FLAG agarose (Sigma). After washing, proteins were eluted for 30 min with 150 μg/ml FLAG peptide or 100 μg/ml HA peptide in Renilla lysis buffer (Promega). The ratio between luciferase activity in eluates and lysates is presented as fold binding over a control reaction. |
10.1371/journal.pgen.1004827 | The IKAROS Interaction with a Complex Including Chromatin Remodeling and Transcription Elongation Activities Is Required for Hematopoiesis | IKAROS is a critical regulator of hematopoietic cell fate and its dynamic expression pattern is required for proper hematopoiesis. In collaboration with the Nucleosome Remodeling and Deacetylase (NuRD) complex, it promotes gene repression and activation. It remains to be clarified how IKAROS can support transcription activation while being associated with the HDAC-containing complex NuRD. IKAROS also binds to the Positive-Transcription Elongation Factor b (P-TEFb) at gene promoters. Here, we demonstrate that NuRD and P-TEFb are assembled in a complex that can be recruited to specific genes by IKAROS. The expression level of IKAROS influences the recruitment of the NuRD-P-TEFb complex to gene regulatory regions and facilitates transcription elongation by transferring the Protein Phosphatase 1α (PP1α), an IKAROS-binding protein and P-TEFb activator, to CDK9. We show that an IKAROS mutant that is unable to bind PP1α cannot sustain gene expression and impedes normal differentiation of IkNULL hematopoietic progenitors. Finally, the knock-down of the NuRD subunit Mi2 reveals that the occupancy of the NuRD complex at transcribed regions of genes favors the relief of POL II promoter-proximal pausing and thereby, promotes transcription elongation.
| Perturbation of the expression level of IKAROS, a transcription factor critical during hematopoiesis, is associated with malignant transformation in mice and humans. The importance of IKAROS expression levels for the control of target-gene regulation was addressed in hematopoietic progenitor cells. The collaboration between IKAROS and the Nucleosome Remodeling and Deacetylase (NuRD) complex can promote gene activation or repression. IKAROS can also interact with the Positive-Transcription Elongation Factor b (P-TEFb) and the Protein Phosphatase 1 (PP1), an important P-TEFb regulator. Immunoaffinity purification of IKAROS interacting proteins and Fast Protein Liquid Chromatography analysis revealed a dynamic interaction between IKAROS, PP1 and the newly defined NuRD-P-TEFb complex. This complex can be targeted to specific genes in cells expressing high levels of IKAROS to promote productive transcription elongation. Based on our results we suggest that, in addition to P-TEFb, the NuRD complex and PP1 are required to facilitate transcription elongation of IKAROS-target genes and normal differentiation of hematopoietic progenitor cells.
| The tumor suppressor IKAROS is a transcription factor critical for hematopoietic multi-lineage priming, cell fate and lineage determination [1]–[5]. Mice homozygote for the Ikaros null mutation (IkNULL) display severe defects in lymphocyte development and function, and develop leukemias and lymphomas with complete penetrance [6]. These phenotypes reflect the requirement of IKAROS to activate the lymphoid program in hematopoietic progenitor cells (HPCs) [4]. IKAROS is also involved in transcriptional regulation of erythroid- and myeloid-specific genes [7]–[13]. The hematopoietic differentiation is affected not only by the presence or absence of IKAROS, but also by its relative expression level [14]. In particular, during B-cell progenitor differentiation, dynamic change of IKAROS expression level has been identified as a key regulator for the expression of multiple target genes [15], [16].
IKAROS controls chromatin organization mainly through association with the Nucleosome Remodeling and Deacetylase (NuRD) complex [5], [17], [18]. NuRD was initially identified as a repressive complex but it was demonstrated afterwards to promote transcription of specific genes as well [5], [19]–[22]. It remains to be defined how this HDAC-containing complex activates transcription. IKAROS contributes to the assembly and stability of the pre-initiation complex (PIC) at promoters [13], [23]–[26] and interacts directly with CDK9, the catalytic subunit of P-TEFb (Positive-Transcription Elongation Factor b) [27]. CDK9 phosphorylates the C-terminal domain (CTD) of the large subunit of RNA Polymerase II (POL II) on Ser2 as well as the SPT5 subunit of DSIF and the E subunit of NELF. These events are required to release promoter-proximal paused POL II and thus, allow productive transcription elongation. Most nuclear P-TEFb is sequestered in the 7SK snRNP repressive complex. This repressive complex is characterized by the snRNP molecule and the proteins HEXIM (HEXIM1 or 2), LARP7 and MePCE [28]. Of interest here, is the dissociation of the P-TEFb from this repressive complex promoted by the sequential activity of the protein phosphatase 2B (PP2B) that favors conformation changes of the 7SK snRNP and protein phosphates 1α (PP1α), involved in CDK9 dephosphorylation at different residues including Thr186 and Ser175 [29], [30]. Dephosphorylated CDK9/P-TEFb is preferentially recruited to promoters by the general factor BRD4 or specific transcription factors such as HIV TAT [31]–[33]. Then, CDK9/P-TEFb becomes catalytically active and promotes the release of promoter-proximal paused POL II when it is “re-phosphorylated” by the TFIIH associated CDK7 [30]. PP1α is one of the three catalytic subunits (α, β or γ) which, together with a regulatory subunit, forms each PP1 enzyme [34]. Interestingly, IKAROS interacts with PP1 and is dephosphorylated by this phosphatase [35]. Whether the IKAROS-PP1 interaction is important for Cdk9/P-TEFb activation and thus, transcription elongation of IKAROS-target genes is not known.
Here, we sought to define the importance of these protein associations for IKAROS and NuRD to function as transcriptional activators. We demonstrate that IKAROS is an adaptor molecule required for the recruitment of the newly identified NuRD-P-TEFb complex to IKAROS-target genes. IKAROS binding to the promoter region of specific genes is also associated with the local recruitment of the CDK9/P-TEFb activator, PP1α. Interestingly, the Mi2/NuRD occupancy at IKAROS-target genes is enhanced when transcription elongation is proficient, and the release of POL II promoter-proximal pausing is decreased in the absence of Mi2. Our data also reveal that the dynamic interaction of IKAROS with the NuRD-P-TEFb complex depends on IKAROS protein levels. Low-levels of IKAROS suffice for Mi2/NuRD recruitment to promoters, but higher levels of IKAROS are required for NuRD-P-TEFb and PP1α to chromatin, CDK9/P-TEFb activation, and productive transcription elongation. Finally, we demonstrate that IKAROS-PP1α protein interaction is required for normal hematopoietic differentiation of primary HPCs.
Several studies have demonstrated physical interaction between IKAROS and NuRD [5], [17], [18], [36] or IKAROS and P-TEFb [23], [27]. To define how IKAROS coordinates chromatin remodeling and transcription elongation activities, we first conducted tandem immunoaffinity purification and mass spectrometry (LC-MS/MS) analysis in Jurkat cells expressing Flag- and HA-tagged IKAROS (Flag-HA-Ik) (Figures 1A, S1A). LC-MS/MS of Jurkat/Flag-HA-Ik eluates indicated that IKAROS associates with PP1α NuRD (Mi2, MTA2, RBBP4 and MBD3) and P-TEFb (CDK9, CYCT1) components (Table 1), whereas the P-TEFb inhibitor HEXIM1 and the negative elongation factor NELF were not amongst the purified proteins. In addition, HEXIM1 did not immunoprecipitate with IKAROS (Figure S1B), thus suggesting that IKAROS interacts with the elongation-competent and catalytically active P-TEFb.
To determine whether IKAROS interactions with Mi2/NuRD and CDK9/P-TEFb define a single complex or multiple complexes, Flag-HA-Ik immunoaffinity-purified complexes were analyzed by size exclusion chromatography. Flag-HA-Ik co-fractionated with NuRD and P-TEFb components as a major peak of ∼2 MDa (Figure 1B). Additionally, Mi2, CDK9, CYCT1, PP1α and IKAROS co-eluted in nuclear extracts of wild type Jurkat cells (Figure 1C). Since IKAROS mainly eluted at 0.5 MDa (Figure 1C), it is likely that only a minority of the total nuclear IKAROS stably associates with the NuRD-P-TEFb complex. Accordingly, silver staining of IKAROS-purified complexes (Figure 1A) allowed the detection of two weak, but above-background, protein bands migrating at the range of the two known CDK9 isoforms [37] (Figure S1C). That only a small fraction of the nuclear CDK9/P-TEFb complex is highly active and capable of stimulating transcription elongation rate has also been shown in different systems [38], [39].
Next, we investigated whether the interaction of IKAROS with CDK9 or Mi2 could be differently modulated by fluctuation of IKAROS protein levels. Protein co-immunoprecipitation (co-IP) experiments were carried out in Ikaros knock-down Jurkat cells (Figure S1D). Co-IP results indicated that IKAROS-CDK9 interaction is more sensitive to IKAROS levels than the IKAROS-Mi2 interaction because IKAROS-CDK9 interaction is impaired in Ikaros knock-down Jurkat cells whereas IKAROS-Mi2 interaction is comparable in non-target as well as Ikaros knock-down Jurkat cells (Figure S1E). This suggests that in hematopoietic cells a fraction of nuclear IKAROS proteins can be dynamically assembled in a multifunctional protein complex containing both NuRD and P-TEFb components and that formation and/or stability of IKAROS-P-NuRD-P-TEFb interaction is influenced by the expression level of IKAROS.
Protein interactions between NuRD and P-TEFb components were then assessed by protein co-IP of Jurkat nuclear extracts. The NuRD-associated proteins Mi2, RBBP4, MTA2, and MBD3 interacted with the P-TEFb components CDK9 and CYCT1 (Figure 1D, S1F), whereas none of these factors interacted with the negative control nuclear protein PCNA (Figures 1D, E). Among the NuRD-associated proteins, only RBBP4 weakly interacted with the general P-TEFb activator BRD4 [40] (Figures 1D, S1G). CDK9, CYCT1, Mi2, MTA2, RBBP4, and MBD3 also co-immunoprecipitated with IKAROS (Figure 1D). DNase1 did not affect Mi2-CDK9 protein interaction (Figure S1H), indicating that DNA is dispensable. Thus, tandem immunoaffinity purification, size-exclusion chromatography and co-IP experiments demonstrate that IKAROS can assemble with the newly defined NuRD-P-TEFb multifunctional complex (Figure 1F).
We examined whether IKAROS could be a licensing factor for NuRD-P-TEFb complex recruitment to chromatin and whether the variation of IKAROS thresholds, which naturally occurs during hematopoiesis [5], [15], [41], could influence NuRD-P-TEFb association with IKAROS and thereby, regulate transcription elongation. To clarify this issue we studied two IKAROS-target genes, c-Kit and Flt3 (Figure S2A), which are cell receptors critical for HPC survival that are downregulated in IkNULL HPCs [42]. We used: (i) bone marrow lineage negative (lin−) HPCs obtained from Ikaros wild type (IkWT), Ikaros heterozygote null (IkHT) or Ikaros homozygote null (IkNULL) mice [6]; and (ii) the mouse G1E2 cells, which are proliferating hematopoietic progenitor-like cells [43]. The expression level of IKAROS in G1E2 cells was sufficient to detect Mi2-CDK9 interaction (Figure 2A).
c-Kit and Flt3 expression correlated with the level of Ikaros expression in lin− HPCs (Figure 2B). Similarly, the expression level of c-Kit and Flt3 was significantly decreased in Ikaros knock-down G1E2 cells (Figure 2C). However, the expression level of additional c-Kit regulators, such as Gata2 [44], Cdk9 and Mi2 (see below) was not reduced in IkNULL or Ikaros knock-down G1E2 cells (Figures 2B, C and S2B).
To investigate whether reduced c-Kit expression is a direct effect of the Ikaros loss, we carried out chromatin immunoprecipitation (ChIP) assays. ChIP experiments indicated that IKAROS was recruited to critical regulatory regions of the c-Kit locus [44] e.g., the −114 Kb enhancer, the c-Kit Transcriptional Start Site -TSS-, and +1 Kb, +5 Kb as well as +58 Kb regions of the Open Reading Frame (ORF) in IkWT lin− HPCs and G1E2 cells (Figure 2D, E). The same regions were occupied by CDK9, and Mi2 in IkWT lin− HPCs, whereas the reduced level of IKAROS affected CDK9 and Mi2 recruitment at c-Kit (Figure 2D, E) but not at c-Fos promoter (Figure S2C), which was selected since c-Fos gene is expressed in HPCs but it is not regulated by IKAROS [45]. ChIP performed with TBP or POL II specific antibodies revealed that the PIC is assembled at c-Kit TSS regardless of Ikaros expression level (Figure 2D, E). However, the recruitment of POL II and POL II with phosphorylated Ser2 at the C-terminal domain (PCTD) at c-Kit ORF was reduced in IkHT, IkNULL lin− HPCs as well as the Ikaros knock-down G1E2 cells (Figure 2D, E), suggesting that IKAROS can positively control c-Kit transcription elongation. This assumption was supported by the significant decrease of CDK9 binding to c-Kit TSS and ORF (Figure 2D, E) and the consequent reduction of c-Kit nascent transcript levels towards the 3′ end of the gene (Figure 2F, G) in Ikaros-deficient cells.
The correlation between IKAROS expression level and the release of promoter-proximal paused POL II was further demonstrated by the analysis of c-Kit traveling ratio, which is determined by the relative ratio of POL II density in gene ORF vs. promoter-proximal regions [46], [47]. As indicated in Table 2, c-Kit traveling ratio decreased in IkHT and IkNULL lin− HPCs, when compared to IkWT.
To address whether IKAROS, CDK9 and Mi2 simultaneously co-occupy the c-Kit TSS, sequential ChIP (re-ChIP) [48] was carried out. Sequential ChIP analysis provided the evidence that in hematopoietic progenitors, these proteins can associate to the c-Kit TSS together (Figure 2H), hence suggesting that transcription elongation is promoted by the IKAROS-mediated recruitment of the NuRD-P-TEFb complex.
Collectively, these results indicate that in HPCs, IKAROS facilitates productive transcription elongation at c-Kit (Figure 2) and Flt3 (Figure S2D, E; Table S1) genes and that reduced IKAROS levels, which do not affect the PIC assembly, can perturb transcription elongation.
Since IKAROS, CDK9 and Mi2 were detected at c-Kit TSS and ORF in IkWT HPCs (Figure 2D, E), we tested whether these factors play an active role during transcription elongation or associate with chromatin at c-Kit ORF independently of productive transcription elongation. Thus, transcription elongation was inhibited with Flavopiridol, which is a specific inhibitor of CDK9 activity [49]. G1E2 cells were treated with 100 nM Flavopiridol, for a short period of time (2 hours) to avoid indirect effects due to general inhibition of gene expression [50]. As expected, CDK9, POL II and CYCT1 protein levels were not reduced in G1E2-treated cells (Figure 3A), and IKAROS phosphorylation [35] appeared to be unmodified (Figure S3A). However, c-Kit and Flt3 nascent transcript levels were significantly reduced (Figures 3B, S3B). Flavopiridol treatment of G1E2 cells did not affect IKAROS recruitment to the c-Kit (Figure 3C) or Flt3 (Figure S3C) promoter region. Furthermore, decreased detection of POL II within the gene ORF, POL II accumulation at c-Kit TSS, and lack of PCTD association at c-Kit TSS and ORF, confirmed that Flavopiridol did not affect POL II loading, hence PIC assembly (Figures 3C, S3C) but substantially reduced transcription elongation (Table 2). Similar results (reduced c-Kit expression, unchanged IKAROS recruitment and lack of PCTD association to c-Kit promoter) were obtained in Cdk9 knock-down G1E2 cells (Figure S3D–F). Thus, transcription elongation block (obtained either by Flavopiridol treatment or Cdk9 knock-down) does not affect loading of IKAROS, CDK9/P-TEFb and Mi2/NuRD at c-Kit TSS region, but impedes P-TEFb and, surprisingly, IKAROS and NuRD recruitment within the gene ORF.
Since the elongation block by Flavopiridol led to an accumulation of Mi2 at c-Kit TSS and reduced association with the downstream regions (Figure 3C), we assessed whether NuRD could directly influence transcription elongation by knock-down of one of the NuRD components, Mi2, in G1E2 cells. The Mi2 knock-down cells showed a reduction of c-Kit and Flt3 mature as well as nascent transcripts (Figure 3D, E). The fact that Mi2 knock-down had a greater effect on transcripts generated at distal rather than proximal regions, suggests that the NuRD complex can assist and facilitate POL II progression during transcription elongation.
IKAROS is a substrate of PP1 activity [35] and PP1α can act as a positive regulator of CDK9/P-TEFb [29]. IKAROS interacts with the α catalytic subunit of PP1 in G1E2 cells (Figure 4A), in COS-7 cells transfected with IKAROS (Figure S4A) and in Jurkat cells (Table 1). Among the PP1 catalytic subunits, we identified PP1α as the most relevant and abundant interacting partner of IKAROS (Table S2). Thus, we investigated whether, by binding to PP1α and CDK9, IKAROS could deliver PP1α to the large P-TEFb complex, thereby activating CDK9/P-TEFb and favoring transcription elongation. In accordance with this assumption, PP1α recruitment was demonstrated at c-Kit TSS in IkWT and much less efficiently in IkHT or IkNULL lin− HPCs (Figure 4B). PP1α recruitment to c-Kit TSS also decreased in Ikaros knock-down G1E2 clones (Figure 4C), even though PP1α expression was similar to control cells (Figure 4D).
The role of the IKAROS-PP1α interaction for CDK9/P-TEFb activation was further delineated by exploring the influence of Calyculin A, a PP1/PP2A inhibitor [34], on c-Kit and Flt3 transcription elongation. Short-term treatment with Calyculin A did not cause toxic effects or cell death (Figure S4B), but induced significant reduction of c-Kit nascent transcripts (Figure 4E), whereas transcription elongation was not reduced upon treatment with inhibitors specific for PP2A (Okadaic Acid) or PP2B (Cyclosporin A) (Figure S4C, D), which are two phosphatases that do not dephosphorylate IKAROS proteins [35]. ChIP assay revealed that IKAROS was not significantly recruited to the c-Kit locus in Calyculin A-treated cells (Figure 4F). In general, CDK9, Mi2 and PCTD occupancy at c-Kit TSS, +1 and +5 regions, and PP1α recruitment at c-Kit TSS were also diminished, whereas POL II accumulated at TSS and +1 region and TBP recruitment did not vary (Figure 4F). Similar outcomes were observed at the Flt3 locus (Figure S4E–H). Calyculin A treatment did not affect CDK9 or PP1α protein levels, but rather led to the appearance of two slow-migrating CYCT1- and IKAROS-specific bands (Figure 4G). These bands, which are likely corresponding to phosphorylated forms of CYCT1 and IKAROS proteins, were expressed at detectable levels. Thus, it can be concluded that the IKAROS-PP1α interaction: (i) contributes to IKAROS dephosphorylation and efficient binding to chromatin; (ii) allows PP1α association with c-Kit and Flt3 TSS and (iii) promotes CDK9/P-TEFb activation in order to release promoter-proximal paused POL II.
The functional role of IKAROS-PP1 interaction during hematopoiesis was further investigated by ex vivo assays in methylcellulose (Figure 5A). IkWT and IkNULL lin− HPCs were transduced with the control pMSCV empty vector (IkWT/GFP or IkNULL/GFP); IkNULL lin− HPCs were also transduced with pMSCV/Ik1 (Ikaros1 isoform, identified as IkNULL/Ik1 HPCs) or pMSCV/Ik1ΔPP1 (a mutant of Ikaros1 carrying the A465/7 mutation, identified as IkNULL/Ik1ΔPP1 HPCs). Ik1ΔPP1 is an IKAROS mutant unable to bind PP1 and therefore, resistant to dephosphorylation [35]. This mutant is reported to possess a shorter half-like than wild type IKAROS and thus, we assessed the stability of Ik1ΔPP1 in IkNULL/Ik1ΔPP1 HPCs by single-cell immunofluorescence (IF) analysis. Since these transgenes possess a Flag-HA tag, IF analysis was carried out with anti-HA antibodies. IF studies indicated that Ik1ΔPP1 accumulates in the nucleus of Ik1ΔPP1-infected lin− HPCs (Figure S5A). As well, in 293T transfected cells, whereby Ik1ΔPP1 was expressed at slightly lower levels than Ik1, Ik1ΔPP1 could interact with several known IKAROS protein partners (Mi2, CDK9, CYCT1 and MTA2) as demonstrated by protein co-IP experiments (Figure S5B).
In transduced lin− HPCs, Ikaros1 and Ikaros1ΔPP1 transgene expression levels were about 5- and 8-fold higher than endogenous Ikaros (Figure 5B), which is comparable to other IKAROS rescue models [3]. As in IkNULL lin− HPCs, c-Kit and Flt3 expression was impaired in IkNULL/GFP lin− HPCs. Importantly, infection of IkNULL lin− HPCs with pMSCV/Ik1 increased c-Kit expression to 74% and Flt3 expression to 46.7% (if their expression levels in IkWT/GFP lin− HPCs is considered as 100%), whereas expression of c-Kit and Flt3 genes was only increased to 33.3% (for c-Kit) and 11.5% (for Flt3) upon pMSCV/Ik1ΔPP1 infection (Figure 5B). Thus, even though Ik1ΔPP1 could slightly increase c-Kit and Flt3 gene expression in lin− transduced HPCs, Ik1 restored transcription elongation of these target genes more efficiently, indicating the functional importance of the IKAROS-PP1 interaction for gene regulation.
Clonogenic assays in methylcellulose revealed that IkNULL lin− HPCs displayed decreased Colony Forming Cell (CFC) activity (1/166 IkNULL lin− HPCs vs. 1/33 IkWT lin− HPCs) as well as reduced colony size, which suggests reduction of cell proliferative potential. IkNULL lin− HPCs produced less mixed CFC (CFU-GEMM) and increased erythroid/megakaryocyte (BFU-E/Mk) colonies (Figure 5C).
Although the expression of the c-KIT receptor at the cell surface of IkNULL HPCs is minimal compared to IkWT HPCs [42], we investigated whether a close-to-normal c-KIT and FLT3 activation could be obtained by the stimulation of IkNULL HPCs with increasing amounts of Stem Cell Factor (SCF or KIT ligand) as well as FLT3 ligand (FL). We observed that IkNULL lin− HPCs were refractory to SCF and FL treatments. This functional limitation was demonstrated by the absence of: (i) induced cell proliferation (Figure 5D); (ii) c-Kit or Flt3 induction (Figure 5E, F); and (iii) hematopoietic commitment and differentiation (Figure S5C) upon SCF and/or FL treatment of IkNULL lin− HPCs. These results along with previously published work [51], suggest that IKAROS regulation of c-Kit and Flt3 expression is critical for CFC activity and differentiation ability of HPCs.
To define the biological contribution of IKAROS-dependent control over transcription elongation, we studied the hematopoietic potential of IkNULL lin− HPCs transduced with Ikaros1 or the elongation-incompetent Ikaros1ΔPP1 mutant. IkWT/GFP lin− HPCs maintained the same hematopoietic potential than non-transduced IkWT lin− HPCs, both in term of colony number as well as colony types. However, IkNULL/GFP lin− HPCs had increased CFU-GEMM and BFU-Mk/E and reduced BFU-E potential compared to non-transduced IkNULL lin− HPCs (Figure 5C; IkNULL vs. IkNULL/GFP). The gain in CFU-GEMM might be caused by an advantageous transduction ability of multipotent progenitor cells, whereas the gain in megakaryocyte vs. erythroid component, as indicated by increased BFU-Mk/E colonies, might be related to the Erythropoietin deprivation during the infection procedure (Figure 5A). Infection of IkNULL lin− HPCs with Ikaros1 significantly augmented CFC activity of transduced HPCs (Figure 5C, table), which correlates with the increased number of CFU-GEMM, BFU-E/Mk and, moreover, BFU-E colonies (Figure 5C, table; IkNULL lin− vs. IkNULL/Ik1 lin− HPCs). Interestingly, CFC activity of IkNULL/Ik1ΔPP1 lin- HPCs was lower than in IkNULL/Ik1 lin− HPCs (Figure 5C, table), but the hematopoietic potential of IkNULL/GFP lin− HPCs and IkNULL/Ik1ΔPP1 lin− HPCs was very similar (Figure 5C, table; IkNULL/GFP lin− vs. IkNULL/Ik1ΔPP1 lin− HPCs). Thus these results suggest that the Ikaros1ΔPP1 mutant is defective in restoring hematopoietic functions and that IKAROS interaction with PP1, which is required for productive transcription elongation, is important for normal hematopoiesis.
P-TEFb can be recruited to gene promoters either by the general P-TEFb activator BRD4 [40] or by transcriptional activators that bind to specific consensus DNA sequences [37]. We previously reported that IKAROS acts as a template for the recruitment of P-TEFb to specific genes in hematopoietic cells [23], [27]. The partnership of IKAROS with the NuRD complex, which is critical for chromatin organization is also very-well established [5], [7], [18], [25]. The results presented here show that NuRD and P-TEFb can be associated together in a multifunctional complex, and that IKAROS is required for NURD-P-TEFb complex recruitment to specific genes in HPCs. Additionally, our results suggest that Mi2/NuRD actively participates in relieving POL II promoter-proximal pausing and contributes to the control of transcription elongation of IKAROS-target genes.
Size fractionation analysis of nuclear extracts and IKAROS immunoaffinity-purified protein complexes revealed that a modest but significant percentage of nuclear CDK9 and CYCT1 (P-TEFb) is stably associated with IKAROS and NuRD components. This association resembles to the small and highly active portion of P-TEFb that is included in the Super Elongation Complexes (SECs), known to stimulate transcription elongation rate [38], [39]. Interestingly, based on LC-MS/MS analysis, it appears that several subunits of the SECs interact with IKAROS in hematopoietic cells (Table S3). Furthermore, the absence of the transcription elongation inhibitor, NELF, and the P-TEFb inhibitor, HEXIM1, among the IKAROS-NuRD-P-TEFb interacting proteins supports the notion that the newly defined complex favors transcription elongation in hematopoietic cells.
Nucleosomes form barriers for the elongating POL II. The main remodeling complex known to facilitate POL II passage through nucleosomes is the histone chaperone complex FACT [52]. Although we cannot exclude that FACT is implicated together with IKAROS and the NuRD-P-TEFb complex in the control of c-Kit elongation, FACT components were not identified by LC-MS/MS analysis of IKAROS-associated proteins. Instead, our data indicate that POL II release and the rate of transcription elongation of these genes are affected by Mi2 knockdown. In addition, Mi2/NuRD occupancy at c-Kit and Flt3 ORF decreased when transcription elongation was blocked by the CDK9 inhibitor Flavopiridol. Therefore, our results suggest that in HPCs, the NuRD complex can act as a chromatin complex that both destabilizes and restores nucleosomal structure in order to assist and facilitate the passage of POL II during transcription elongation. Accordingly, the NuRD complex can support nucleosome remodeling through the Mi2-associated helicase activity and histone deacetylation through the HDAC-associated activity [53], [54].
Initially regarded as a co-repressing complex because of its association with histone deacetylases and transcriptional repressors, the NuRD complex has also been associated with permissive chromatin and gene activation [5], [19]. More recently, it has been reported that the NuRD-mediated repression of a subset of pluripotency genes in ES cells occurs in a dynamic equilibrium with activation signals in order to fine-tune expression of these genes in response to differentiation stimuli [20]–[22]. Our findings bring a novel perspective to the role of NuRD in transcription regulation since we demonstrate a functional link between chromatin-associated activities that are required for the control of early transcription regulation i.e., NuRD-dependent chromatin remodeling associated with gene priming and PIC assembly [55], and productive transcription elongation whereby Mi2/NuRD contributes to P-TEFb-dependent relief of paused POL II and transcription elongation.
During differentiation of B-cell progenitors, the dynamic change of IKAROS expression level has been identified as a key mechanism for multiple target gene expression [15], [16]. Perturbation of IKAROS expression level has deleterious effects in double-positive thymocytes and in pre-B cells whereby reduced IKAROS function is associated with malignant transformation in mice and humans [41], [56], [57]. Furthermore, the IKAROS haploinsufficiency is reported to promote acute lymphoblastic leukemia with a high risk of relapse [58]. Our results suggest that chromatin association of the NuRD-P-TEFb complex to IKAROS target-genes depends on IKAROS expression level, hence providing a reasonable explanation for IKAROS dosage effects observed in hematological malignancies. In IkWT lin− HPCs and G1E2 cells, a fraction of IKAROS associates with P-TEFb and NuRD and this complex is recruited to c-Kit and Flt3 genes. In IkHT lin− HPCs and Ikaros knockdown G1E2 cells, IKAROS, Mi2/NuRD and CDK9/P-TEFb are recruited less efficiently to gene regulatory regions and transcription elongation is affected although transcription initiation occurs normally and POL II is in a promoter-proximal paused configuration. Finally, in IkNULL HPCs, transcription initiation occurs normally but P-TEFb and NuRD recruitment to c-Kit and Flt3 loci is impaired. Then, the enrichment of elongation-competent Ser2-phosphorylated POL II at gene ORF drops to almost background values. As a result, transcription elongation is profoundly reduced.
A large set of hematopoietic lineage-specific genes are characterized by permissive chromatin organization and PIC assembly at their promoters in HPCs [59]–[65]. IKAROS is required for the establishment of lineage-specific transcriptional programs [4], [5], [51]. Based on our results, we posit that when expressed at higher levels in HPCs and lymphoid progenitors, IKAROS induces rapid and dynamic expression of multiple genes poised for expression through the relief of the promoter-proximal paused POL II and transcription elongation. During alternative lineage-specification, IKAROS levels decrease, the IKAROS-NuRD-P-TEFb complex associates less efficiently to chromatin, and transcription elongation of different activated genes targeted by IKAROS declines.
PP1α is involved in CDK9 dephosphorylation at Thr-186 and Ser-175 [30]. Dephosphorylation of Thr-186 and Ser-175 facilitates P-TEFb dissociation from the 7SK snRNP repressive complex. While the importance of Ser-175 phosphorylation/dephosphorylation is debated, dephosphorylation of Thr-186 is known to promote binding of the CDK9/P-TEF complex at genes TSS [29]. At the TSS, Thr-186 is phosphorylated by the TFIIH-associated CDK7, an event required for the catalytic activity of CDK9 (P-TEFb activation) and thus, the release of promoter-proximal paused POL II [66]. Hyperphosphorylation of IKAROS negatively affects its stability and DNA binding affinity [67], [68]. IKAROS dephosphorylation by PP1 [35] enhances IKAROS binding to DNA and, based on results presented here, it can also favor the transfer of this phosphatase to CDK9, thereby contributing to P-TEFb release from the 7SK snRNP and CDK9 activation [29]. Indeed, immunoaffinity purification and FPLC analyses suggest that PP1α associates with IKAROS, NuRD and P-TEFb in a 2 MDa complex that does not include subunits of the 7SK snRNP repressive complex. Since we found that PP1α is particularly abundant in close proximity to the TSS of the transcriptionally active c-Kit and Flt3 genes, and PP1α recruitment to these TSS is highly influenced by IKAROS concentration, it can be argued that IKAROS contributes to chromatin recruitment of PP1α at the TSS of target genes hence, facilitating CDK9/P-TEFb association through dephosphorylation of CDK9 Thr-186 and possibly, Ser-175 [29], [69]. Furthermore, although CDK9 phosphorylation by CDK7 activates CDK9 and promotes POL II release, it has been demonstrated that imbalanced CDK9 hyperphosphorylation by CDK7 can have the opposite effect [30]. Thus, the relative higher abundance of PP1α at the TSS (when compared to the other regions bound by the IKAROS-NURD-P-TEFb) might be required to prevent excessive phosphorylation of TSS-bound CDK9 by the TFIIH-associated CDK7.
The importance of the IKAROS-PP1α network was demonstrated by results obtained with the PP1α inhibitor Calyculin A and with the Ik1ΔPP1 mutant. First, Calyculin A treatment, which leads to IKAROS hyperphosphorylation and reduced DNA binding [35], resulted in decreased recruitment of PP1α, Mi2 and CDK9 to c-Kit and Flt3 promoters but did not affect TBP and POL II recruitment. Thus, PP1α activity is critical for IKAROS as well as NuRD and P-TEFb recruitment to these promoters and for transcription elongation, while being dispensable for PIC formation. Second, the rescue of IkNULL HPCs attempted with the Ik1ΔPP1 mutant, which interacts with Mi2, MTA2, CYCT1 and CDK9 (Figure S5B) but does not efficiently interact with PP1 [35], demonstrated that without the interaction with PP1, IKAROS cannot exert its normal function during hematopoiesis.
It is worth noting that PP1 can contribute to co-transcriptional pre-mRNA splicing control [70], [71], an event that could also favor transcription elongation of IKAROS target genes. In fact, LC-MS/MS analysis suggested that some proteins implicated in the process of transcription termination can interact with IKAROS in hematopoietic cells (Table S3). Based on our results we suggest that IKAROS, NuRD and P-TEFb are not only active at the TSS but they facilitate transcription elongation and could also influence transcription termination.
Thus, IKAROS control over transcription elongation at genes such as c-Kit and Flt3 is likely to provide a rapid adjustment of their expression levels, and be a critical mechanism affecting HSC/HPCs interaction with the niche, cell fate and stress response [72]–[75].
In conclusion, we have demonstrated that IKAROS is the DNA-binding subunit of the newly characterized NuRD-P-TEFb multifunctional complex, a chromatin-associated complex that contains chromatin remodeling (NuRD) as well as gene transcription elongation (P-TEFb) activities. IKAROS is important for CDK9 and Mi2 interaction and combined recruitment to actively transcribed genes in hematopoietic cells. We demonstrate that low IKAROS expression level does not preclude appropriate promoter organization but impairs productive elongation, whereas higher IKAROS levels are necessary to relieve promoter-proximal paused POL II and efficient transcription elongation of target genes (Figure 6). Our results suggest that NuRD can assist POL II transcription elongation complex throughout the ORF of IKAROS target genes and provides mechanistic cues to explain the central role played by IKAROS as transcriptional activator during hematopoietic lineage decision, differentiation, and interaction with the niche.
These assays were done essentially as previously reported [7], [23], [27], [48].
Protein complexes were purified from Jurkat nuclear extract or IKAROS immunoaffinity-purified complexes on an AKTA Purifier FPLC System (GE Healthcare) using a Superose 6 10/300GL column (GE Healthcare).
Flag-HA sequential immunoaffinity (or Tap-Tag) purification was carried out as described in Nakatani et al. [76] starting from nuclear extracts prepared from pOZ-N-Flag-HA-IRES-ILR2 (mock sample) or pOZ-N-Flag-HA-IKAROS-IRES-ILR2- (Flag-HA-Ik sample) Jurkat cell clones. LC-MS/MS analysis was performed at the Taplin Mass Spectrometry Facility (Harvard University, Boston).
Homozygote (IkNULL) and heterozygote (IkHT) Ikaros null mice were genotyped by PCR as described [6]. Animals were sacrificed by cervical dislocation. Animal experiments were conducted in accordance with the Canadian Council on Animal Care (CCAC) guidelines and approved by the Maisonneuve-Rosemont Hospital animal care committee (approval numbers 2011-16 and 2011-17). Lineage negative (lin−) HPCs were purified using the easySep mouse hematopoietic progenitor enrichment kit (StemCell Technology).
pMSCV/Ik1 and pMSCV/Ik1ΔPP1 vectors were generated by cloning the murine Ikaros1 cDNA or the mutant Ikaros1ΔPP1 cDNA, both with a Flag and HA tag at their N-terminal regions, into the MSCV-pgk-EGFP vector (Dr G. Sauvageau, IRIC). Retroviral infection of lin− HPCs was carried out as described [77]. Lin− HPCs were cultured for 3 days in medium containing 50 ng/ml SCF, 10 ng/ml IL3, 10 ng/ml IL6 and 5×10−5 M β-mercaptoethanol, without Erythropoietin. Transduced lin−/GFP+ HPCs were isolated on a FACS Aria III sorter (BD Biosciences) based on green fluorescence. HPCs were seeded in triplicate on complete methylcellulose medium (MethoCult Stemcell Technologies). Colonies were scored at day 14.
Unpaired Student's t-test was used to determine the level of statistical significance (P-value).
Additional Materials and Methods information can be found in Text S1.
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10.1371/journal.pcbi.1003641 | A Signature of Attractor Dynamics in the CA3 Region of the Hippocampus | The notion of attractor networks is the leading hypothesis for how associative memories are stored and recalled. A defining anatomical feature of such networks is excitatory recurrent connections. These “attract” the firing pattern of the network to a stored pattern, even when the external input is incomplete (pattern completion). The CA3 region of the hippocampus has been postulated to be such an attractor network; however, the experimental evidence has been ambiguous, leading to the suggestion that CA3 is not an attractor network. In order to resolve this controversy and to better understand how CA3 functions, we simulated CA3 and its input structures. In our simulation, we could reproduce critical experimental results and establish the criteria for identifying attractor properties. Notably, under conditions in which there is continuous input, the output should be “attracted” to a stored pattern. However, contrary to previous expectations, as a pattern is gradually “morphed” from one stored pattern to another, a sharp transition between output patterns is not expected. The observed firing patterns of CA3 meet these criteria and can be quantitatively accounted for by our model. Notably, as morphing proceeds, the activity pattern in the dentate gyrus changes; in contrast, the activity pattern in the downstream CA3 network is attracted to a stored pattern and thus undergoes little change. We furthermore show that other aspects of the observed firing patterns can be explained by learning that occurs during behavioral testing. The CA3 thus displays both the learning and recall signatures of an attractor network. These observations, taken together with existing anatomical and behavioral evidence, make the strong case that CA3 constructs associative memories based on attractor dynamics.
| A type of neural network called an “attractor network” is thought to underlie memory associations. Importantly, when such a network is presented with part of a memory, the network activity is attracted to the complete memory. However, it has been difficult to obtain clear experimental evidence for such attractor networks. Indeed, recent “morphing” experiments that were specifically designed to observe these attractor dynamics in the hippocampus did not obtain the expected results, leading to a controversy on the validity of the attractor hypothesis of memory. Here, we have built a computational model of the relevant hippocampal areas, including its core anatomical and physiological features, and through the use of large-scale computer simulations reveal in detail the physiological properties expected of the hippocampal attractor network during morphing experiments. We show that the experimental results obtained are actually those to be expected of an attractor network when the specifics of the experimental protocol are taken into account. Most importantly, the results directly demonstrate the attraction of CA3 activity to a stored pattern. Our results, together with previous behavioral and in vitro studies, provide strong evidence that CA3 is an attractor network for associative memory.
| Theoretical work has shown how networks with excitatory recurrent connections can function as an associative memory [1]–[4]. Specifically, Hebbian plasticity at the synapses of recurrent connections leads to the association of the elements of a memory. Information stored in this way can be recalled given external input of a partial pattern, thus displaying “pattern completion” [5], [6]. This “attraction” of network activity to a stored pattern provides a useful form of associative memory and has inspired much theoretical and experimental work. Among hippocampal sub-regions, CA3 is unique in having extensive excitatory recurrent connections [7], [8]. This property, together with the finding that the synapses of these recurrent connections can undergo Hebbian plasticity [9], [10], has led to the hypothesis that CA3 has attractor dynamics and serves as the main site for associative memory storage in the hippocampus [11]–[16].
Despite the influence of the attractor concept, it has been difficult to obtain direct experimental support for attractor networks in the hippocampus. Experiments specifically designed to observe the electrophysiological signature of attractor dynamics in CA3 have been problematic (for a review, see [17]). The experiments designed to identify attractors first established memories of two environments with different shapes (square/round) but unaltered distal cues; the environment was then gradually morphed from one to the other [18]–[20]. Given that attractor networks can display winner-take-all dynamics, the expectation was that, during such morphing, the network would first be attracted to one stored memory and would then make a sudden transition to the other. Because the firing patterns in CA3 changed gradually rather than suddenly, it has been argued that the results are inconsistent with the properties of attractor dynamics and that a different function of CA3 should be entertained [21]. Alternatively, it has been suggested that sudden transitions may not be an appropriate criteria for identifying attractor networks [22]. Here, we directly address this issue, which is central to understanding the hippocampal contribution to associative memory.
We have developed a computational model of CA3 and its input structures and have used this model to simulate the morphing experiments described above [18]–[20]. This model was constrained not only by the properties of CA3, but also by the properties of the input to CA3 from the dentate gyrus (DG) and entorhinal cortex (EC). Furthermore, because CA3 cells fire selectively during only a small fraction of the theta cycle [23], [24], we modeled the dynamics of CA3 that could occur in a comparable short time segment. With this model, we have been able to account for the results obtained during morphing experiments and to analyze how the CA3 recurrent connections affect network function and dynamics. Our analysis clarifies the criteria that should be applied to identify attractor dynamics under the conditions of morphing experiments. These criteria are satisfied by the data. Our simulations show that CA3 only satisfies these criteria when recurrent excitation is present, leading us to conclude that intra-CA3 processes in fact support attractor dynamics. Notably, for small morphs, the pattern of activity in CA3 is attracted to a stored pattern, whereas the pattern in DG, a region that provides input to CA3, is not. We have also analyzed an additional experimental observation, the hysteresis observed in CA3 recordings during morphing [19]. Our analysis suggests that this hysteresis arises from CA3 plasticity, thus suggesting a new method for observing how experience affects CA3 attractor dynamics.
The model that we developed is illustrated in Figure 1A. We modeled pyramidal cells of CA3 (NCA3 = 10,000) and the granule cells of the dentate gyrus (DG, NDG = 800,000) as one-compartment integrate-and-fire neurons [25]. The voltage of each neuron i is determined by the input feedforward excitatory input, IFF, the recurrent excitatory input, IREC, the recurrent inhibitory input, IGABA, and the after-hyperpolarization, IAHP, currents. Both DG and CA3 cells receive feedforward excitatory current from the EC (NEC = 160,000), IEC, and recurrent inhibition from their respective interneuron networks, IGABA. CA3 cells also receive feedforward excitatory current from DG cells, IDG, and recurrent excitatory current from the recurrent collaterals of CA3, ICA3. We use as parameters the average input resistance (Rn = 33 MΩ) [26], the membrane time constant (τn = 30 ms), and the firing threshold (T = −50 mV). Voltage is reset to rest (VREST = −65 mV) after each spike. The after-hyperpolarization maximum current is set to AAHP = −2 nA with τAHP = 7 ms decay [27]. To emulate an absolute refractory period caused by sodium channel inactivation, cells that emitted a spike were not allowed to spike for the following period τSPIKE = 2 ms. Inhibition is global within each region (CA3 and DG) and occurs with a delay of 3.3±0.4 msec [28] relative to the first spike succeeding the previous inhibitory current discharge [29]. The membrane potential of neuron i evolves according to:(1.1)
The feedforward excitatory input current of cell i, , is computed through the arithmetic sum of all excitatory post-synaptic currents from synapses reaching i multiplied by a feedforward gain factor . For DG cells: ; while for CA3 cells: . was estimated as 0.68 nA in order to allow network oscillation within gamma frequency (∼35 Hz).
The entorhinal input current, IEC, of each CA3 and DG cell is computed as a linear combination of the inputs from the lateral and medial entorhinal cortices regulated by a mixing factor α (Equation 1.2). There is no data available to directly estimate α, so we quantitatively estimate it through a parametric search methodology. The inputs of the lateral and medial entorhinal cortices are computed independently and are normalized by the mean maximum value considering all positions (Equations 1.3 and 1.4). Normalization of the input allows an interpretation of α with respect to the overall size of the EPSC originated in each of the entorhinal cortices. Feedforward synaptic weights, WMEC and WLEC, are randomly assigned from a distribution that corresponds to the measured distribution of synapse size [30], [31]. The number of EC cells that converge into the DG and CA3 can be estimated from the measured spine density of 2.3 spines/µm and dendrite length of 3000 µm [32]. Considering that each spine has one synapse, we estimate for the measured spine density and dendritic length [33] that each DG cell receives input from 1200 MEC and 1500 LEC cells, while CA3 cells receive inputs from 1400 MEC and 1500 LEC cells [34], [35]. To emulate the morphing experiment (see below), the total EC input of each cell i, IiEC, is defined for each of the Nr positions r(x,y) and Nc wall shapes c:(1.2)(1.3)(1.4)
The DG input current to CA3 cells is set respective to the normalized activity of a randomly selected presynaptic DG cell multiplied by a relative gain factor β. Activity of DG cells is computed a priori as a mean rate (see Data analysis section) and is set constant for a specific position and morphing stage. The input of the DG to CA3 neuron i is defined as:(1.5)
The recurrent CA3 input current of cell i, is determined by the non-linear threshold function of the arithmetic sum of the recurrent excitatory current, : (1.6)where = 20 nA is the asymptotic feedback excitatory current and is the recurrent excitation threshold, considering a non-linear mapping from input to excitatory current [36]. Recall threshold is defined as the threshold complementary . is computed as the sum of the product of the afferent activity, , and the specific synaptic weight, . is modeled as a step function with 3 msec duration and release lag, , of 1 msec [37]: (1.7)
To emulate the experimental procedure and the ensuing hippocampal neuronal dynamics, the activity of the model was computed over the trajectory of real rats available online [38]. All trajectories were obtained from sessions in a square environment, and morphing was encoded in the activity of LEC neurons. We do not implement any direct source of noise in our simulations. Indirectly, noise arises naturally as fluctuation of EPSPs from the combination of a high-resolution spatial representation (computed bins of 1 cm2 compared to place fields of >200 cm2) with the natural tracking of the position of the animal. Noise also arises from the IPSP delay, which is set probabilistically with a normal distribution. Neural activity was computed over sessions of 10 minutes (T = 600 sec). Each gamma cycle (t = 36.5 msec) was computed independently with a randomly selected IPSP delay determined by a normal distribution with specific SD (d = 3.3±0.4 msec) and with the potential of all neurons initialized at rest (Figure 1B). Different cells could assume multiple rate values (see Data analysis section) because the release of action potentials was probabilistic: the cells with the strongest input fire in every such simulated cycle and thus show a high probability (rate, λ). Cells that fire just after the strongest cell will fire in most simulated cycles approximating the maximum rate, whereas cells with less excitation may fire late in the gamma cycle but often do not fire at all, thus displaying a low rate. Recurrent excitation is applied within the active window of the gamma cycle and can cause a cell with low feedforward excitation to produce an action potential, increasing its rate.
Activity of EC cells is dependent on the current position of a virtual rat, r(x,y), which navigates through the environment following empirically determined trajectories of real rats (see below), and the current progression in the morphing procedure, c. The computation is analogous to previous work [34]. The activity of MEC cells is defined by a mathematical description of grid cells [39] and is made insensitive to morphing [19], [40] (Figure 1C, left), unless when noticed otherwise. To simulate the conditions that lead to global remapping in the CA3 [21], [40], grid cell realignment is implemented by setting a different angular and position phase but letting the same spatial frequency. Both MEC and LEC rate maps are tailored to fit the observed spatial information score [41]. Morphing is encoded in the activity of the LEC cells by allowing a sharp transition between two independent rate maps at a morphing degree specific for each cell defined randomly following a uniform distribution [34] (Figure 1C, right). The LEC selectivity to morphing is grounded in the observed selectivity of LEC cells to objects [42], [43], the fact that it receives strong input from sensory driven areas [44]–[46], and the finding that rate remapping in the CA3 is impaired by LEC lesions [20]. Importantly, although we assume a sharp transition for the response of LEC cells during morphing, the fact that the point of the transition is different for each cell and that the activity of many cells is summed makes our implementation equivalent to the case in which each cell had a smooth or non-uniform response to morphing. LEC and MEC activity was produced with a resolution of 1 cm×1 cm.
Excitatory input for the EC and DG was computed using the virtual rat's position as a reference (x,y). The model was updated with a step size of 1 msec from the beginning of the gamma cycle and the time of the first spike and 0.1 msec steps in the interval between the first spike and the release of GABA. Spikes were time stamped for further analysis. Due to the expected low activity levels of the DG [47], [48], only the cells with mean weight strengths within the upper 10% percentile were simulated.
To emulate the rat's exposure to the square and round environments, the recurrent CA3 weight matrix is defined based on the history of the firing of cells on the square and round environments without recurrent excitation. As we are not interested in the dynamics of plasticity prior to morphing experiment, we used an interleaved procedure to define the recurrent CA3 weight matrix as follows: to enhance orthogonalization of CA3 activity following the CA3-DG interaction [16], each CA3 cell is assigned to a cluster through a k-means algorithm using spatial correlation as a distance metric. The number of clusters is set to maximize the grouping of the data [49]. Cells belonging to the same cluster (C) are interconnected with a weight inversely proportional to the size of the cluster (n(C)) so that the sum of all synaptic weights to each cell is equal to 1:(1.8)
The training is performed for the two extreme shapes of the environment, and the synaptic weight between two cells is defined as the maximum value over the two conditions: (1.9)
Recurrent CA3 weights are updated at the end of every session: a temporary connection matrix is built using the above clustering method, and the weights interconnecting active cells are updated by a convex sum-ruled by a learning factor (LRATE) between the previous weights and the weights in the temporary connection matrix:(1.10)
Data analysis includes construction of 16×16 bin rate maps, place fields analysis, population vector correlation, rate overlap, and spatial correlation and is performed following the same procedures and methods as reported for the experimental data [19]. In summary, the outcome of the simulations was a list of time-stamped spikes that could be related to the r(x,y) coordinate in which they were emitted (r(t)). Space was discretized in 16×16 bins (Nr = 256, equivalent to 5×5 cm). For the specific case of the DG input to CA3 and the activity map of MEC and LEC cells, space was discretized in 80×80 bins (Nr = 6400, equivalent to 1×1 cm). For each bin, the firing rate was calculated by averaging the number of spikes at a certain position and dividing it by the average occupancy of that bin (Figure 1D). Rate maps were smoothed by a Gaussian kernel (g) of h = 5 cm sd: (1.11)
Cells with a mean firing rate above 0.1 Hz in at least one of the morphing steps were considered active. Place fields were determined by the existence of continuous bins (n>8 and n<128) with a peak rate no less than 2 Hz, with all units above 20% of this peak value.
The population vector (PV) correlation was calculated by correlating the response vector of all cells in a specific bin and correlating it to: (a) the same response vector under a different morphing condition and (b) a response vector of a different bin localized 50 cm away under the same morphing condition. Only active cells with a firing rate above 1 Hz in the two conditions were considered for the PV.
The overlap between two rate maps was measured by dividing the mean rate displayed in the less active condition by the mean rate in the more active condition. The spatial correlation was defined as the pair-wise correlation of the rate maps considering each bin. In our simulations, to correct for sampling error, all comparisons in the morphing experiment between rate maps were performed using simulation data from different trajectories.
In the experiments of Leutgeb et al. [18], [19], the walls defining the environment were morphed from a square shape (1) into a circular shape (7) over five intermediate shapes (2–6) while distal cues were kept constant. Morphing occurred after the animals were initially exposed to both the square and circular environments, thereby establishing a memory for these extremes. The classic notion of attractor dynamics specifies that there will be an abrupt transition in the cell firing pattern as the environment is gradually morphed [21]. However, no such transition was observed (Figure 3A of [19]). To the contrary, the population vector (PV) correlation, which can be used to quantify the difference in recorded CA3 population responses in two different environments, changed gradually. These changes were caused by alterations in the cells' peak firing rate (either up or down) without modification of the identity of the active cells, a process called rate remapping [19], [34]. Importantly, DG and CA3 behaved differently; for the smallest morphs (environments 1 to 2 or 7 to 6), the change in the PV correlation was much larger in the DG than in CA3 (Figure 3A of [19]), even though CA3 is a monosynaptic target of DG [50]. However, for large morphs (environments 1 to 5 … or 7 to 3 …), the observed PV changes were the same for the two regions.
To simulate these morphing experiments, we constructed a model of the DG and CA3 networks (Figure 1A). CA3 cells were modeled as having input from DG, both lateral and medial parts of the EC, and recurrent excitatory input from other CA3 cells. DG cells were modeled as having input only from the EC. We modeled these inputs using a realistic number of contacts and realistic synaptic strength distributions (see Methods). Feedback inhibition was modeled separately in DG and CA3, giving rise to gamma frequency oscillations, as observed in these structures [29], [51], [52]. The delay of feedback inhibition (3.3±0.4 msec) [28] was made slower than that of recurrent excitation (1 msec) [25], [37]. Memories of environments 1 and 7 were set in the recurrent CA3 connections (see Methods). Rate was determined as follows (Figure 1B). The cell with strongest input will be the first to fire in a gamma cycle, triggering feedback inhibition. Other neurons that reach threshold may fire at some later time; still others with low excitation are unlikely to fire at all. However, because of noise in the system, firing is determined probabilistically. We thus take this probability of firing during a gamma cycle as measure of rate (see Methods).
With this biologically constrained model, we computed the activity of CA3 and DG cells in different morph states by analyzing the spike probability as the simulated rat traversed the environment (paths were taken from experimental data [38], [53]). The rat's location was represented by the activity of grid cells of the medial entorhinal cortex (MEC) (Figure 1C, left) [38], [53], whereas sensory information about the walls of the environment was represented by the activity of the cells of the lateral entorhinal cortex (LEC) (Figure 1C, right) [42], [43]. Both MEC and LEC maps were constrained by data (see Methods). Rate maps were computed from the simulated neural activity and the trajectories (Figure 1D). There were three open variables that we could not obtain from the literature: the relative strength of the input from LEC or MEC (α); the ratio of DG-to-EC input (β); and strength of the recurrent synapses (1-δ). We estimated these parameters computationally by searching the best fit to the experimental data using as reference the available metrics of both population and single-unit activity. This strategy allowed a direct comparison between the simulated data and experimental data using exactly the same methods. If the reader is not interested in the technical issue of parametrical optimization, he or she may wish to go directly to the next section, where we apply the model to the morphing data and analyze the evidence of attractor dynamics in the CA3.
Through the parametric optimization of the relative strength of the input from LEC or MEC (α), the simulated DG population data reproduced the main features of the experimental data (Figure 2). In our simulations, an average of 3.5% of DG cells were active at each session, in accordance with experimental measurements [47], [48]. DG cells exhibited place fields that independently rate remapped during morphing (Figure 2A), as observed in previous modeling studies [31], [34] and in the experimental data [19]. The distribution of the number of place fields per active cell was similar to that observed experimentally (Figure 2B). Simulated place fields had a peak rate of 11.92 Hz±7.87, comparable to 11.54 Hz±8.16 in Leutgeb et al. (2007).
The parameter α was optimized by searching in the range of valid α values (0–1) the value with which the simulated neural activity of the DG would better fit experimental data [19]. Both individual neurons, cumulative change in the average firing rate (rate overlap) and spatial correlation, and population activity metrics, PV correlation and PV autocorrelation, were used as metrics for the optimization process (see Methods). When we analyzed the activity of individual neurons, we observed that, for small and large morphs, strengthening LEC influence (high α) led to a higher decorrelation between firing rate distributions of individual cells than when MEC influence was high (low α) (Figure 2C). In the extreme case when considering only the LEC input (α = 1), the rate maps of individual DG cells in the two extreme shapes were uncorrelated. Strong LEC influence led to a higher change in the average firing rate of individual cells (lower rate overlap) compared to the condition of strong MEC influence (Figure 2D). If we interpret these results in terms of rate remapping (in which non-spatial information is encoded in the rate of spatially stable place fields) [34], [54], we observe that there is a trade-off between the ability of the DG cells to encode the wall shape information in the peak rate of place fields and the maintenance of the position of place fields. Interestingly, the experimental observation indicates that there is a compromise with balanced MEC and LEC influence (Figure S5 of [19]).
When we analyzed the population activity of DG neurons, we observed that stronger LEC contribution yielded stronger PV decorrelation for every pair of box shapes if compared to conditions in which the MEC input was greater (Figure 2E) with the best model fit, as in the analysis of single cells, obtained with a balanced MEC and LEC input (α = 0.5). We next investigated whether the encoding of the wall shape information disturbed the ability of the DG population to produce orthogonal representations of unrelated positions by measuring the autocorrelation of PVs obtained in the same box shape but at positions located 50 cm away (Figure 2F). High correlation would indicate a high overlap between representations of different positions, and a lower correlation would imply otherwise. We observed that strong LEC input resulted in PVs more strongly correlated at distant positions if compared to the condition with higher MEC influence. This observation indicates that also at the population level there is a trade-off between the ability of the DG to encode a specific position and a wall shape. Importantly, considering all population and individual cell metrics, an input with balanced MEC and LEC contribution provided the best fit to the experimental data [19].
Having established how to correctly simulate the DG and thus its input to CA3, we analyzed CA3 responses during morphing. We first analyzed the CA3 population response without recurrent connections. In our simulations of such a network, CA3 cells exhibited several properties consistent with the data. The distribution of the number of place fields per active cell were similar to that observed experimentally (Figure 3A,B). Peak place field firing was at 12.45 Hz±7.73 in simulation, which is comparable to 13.13 Hz±7.97 reported by Leutgeb et al. (2007). However, although rate remapping was observed during morphing, it was not consistent with the experimental data (Figure 3C–F), and this was true irrespective of the ratio (β) of DG-to-EC input. With increase of β, there was a general reduction of the correlation between rate maps in different environments (Figure 3C) and virtually no change in the average rate of the cells (Figure 3D). With respect to the population response to morphing, high values of β resulted in an overall increase of PV decorrelation when compared to the condition with low β (Figure 3E), thus not fitting the data [18]–[20]. Yet stronger DG input decreased the CA3 PV autocorrelation in distant positions (Figure 3F). In conclusion, we were unable to fit the CA3 data using a model without recurrent collaterals.
We next analyzed whether the morphing data could be accounted for if recurrent collaterals were included (Figure 4). Synaptic weights of the CA3 recurrent collaterals were set based on the population activity in environments 1 and 7 (see Methods), emulating the experimental protocol in which the animals were familiarized to the two extreme shapes before the experiments. The addition of the excitatory feedback from the recurrent collaterals did not impair the formation of place fields and their ability to rate remap. Importantly, by increasing the strength of the recurrent synapses (1-δ), there was an increase of the correlation between rate maps of the same cell between different environments (throughout all morphs), leading to an almost flat response, as observed experimentally (Figure 4A). Such enhanced stability of the firing rate distribution of individual cells indicates that the place fields are present and unmoved throughout the morphing.
We next examined whether single cells were still responsive to morphing by measuring the cumulative change in the average firing rate of individual cells as morphing progressed. We found that the addition of the recurrent collaterals affected the average change in rate differently for small and large morphs (Figure 4B): for small morphs, the average change in rate was less than in the condition without recurrent collaterals; for large morphs, the average change in rate was higher than in the condition without recurrent collaterals. This indicates that not only are different wall shape conditions successfully encoded in the individual cells rate maps, but also that, for very similar inputs, the system attracts the average rate response to the stored pattern. Thus, the addition of the recurrent collaterals favors a code in which the information about the environment is encoded by the peak rate of place fields located at fixed positions. We found similar results when analyzing the population response to morphing: there was an overall increase in the PV correlation measured between sessions with different wall shapes approximating experimental observations (Figure 4C). For the parameters that led to the best model fit (1-δ = 95%), there was a stronger increase in the PV correlation for the small morph than for the large morph (Figure 4D). Moreover, we observed an additional reduction in the PV autocorrelation obtained in distant positions, approximating the observed value (Figure 4E). This indicates that the activity of the recurrent collaterals enhances the ability of the network to discriminate between unrelated positions. Altogether, these analyses show that the addition of recurrent collaterals allows an accurate description of rate remapping as seen in both the single-cell and population responses.
With all parameters set, we next directly compared the response to morphing in DG and CA3. The simulated data not only provided a model fit of the individual region response to morphing, but also provided a reasonable description of the relation between the population response of the CA3 and the DG to morphing (Figure 5). The experimental finding that DG population activity was more strongly affected by the small morph than the CA3 population activity was only observed when recurrent collaterals were present (Figure 5A). Notably, although the CA3 PV correlation was increased during both small and large morphs by the recurrent collaterals, this effect was stronger for small morphs than for large morphs, indicating the existence of a basin of attraction (ΔPV correlation of 0.35 for small morph against 0.20 for large morph, Figure 4D). Further, we found additional evidence for a basin of attraction by analyzing how single CA3 cells changed their firing rate throughout morphing (Figure 5B); for the large morphs (1–7) in which little effect of the attractor dynamics is expected, we found a higher change in the average firing rate in CA3 when compared to DG in the presence of recurrent collaterals (rate overlap in CA3 is ∼0.2 lower than in DG), setting the baseline of how the rate of DG and CA3 cells is affected by morphing. For the small morphs (1–2), the condition in which the attractor dynamics would be effective, in the CA3 there was a lower change (rate overlap in CA3 is ∼0.05 higher than in DG) in the average firing rate in CA3 than in DG when recurrent collaterals were included. These results indicate that, even though CA3 cells are naturally more sensitive to changes in the environment when it is out of a basin of attraction (as seen by the baseline results of the large morph), when we consider the conditions in which attractor dynamics are effective, there is a lower sensitivity to change in the CA3 cells. Notably, we also found that the addition of collaterals contributed to the spatial stability of place fields in the CA3; only in the presence of recurrent collaterals were individual rate maps of CA3 cells less affected by morphing than individual rate maps of DG cells (Figure 5C). The analysis of the dynamics of CA3 rate coding also revealed the role of the feedforward excitation and competitive inhibition in pattern separation, as there is a considerable reduction in the PV autocorrelation between two distant and unrelated areas (Figure 5D). Also, consistent with previous findings that a two-stage process increases spatial specificity [35], we observe a reduction of the mean number of place fields in CA3 (Figure 5E). These results allow the identification of the specific role of the neural circuits of DG and CA3 in memory: while the convergence of excitatory feedforward input and the internal inhibitory competition cause pattern separation, the recurrent excitation has a major role in pattern completion.
Importantly, neither in the reported data nor in the simulation was there any evidence that morphing produced a sharp rather than a graded transition in any of the computed measurements (Figure 5A). Thus, three important conclusions follow. First, the recurrent connections in CA3 do have an attractor function; during small morphs, they “attract” the dynamics toward a stored pattern (e.g., the square or the round shape). Second, this attractor dynamics modulates rate remapping, thereby leaving the spatial information intact. Third, despite this attractor function, the CA3 firing pattern undergoes a graded rather than abrupt change during morphing over intermediate states (i.e., 4 and 5).
To characterize the mechanisms by which the recurrent collaterals affect the population response to morphing, we analyzed the dynamics of single cells during small morphs in the presence and absence of recurrent excitation. The small morph is a condition in which there should be a moderate but still noticeable change in the input pattern to CA3. In the presence of attractor dynamics, the input pattern will be within the stored pattern basin of attraction and thus pattern completion should be observed. We analyzed the firing pattern produced under these conditions and the subsequent influence of recurrent excitation. The small morph had three important effects (Figure 6). First, in cells whose total feedforward input, including the excitatory current from EC and DG, was strong (0.9 nA) and led to a spike, the presence of recurrent excitation did not yield a significant increase in the probability of an action potential (Figure 6A). Second, in CA3 cells that were part of the stored pattern but received DG/EC input after morphing that was subthreshold (0.6 nA), the recurrent input triggered a spike, thereby producing pattern completion (Figure 6B). In the absence of recurrent excitation, such cells would not fire. This explains why there is a higher PV correlation between the population responses to a stored pattern and a small morph in the presence rather than in the absence of recurrent excitation (Figure 5A). Thus, in this way, the internal dynamics provided by the CA3 recurrent synapses attracts a cell toward a stored pattern, thereby producing rapid pattern completion within a single gamma cycle [25]. Third, what the dynamics of the attractor cannot do is erase spikes that have already occurred. Consider that, after a small morph, a cell is strongly excited by DG/EC that is not part of the nearby stored pattern (Figure 6C). Because this spike has occurred and cannot be erased, the total activity during the short firing period cannot be identical to the stored pattern. Likewise, activity induced by additive noise cannot be suppressed. Thus, although recurrent excitation can attract CA3 to a stored pattern, this attraction cannot be perfect.
The understanding that attractor dynamics cannot eliminate spikes that are not part of the stored pattern has further implications. In the morphing experiments, the smallest morphs (1, 2) displayed a PV less than 1 (0.9). However, because two measurements in environment 1 (albeit with intermediate sessions in all other environments, i.e., 1-1′ is obtained from the sequence 1-2-3-4-5-6-7-1 with six intermediate sessions) also showed a PV correlation of 0.9 [19], it was suggested that an attractor mechanism made the response in environment 2 identical to that in environment 1. Our analysis, however, suggests that such perfect attractor reconstruction cannot occur, and we suggest an alternative explanation: that the intermediate sessions between the two recordings in environment 1 altered the stored attractor, thereby reducing the correlation in the 1-1′ morphing to 0.9. Thus, the 1-2 environments evoked different responses because the attractor system does not work perfectly as explained above, whereas the 1-1′ environments evoked different responses because of the learning produced in intermediate environments. We simulated the morphing procedure with varying learning rates and observed that the 1-1′ (1-2-3-4-5-6-7-1) and the 1-2 correlation were not equally affected by the exposure to different environments (Figure 7A). Interestingly, because of the sequence in which the wall shapes were changed, the correlation of the 1-1 morphing changed more thoroughly to higher learning rates due to the fact that there were more intermediate trials (n = 6) between the comparisons when compared to the correlation of the 1-2 environments (n = 0), which allowed that, for a specific learning rate, both comparisons are equivalent.
Subsequent work supports our interpretation: when 1-1 comparisons are made without exposure to intermediate environments, the PV correlation was higher (0.93 in Figure 5 of [20], 0.96 in Figure S5 of [55]). In the same studies, the PV correlation was lower [0.90 in 20,0.91 in 55] if there were intermediate exposures to other environments and was progressively reduced with the number of such exposures, as would be predicted if these exposures produced learning and a modification of the stored attractors. Further evidence of experience-dependent plasticity in the recurrent collaterals is that hysteresis was observed in CA3, but not in the DG [19]. In our simulations with learning in the recurrent collaterals, we observed comparable levels of hysteresis in the CA3 rate maps (Figure 7B).
We next investigated how place cells respond under conditions in which environmental change does produce grid cell realignment. We investigated how grid cell realignment affects the population response to morphing in the CA3. Grid cells were shown to realign when the animal is trained at the same location but in different boxes or at different locations but with the same box [40]. Under these conditions, place fields do not remain stable at the same positions, characterizing global remapping [40], [54]. During morphing, grid cells seems to realign at an intermediate position, causing an abrupt change in the CA3 population neuronal activity [21], [56] (Figure 8, left). To verify whether our model produces results in accordance to the literature, we realigned the grid cell population in the middle of morphing (see Methods) and computed the activity of CA3 cells (Figure 8, right). We observed that, following the realignment of the grid cells, there was an intense and abrupt change in the PV correlation. This effect is further supported by the observation that the change in the PV correlation during morphing is graded when grid cells are stable [19]. Our data thus corroborate the view that grid cell stability is required for rate remapping in the DG and CA3.
We have addressed the question of whether the CA3 memory system can be considered an attractor network in the face of ostensibly conflicting experimental results. Using a simulation of the EC/DG/CA3 system, we show that firing patterns recorded in CA3 during the morphing of an environment are in accord with what is expected if CA3 is an attractor network. When the environment is subject to small morphs, DG granule cells, which do not have recurrent synapses, change their firing patterns substantially. In contrast, CA3 cells, which do share recurrent plastic connections, change much less, indicating an attraction to a stored pattern. Importantly, our simulated observations are in accord with experimental data [19], [57]. Given that DG provides strong input to CA3 [50], attraction of CA3 cells to a stored pattern must be due to recurrent activity within CA3 itself. Our simulations show that the recurrent collaterals of CA3 can produce these dynamics and do so within a short time interval consistent with the theta-phase specific firing of CA3 cells [24].
Importantly, our work clarifies the issue of whether sharp transitions during morphing are a requirement for demonstrating that a network follows an attractor dynamic. The argument that CA3 might not be an attractor network [21] was based on the observation that sharp transitions in PV correlation did not occur during morphing, thus not displaying a criterion of attractor networks. This criterion was suggested by work in which attractor networks were activated by brief external inputs and were then allowed to evolve to a stored pattern after the external input was removed [2], [11], [12]. The sharp transition occurs because without external input, attractor networks are all-or-none; with dynamics unconstrained by external input, the network uses internal dynamics to converge to the closest of the stored memories. For this reason, the final state of the network does not show intermediate states, and sharp transitions are expected. Such a feature is, however, not applicable to the hippocampus because external input from the EC and from DG to CA3 is never absent. Under these conditions, our simulations show that sharp transitions do not occur (Figure 5C and 6A). Thus, under the conditions of the morphing experiment, sharp transitions are not an appropriate criterion for identifying an attractor network.
There is a specific case in which sharp transition can be observed in the CA3 [21], [56]: if the animal is familiarized with the two extreme shapes with altered distal cues, a different spatial coordinate system is assigned for each memory. As the EC globally remaps with different distal cues, a sharp transition in the CA3 will occur but will be caused by changes in cortical activity and cannot be attributed to attractor dynamics in CA3 [40]. In the experiments that we have analyzed here, distal cues were kept unaltered, and this prevents global remapping in MEC.
For CA3 to function as an associative memory, the recurrent synapses must be able to undergo activity-dependent changes in their synaptic strength. Indeed, work in the slice preparation has clearly shown that these synapses can undergo long-term potentiation (LTP) [9], [10], but there has been no previous in vivo demonstration that these synapses can change in response to environmental stimuli. We argue that aspects of the data reported by Leutgeb et al. [19] strongly argue that the attractors formed in CA3 are continuously subject to learning. Indeed, this is demonstrated by the fact that exposing rats to intermediate environments is sufficient to produce a modest change in CA3 PV correlation and thus its synapses (Figure 7) [20], [55]. The key observation is hysteresis of the PV; if an altered environment is interposed between two test sessions in the same environment, the PV in the two identical environments will be slightly altered. Importantly, this hysteresis is not observed in DG [19], strongly suggesting that it occurs because of the plasticity within the recurrent connections of CA3. Indeed, we are able to reproduce these hysteresis effects in our model that simulates the effects of experience-dependent Hebbian plasticity in the CA3 excitatory recurrent connections.
This analysis of morphing suggests future experiments investigating the role of attractors and their modification by learning. Given that attractor dynamics can now be more precisely identified, it would be of interest to test directly whether NMDAR action during learning of the square/round environments is necessary for attractor formation, as would be predicted based on in vitro studies analyzing pattern completion [58]. Indeed, following this prediction, NMDAR seems to be required during memory formation, as shown by the fact that pattern completion during subsequent recall is prevented [59]. NMDARs are not required during memory recall [60]. This is consistent with the observation that the latter effect depends on the fast dynamics of our model. Additionally, a second type of analysis could investigate discretization during learning [61]; it has previously not been possible to experimentally address the question of how finely the world is divided, but it is now approachable through the study of CA3 attractors in particular, by addressing both the temporal and the spatial ranges of this memory segmentation. In addition, we can speculate that GABA-dependent dendritic shunting of spike-time-dependent learning can assure that also the learning dynamics is restricted to single gamma cycles [62]. From the model presented here, we will be able to estimate the average size of the population of CA3 neurons that define a distinct memory, their interrelation, and the drift that they might be subject to. In addition, their embedding in a theta-gamma code raises the question of whether single memory segments defined in a single gamma cycle are, in turn, integrated in hierarchical structures following the theta rhythm. Further, the drift of CA3 memory that we have identified would suggest that, for a more permanent storage of memory segments, other structures will have to be engaged to solve the so-called plasticity-stability dilemma [63]. Finally, the ability of rather short exposures to altered environments to change the attractor properties of CA3 facilitates the study of learning in a defined network. This may allow the analysis of the spike patterns that lead to learning, the role of neuromodulators, and the role of repetition/replay in producing long-lasting synaptic modification.
The demonstration that CA3 cells display the properties expected of an attractor network carries special significance because it provides the key remaining evidence, i.e., analysis of in vivo data, that is necessary to establish the associative memory function of CA3. As discussed, the existence of modifiable recurrent connections in CA3 suggested that CA3 is an attractor network. Consistent with this hypothesis, a mutation that disables synaptic plasticity in CA3 prevents behavior that is dependent on pattern completion [59], [64]. Additionally, signatures of experience-dependent plasticity and pattern completion have been obtained in vitro with CA3 slices [58]. Thus, taken together, the anatomy, the behavioral experiments, in vitro electrophysiology, and our analysis of in vivo recordings make a strong case that CA3 is, in fact, an associative memory structure that follows attractor dynamics. The CA3 network analyzed here is thus among the very few cases in which the evidence regarding network, cellular, and anatomical properties has converged to explain an important aspect of memory and behavior.
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10.1371/journal.pbio.0050293 | A sex-ratio Meiotic Drive System in Drosophila simulans. II: An X-linked Distorter | The evolution of heteromorphic sex chromosomes creates a genetic condition favoring the invasion of sex-ratio meiotic drive elements, resulting in the biased transmission of one sex chromosome over the other, in violation of Mendel's first law. The molecular mechanisms of sex-ratio meiotic drive may therefore help us to understand the evolutionary forces shaping the meiotic behavior of the sex chromosomes. Here we characterize a sex-ratio distorter on the X chromosome (Dox) in Drosophila simulans by genetic and molecular means. Intriguingly, Dox has very limited coding capacity. It evolved from another X-linked gene, which also evolved de nova. Through retrotransposition, Dox also gave rise to an autosomal suppressor, not much yang (Nmy). An RNA interference mechanism seems to be involved in the suppression of the Dox distorter by the Nmy suppressor. Double mutant males of the genotype dox; nmy are normal for both sex-ratio and spermatogenesis. We postulate that recurrent bouts of sex-ratio meiotic drive and its subsequent suppression might underlie several common features observed in the heterogametic sex, including meiotic sex chromosome inactivation and achiasmy.
| Mendel's first law of genetics states that two alleles of a heterozygote are transmitted to the next generation at an equal ratio. The cornerstone of population genetics, this law states that the evolutionary fate of genetic variants is solely governed by their contribution to the good of their carriers. However, meiotic drive genes—which skew transmission in their own favor—can evolve under certain circumstances, even though they cause harm to the genome as a whole. Meiotic drive elements are often enriched on the two sex chromosomes (i.e., the X and the Y) because of a lack of recombination between them. Here we describe the genetic and molecular characterization of a meiotic drive distorter on the X chromosome in Drosophila simulans. This distorter apparently formed de nova from yet another new gene. To fight back against this harmful distorter, the D. simulans genome has evolved an ingenious mechanism based on DNA sequence homology. We postulate that repeated meiotic drive invasion and its suppression could be a major mechanism for genome evolution, underlying the ultimate cause for the inactivation of sex chromosome during meiosis and the occasional loss of recombination (achiasmy), which is observed only in the heterogametic (XY) sex.
| Sex chromosomes are believed to evolve from a pair of autosomes [1–3]. An incipient Y chromosome, like an autosome, is largely euchromatic and free to recombine, except for a small region determining sex, as exemplified by species such as the papaya plant [4] and the medaka and stickleback fish [5,6]. On an evolutionary time scale, the nonrecombining region of the Y will generally expand to include most or all of the chromosome, accompanied by an accumulation of transposable elements and other repetitive sequence, as well as mutational inactivation of most of the protein-coding genes. Only a small number of genes remain active in a mature Y chromosome, such as that in humans or Drosophila. Some Y-linked genes are vestiges of the degeneration process, while others have originated from autosomes as a result of recruiting male-specific genes such as those that function in spermatogenesis [7–10]. Accompanying the evolution of sex chromosomes, at least two problems of biological significance arise. One problem is the unequal gene dosage of sex-linked genes between the XY sex and the XX sex. Because of Y degeneration, most genes on the X have only one active copy in the XY sex but two in the XX sex. Myriad strategies to compensate the dosage inequality have been exploited by various species, and some of these mechanisms are now understood in molecular detail in model organisms of fly, worm, and mouse [11].
Another substantial but less obvious problem consists of genetic conflicts over the sex ratio among various parts of a genome, which would allow optimal transmission of their own genes. A corollary to sexual reproduction is Fisher's well-known principle that the sex ratio must be equal for a panmictic population of dioecious species [12]. However, as noted long ago, Fisher's principle applies only to autosomal genes but not to sex-linked genes. Genes linked to one or the other sex chromosome would have a selective advantage were the sex ratio in the population skewed [13]. Because of the genetic isolation between the sex chromosomes, mutations biasing the sex ratio can easily accumulate and enhance each other as long as their deleterious effects are offset by their biased transmission. Thus, the evolution of sex chromosomes leads to an intrinsic conflict among the X, the Y, and the autosomes with regard to sex ratio.
Many cases of sex ratio distortion (sex-ratio hereafter) have been documented, particularly in taxa where intensive laboratory investigation is possible [14]. Because of the biased sex ratio, suppressors unlinked to a distorter are strongly selected to restore the Fisherian sex ratio [15]. The occurrence of sex-ratio in a population can often be transient and easily escape notice. However, recurrent bouts of sex-ratio invasion and suppression can modify the genetic architecture of gametogenesis to such an extent that hybrid incompatibility can be driven to evolve among isolated populations. In other words, genetic conflicts can be a key mechanism for speciation [16–18].
Several cases of sex-ratio have been reported in D. simulans [19–23]. In a companion paper, we reported the cloning of an autosomal sex-ratio suppressor [24]. As Fisher's principle predicts, there must exist an X-linked sex-ratio distorter to which this suppressor corresponds. Here we report the characterization of such a distorter. More generally, we speculate that sex-ratio distortion might underlie the evolution of meiotic sex chromosome inactivation and achiasmatic meiosis, two biological phenomena whose evolutionary origins still remain mysterious.
We previously cloned a D. simulans gene, not much yang (nmy, polytene chromosome position 87F3), in which the homozygous male mutant displays a female-biased sex ratio. This gene belongs to the Winters sex-ratio system, one of three independent sex-ratio systems found in this species [24]. We inferred that the wild-type (Nmy) function is a suppressor of sex-ratio distortion, and that there must be a corresponding X-linked sex-ratio distorter according to Fisher's principle of sex ratio evolution. By happenstance, we found an X chromosome that did not express the sex-ratio phenotype in homozygous nmy males (Figure S1). This X chromosome was thought to have a loss-of-function mutation in the gene(s) causing sex-ratio. We designate the mutant gene as distorter on the X (dox). Other X chromosomes, including one from the stock y wam v2 f66, did express sex-ratio when tested in the nmy background and were postulated to carry the distorting allele Dox (Figure S2).
A preliminary mapping of dox was carried out through the scheme described in Figure 1. Recombinant X chromosomes were tested for sex ratio (proportion of females or k) in the nmy background (I in G5 of Figure 1) as well as in the nmy/+ background as control (II in G5). A total of 148 X chromosomes tested could be grouped into eight genotypic classes (Figure 2A–2H). Several inferences can be drawn from the results. First, there are two and one recombinants in classes E and F, respectively, which are exceptional and thus informative in the mapping of dox, allowing it to be placed closely proximal to v at a distance of about 6% (3/51) of the w–v interval. Second, the major sex-ratio distorter shows less strength of distortion when a gene in the vicinity of f is absent. The reduced distortion can be inferred by comparing classes A (k ± standard error of the mean [SEM] = 0.803 ± 0.014, n = 22), D (0.797 ± 0.019, n = 7), and F (0.786 ± 0.020, n = 22, excluding one losing the major distorter) with class G that has a significantly lower sex ratio of 0.655 ± 0.013 (n = 24) (t-test, p << 0.001). The first three classes have similar sex ratios (analysis of variance [ANOVA], p = 0.748). We call the gene near f an enhancer of Dox (E(Dox)) because it alone does not cause sex ratio distortion (class H, 0.509 ± 0.004). In light of the above reasoning, the five class B recombinants showing sex-ratio probably have an inferred genotype of w Dox v2 e(Dox) (0.686 ± 0.018), the same as class G with respect to sex-ratio distorter and enhancer (t-test, p = 0.157). Finally, based on numerous SNP sites found between the two parental X chromosomes, we genotyped a selected subset of the 148 chromosomes and narrowed the location of dox to a region of 215 kb between CG15316 (8E1–4) and nej (8F7–9), which falls within an interval defined by two visible markers lz and v.
The fine mapping of dox began with the construction of two X chromosomes of lzs Dox v2 f66 and y wam dox, whose phenotype with regard to Dox was confirmed by testing in a homozygous nmy background through the scheme described in Figure 1 (G3 through G5). A cross of + + lzs Dox v2 f66/y wam + dox + + females to Ubx/D males was set up, and 324 recombinants with crossovers between lz and v were obtained. We picked 22 lzs and 21 y wam v2 f66 X chromosomes with crossovers falling between CG15316 and nej to further test their sex-ratio phenotype in a homozygous nmy background, again using the scheme in Figure 1 (G3 – G5) (Figure 3A). The cross in G4 was carried out at 18 °C so that the sex-ratio phenotype of Dox can be fully expressed in G5 [24]. Each of the 43 recombinants was unambiguously classified as either Dox or dox.
Four SNP markers were found in the CG15316–nej region (Table S1), and these were used to demarcate the crossover points for the 43 recombinants. There are two Dox and five dox lines, with their crossovers falling between the markers 5dox_III and C14/C17. We sequenced ∼31 kb embracing this region (Figure 3A). The two parental alleles are identical for the 20,791 bp within the 5dox_III–C14/C17 interval, except for a deletion of 105 bp (Δ105) in dox (Figure 3B). We confirmed the predicted presence or absence of the Δ105 element in the final seven informative recombinants.
We sequenced six other D. simulans strains in the region between the primer pair DoxF4-DoxR4 that spans the Δ105 sequence (Figure 3B; Text S3). Two types of haplotypes were recognized. One is from the SR6 X chromosome that carries the Paris sex-ratio distorters [24,25]. Three copies of a 360-bp repeat were found within this haplotype. The other type is shared by all the other strains, with an insert of 3,833 bp found within the last 360-bp repeat.
This 3,833-bp fragment has sequences homologous to the last three exons from the gene CG32702 of D. melanogaster. The CG32702 ortholog is missing in the current annotation of D. simulans genome (Release 1.0, http://genome.ucsc.edu/). However, we did obtain a sequence of 18.7 kb covering the orthologous CG32702 region in D. simulans as well as its full length cDNA of 11,550 bp (Figure 3B and Text S1). The transcript consists of 15 exons, largely agreeing with the computational annotation of this gene in D. melanogaster, except for differences in two splice sites and one extra exon at the 5′ end.
Apparently, this 3,833-bp insert (designated Tp3833) was duplicated and transposed from a sequence of 3,549 bp (designated Tp3549) in the 3′ region of CG32702. Note that one copy of the 360-bp repeat is also present next to Tp3549, suggesting that this repeat may have facilitated the transposition. The last two exons and part of exon 13 (Ex13) of CG32702 are still intact in the Tp3833 region (CG32702d, Figure 3B).
Sequences from the homologous region between DoxF4-DoxR4 were obtained from one strain of each of the sibling species D. sechellia, D. mauritiana, as well as D. melanogaster. All species resemble SR6 in having various copy numbers of the 360-bp repeat (Figure 3C). A phylogenetic analysis of these 360-bp repeats shows a monophyly of the eight copies from D. melanogaster, but a reticulate relationship among the rest, suggesting shared evolutionary history of this intergenic region in the D. simulans clade (Figure S3). However, it remains to be determined whether a Tp3833-like sequence can be found in D. mauritiana. The existence of a functional Nmy strongly suggests that a corresponding sex-ratio distorter like Dox may still segregating in this species [24].
Within Tp3549, a fragment of 1,458 bp replaces a fragment of 1,408 bp downstream of the 3′ end of CG32702 in D. melanogaster, and these two sequences have no homology (Figure 3B). Database searches suggest that the 1,458-bp sequence is absent from D. sechellia, D. yakuba, and D. erecta, but some similar fragments of 300–600 bp can be found dispersed in these genomes, often in multiple copies. Transcripts within Tp3549 were detected, and a new gene, which we designate as Mother of Dox (MDox), is defined (see below). Tp3549 and Tp3833 thus represent a fluid portion of the Drosophila genome that occasionally gains new functions.
Initially the truncated version of CG32702 (CG32702d) appeared to be the best candidate for Dox because of its perfectly conserved open reading frames (ORFs) and intron–exon boundaries (Figure 3B). However, we have not detected transcripts from CG32702d. Extensive 5′- rapid amplification of cDNA ends (RACE) experiments using gene-specific primers targeting CG32702d all failed. A 3′-RACE experiment did recover cDNAs, but they could be transcribed from the 3′-end of CG32702, not of CG32702d. There is a divergent site (C/A) between Tp3549 and Tp3833 in the 1,919-bp region corresponding to the last three exons of CG32702. Using the primers CG32702seqF26 and CG32702seqR26 (F26 and R26 in Figure 4; Text S3), only the CG32702 sequence can be amplified from cDNA (Figure S4). CG32702d is therefore unlikely to be transcribed or its expression is too low to be detected by reverse-transcription PCR (RT-PCR).
On the other hand, we have detected transcripts that cover the region of Δ105 in the opposite direction of CG32702d (Figure 4). Two transcripts from the allele Dox were recovered with either four or three introns. Their full lengths are 2,781 bp and 2,690 bp, respectively. From the allele dox, we have also recovered two full-length cDNAs identical to those of Dox, except that the exon III, 42 bp in length, is missing because of the deletion Δ105 (Figure 4). This 42-bp element is tandemly repeated in the cDNA of Dox but has only one copy in that of dox. Within Tp3549, we have also recovered a full length cDNA antisense (2,564 bp) to the 3′ end of CG32702 (Figure 4). MDox like Dox also has three introns in exactly the same sites, as well as the tandem repeats of 42 bp present in its cDNA (Figure 4).
Surprisingly, all transcripts from the Dox and MDox loci have very limited coding potential. The largest ORFs of MDox in all three frames are shown in Figure 4, and all but one fail to match any known sequences by BLASTX searches through the nr database. The one ORF that was predicted by Genscan encodes 62 amino acids (aa), and this ORF highly matches (57/62 or 91% identity) the C terminus of a D. melanogaster gene named CG8664 (located in region 15F7, proximal to the gene f). Similarly, only one of the largest ORFs of Dox has BLASTX hits. This is again the ORF of 69 aa predicted by Genscan and is homologous to the 62 aa ORF of MDox, although only part of it matches to CG8664 (38/44 or 86% identity) due to a frameshift mutation. CG8664 has no known biological functions or phenotypes. In the orthologous position of CG8664 in the current D. simulans genome annotation, a fragment of 2,084 bp, instead of a CG8664 homolog, has been found. Part of this 2,084-bp fragment, approximately equivalent to the 1,458-bp element mentioned above (Figure 3B), is recognized and has a high similarity (99.3% identity) to a region within Dox. If the existence of this partial paralog of Dox in the f region is confirmed by experiment, it would be interesting to test it as the candidate gene for E(Dox).
The pair of tandem 42-bp elements essential for a functional Dox are located within an ORF of 157 aa (Figure 4). A 14-aa domain encoded by this 42-bp element has no known functions. If this ORF is ever translated, the tandem 14-aa domains appear to be required for the wild-type function of Dox as a sex-ratio distorter. Coding or noncoding, the molecular mechanism underlying the effect of Dox in rendering Y-bearing sperm dysfunctional awaits further experimental investigation. The wild-type function of MDox is not known, although the presence of the critical tandem repeats of the 42-bp element suggests its biochemical similarity to Dox.
The phenotype of Dox as a sex-ratio distorter is uncovered if its suppressor, Nmy, is nonfunctional (Figure 5A) [24]. The Nmy transcript appears to form a stem-loop structure with a double-stranded RNA (dsRNA) stem of 345 bp, and small interfering RNAs (siRNAs) produced from the dsRNA stem could target and suppress Dox [24]. Hence, homology between Dox and Nmy is anticipated. Indeed, sequence comparisons suggest that Nmy originated from Dox through a retrotransposition event [24]. Specifically, the 345-bp dsRNA sequence from Nmy has extensive homology to the potential ORF of both Dox and MDox that contains exon III (Figures 5B). The critical 42-bp element falls within an 85-bp region that has a perfect match with the stem region (positions 264–390 in the alignment of Figure 5C). Whether or how either Dox or MDox is regulated by these hypothetical siRNAs is currently under investigation.
The possibility that Dox evolved solely as a sex-ratio distorter and for no other reasons is supported by the normal phenotype of the double mutant dox; nmy. We have shown previously that the etiology of the Winters sex-ratio is the degeneration of the Y-bearing spermatids during their maturation, as observed both through transmission electron microscopy (TEM) and through light microscopy [24]. We carried out similar observations of the spermatogenesis of dox; nmy males at 16 °C. All stages of spermatid maturation appear to be normal as also found in Dox; Nmy wild type (Figure 6A–6D, in comparison to Figures 4 and 5 in [24]). Quantitatively, 5.7% (n = 1108) of spermatid heads appear to be abnormal under TEM, in a proportion similar to wild-type Dox; Nmy (5.8%, n = 1903). With 4',6-diamidino-2-phenylindole (DAPI) staining, no abnormal spermatid head was observed among the 1,416 heads examined. Consistent with these cytological observations, the sex ratio of progeny from the dox; nmy males at 16 °C was 54%. As a comparison, a dox; Nmy male was similarly examined. No abnormal heads were observed among 1,058 spermatids, and the sex ratio when tested was also 54%. All the evidence together suggests that Dox is not an essential gene and is fully dispensable. Nmy is also dispensable if Dox is absent.
The fate of a sex-ratio system can be loss, fixation, or stable polymorphism. Apparently, the Winters sex-ratio is still segregating in D. simulans [24]. The same is true for the Paris sex-ratio system that has been found in the same species [21]. Evidence from molecular population genetics shows that the Paris SR6 X chromosome has swept through African and Indian Ocean islands only recently (less than 20 thousand years ago [ka]) [25]. The presence of a functional Nmy suppressor in D. mauritiana suggests that the Winters sex-ratio evolved in the ancestor of the D. simulans clade [24]. The following genetic evidence will enforce the above conclusions and help to compare the evolutionary history of these two sex-ratio systems.
We have introgressed the Y chromosome of D. sechellia into D. simulans (D. sim Y[sech]) in a background isogenic to simB (Figure S6). The success of this introgression was confirmed by fingerprinting with a Y-specific probe Y5g (Figure 7A and Figure S2). The D. sechellia Y chromosome was thus tested against the driving effect of either Dox (Winters) or SR6 (Paris). The Dox/Y[sech] male expresses sex-ratio if nmy is homozygous, but does not if one copy of the functional Nmy gene is present (Figure 7B). Hence the D. sechellia Y chromosome is equally sensitive to Dox as is the D. simulans Y chromosome. Intriguingly, SR6/Y[sech] males exhibit male-biased sex ratio distortion (k = 0.33). Unfortunately, similar introgression of the Y chromosome from D. mauritiana cannot be made because D. sim Y[mau] is sterile [26].
The above observations are consistent with the earlier estimate that the origin of Dox predates speciation among D. simulans, D. mauritiana, and D. sechellia about 200–400 ka [27], whereas SR6 arose in D. simulans after the speciation [24,25]. Assuming that an Y-linked distorter causing male-biased sex ratio distortion has little chance of persistence as compared to an X-linked one causing female-biased sex ratio distortion [13,28], we suppose that the Y[sech] still bears sensitive sequence to Dox as in the ancestral Y of the three species. We suggest that the male-biased sex ratio expressed by SR6/Y[sech] is a sign of evolutionary independence between the Y[sech] and the SR6 distorters.
The etiology of SR6 has been attributed to loss or breakage of the D. simulans Y chromosome during meiosis II in SR6/Y[sim] males [29,30]. Our results support earlier findings by showing that male progeny from the SR6/Y[sim] father are sterile at a frequency of 19%. However, the frequency of sterile male progeny from an SR6/Y[sech] father is only 3% (Fisher's exact test, p < 0.0001), a number that is not different from the control (2%, Fisher's exact test, p = 0.284) (Figure 7C). It is possible that SR6 does not cause loss or breakage of the Y[sech], hence the etiology of the male-biased sex ratio may be different from similar male sex-ratio (msr) cases reported in D. pseudoobscura [31] and in D. affinis [32], where a large number of nullo XY sperm are produced. The unique cytological mechanism underlying the male-biased sex ratio in SR6/Y[sech] males again suggests that the unequal sex ratio is a neomorph created by a genetic incompatibility between the two chromosomes, rather than a shared evolutionary history of sex-ratio.
A sex-ratio meiotic drive distorter, Dox, has been identified. Dox is a new gene that arose from yet another new gene MDox. Intriguingly, both MDox and Dox appear to be transcribed as noncoding RNAs or as mRNAs with very limited coding potential. Dox was also the precursor for the origin of the autosomal suppressor Nmy by a retrotransposition process. Dox functions solely as a sex-ratio distorter and is not essential, because the mutant dox males have normal sex ratio and spermatogenesis. The Dox/Nmy system is the first that has been characterized at the molecular level for sex-ratio meiotic drive, a widespread biological phenomenon that is promoted by the evolution of heteromorphic sex chromosomes.
The gene structures of Dox and Nmy strongly suggest that an RNAi mechanism is involved, just as in numerous transgenic studies where inverted repeats (IR) were used to silence target genes in eukaryotes (e.g., [33]). Most likely, the suppression of Dox by Nmy is through a classic RNAi pathway, also known as post-transcriptional gene silencing (PTGS), which has been under intensive genetic and biochemical studies (reviewed in [34]). In essence, 21–23 nucleotide (nt) siRNAs processed from dsRNA are responsible for guiding the active RNA-induced silencing complex (RISC) to homologous mRNA, resulting in the latter's subsequent cleavage [35]. The PTGS model for Dox/Nmy interactions can be readily tested by comparing the steady-state mRNA levels of Dox between Dox; Nmy and Dox; nmy males, and by detecting the binding of specific siRNAs with the RISC components. Because PTGS happens in the cytoplasm, and spermatid nuclei within a cyst share the same cytoplamic syncytium, the final gene product of Dox likely has a localized deleterious effect in the Y-bearing spermatid nuclei, whereas the presence of a Y or absence of an X must provide the primary cue that eventually leads to abnormal maturation of the Y-bearing sperm heads.
Alternatively, a different type of RNAi mechanism could be involved in the Dox/Nmy interaction. A class of small RNA in the size range 24–29 nt has been identified as silencing intermediates in the control of repetitive sequences such as retrotransposons [36]. Unlike the classic RNAi machinery, the core proteins do not require DCL-1, DCL-2 and AGO2, and a different type of RNAi pathway (repeat-associated small interfering RNAs or rasiRNAs) has been proposed [36]. The rasiRNA pathway has also been shown to be responsible for silencing a possible cryptic sex-ratio meiotic drive distorter, Ste, in D. melanogaster [37]. The Y-linked Su(Ste) suppresses the deleterious effects of the X-linked Ste, including male sterility and meiotic drive [38]. Both Ste and Su(Ste) consist of repeats that share extensive homology, and rasiRNAs were shown to be the information carrier for the target specificity [36,39,40].
A third possible mechanism for silencing Dox might be at transcriptional level in a manner of co-suppression as first observed in plant transgenics, where the expressions of both an endogenous gene and the homologous transgene were down-regulated [41]. This type of transcriptional gene silencing (TGS) has been demonstrated in Drosophila [42,43], and it requires physical contacts between homologous sequences and Polycomb group (PcG) proteins [44]. Note that an intact pair of inverted repeats is not required for an efficient TGS (e.g.,[44]). In our case, nmy[1427] is a loss-of-function mutation that does not have an intact pair of inverted repeats but does have a 1.2-kb sequence paralogous to Dox, arguing against this type of TGS as a strong candidate mechanism for silencing Dox [24].
The X chromosomes of many species are condensed precociously in prophase of meiosis I when active transcription peaks in the autosomes [45]. The existence of meiotic sex chromosome inactivation (MSCI, also known as X chromosome allocycly) has been well established in several model organisms, either directly through the observation of precocious heterochromatin sex bodies [46–48], or indirectly from genetic analysis of X-autosome translocations [49] as well as with genome-wide gene expression studies [50–55]. Recently, MSCI has been demonstrated in D. melanogaster by assaying transgenic expressions in the X chromosome [56]. Though sex bodies are the direct evidence for MSCI, they have not been observed in most species examined so far, including Drosophila [57]. The status of MSCI may be assayed with more sensitive methods such as the detection of histone modifications that relate to transcriptional activity (e.g., [48]).
There are several hypotheses for the evolution of MSCI. One hypothesis is that MSCI evolves because of a need to suppress recombination between the two sex chromosomes [58]. Another hypothesis was coined as the SAXI hypothesis (sexual antagonism and X inactivation). Because the X spends 2/3 of its evolutionary history in females, the X will be depleted of male-specific genes, and a feminized X would be under selection to be silenced during male meiosis [59]. A third hypothesis has been suggested in the light of the discovery of the meiotic silencing of unpaired DNA (MSUD) in Neurospora crassa [60,61]. MSUD is reasoned to have evolved for defending against invasion of transposons [62–64]. The connection between MSCI and MSUD is supported by the silencing of unpaired chromosomal fragments in the mouse and worm [65,66].
Each of the three hypotheses for MSCI captures only some specific features consequent to the evolution of sex chromosomes, and hence provides only a partial and proximate explanation for the evolution of MSCI. The ultimate cause of MSCI, of course, must be the degeneration of the Y or W chromosome. Following this line of reasoning, we propose yet another hypothesis that we call “the drive hypothesis” for the evolution of MSCI: there is a constant requirement for silencing sex-linked genes including potential sex-ratio distorters during meiosis because of an intrinsic conflict over the sex ratio within a genome accompanying sex chromosome evolution.
We believe that the drive hypothesis provides mechanistically superior explanations to the other hypotheses for the evolution of MSCI for the following specific observations: (1) There is a predominant pattern of generating testis-specific genes through retrotransposition between the X chromosomes and autosomes [67–70]. Some of the retrotransposed sequences might be involved in creating new distorters and suppressors. (2) MSCI in the worm and mouse is dependent on a putative RNA-dependent RNA polymerase, suggesting the involvement of an RNAi-like mechanism [71]. The Dox/Nmy case provides a strong mechanistic connection between meiotic drive and the evolution of MSCI. Admittedly, it is not an easy task to test empirically and discriminate among the hypotheses described above. Although the drive hypothesis emphasizes the importance of meiotic drive in the evolution of MSCI, other sex-chromosome–specific features such as suppressed recombination, degeneration, and depletion of sex-specific genes could well be different facets of the same evolutionary process. These features may share biochemical components and have reinforced each other over evolutionary time.
In addition to MSCI, achiasmatic meiosis is another evolutionary oddity that might also be rooted in sex chromosome evolution. An achiasmatic meiosis has no crossovers between homologs, even of autosomes. Between 20 and 30 independent evolutionary occurrences of achiasmatic meiosis have been recorded, and all of them are observed in the heterogametic sex [72], with one possible exception in Tigriopus californicus [73,74]. Nevertheless, it is reasonable to generalize that achiasmatic meiosis evolves only in the heterogametic sex, as a result of occasional spillover of the molecular machinery that is simultaneously responsible for both suppressing meiotic drive and recombination between the sex chromosomes. This connection between achiasmy and heterogamety was actually presaged by J. B. S. Haldane and J. S. Huxley [75,76], but here we specify meiotic drive as the major evolutionary cause. One would wonder why there are so many sex-ratio meiotic drive cases reported in Drosophila. Other than the geneticists' proclivity for counting flies in lab, does achiasmatic male meiosis in this genus also contribute to the abundance of meiotic drive systems?
Meiotic drive in ZW females is mechanistically different from that in XY males. For XY males, sex-ratio meiotic drive can be achieved either by loss of a sex chromosome during meiosis (meiotic drive sensu stricto) or by abnormal postmeiotic development (gametic drive or meiotic drive sensu lato, as used throughout this article). To prevent gametic drive, stringent control of X-linked genes must be achieved through means such as MSCI. However, in ZW females, only one of the four haploid products will eventually end up in the functional egg during oogenesis and meiotic drive sensu stricto would be an easier means to achieve biased transmission than gametic drive.
For the ZW species, centromere structure and centromere-binding proteins may play an important role in meiotic drive (“centromere drive”) [77], and there may be no particular need for evolving MSCI as a defense against gametic drive, although in principle, occasional female gametic drive may still evolve such as a polar body distorter that can kill an egg. Indeed, there is a general lack of cytological observation of MSCI in the ZW females [57,78], with seemingly one proved exception in the caddis fly Glyphotaelius pelludidus [79]. Consistent with this observation, there are only two independent origins of ZW achiasmy [72]. Another explanation for the absence of Z allocycly might be due to a lack of selection pressure to evolve sex-ratio distorters on the Z chromosome, because male-biased sex-ratio distorter cannot persist long in a population [13], therefore it might have left little trace of impact on genomic evolution prior to its disappearance. The question of whether the Z chromosome lacks MSCI deserves special attention because its general absence would be inconsistent with the SAXI or MSUD hypotheses.
The D. simulans stocks are: (1) y wam v2 f66 from the Tucson Drosophila Stock Center; (2) w; e and simB (w; nt; III) [80]; (3) C(1)RM y w/lzs and Ubx/D from J. Coyne; (4) SSR12-2-7 (w; nt; nmy) [24]; and (5) Paris sex-ratio X chromosome SR6 and its standard ST8 as described previously [81]. The SR6 and ST8 X chromosomes are maintained by backcrossing males to females of the stock C(1)RM y w/lzs every generation. The D. sechellia stock 3588 is from A. Clark [23].
The D. mauriatiana × D. simulans introgression lines have been described before [18,80]. The following lines were used in this study: heterozygous introgression lines P40–46 nmy, P38–11 nmy, P38H77 Nmy, P40L12 nmy, and P40B13 Nmy, all having the genotype w; nt; P/ III (Nmy) where P represents the various semi-dominant P-element transgenes P[w+] marking the introgressed D. mauritiana material nearby, and III (Nmy) represents the third chromosome III with Nmy. These chromosomes are maintained by backcrossing P/III males to simB females every generation. The SSR (skewed sex ratio) line Q15.3 is from a previous D. simulans × D. sechellia hybridization experiment [23]. Several stocks were constructed for this study: C(1)RM y w/w; nt; III by backcrossing females of C(1)RM y w/lzs to simB males for >19 generations. C(1)RM y w/w dox; nt; nmy was constructed through a scheme described in Figure S5. Another stock, w/Y[sech]; nt; III, which is isogenic to simB except that the Y chromosome is from D. sechellia 3588, was constructed through a scheme described in Figure S6. The stock y wam was constructed from the stocks w; e and y wam v2 f66. Another stock, C(1)RM y w /lzs v2 f66, was constructed from C(1)RM y w/lzs and y wam v2 f66.
All flies were reared on cornmeal-molasses-agar medium sprinkled with yeast grains at room temperature (22 ± 1 °C) unless otherwise indicated. The sex ratio of a male was scored by mating this male with three tester virgin females, usually of the stock w; e, for 7 d before clearing all adults. The progeny were sexed and counted three times until the 19the day. Sex ratio (k) was calculated as percentage of females.
SNPs were discovered by sequencing 500–1,000–bp PCR products from relevant X chromosomes. The primers were designed by targeting the D. melanogaster genome (http://www.flybase.org/), and the virtual PCR products were compared to the D. simulans sequences (http://genome.wustl.edu/tools/blast) for correcting any mismatches within the primers (Table S1). Genotyping was done directly by sequencing. Some other key reagents/kits are: LA Taq long PCR kits (Takara); EZ-Tn5 Insertion Kit for sequencing large DNA fragments (Epicentre); Lambda ZAP II vector for genomic library (Stratagene); TRIZOL Reagent for RNA isolation, SuperScript II Reverse Transcriptase and 3′ or 5′–RACE kits (Invitrogen).
Light microscopy and TEM procedures have been described previously [24].
All sequences have been deposited in the GenBank database (http://www.ncbi.nlm.nih.gov/Genbank/index.html) and have been assigned the accession numbers EF596886-EF596899.
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10.1371/journal.pgen.1001353 | Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies | Genome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulated in the literature on biological pathways and interactions. It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries. Although a number of methods have been developed recently to prioritize genes using prior biological knowledge, such as pathways, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway. However, how genes are related with each other in a pathway may be very informative to identify association signals. To make use of the connectivity information among genes in a pathway in GWAS analysis, we propose a Markov Random Field (MRF) model to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form, and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease. Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene–based method. We also illustrate the usefulness of our approach through its applications to a real data example.
| Statistical methods used in most GWAS are based on the analysis of single markers. Prior biological information about markers, genes, and pathways is not commonly incorporated in the detection of associated disease loci. Recently a number of methods have been developed to incorporate such information, and it has been shown that they may make use of prior biological knowledge in association analysis. However, most of these methods ignore the regulatory relationships and functional interactions among genes. In this article, we propose a statistical method that can explicitly model the interactions of genes in a neighborhood defined by the topology of a pathway. Simulation studies and a real data example show that the proposed method can improve the power of identifying associated genes when they are in the neighborhood of other genes whose association has been firmly established in previous studies.
| In genome-wide association studies (GWAS) researchers examine a large number of markers across the genome in many individuals to identify associations between genetic variants and disease, or to prioritize markers for follow up studies. However, most of the times the signals from individual markers are weak and the sample size is not large enough to have adequate power for true discoveries, especially when the minor allele frequency is low. Various approaches have been developed to increase statistical power, including aggregating multiple markers from the same gene or in the same haplotype block region and incorporating information from other sources into the GWAS analysis. It has been found that the gene level analysis has the ability to identify new associations in addition to those identified using individual Single Nucleotide Polymorphisms (SNPs) [1], [2]. Gene-based analyses include those using the most significant SNP within and near a gene [1]; combination statistics (Fisher, Sidat, and Simes) from all individual markers [2]; Principal Component Analysis (PCA) regressions [3] and the sparse partial least squares regressions [4]. To incorporate prior biological knowledge, one information rich resource is biological pathways. It is believed that genes interact with each other in biological processes, and it is conceivable that they may jointly affect the risk of a complex disease. There exist an abundance of databases containing known gene pathways and protein-protein interactions, such as KEGG, BioCarta, GenMAPP, and HPRD. A number of gene prioritization methods incorporating prior biological knowledge have been developed for GWAS. Some examples include Prioritizer [5], Endeavour [6], CGI [7], CANDID [8], GeneWanderer [9], CIPHER [10], GIN [11], and the pathway based gene set enrichment approach [1]. These methods have shown that incorporating prior biological information in GWAS is useful. However, they do not consider functional relationships among genes. The general input of these approaches is a list of genes as a set, in which genes are treated as exchangeable without taking into account the regulatory relationships among them. As a result, information from the pathway topology and interactions among genes is usually ignored. However, how genes are functionally related to each other in a pathway may be very informative for GWAS analysis and such information can be utilized to increase the power of detecting real associations. When associations have been firmly established for some genes either through GWAS or prior candidate gene-based studies, we can take advantage of this knowledge to examine other genes related to these known genes through the same pathways they all participate in.
In this paper we propose a Markov Random Field (MRF) model to incorporate biological pathway information in GWAS. MRF has been considered by several authors to combine data from different sources in genomics studies, e.g., a spatial normal mixture model [12] for gene expression and CHIP-chip data, a Gamma-Gamma model and MRF for mRNA microarray data [13], and prioritizing genes by combining gene expression and protein interaction data [7]. However, little has been done in the context for GWAS, with the exception of Li et al. [14] who proposed a hidden MRF for GWAS. But their method is developed in the context of jointly analyzing markers in linkage disequilibrium.
We first present a motivating example from a GWAS of Crohn's disease [15] for the proposed method. As will be shown next, the result clearly suggests that genes in the same neighborhood within a pathway tend to show similar association status. This Crohn's disease cohort includes 401 cases and 433 controls, and the Illumina HumanHap300 BeadChip (Illumina, San Diego) were used for genotyping. We first mapped SNPs to genes and then applied PCA regressions to obtain gene-level p values of the association tests with Crohn's disease status [3]. More details about this data set are given in the Materials and Methods section. We then obtained pathway and interaction from BioCarta (http://www.biocarta.com/), GeneMAPP [16] and KEGG [17]. We consider a total of 3,735 genes in over 350 pathways. Genes on the same chromosome that are within 1 million base pairs are excluded to avoid effects caused by possible linkage disequilibrium. To see whether genes connected with each other in the same pathway tend to show similar evidence for association, we use a cut-off value 0.15 where genes whose p values are below this cut-off are considered interesting and labeled with 1. Note that we use a relatively loose threshold so that a sufficiently large number of genes are called “interesting” and this loose cut-off also reflects our belief that many genes have weak effects and only show moderate evidence of association. In a pathway , we consider the number of edges connecting a pair of “interesting” genes, which depends on the labels of all genes. We denote this number by . A large value of would suggest that “interesting” genes are more likely to be neighboring genes. To assess the statistical evidence for the tendency to observe large values, we employ a permutation procedure as follows. In each permutation, we randomly permute the “interesting” labels of all genes and derive a permuted statistic and these permuted statistics are used to arrive at an empirical distribution of under the null hypothesis that there is no tendency for neighboring genes to have similar disease association status, i.e. “interesting” or not. We then compare the observed statistic with the empirical distribution. Finally the p value of the observed in this empirical distribution is calculated. A p value close to 0 indicates that “interesting” genes tend to be neighbors. This procedure is repeated for all pathways, and the histogram of p values of for all pathways is plotted in Figure 1. It is evident that this distribution is highly skewed to the left, which suggests associated genes tend to be neighbors in a given pathway.
In the rest of this article, we first introduce our model and statistical inferential procedures. The performance of our methods is then assessed through both simulation studies and real data applications.
We start by considering a simple model in which a pathway is represented by an undirected graph where is a set of genes (nodes) and are directly connected} denotes the set of all edges. For the th gene in , let denote the set of its neighbors, and denote the number of its neighbors. Let denote the true association status where
The values are referred to as labels of a node hereafter. Let denote the labeling of . Thus is a spatial random vector whose elements may be correlated with each other. Note that each node can be labeled either or , and so there are a total of unique configurations of the pathway. The ultimate goal is to infer the value of based on the pathway topology and the observed association data.
To formalize the idea that neighboring genes tend to have similar association status, we need a probability measure so that nodes connected with each other tend to have the same labels. Here we consider a nearest neighbor Gibbs measure [18] that has the following form:(1)where are the prior parameters or hyperparameters, and are the indicator functions, , and is a normalizing function that is the sum over all possible configurations:(2)
Note that it is prohibitive to evaluate when is large. Here and assign prior weights to edges connecting two non-associated nodes and two associated nodes, respectively. The function will be elaborated in more details in the context of the conditional probability later.
In (1), the second sum is taken over all edges connecting direct neighbors in which both end nodes are labeled −1, and the third sum is taken over all edges in which both end nodes are labeled 1. Positive and will put more weights on configurations in which directly linked nodes have the same labels, which is desirable in our context. The hyperparameter determines the marginal probability of when , i.e., all nodes are treated as singletons that are independent:
The simple form Gibbs measure in (1) has the Markov property that makes it attractive to model a biological pathway, in which directly linked genes interact with each other. It defines a MRF, which by definition is a probability measure that satisfies , where denote all nodes but , and is the set of all direct neighbors of node . Please see Materials and Methods for details.
Now we discuss the posterior distribution of association status after combining the evidence from the observed association statistics at the gene level and the structure of the gene pathway. Before we proceed, it is necessary to present the likelihood function of the observed data. We consider the situation where the observed evidence of association is summarized by values, which are assumed to be conditionally independent given the true association status . Under the null hypothesis of no association, each p-value has a uniform (0,1) distribution. In this article, we consider , where and is the CDF (Cumulative Distribution Function) of . Therefore, under the null hypothesis of no association, i.e., , the density of is . However, if there is association between the gene and disease, i.e., , the distribution of is usually unknown. For simplicity, we assume that it is from , where is the location parameter and is the scale parameter that usually depends on the true effect size, allele frequencies, and the sample size. To account for the uncertainty about the parameters, we can put prior distributions on and , and marginalize over them to obtain the predictive density of . Here we consider conjugate priors and , or We denote that are hyperparameters. The prior mean of is and its variance is . The prior mean of is and the prior variance is . This prior is of conjugate form so that the integration over and is analytically tractable. We note that the hyperparameters can be estimated from the observed data via an empirical Bayes method (see Text S2, Figures S1 and S2). Under this prior setting, the marginal density of is
This is equivalent to when 2, and others.
The joint marginal density of is
Thus, the posterior distribution of given the observed data is (3)
Similar to the MRF interpretation of the prior distribution (1), the posterior also has a nice conditional distribution and is actually a MRF, as will be shown in the Materials and Methods section.
When is large, since it is prohibitive to evaluate posterior probabilities on the entire space of configurations, we implement a Markov chain Monte Carlo (MCMC) method to sample from the posterior distribution. Naturally a Gibbs sampler is well suited for a MRF. As will be shown later, due to the MRF property, the posterior has a nice closed-form conditional distribution that can greatly facilitate the MCMC.
Most GWAS lead to a set of candidate genes/SNPs that will need to be validated in follow-up studies. Therefore, it is important to include as many truly associated genes as possible among the top ranked genes. Our proposed method allows us to rank order genes as detailed below.
There are several ways of inferring the labels according to the posterior distribution of . The first one is to use maximum a posteriori (MAP) estimate, which is the configuration with the largest posterior probability, a reasonable point estimate for . Let us denote it by . The MAP is the maximizer of the joint posterior distribution:
A Gibbs sampler outlined above can be applied to stochastically search for the solution to the above optimization problem. Multiple restarts with different initial configurations are recommended. An alternative approach is to base the estimate on the posterior conditional probability of given the observed data and all the other nodes . We can estimate by maximizing this conditional probability (MCP): (4)
The advantage of this approach is that the above problem is trivial to solve. As will be explained in equation (8) of the Materials and Methods section, the second term in formula (4) can be evaluated in closed form. Besag [19] proposed an algorithm known as iterated conditional modes (ICM) that iteratively updates . Note that the convergence of ICM is assured because the posterior is proportional to which never decreases at any iteration because the first term is non-decreasing and the second one is a constant. So it is easy to see the ICM will converge to a local maximum in the posterior distribution. Since the ICM runs fast and usually converges in several iterations, multiple restarts with different initial configurations are recommended. Finally the resulting configurations can be compared by evaluating up to a normalizing constant to pick the largest one.
The inference can also be based on the marginal posterior probability. Let . We consider a decision rule in the form , where is an indicator function and is the sought decision threshold. If , the decision is positive (also referred to as discovery) and gene is called to be associated with the disease. Likewise if the decision is negative. To address the problem of multiple comparisons, we consider loss functions associated with making wrong decisions (false discoveries and false negatives), and solve the decision problem by minimizing the expectation of the loss functions under the posterior distribution. Here we consider two loss functions. First, if we are interested in the 0-1 loss function , we may want to minimize the expected loss (5)under the posterior distribution of . The solution is . Note that assigns equal loss to the false positive and false negative errors. This is to minimize the expected frequency of making wrong calls for the association status. Note that the performance of the decision rule is based on the frequentist operating characteristic in the Bayesian framework, which is common in medical decision makings [20]. The second loss function we consider is the false discovery rate (FDR): (6)
Suppose the goal is to control the expected FDR, under the posterior distribution, such that it is no more than , i.e., . If we rank order all genes by their posterior probabilities from the largest to the smallest, and let denote the th order statistics, then the solution is to choose a cut off value where is the largest integer that makes . We should mention that more complicated loss functions can be considered under the framework of our model. See Müller et al. [20] for other examples.
First we use simulated data to study the performance of the proposed method. The simulation is based on a simple 6-node network shown in Figure 2. Genes G1 through G3 are assumed to be associated with the disease (labeled +1) while G4 through G6 are not associated with the disease (labeled −1). Data are simulated from a disease model as follows. We assume G1, G2 and G3 have independent effects on disease risk and each has a disease related SNP. The genotypes and minor allele frequencies of these three SNPs are denoted by and , respectively, where for A multiplicative genetic model is assumed for the risk of having the disease. More specifically, for an individual with genotype , the risk is , where is the baseline risk of those carrying two normal alleles in all three genes, and is the relative risk, or effect size, of gene , . For each SNP the Hardy-Weinberg equilibrium (HWE) is assumed to hold in the general population so that the genotype probabilities are , , and for , 1, and 2, respectively. In the simulation we use three minor allele frequencies = (0.05, 0.10, 0.15), three disease prevalence values = (0.05, 0.10, 0.15), and six effect sizes = (1.05, 1.10, 1.15, 1.20, 1.25, 1.30). As a result, there are a total of 54 settings of , for each of which we first let and , and then calculate the baseline risk , and finally obtain the conditional distribution of the genotypes given the disease status. Then genotypes of G1, G2 and G3 of 600 cases and 600 controls are simulated according to the conditional genotype distribution. The values of the three causal genes are calculated from a logistic regression of the data. For G4 through G6, the values are simulated from Uniform(0,1). The power of detecting the true association depends on the disease model. In this case, larger values of relative risk, MAF and prevalence corresponds to association tests with higher power.
In the simulation we set the hyperparameters = (−1, 0.25, 0.01) where more weights are assigned to edges connecting two associated genes. This corresponds to a prior belief that the probability of association is roughly between 0.35 and 0.50. The hyperparameters are set to (3, 1, 10, 1) where a large value of puts a large prior variance on , which allows a wide range of values for both and . For each simulated data set, the posterior probabilities are enumerated since there are only 64 possible configurations in this simple example. The simulation is repeated 500 times. We compare the proposed method using the posterior mean with the one using the value, and apply cut-off values of 0.7 and 0.05 for posterior probabilities and values, respectively. For each simulated data set, we calculate the false positive rate (FPR), sensitivity (Sens.), and false discovery rate (FDR) by thresholding on values and posterior probabilities. In addition, genes can be rank ordered by the two methods and the area under the Receiver Operating Characteristic curve (AUC) can be calculated. The average values of the three rates plus the AUC over the 500 simulated data sets are shown in Table 1. As can be seen, the proposed method of the posterior probability has higher sensitivity, smaller false discovery rate, and higher AUC than the value thresholding in every setting of the prevalence, MAF and effect size, while the FPR of both methods are controlled at 0.05.
The second simulation study is based on the network shown in Figure 3. This network was adapted from BioCarta “Human Rho cell motility signaling pathway” and we deleted a few genes that are either absent from our Crohn's disease data or not connected to others. We assume three different sets of truly associated genes, plotted in triangles, rectangles and pentagons, each of which contains three, five, and seven nodes, respectively. To simulate different levels in the power of the association tests, for each gene with , the value is computed from a two-sided test where scores are randomly drawn from , and , respectively, corresponding to the power 0.16 (low), 0.32 (median) and 0.51 (high) in the association tests. The values for are generated randomly from Uniform(0, 1) as before.
To examine the effects of hyperparameters of the network, we consider eight priors, listed in Table 2, that roughly form four main groups indexed by numbers 1 through 4, and two subgroups indexed by letters and . For each set of hyperparameters a Gibbs sampler is run to draw samples from the corresponding prior distribution, and we can estimate , the prior mean, and and where , the probabilities of edge linking two nodes with identical labels. The averages of the estimated probabilities are listed in the last three columns of Table 2. The average prior means of all nodes are about 0.05, 0.15, 0.25, and 0.4, respectively for the four main groups. Roughly speaking, it means that group 1 is in favor of a small number, and group 4 a large number, while groups 2 and 3 in between, of nodes labeled with +1. Furthermore, values of in subgroup are larger than those in subgroup , meaning that nodes with identical labels are more likely to be next to each other apriori in subgroup than subgroup , as can be seen from the last two columns in Table 2. On the other hand, because the posteriors are found to be insensitive to the hyperparameters when is large, they are set to (3, 1, 10, 1) as in the previous example.
We simulate 200 data sets for each combination of the three power settings (low, median and high) and three truly associated sets (3, 5, and 7 nodes). For each data set, we run eight Gibbs samplers using eight different hyperparameters described above. Each Gibbs sampler is run with 100 restarts and each start contains 100 steps. We compare the average AUC of 200 simulated data sets using value and the posterior mean and plot the results in Figure 4. In general, the AUC of the proposed method is larger than that using values alone. It achieves good AUC if the prior mean is close to the truth, especially when the power is low. For example, in the middle column panels where there are 5 truly associated genes, prior settings 2 and 3, favoring median number of truly associated nodes, outperform prior settings 1 and 4. Similarly, in the right panel where the true model contains 7 genes, prior settings 3 and 4, which are in favor of large models, perform better than the other prior settings. Furthermore, priors in subgroup b are better than subgroup a in general. It is not surprising because the priors in subgroup b encourages nodes labeled with +1 to group together, which agrees with the simulation setting.
To evaluate the control of the false positive rates and the false discovery rates of the proposed methods in relatively large pathways with only a few associated genes, we conduct a third simulation study based on a simulated network shown in Figure 5 that contains 60 nodes. We consider three truly associated gene sets, namely (2, 11, 19), (2, 11, 19, 41), and (2, 11, 19, 20, 41), and label them as models 1, 2 and 3 in Table 3. Similar to the previous study, we simulate values from scores randomly drawn from , and , corresponding to weak, median and strong associations, respectively. Three prior settings are considered for , namely (−1.5, 0.15, 0.02), (−1.50, 0.10, 0.01) and (−2, 0.2, 0.01), whose average prior probability is approximately 0.2, and average prior probabilities for are roughly 0.13, 0.11, and 0.08, respectively. For the proposed method, we consider three decision rules. The first one (PM1) uses the posterior mean with a cut-off value as in (5), the second one is MCP as in (4), and the third one (PM2) is the method to control the FDR at 0.1 as in (6). Then we compare them with the value method (P value) with a cut-off value set at 0.05 and the correction method (BH) of Benjamini & Hochberg (1995) [21]. For each scenario we simulate 100 data sets, and run a Gibbs sampler with 100 restarts where each start contains 100 iterations. For each simulated data set, we calculate the FPR, sensitivity (Sens.), FDR, and AUC as before. Table 3 lists the average values of the 100 simulation runs. In general PM1 and MCP control the FPR below the 0.05 level and have lower FDR than the value while achieving better or similar power as the value method. In terms of controlling FDR, PM2 controls the FDR around 0.1, and it has smaller FPR or better power than the BH method in most cases when it achieves similar or better FDR.
We use one Crohn's disease [15] data set to further evaluate the performance of the proposed method. Details of this data can be found in the Materials and Methods section.
We run our algorithm on 289 pathways that have at least 20 genes with non-missing values. The hyperparameters are chosen such that the average prior mean is roughly between 0.2 and 0.4 based on the simulation findings. To evaluate the performance, we consider 32 target genes that are confirmed to be related to the Crohn's disease [22]. Among these genes, 10 genes can be mapped to 66 pathways. In Figure 6 we plot the AUC values of the rankings by values on the axis and posterior means on the axis for pathways containing three or more target genes. A majority of AUC values are improved if genes are rank ordered by the posterior mean. The average AUC based on values is 0.568 while on posterior means is 0.613. To see what causes the rank changes of genes in the posterior probability, in Figure 7 we show the Human IL-2 Receptor Beta Chain in T cell Activation pathway from BioCarta. Genes in this pathway are densely connected. To aid visualization, we randomly remove some edges. Significant genes whose values are below 0.05 are colored in cyan, genes with improved ranks are colored in light blue and others are colored in pink. It can be clearly seen that genes colored in light blue have more connections with the significant genes, and are more heavily linked among themselves, compared to other genes in the pathway. Genes that have many interactions with each other may play important roles in the biological processes in the pathway. When they are connected to many significant genes, it might be reasonable that they are more likely to associate with the disease than other genes.
In this article we introduced a Bayesian method to incorporate prior knowledge of biological pathways into GWAS. This approach uses a MRF as a prior distribution to model the interactions among genes that participate in the same pathway. We showed that the posterior distribution is also a MRF and can be sampled via a Gibbs sampler. Inferences based on the posterior distribution allow us to combine data from the association study with prior information of biological pathways. In particular, this framework considers the topology of all genes in a pathway, which has not been fully utilized in many of the existing methods. The simulation studies and real data example suggest that the proposed method has higher power to identify genes associated with disease.
One limitation of the MRF model is that the Gibbs sampler tends to move around local maxima for a long time and thus can be slow in convergence to the posterior distribution. We recommend to run the Markov chain Monte Carlo with multiple random restarts, and examine the sampling distribution of network statistics, like the number of genes labeled with +1 and the proportion of edges linking genes with identical labels. In our studies, we found that a Markov chain initially moves very rapidly from its starting state, usually within the first 10 to 20 steps, before it reaches some steady states and stabilizes for a long period thereafter. We suggest running 100 Gibbs steps for each random starting state, and conducting the simulation with 100 restarts. The computing time of this scheme typically takes a few minutes on a PC for a pathway of about 30 genes. We should also mention that the characteristics of the MRF defined in (1) depend on both the hyperparameters and the structure of the network under consideration. Consequently there does not exist a set of hyperparameters that can be suitable for all pathways. To assist the specification of hyperparameters, we provide an algorithm of estimating hyperparameters based on a conditional empirical Bayes approach in Text S2. It is recommended that these values would be used in initial attempts and it would be better to test several other variants of hyperparameters, possibly through fine-tuning the initial values. It is helpful to draw samples from the prior distribution to assess the effects o f different prior settings. One limitation of pathway-based analysis is that not all the genes can be associated with pathways. It is likely with knowledge accumulation, more genes will be mapped to pathways. An R package is under construction and will be made publically available soon.
The nearest neighbor Gibbs measure on gene pathways in formula (1) defines a MRF and its conditional distribution has a logistic regression form as shown below.
To see that the posterior distribution is also a MRF, note that for node ,
Thus, the conditional posterior distribution of given all other nodes only depends on its neighbors, which means the posterior distribution is also a MRF. The conditional posterior log odds of is(9)
where is the marginal likelihood ratio. Therefore, (9) is the product of the marginal likelihood ratio, reflecting the evidence from the data for association with the disease, and the conditional prior odds, reflecting the effect from interactions among neighboring genes from the biological pathway.
To make it clear, we can rewrite (9) in the form of a system of auto-logistic regression equations:(10)where
There are a few observations. First, it is easy to see that the posterior conditional logit form in (9) is the same as the prior conditional logit in (8) except its intercept is . Thus, the observed log likelihood ratio provides a fixed additive effect to the prior logit. Second, the coefficient matrix is symmetric, i.e., . If gene and are not neighbors, then and they are conditionally independent. On the other hand, if they are neighbors, then the impact between each other is equal. Third, genes and are in general correlated in their joint posterior distribution, even if they are not neighbors and are conditionally independent. Moreover, the more common neighbors they share with each other, the stronger the correlation between the two.
To sample from the posterior distribution, here we implement a Gibbs sampler that is well suited for a MRF. The algorithm is described as follows. First we set an initial value for , say . Then in step , we update the labels sequentially for according to (10): to obtain from . In each cycle we may want to randomize the order in which the nodes are updated.
The Crohn's disease [15] data set is used to evaluate the performance of the proposed method in the Results section. Crohn's disease is a type of inflammatory bowel disease characterized by chronic inflammation of discontinuous segments of the intestine. The disease is found to be related to the interaction of several factors including genetic susceptibility, the intestinal microbial flora of the patient, the patient's immune response to these microbiota, and environmental triggers [25]. It has been well established that Crohn's disease has a strong genetic component [26].
The cohort used in the analysis includes 401 cases and 433 controls. SNPs with a call rate greater than 0.9, minor allele frequency greater than 0.01, and HWE value greater than 0.001 are kept, while subjects with a call rate less than 0.95 are removed from the analysis. Finally 397 cases and 431 controls remain in the analysis. SNPs are considered being mapped to a gene if their physical locations are within 10 kb from the start or end point of the gene as given by Refseq annotation at the NCBI website. Gene level values are obtained by regressing disease status on PCA components that account for at least 85% of the variation [27]–[29]. The pathways and genes in each pathway as well as the gene-level p values can be found at http://bioinformatics.med.yale.edu/group/software.html.
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10.1371/journal.pntd.0003776 | Minimizing the Risk of Disease Transmission in Emergency Settings: Novel In Situ Physico-Chemical Disinfection of Pathogen-Laden Hospital Wastewaters | The operation of a health care facility, such as a cholera or Ebola treatment center in an emergency setting, results in the production of pathogen-laden wastewaters that may potentially lead to onward transmission of the disease. The research presented here evaluated the design and operation of a novel treatment system, successfully used by Médecins Sans Frontières in Haiti to disinfect CTC wastewaters in situ, eliminating the need for road haulage and disposal of the waste to a poorly-managed hazardous waste facility, thereby providing an effective barrier to disease transmission through a novel but simple sanitary intervention. The physico-chemical protocols eventually successfully treated over 600 m3 of wastewater, achieving coagulation/flocculation and disinfection by exposure to high pH (Protocol A) and low pH (Protocol B) environments, using thermotolerant coliforms as a disinfection efficacy index. In Protocol A, the addition of hydrated lime resulted in wastewater disinfection and coagulation/flocculation of suspended solids. In Protocol B, disinfection was achieved by the addition of hydrochloric acid, followed by pH neutralization and coagulation/flocculation of suspended solids using aluminum sulfate. Removal rates achieved were: COD >99%; suspended solids >90%; turbidity >90% and thermotolerant coliforms >99.9%. The proposed approach is the first known successful attempt to disinfect wastewater in a disease outbreak setting without resorting to the alternative, untested, approach of ‘super chlorination’ which, it has been suggested, may not consistently achieve adequate disinfection. A basic analysis of costs demonstrated a significant saving in reagent costs compared with the less reliable approach of super-chlorination. The proposed approach to in situ sanitation in cholera treatment centers and other disease outbreak settings represents a timely response to a UN call for onsite disinfection of wastewaters generated in such emergencies, and the ‘Coalition for Cholera Prevention and Control’ recently highlighted the research as meriting serious consideration and further study. Further applications of the method to other emergency settings are being actively explored by the authors through discussion with the World Health Organization with regards to the ongoing Ebola outbreak in West Africa, and with the UK-based NGO Oxfam with regards to excreta-borne disease management in the Philippines and Myanmar, as a component of post-disaster incremental improvements to local sanitation chains.
| When an outbreak of infectious disease occurs in a low-resource setting, the rapid construction of emergency healthcare facilities may significantly reduce mortality. The facilities also result in the generation of large volumes of highly contaminated fecal waste that represents a potential basis for further disease transmission. Infection protection and control strategies at healthcare facilities must therefore include measures to establish and maintain good water supplies, sanitation and hygiene (WASH). Even where the pathogen of concern is not waterborne, health-care providers have a ‘duty-of-care’ to protect workers and neighboring communities from all excreta-borne diseases. In this study, the authors successfully demonstrated, for the first time, the in situ disinfection of wastewaters from cholera treatment centers during the Haiti cholera outbreak, using a low-cost physicochemical method. The approach is currently being adapted by NGOs to help manage human excreta in other emergency settings, including the current Ebola outbreak. Although the Ebola virus is relatively fragile, it may exist in high concentrations in the bodily fluids (including feces) of those with the disease. The approach to in situ disinfection of excreta described here may therefore support infection control in outbreaks of Ebola and other infectious diseases.
| Outbreaks of specific infectious diseases that may potentially be transmitted by human excreta, including cholera, Ebola and hepatitis A and E present a challenge to existing WASH (water, sanitation and hygiene) practices and a greater focus on practical in situ disinfection of human waste may offer an effective first step in the development of a longer-term sanitation ladder to support infection control. The research presented here focuses on an innovative in situ disinfection technique, which to date has been mainly applied in the context of a cholera outbreak, but which could potentially, and in the near future, provide a health protection intervention within the context of other outbreaks of neglected tropical diseases, including Ebola.
Ten months after the devastating earthquake of 12th January 2010, cholera appeared in Haiti for the first time in nearly a century. The outbreak escalated and as of 21st November 2014, the resulting mortality had reached 8,505 and the cumulative morbidity had reached 717,203—equivalent to approximately 6.9 percent of the national population [1] [2]. According to the WHO [3], the outbreak accounted for 57% and 53% of global cholera cases, and 58% and 37% of global cholera deaths reported in 2010 and 2011 respectively. Morbidity levels have probably been significantly higher than these figures suggest, as globally only a minority of cholera cases may be reported to the relevant authorities [3].
Cholera is a severe, acute, dehydrating diarrheal disease of humans, which, in the absence of adequate rehydration, can lead to death in both children and adults within twelve hours. The case-fatality rate for severe cholera without treatment can be as high as 50% [4]. The disease results from infection by a pathogenic strain of the bacterium Vibrio cholerae, which is capable of producing a potent toxin. Since the first recorded cholera pandemic, which began in 1816, the pathogen has spread and evolved rapidly [4] [5]. The ongoing seventh cholera pandemic began in 1961 and there is now good molecular evidence to suggest a close relationship between the Haitian isolates of V. cholerae and variant V. cholerae El Tor O1 strains isolated in Bangladesh in 2002 and 2008, and a more distant relationship with isolates currently circulating in South America [6] [7].
Established cholera control strategies call for a combination of interventions, including improvements to the quality and quantity of drinking water supplies, provision of consistently functional sanitation chains and promotion of effective hygiene practices. Under certain circumstances, the administration of oral vaccines to ‘at risk’ communities may also be recommended [8] [9]. Treatment of infected individuals is largely based on oral (or in more serious cases, intravenous) rehydration [4]. For the most severe cases, a suitable antibiotic, such as tetracycline, doxycycline or azithromycin, may be administered [10]. However, it has been widely recognized that treatment alone will not break the cycle of disease transmission and that improvements of WASH infrastructure are essential to achieving sustained control, elimination, or eradication of many tropical diseases [11] [12] [13] [14].
Doctors Without Borders (Médecins Sans Frontières, or MSF) is an international medical humanitarian organization that delivers emergency aid to people affected by armed conflict, epidemics, natural disasters, and exclusion from healthcare. It has operated successfully in numerous cholera emergencies during the past four decades, and has formulated effective field response strategies to cholera outbreaks, including the design and operation of appropriate water and sanitation technologies. The organization has been active in Haiti for over twenty years and, in collaboration with the Haitian Ministry of Public Health (MSPP), has been a leading provider of treatment to cholera patients in the country since the beginning of the outbreak, treating more than 300,000 patients by September 2014 [15] [16] [17].
The established MSF protocol for dealing with human fecal wastes in emergencies involves the addition of 2% chlorine solution to each bucket of patient feces or vomit and the construction and operation of soil infiltration pits or trenches to dispose of the large volumes of waste produced by CTC operations [18]. However, this approach is only appropriate when the water table remains at least 1.5 meters below the lowest point of the excavated pit or trench. In the densely populated Haitian capital of Port-au-Prince, the water table may be considerably higher (as little as 30 cm below the surface during periods of heavy rainfall), and clearly, under these circumstances, infiltration cannot provide safe disposal of the infectious human wastes arising [19].
From the outset, the response of many of the international organizations operating in the wake of the Haiti cholera outbreak was to instigate road haulage (by truck) of all fecal waste originating from cholera patients (chlorinated or otherwise) to a centralized waste pit at the Truitier landfill site on the outskirts of Port-au-Prince. This facility is situated close to the impoverished and densely populated community of Cité Soleil on the western outskirts of the capital, a few hundred meters from the coast and on the aquifer of the Cul-de-Sac plain, traditionally a source of raw drinking water for the city of Port-au-Prince [20]. At an early stage of the emergency response, water and sanitation engineers of MSF-OCA (Médecins Sans Frontières–Operational Centre Amsterdam) concluded that using the Truitier landfill site for the disposal of cholera wastes represented a clear hazard to human health and the organization therefore decided that it was not prepared to countenance this practice. The organization further decided that the practice of ‘super-chlorination’ followed by disposal to the environment was also unacceptable.
The principal reasons for these decisions were:
Road transportation of significant quantities of contaminated wastewater was considered hazardous to human health, particularly within the complex, often chaotic, urban context of post-emergency Port-au-Prince.
The contaminated wastewater arising from CTC is characterized by extremely high concentrations of readily-oxidizable matter. It would therefore be imprudent to assume that a wastewater disinfection process based on chlorination would consistently disinfect the waste to an adequate degree [21], given that the ability of these in situ disinfection strategies to reduce target pathogens had not been formally assessed [19].
It has been suggested that certain strains of V. cholerae (the “rugose” phenotype) may be more resistant to chlorine-based disinfection as a result of exopolysaccharide production, which promotes cell aggregation. Such strains may therefore pose an elevated risk to human health [22] [23].
Even if ‘super-chlorination’ were able to reduce Vibrio cholerae numbers to levels that did not pose a significant risk to those living downstream of CTC operations, the production of combined chlorine residuals and the relatively high operational costs associated with this process would likely make it both environmentally and financially unacceptable in the medium- to long-term. Moreover, this approach to disinfection does not significantly remove suspended material.
By October 2010, the rapid spread of the Haitian cholera outbreak had resulted in a pressing need for CTC facilities throughout the country and a novel, low-cost and consistently-effective way to treat and disinfect the wastewaters from MSF CTC operations was therefore urgently required.
In Port-au-Prince, a partly-commissioned MSF maternity hospital (‘Delmas 33’) was converted by the organization into a CTC within a matter of days. By the time its operational life ceased in early March 2011, more than 3,000 cholera in-patients had been treated at the facility. It was then converted back into a maternity hospital, and a new MSF CTC was established on nearby tennis courts. In total, at these two CTC, MSF water and sanitation engineers were required to treat and dispose safely of over 320,000 liters of wastewaters, which were potentially infected with high levels of Vibrio cholerae. It has been estimated that wastewaters from CTC, especially those components derived from patient stool buckets, may contain more than 107 Vibrio cholerae per 100 ml [24] [25] [26]. Such wastes must therefore be treated and disposed of with extreme caution. Within the Haitian context, rapid intervention to provide effective disinfection of this wastewater was essential in order both to control disease transmission and to respond to the prevailing concerns of the local populace with regard to the management of cholera wastes by international organizations.
The onsite treatment of CTC wastewaters within the challenging context of medical emergencies needs to be relatively low-cost, logistically simple, rapid to deploy, immediately effective and capable of removing microbial pathogens significantly more effectively than conventional treatment technologies. Such systems have rarely been established, and no peer-reviewed literature that critically evaluates their operational performance is available. However, the concentration of Vibrio cholerae in CTC wastewaters and the potential risk to public health that the pathogen represents may be estimated from previous studies. During a two-year investigation of cholera carriers in the Philippines, Dizon et al. [27] measured the numbers of Vibrio cholerae per gram of feces among human populations in areas of the country in which the disease was endemic or epidemic. The feces of ‘simple carriers’ contained between 102 and 105 Vibrio cholerae per gram of feces, whereas the feces of patients presenting symptoms of ‘mild cholera’ were shown to contain between 106 and 109 Vibrio cholerae per milliliter of stool (on their first day of illness). Howard et al.[24] [25] examined the wastewater from a hospital operated by the UK-based NGO Oxfam in Bangladesh, which admitted between two and forty confirmed cholera cases per day. The authors recorded levels of Vibrio cholerae between 5 x 105 and 5 x 107 per 100 ml of wastewater. It is worth noting that the level of Vibrio cholerae was demonstrated to exceed that of thermotolerant coliforms in this instance.
In the work described here, the authors aimed to use the best available expertise to design, construct rapidly and operate an effective onsite CTC wastewater treatment system that would protect the health of the inhabitants of Port-au-Prince from the potential risk of disease associated with contaminated wastewaters. Further, it was considered essential that this innovative technology should be subjected to a robust critical risk evaluation of each stage of the project cycle. This was designed to maximize human health protection at the time of the emergency and to enable MSF and other NGO (potentially operating in other parts of the world), to gain the fullest possible benefit from the resulting evidence-base.
Based on initial estimations of Vibrio cholerae levels in the CTC wastewaters and with reference to the available literature, a wastewater management strategy, involving four consecutive and distinct barriers to the transmission of Vibrio cholerae, was proposed as follows:
Initial chlorination of patient feces within stool buckets immediately following collection by MSF health-care professionals, as already practiced according to MSF protocols [18] [28];
Storage of pooled CTC wastewaters in open tanks—in practice for up to twelve weeks (average six weeks, minimum three) at relatively high ambient temperatures—resulting in a further reduction in levels of enteric microorganisms as a result of natural biological, chemical and physical processes;
The design and operation of a novel batch-operated onsite wastewater treatment and disinfection plant, as described in detail below; and finally
Controlled effluent disposal within soil infiltration trenches according to existing MSF protocols [18].
In-house MSF water and sanitation expertise, supported by expert external advice, were used to develop a shortlist of three technologies that might meet the objective of achieving effective, robust and relatively low-cost onsite treatment of the CTC wastewaters:
Protocol A: Coagulation/flocculation and disinfection of the wastewater with hydrated (slaked) lime (Ca(OH)2) at high pH levels, using a treatment system that was based on the methodology of Taylor et al. [29] [30];
Protocol B: A novel approach involving disinfection at low pH levels using hydrochloric acid, followed by pH neutralization and subsequent coagulation/flocculation, achieved using aluminum sulfate (or an alternative low-cost coagulant); and
Protocol C: Septic tank treatment combined with an anaerobic filter.
Protocol C was rejected at an early stage, as it was considered that this approach would take too long to establish and would be insufficiently robust to operate effectively and reliably within an emergency setting. Subsequently, batch treatment systems based on Protocols A and B were designed, operated, and monitored within two CTC operations in Port-au-Prince, over a period of six months.
The main goal of the treatment was to achieve a level of disinfection of the highly contaminated fecal waste that was adequate to release the effluent and the sedimented sludge into the environment without introducing a new disease transmission route. Effective disinfection was achieved through the combined and simultaneous action of two mechanisms, namely:
The exposure of the pathogens to an alkaline (‘protocol A’) or acidic (‘protocol B’) environment, resulting in pathogen deactivation
The physical removal of the pathogen as a result of coagulation and flocculation and sedimentation. The sedimented sludge was subsequently treated in drying beds before incineration or controlled infiltration to soil (see details on the next section)
Gram-negative (Gram-) and Gram-positive (Gram+) bacteria are both sensitive to high pH, although Gram- bacteria (including Vibrio cholerae) tend to be more susceptible to high pH levels because of their relatively thin peptidoglycan layer: the Gram- layer is in fact only about 2 to 3 nm thick, whereas the Gram+ layer is about ten times thicker [31]. The peptidoglycan layer stabilizes the cytoplasmic membrane of intact bacterial cells against the pressure exerted by the cytoplasm [32]. Therefore, the thinner peptidoglycan layer associated with Gram- bacteria may less effectively prevent the cytoplasmic membrane from bursting once it is weakened by a high pH environment [33].
There are multiple hypotheses as to how strong and weak, organic and mineral acids inhibit or destroy bacteria. In general, acids have antimicrobial activity both in their undissociated and dissociated forms (although the former has a stronger antimicrobial effect) [34]. One of the prevailing hypotheses is that strong acids inhibit or destroy microorganisms by interfering with the permeability of the microbial cell membrane. The acidic solution interferes with the substrate transport and with the oxidative phosphorylation from the electron transport system. This results in the acidification of the cell content, which is considered to be the principal (but not the only) cause of inhibition and death [35]. It has also been suggested that some acids may also inhibit or kill bacteria by blocking amino-acid uptake through the membrane [36]. Moreover, some acids may enter the bacterial cells as undissociated molecules that are soluble in phospholipid membranes and then acidify the cell interior [37].
For the purposes of this study, thermotolerant coliforms were used as an index of disinfection efficacy. These bacteria primarily originate from the intestines of warm-blooded animals and are widely used as an indicator of the presence of fecal material in water. Although it was not feasible under the conditions of the study described here to enumerate the pathogen Vibrio cholerae directly, all available evidence suggests that for the extreme pH levels achieved during the treatment protocols described here, thermotolerant coliforms represent an acceptable conservative indicator of the presence of Vibrio cholerae in that they exist at high concentrations in human feces and are, like Vibrio cholerae, a Gram- bacterium of primarily enteric origin. Although future work on the behavior of specific pathogens to low-cost on-site disinfection processes is warranted, the authors believe that the approach taken here represents a robust approach to estimating the risk of pathogen transmission.
Another aspect of this study that was partly limited by the constraints of an emergency setting was chemical analysis of the wastewater. However, wastewater from cholera treatment centers is commonly composed of human feces and sullage (graywater) from personal washing facilities and hygiene practice. Therefore the main components of the wastewater are generally known though the alkalinity, the buffering capacity, the relative concentrations of organic matter and other components are liable to vary between CTC and with time. Several studies have previously determined the composition of wastewater derived from various infectious (including tropical) disease hospital departments [38] [39] [40] [41] [42]. However, although the quantity of alkaline or acidic solution required to achieve adequate treatment and disinfection by the protocols described here will depend on a number of wastewater characteristics (e.g., organic content and alkalinity) it is important to note that the protocols are defined by pH ‘end-points’, in that reagents are added until a prescribed pH level is reached so that variability in wastewater composition ceases to be a factor in treatment efficacy. Since this will be the case in other future applications of the protocols, the authors believe that the inability to obtain detailed data on the wastewater composition under the conditions of this study does not constitute a significant weakness in the research.
Initial laboratory pilot-scale studies of Protocols A and B, using simple five-liter beakers, were followed by full-scale batch treatment of the wastewater using both protocols, initially at the ‘Delmas 33’ CTC. At a later stage, and following closure of this facility, a new, full-scale wastewater treatment facility was established at the nearby ‘Delmas-Tennis Court’ CTC. The results reported on this paper refer exclusively to the analysis of batches that were treated when adequate monitoring equipment had become available in the field. Protocol A was used for the treatment of two batches of wastewater and Protocol B was used for the large-scale treatment of six batches of CTC wastewater, the batch volumes being in all cases between 10 and 15 m3.
A detailed risk assessment was undertaken for each stage of the project. This included details of operator hygiene requirements and the appropriate use of personal protective equipment to minimize operator contact with potentially corrosive chemicals and infectious agents [43].
Simple jar-test studies, using five-liter beakers, were initially used to investigate the efficacy of Protocol A with regard to the removal of thermotolerant coliforms and suspended solids (or turbidity). At the inception of each jar-test, a small sample of untreated wastewater (approximately 30 ml) was taken and the following parameters were tested for: turbidity–recorded as nephalometric turbidity units (NTU); presumptive thermotolerant coliforms–recorded as colony-forming units (CFU) per 100 ml; pH level; and quantities of chemical reagents used–recorded as grams or milligrams per liter.
The first step of each jar-test experiment involved the step-wise addition of hydrated lime slurry (Ca(OH)2) to the wastewater, until the pH of the mix reached a level between 11.4 and 12.2. This was immediately followed by three minutes of ‘rapid-mixing’, followed by 15 minutes of ‘slow-mixing’ (both steps being achieved manually in the absence of a mechanical jar test-rig). The contents of the beaker were then left to settle overnight. The supernatant was subsequently removed and its pH level adjusted to approximately 7 by the addition of concentrated hydrochloric acid (HCl). At the end of each jar-test process, a small sample (approximately 50 ml) of supernatant was removed and tested for the same set of wastewater quality parameters as mentioned previously.
Jar-test studies were similarly performed in order to investigate the efficacy of Protocol B. At the inception of each jar-test, a small sample of untreated wastewater (approximately 30 ml) was tested for the same set of parameters as in Protocol A.
The first step of the jar-test experiment for Protocol B involved the addition of hydrochloric acid (HCl), at a quantity sufficient to decrease the wastewater pH to a level between 3.7 and 3.9, so as to achieve disinfection of the wastewater. This was immediately followed by ‘rapid-mixing’ for one minute. Following overnight sedimentation, the wastewater was adjusted to a pH level of approximately 7, by the addition of the hydrated lime slurry that was also used for Protocol A. At this point, another small sample (approximately 30 ml) of supernatant was removed for analysis as before.
Aluminum sulfate (75 to 150 mg/L—either as Al2(SO4)3 * 16H2O or as Al2(SO4)3 * 18H2O) was next added to the beaker as a coagulating agent, in order to achieve suspended solids removal, and consequently, to achieve a further reduction in microbial levels. The addition of aluminum sulfate was immediately followed by three minutes of rapid-mixing, followed by 15 minutes of slow-mixing.
Following the slow-mixing phase, the wastewater was allowed to settle in the five liter beaker reactor for one hour. Once again, a small sample of supernatant (approximately 30 ml) was removed for analysis, as before.
Laboratory jar-testing of the high pH treatment process (Protocol A) using hydrated lime (Ca(OH)2) and, at a later stage, the low pH treatment process (Protocol B) using aluminum sulfate, was followed by full-scale batch treatment. Here, wastewater and coagulants (added at concentrations suggested by the jar-tests) were combined within regimes that mimicked, as closely as possible, initial rapid-mixing followed by slow-mixing, and finally settlement for a minimum period of fourteen hours, all within a 30 m3 circular open tank. Figs 1 and 2 outline the full-scale treatment procedures adopted for each protocol.
The 30 m3 treatment tank (reactor) was filled to a maximum level of approximately two-thirds of the total capacity of the tank. The wastewater was then mixed by re-circulation using a gasoline-fueled centrifugal pump so as to obtain a homogenous mix. The established set of bacteriological and physico-chemical parameters measured during the pilot-studies was determined for the wastewater influent from grab samples of approximately 30 to 50 ml. In addition, the COD (mgO2/L) of the reactor influent was measured.
The lime slurry was prepared by the addition of hydrated lime to chlorinated drinking water, at a concentration of approximately 20 g/L, in a 200 liter drum, placed on a platform above the reactor tank, directly above the influent pipe. Lime slurry was continuously added to the wastewater, in an attempt to achieve rapid-mixing (with the inflow hose running parallel to the tank wall by means of an ‘elbow-joint’), until the pH level of the circulating wastewater was measured to be greater than, or equal to, 11.4.
Once the target pH level had been reached, the pump was operated continuously at a relatively low revolution rate for approximately 15 minutes, in order to achieve slow-mixing, and consequently to aid flocculation of the reactor contents. The pump was then switched off and the wastewater was left to settle for at least fourteen hours. A small grab sample of the resulting (‘partially treated’) supernatant was removed for analysis using the same set of parameters used to test the untreated wastewater influent. After measuring the depth of sludge in the tank, the supernatant was carefully pumped into a nearby 3.8 m3 tank, taking care not to re-suspend the sludge. The contents of this tank were then adjusted to a pH level of between 7 and 8, by the addition of HCl. A final sample of supernatant was removed for analysis as before.
Provided that the effluent had reached a quality considered to be ‘satisfactory’ (defined as having achieved a turbidity level of less than 50 NTU, a pH level of between 6 and 8, and containing fewer than 1,000 thermotolerant coliform CFU per 100 ml), this ‘final effluent’ was then carefully infiltrated into onsite soil trenches. If the effluent quality failed to meet these quality criteria, the entire treatment procedure was repeated before the final effluent was allowed to be infiltrated to the soil.
The process of tank filling was identical to that followed under Protocol A and grab samples of the influent were analyzed for the same parameters prior to treatment.
HCl was then added to the tank contents until the pH level of the circulating wastewater was recorded to be equal to, or lower than, 3.9. Once the target pH level had been reached, the contents were recirculated slowly by pumping for five minutes to ensure that the pH level within the reactor was as homogenous as possible. The pump was then switched off, and the tank contents were left to stand for a minimum period of no less than fourteen hours.
When the depth of sludge in the tank had been measured, the supernatant was carefully pumped (taking care not to re-suspend the limited quantity of sludge that had been produced at this stage) into the nearby smaller tank (3.8 m3). The contents of this tank were adjusted to a pH level of between 6 and 7, by the addition of lime slurry, before a grab sample was removed for analysis using the same set of parameters as before. The wastewater at this stage was considered to be ‘partially treated’.
It is perhaps worth noting that, while the addition of HCl, as described above, did not in itself result in coagulation/flocculation, it was considered useful to take advantage of unaided overnight sedimentation before the supernatant was removed for subsequent coagulation/flocculation the next day. The remaining, relatively small quantity of ‘low-pH (disinfected) sludge’ removed from the bottom of the treatment tank in Protocol B was blended with the much larger volume of ‘high-pH sludge’ produced by Protocol A, in order to produce a pH-neutral blend.
A concentrated solution of aluminum sulfate was prepared by dissolving approximately 300 g of the hydrated salt in 1 liter of chlorinated drinking water. Four transparent beakers, each containing 1 liter of wastewater, were used for jar-tests, with the aim of determining the quantity of coagulant needed to achieve adequate sedimentation. This was found to be approximately 100 mg/l. The aluminum sulfate solution was added to each 3.8 m3 tank, with manual ‘rapid-mixing’ achieved using a short stirring rod for approximately 5 minutes (flash-mixing), followed by a manual slow-mixing phase of about 15 minutes, using a longer stirring rod (to improve the formation of flocs).
The wastewater was then left to settle for approximately one hour and a grab sample of approximately 30 ml of supernatant (‘final treated effluent’) was tested for the standard parameters, as before. Provided that the effluent had achieved the ‘satisfactory’ quality previously specified under Protocol A, the effluent was carefully infiltrated to the soil. Again, if the quality criteria for satisfactory final effluent had not been met, the coagulation/flocculation procedure, using aluminum sulfate, was repeated. If the effluent quality level had still not met the specified quality standards at this stage, the entire treatment process, including low-pH disinfection and coagulation/flocculation, would have been repeated, but in practice this was never required (Fig 1).
All sludge had been exposed to either the high or low pH environment (that had each demonstrated more than 3-log reduction in levels of thermotolerant coliforms in the supernatant) for at least twenty four hours. Bacterial enumeration of sludge is not possible by membrane filtration as the solids block the pores of the nitrose-cellulose filter. The alternative enumeration method by ‘multiple tube’ (most probable number) was not feasible under the emergency field conditions at the CTC. Therefore, although it can be assumed from the analysis of the supernatant that significant disinfection had been achieved throughout the contact tank during the coagulation-flocculation and subsequent sedimentation stages, the precautionary principle was used in all subsequent handling of the sludge. All sludge was air dried for at least fifteen days and then either carefully placed in infiltration pits (as continues to be common practice for the disposal of fresh, untreated human excreta in many CTC operations around the world) or incinerated along with solid hazardous health-care wastes, as recommended by Gautam et al. [40]. The authors therefore conclude that the hazard of human infection from the sludge was appropriately managed.
The following set of physico-chemical and bacteriological analyses was performed on all Protocol A and Protocol B samples (both during pilot-scale studies and full-scale plant operation). The main aim of all analyses was to determine the degree of reduction in turbidity (NTU) or total suspended solid (TSS), and thermotolerant coliforms (CFU per 100 mL). Measurements of COD concentration were only achieved during full-scale operation of Protocol A. All analyses were undertaken on grab samples, typically 30–50 ml of the wastewater, taken either from the five-liter beakers (pilot-scale trials) or from the full-scale treatment tanks.
Initially, turbidity levels were recorded (as NTU) following a simplified 'turbidity tube' method [44]. This method was later replaced by a spectrophotometric protocol, using a Hach portable turbidimeter (model 2100P), which operated within a wavelength range of 400 to 600 nm. All turbidity data reported here were recorded spectrophotometrically.
Measurement of total suspended solids (as mg/L) was achieved by filtration of the sample through a glass-fiber filter, according to standard methods [45]. As an oven was not available in the field, filters were dried at ambient temperature (normally greater than 30°C) until constant weight was achieved (normally within 48 hours).
pH levels were measured several times during both protocols to minimize the quantity of reagents used to achieve adequate disinfection (and in the case of Protocol A, to ensure effective coagulation and flocculation). The pH level was also frequently measured during the later neutralization phases (both protocols) in order to achieve a final pH level of between 6 and 8. A Palintest Micro 500 pH meter was used for all measurements. pH buffers (7.0 and 4.0) were used for pH meter calibration and pH probes were stored in a saturated KCl solution. In addition, because of the potential for damage to the probe at high and low pH levels, simple pH litmus paper strips were frequently used to verify the pH values obtained.
COD (as mgO2/L) was measured using a simplified spectrophotometric field kit (Palintest). The samples were digested at 150°C for two hours in a strong solution of sulfuric acid, in the presence of chromium and silver salts. The tubes were then cooled and the color was measured using the Palintest photometer. Four test kits were used, with a maximum detection level of either 2,000 mg/L or 20,000 mg/L, for analysis of the influent, and either 150 mg/l or 400 mg/l, for analysis of partially, or fully-treated wastewaters.
During the field conditions encountered, the quantities of all chemical reagents used were recorded as accurately as possible during all operations.
Presumptive counts of thermotolerant coliforms were recorded as colony-forming units (CFU) per 100 ml, following membrane filtration through a sterile nitrose-cellulose membrane filter (0.45 μm) (using a DelAgua water-testing kit, sterilized by the production of formaldehyde, formed from burning methanol). Acidic and alkaline samples were washed through the filter with an excess of distilled water for one minute, to ensure that the pH level of the membrane prior to incubation approximated 7. Following filtration, the filters were incubated at 44°C ±1°C for 18 to 24 hours, on sterile absorbent pads, soaked in membrane lauryl sulfate broth (Oxoid). Samples were diluted according to their predicted bacterial counts using de-ionized water. Following incubation, all yellow colonies greater than 2 mm in diameter were enumerated and recorded as CFU of presumptive thermotolerant coliforms per 100 ml of the original sample.
The results from the final eight treatment batches, which were performed when adequate monitoring equipment had become available in the field, are summarized in Table 1. A mean wastewater volume of 12.8 m3 was treated in each of the batch processes reported here, six of which were executed according to the low pH procedure (Protocol A), and two of which were executed according to the high pH procedure (Protocol B).
Treatment by both Protocols A and B achieved an effectively clarified effluent, with a turbidity reduction consistently greater than 80% and a mean reduction of 93% (1.1 log). Mean TSS reduction was 92% (1.1 log). Removal of thermotolerant coliforms was consistently greater than 99.8% (2.7 log), with a mean reduction of 99.9% (3 log). The mean removal of COD (calculated with reference to an average value for untreated wastewater in the absence of sufficient data) was consistently higher than 99% (2 logs).
The rate of consumption of chemical reagents during the full-scale treatment operations (when adequate monitoring equipment had become available in the field) is summarized in Table 2. A comparison of the two protocols suggests that, overall, Protocol B was more efficient in terms of the total mass of reagents required to achieve the desired treatment outcome. Protocol B was demonstrated to require on average 1.30 L of HCl per m3 of wastewater, compared with 2.25 L HCl per m3 wastewater for Protocol A. Additionally, a mean dose of 0.47 kg of Ca(OH)2 was required per m3 of wastewater for Protocol B, compared with 3.96 kg Ca(OH)2 per m3 of wastewater for Protocol A. The mean residual aluminum level in the treated effluent from Protocol A was shown to be 0.05 mg per L and 0.07 mg/l for Protocol B. Levels of residual aluminum were never reported to exceed 0.1 mg/l. The mean volume of sludge produced was 6% (vol./vol.).
Operating a novel wastewater treatment plant during the Haitian cholera outbreak presented significant logistical problems, the foremost of which were limited access to adequate supplies of good quality chemical reagents (including lime, alum and hydrochloric acid) and inadequate provision of resources and facilities to support effective operational research. Notwithstanding these obstacles, a novel CTC wastewater treatment and disinfection system was designed and operated successfully, and provided a potentially very useful knowledge-base for further development and application of the technology in other settings.
During the entire operational stage (rather than solely during the final phase reported above), Protocol A demonstrated a greater requirement for chemical reagents than Protocol B (in terms of mass of chemicals to be transported into the field per m3 of wastewater to be treated). It is important to note here that variance in the mass of hydrated lime used per unit of wastewater during the execution of Protocol B was much higher than was predicted by initial laboratory tests. This is probably the result of, not only variations in the characteristics of the wastewater between each batch, but also variations in the purity (as percentage weight of CaO) of successive batches of the lime obtained during the challenging circumstances encountered at the time of the study. Additionally, plant operation was undertaken in conjunction with operator training. During initial plant operation (data not reported here) operators were trained to prevent excessive use of reagents that was unnecessary to meet the treatment objectives. The residual levels of aluminum recorded in the treated effluent produced under Protocol B suggest that addition of this coagulant (which is commonly used in drinking water treatment systems), is unlikely to represent a significant risk to the health of human populations living downstream of the treatment system [46].
The recorded average volume of produced sludge, at 6% (vol./vol.), was slightly higher than the values recorded in the literature for coagulation/flocculation using hydrated lime and aluminum sulfate [47] [48]. However, sludge volumes slightly in excess of those stated in the literature may be deemed acceptable for this kind of experimental field-work, especially given the practical constraints observed at the time these trials were undertaken. For example, during certain phases of the project, the removal of supernatant was found to have been performed under sub-optimal conditions. This was because it took up to one month to train personnel adequately so as to optimize the process and minimize the sludge volume.
An additional study of the microbiological characteristics of the sludge, before and after its subsequent treatment by solar drying and prior to its incineration or controlled discharge to a protected soil infiltration pit is warranted in the future. This would need to be done using a multiple tube (most probably number) approach rather than the membrane filtration method available to the authors during this study. However, it is important to note that, although pathogens would have been concentrated in the sludge during the treatment process, the evidence suggests that the extreme pH levels to which they were subjected (for an extended period of time) would have resulted in a highly significant reduction in the concentration of viable organisms and that controlled soil infiltration, according to the protocols used elsewhere for untreated CTC wastewaters, constitutes a rational management of the risk of onward human infection.
A relatively simple cost analysis demonstrated that labor costs per unit of treated fecal waste for Protocols A and B are roughly equivalent to those of the super-chlorination approach to disinfection, the efficacy of which has been questioned [21] [19] [22] [23]. Moreover, significant financial savings, in relation to reagent costs, may be achieved using the protocols presented here. Further details are provided in the Supporting Information files.
In light of the recent findings of a panel of experts reporting to the United Nations, the research presented here is timely [49]. The report states that “[…] to prevent introduction of contamination into the local environment, United Nations installations worldwide should treat fecal waste using on-site systems that inactivate pathogens before disposal. These systems should be operated and maintained by trained, qualified […] staff or by local providers with adequate oversight […]” [50]. Although the authors of the report do not prescribe an appropriate microbiological quality standard that might be met by disinfection of the wastewater prior to discharge into the environment, it is interesting to note that concentrations of thermotolerant coliforms in the treated wastewater reported in the study reported here consistently met the WHO bacteriological guideline values for agricultural reuse, i.e., fewer than 1,000 CFU/100 ml [51]. In fact, the quality of the final effluent achieved by both full-scale treatment protocols was consistently higher than the minimum standard initially agreed for disposal by direct infiltration.
The high rate of disinfection achieved using both physico-chemical treatment protocols described here clearly suggests that this innovative technology may be an appropriate and potentially valuable option for the onsite-disinfection of CTC wastewaters generated in the emergency settings encountered during cholera epidemics and potentially may offer a valuable form of wastewater and human excreta disinfection during outbreaks of other infectious diseases. For example, although the Ebola virus is considered to be ‘fragile’ beyond the environment of bodily fluids (including feces), its potential presence in large numbers in the feces of Ebola patients and its relatively low infective dose [52] [53] present a potent hazard to health care workers. The disinfection options presented here may be readily adapted to provide an important in situ excreta disinfection step as part of an integrated infection control framework.
More accurate determination of the chemical consumption for both protocols is currently being achieved through laboratory experimentation, but a key finding of the field work reported here, that chemical consumption during the execution of the low pH treatment process (Protocol B) was significantly lower than that during the high pH process (Protocol A), appears to be valid. This consideration potentially has significance for international medical organizations that may wish to use this technology during future disease outbreaks, especially in scenarios where reducing the quantity of chemicals, either purchased locally or imported, may be a high priority.
The evidence available from the published literature suggests that the organism Vibrio cholerae is highly likely to respond to extreme levels of pH achieved in the protocols presented here in a similar manner to thermotolerant coliforms (including Escherichia coli). However, a future investigation, which compares the behavior of Escherichia coli and other commonly used indicator bacteria (such as intestinal enterococci), with that of Vibrio cholerae would probably provide valuable additional information to help optimize the treatment technologies presented here.
The engineering priority now must be to monitor these treatment systems under more highly-controlled conditions, in order to refine the treatment processes and validate the data reported here, which were achieved under challenging field conditions. A longer-term challenge for microbial ecologists is to develop a better understanding of how toxigenic strains of V. cholerae and other excreta-borne pathogens behave in the environmental niches present in wastewater treatment plants [54], but the technology outlined here may have broader application to scenarios in which hygienic management of sludges and wastewaters has to be achieved rapidly and at relatively low-cost. The authors are therefore currently exploring its application to other NTD outbreak settings and to the broader issue of urban excreta management in low-income communities. However, it is essential that those actively involved in WASH operational research should take a multi-disciplinary approach to the issue of controlling disease transmission from human excreta and should avoid the tendency to focus exclusively on infrastructural interventions [11]: those responsible for designing and operating new wastewater treatment technologies in emergency settings should always consider the broader and longer-term public health context of their interventions and should fully evaluate all new technologies within the rational risk management framework of ‘sanitation safety planning’.
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10.1371/journal.pgen.1007296 | The TIE1 transcriptional repressor controls shoot branching by directly repressing BRANCHED1 in Arabidopsis | Shoot branching is a major determinant of plant architecture and is regulated by both endogenous and environmental factors. BRANCHED1 (BRC1) is a central local regulator that integrates signals controlling shoot branching. So far, the regulation of BRC1 activity at the protein level is still largely unknown. In this study, we demonstrated that TIE1 (TCP interactor containing EAR motif protein 1), a repressor previously identified as an important factor in the control of leaf development, also regulates shoot branching by repressing BRC1 activity. TIE1 is predominantly expressed in young axillary buds. The gain-of-function mutant tie1-D produced more branches and the overexpression of TIE1 recapitulated the increased branching of tie1-D, while disruption of TIE1 resulted in lower bud activity and fewer branches. We also demonstrated that the TIE1 protein interacts with BRC1 in vitro and in vivo. Expression of BRC1 fused with the C-terminus of the TIE1 protein in wild type caused excessive branching similar to that observed in tie1-D and brc1 loss-of-function mutants. Transcriptome analyses revealed that TIE1 regulated about 30% of the BRC1-dependent genes, including the BRC1 direct targets HB21, HB40 and HB53. These results indicate that TIE1 acts as a positive regulator of shoot branching by directly repressing BRC1 activity. Thus, our results reveal that TIE1 is an important shoot branching regulator, and provide new insights in the post-transcriptional regulation of the TCP transcription factor BRC1.
| Shoot branching is a key factor that not only affects plant survival but also determines food productivity in crop species. BRANCHED1 (BRC1) integrates internal and external signals to determine shoot branching. However, the regulation of BRC1 at the protein level remains elusive. We found that TIE1 (TCP interactor containing EAR motif protein 1) plays an important role in the control of shoot branching in Arabidopsis. Higher TIE1 expression levels lead to bushier Arabidopsis plants. TIE1 directly interacts with BRC1 and represses BRC1 transcriptional activity. Furthermore, BRC1 downstream target genes are downregulated by TIE1. Our findings demonstrate that TIE1 acts as a key repressor of BRC1 activity and positively regulates shoot branching.
| Shoot branching greatly affects plant architecture, one of the most important agronomic traits. The manipulation of shoot branching patterns is an efficient way to promote and manage crop production [1]. Shoot branching is a developmental process with a high plasticity and tightly regulated by diverse endogenous and environmental stimuli. The development of shoot branches starts from the initiation of axillary meristems (AMs) in the leaf axils. The AMs then develop into small buds with a few leaves, which either remain dormant or grow to form branches in response to internal or external cues [2].
Genetic analyses have identified several important transcriptional regulators, which form a complex regulatory network during the initiation of AMs [3]. However, few transcriptional regulators have been found to control local bud activity. TEOSINTE BRANCHED1 (TB1) is an important domestication gene of maize that plays a central role in the control of shoot branching. TB1 is a founder member of the TCP (TB1/CYCLOIDEA/PCF) family of transcription factors conserved in the plant kingdom. In both monocots and dicots, orthologs of TB1 play a pivotal role in the control of bud activity. Examples of this are the rice FINE CULM 1/OsTB1, sorghum SbTB1, Arabidopsis BRANCHED1 (BRC1), and tomato, pea and potato BRC1-like genes [4–9]. The Arabidopsis BRC1 gene is predominantly expressed in developing axillary buds (axillary meristems, bud leaf primordia and subtending vascular tissue) and its expression levels decrease as buds grow out. BRC1 acts as a suppressor of bud activity: loss-of-function brc1 mutants display accelerated initiation of axillary meristem formation, faster bud development and more branches [6,10].
Increasing evidence indicates that BRC1 is an integrator of diverse internal and external signals that control bud activity. The branch-suppressing hormone strigolactone (SL) controls shoot branching in part by positively regulating BRC1 at the transcriptional level in Arabidopsis and pea [6,8,10–12]. The brc1 mutants are insensitive to SL treatments and epistatic to smxl6 smxl7 smxl8 triple mutants [8,13, 14]. On the contrary, the hormone cytokinin (CK) negatively regulates BRC1 expression and promotes shoot branching in rice and pea [8,15], although the branching of Psbrc1 pea mutants still respond to CK treatments [8]. Likewise, sugar is an important nutritional and signaling element proposed to be necessary for axillary bud outgrowth. BRC1 transcript levels are reduced after sucrose application to buds [16–18], whereas low sucrose levels upregulate TB1 expression in wheat [19]. In addition to the endogenous signals, BRC1 is also regulated by numerous external inputs. For example, changes in light quality (i.e. a reduction in the red-to-far red light ratio) upregulate BRC1 and lead to suppression of bud growth [20,21].
A TB1 upstream regulator, IDEAL PLANT ARCHITECTURE1 (IPA1), has been identified in rice. IPA1 is a transcription factor that promotes the expression of OsTB1 by directly binding to its promoter region [22]. TB1 downstream targets [23,24] and also BRC1 targets begin to be characterized. Three HD-ZIP transcription factor-encoding genes, HOMEOBOX PROTEIN (HB) 21, HB40 and HB53 have been shown to be directly regulated by BRC1 in Arabidopsis [18]. These HD-ZIPs and BRC1 together, upregulate 9-CIS-EPOXICAROTENOID DIOXIGENASE 3 (NCED3), which encodes a key enzyme in ABA biosynthesis to promote bud dormancy. However, although the transcriptional regulation upstream and downstream of BRC1 begins to be understood, the regulation of BRC1 activity at the protein level is still very poorly known.
TCP Interactor containing EAR motif protein 1 (TIE1) was identified as a nuclear transcriptional repressor that regulates leaf development [25]. Overexpression of TIE1 in the activation-tagging mutant tie1-D causes hyponastic leaves, while the disruption of TIE1 leads to epinastic leaves. The TIE1 protein interacts with CIN-like TCP transcription factors and it also recruits the transcriptional corepressors TOPLESS (TPL)/TOPLESS-RELATED (TPR). Formation of this protein complex leads to a repression of the activity of CIN-like TCP transcription factors. The association of TIE1 with these TCPs further leads to an altered expression of TCP target genes, such as LOX2, AS1 and IAA3. However, tie1-D and jaw-D, in both of which the TCP activity was downregulated, did not display completely identical phenotypes, which indicates that TIE1 may also bind other transcription factors and regulate additional biological processes. In addition, TIE1 is regulated by E3-ligase proteins termed TIE1-associated RING-type E3 ligases (TEARs) [26]. TEARs interact with TIE1 and are responsible for TIE1 degradation, which boosts CIN-like TCP activity during leaf development.
Here, we report that the transcriptional repressor TIE1 positively controls shoot branching by directly regulating BRC1 protein activity. We demonstrate that overexpression of TIE1 leads to higher bud activity and more branches, whereas disruption of TIE1 causes reduced bud activity and branch suppression. TIE1 is predominantly expressed in axillary buds and is negatively regulated as buds grow out. TIE1 represses the expression of BRC1 target genes, probably by directly interacting with BRC1 and antagonizing its activity. Our data reveals a novel molecular mechanism by which plants control BRC1 activity accurately and flexibly via TIE1 at the protein level to determine bud activity in response to endogenous and environmental cues.
We previously identified a transcriptional repressor, TIE1, essential for the control of leaf development [25]. The gain-of-function tie1-D mutant, obtained by T-DNA activation-tagging, displays strong leaf developmental defects. We noticed that tie1-D also produced an excessive number of branches (S1 Fig), suggesting a possible role of TIE1 in the control of shoot branching. To test this possibility, we first generated transgenic lines carrying the construct 35S-GFP-TIE1, in which the TIE1 coding sequence (CDS) fused to the GREEN FLUORESCENT PROTEIN (GFP) was driven by the Cauliflower Mosaic Virus 35S promoter (CaMV35S). The 35S-GFP-TIE1 plants displayed epinastic leaves as observed in tie1-D mutants (Fig 1A), which indicated that the GFP-TIE1 fusion protein was functional. We analyzed three 35S-GFP-TIE1 independent transgenic lines and found that all three lines produced more branches than the wild-type controls, and recapitulated the branching phenotype of tie1-D (Fig 1A–1C and S1 Fig). Because the homozygous tie1-D plants are sterile, we investigated the branching phenotype of plants of the fertile 35S-GFP-TIE1-19 line in detail. The results showed that although this TIE1 overexpression line generated fewer rosette leaves than the wild-type plants (Fig 1A–1C), almost all the buds grew out to form branches, whereas in the wild-type controls most buds remained small at this stage (Fig 1B and 1C). These results suggest that TIE1 is a positive regulator of axillary bud activity and shoot branching.
TIE1 belongs to a gene family with a high functional redundancy [25]. To overcome the difficulties caused by such genetic redundancy, we used a dominant-negative strategy to interfere with the function of all the TIE family members. We generated a 35S-TIE1mEAR-VP16 construct in which the TIE1 repressor was changed into an activator by mutating the EAR motif of TIE1 and fusing it to the VP16 activation domain. This approach has been successfully used to disrupt TIE genes redundancy in our previous study [25]. We examined all the rosette leaf axils of wild-type and 35S-TIE1mEAR-VP16 transgenic plants (Fig 2). Under the same growth conditions in which wild-type controls produced several branches, TIE1mEAR-VP16 plants only had axillary buds but no branches (Fig 2A–2C). In addition, the degree of axillary bud development in the mutant and wild-type plants was classified into four classes, based on absence/presence of visible axillary leaf primordia and on axillary bud size (Fig 2D). The detailed analysis of the branching phenotypes showed that axillary bud development was obviously delayed in TIE1mEAR-VP16 plants when compared to wild-type controls (Fig 2D): TIE1mEAR-VP16 plants produced fewer branches than controls, and had more axils without a visible axillary bud. These results indicate that, like BRC1, TIE1 regulates shoot branching from the early stages of axillary bud development.
To characterize in more detail the spatial and temporal expression patterns of TIE1 during bud development, a 2790-bp genomic fragment upstream of the TIE1 translation start codon was fused to the β-GLUCURONIDASE (GUS) reporter gene to generate a TIE1pro-GUS construct [25]. GUS staining analyses of the TIE1pro-GUS transgenic lines revealed that TIE1 was predominantly expressed in developing axillary buds (Fig 3A–3G). In young axillary buds, signal was detectable throughout the leaf primordia (Fig 3A, 3C and 3E). As the buds developed, GUS signal became progressively more restricted to the base of the buds, and to the bud leaf vasculature (Fig 3B, 3D, 3F and 3G). When buds grew out into shoots, GUS activity was almost undetectable (Fig 3H). In addition, GUS accumulated in the stem vasculature, in particular in the phloem (Fig 3I), young leaf veins (Fig 3A and 3B) and sepal vasculature (Fig 3J). The expression patterns of TIE1 during axillary bud development resemble those of BRC1 [6]. These results are consistent with an important role of TIE1 in the control of axillary bud activity.
To investigate the molecular mechanisms by which TIE1 regulates shoot branching, we performed a yeast two-hybrid screening of an Arabidopsis transcription factor library to identify TIE1 interactors, using as a bait of a protein containing the N-terminal (N-t) 108 amino acid residues of TIE1 [25]. The results showed that the N-t region of TIE1 interacted with BRC1 but not with BRC2. We therefore cloned the CDS of Arabidopsis BRC1 and BRC2 to verify this interaction. The yeast-two-hybrid assays confirmed that BRC1 interacted with TIE1, whereas BRC2 did not (Fig 4A). We then performed additional experiments to further investigate BRC1-TIE1 interaction in vitro and in vivo. First, we expressed BRC1 fused to the MALTOSE BINDING PROTEIN (MBP-BRC1) and His-tagged TIE1 in Escherichia coli and purified them for in vitro pull-down assays. Specific binding of His-tagged TIE1 was detected in the MBP-BRC1 after eluting six times, while no band of His-tagged TIE1 was observed in the control MBP, indicating that TIE1 interacted strongly with BRC1 in vitro (Fig 4B). Then, we confirmed the association of BRC1 with TIE1 in vivo by BiFC and firefly luciferase complementation imaging assays (Fig 4C and 4D). Finally, BRC1 and TIE1 interaction was further confirmed by acceptor photobleaching fluorescence resonance energy transfer (APB-FRET) using transient assays in Nicotiana benthamiana leaves (Fig 4E).
BRC1 contains several important domains including a TCP domain for dimerization and DNA binding, and an R domain of unknown function [27]. To map the regions of the BRC1 protein necessary for interaction with TIE1, we generated a series of BRC1 deletions lacking different regions of the protein (Fig 4F). Yeast two-hybrid assays showed that the TCP domain was necessary and sufficient for the interaction between TIE1 and BRC1, whereas the R domain was not required for the interaction. We further assayed three BRC1 proteins with point mutations in the TCP domain, two in the basic region and one in helix II. All three mutations disrupted the TIE1-BRC1 interaction (Fig 4F), whereas a point mutation in the R domain did not affect the interaction. These data demonstrate that BRC1 interacts with the N-t region of TIE1 through its TCP domain. These results together with the observed overlapping expression patterns of BRC1 and TIE1 in axillary buds support the existence of this interaction in planta.
We further examined whether other members of the TIE family could interact with BRC1. Yeast two-hybrid analyses showed that TIE2 and TIE4 also interacted with BRC1, while TIE3 did not (S2 Fig). These results suggest that several members of the TIE protein family may control shoot branching by directly interacting with BRC1.
We then examined whether BRC1 transcriptional activity could be regulated by interaction with the transcriptional repressor TIE1. For that we used the reporter construct HB53pro-LUC, in which the LUCIFERASE (LUC) gene is driven by a 2000 bp promoter region of HB53, which is a BRC1 direct target gene [18]. We co-infiltrated Nicotiana benthamiana leaves with HB53pro-LUC and 35S-BRC1 (and a 35S-MYC-GFP control). LUC activity analysis indicated that BRC1 was able to activate directly the HB53pro-LUC. In contrast when HB53pro-LUC and 35S-BRC1 were co-infiltrated with 35S-TIE1-MYC-GFP, the activation of HB53pro-LUC was very much reduced (Fig 5A), suggesting that TIE1 inhibits BRC1 transcriptional activity. We have shown that TIE1 serves as a bridge between TCPs (with its N-t region) and the corepressors TPL/TPRs (with its C-t region) [25]. To further examine the effects of the TIE1-BRC1 interaction, we generated a fusion protein BRC1-TIE1C in which BRC1 was physically linked with the C-t of TIE1 (from the 108th amino acid residue to the stop codon). The fusion protein was expressed in wild-type plants using a CaMV 35S promoter. The detailed branching phenotypes of these transgenic plants were analyzed (Fig 5B and 5C). The 35S-BRC1-TIE1C transgenic lines produced about six branches, whereas the control 35S-TIE1C transgenic plants produced two to three branches under the same growth conditions (Fig 5B and 5C). The increased branching of 35S-BRC1-TIE1C transgenic lines indicated that BRC1 protein activity was affected by the fusion with TIE1, probably due to the recruitment of TPL/TPRs corepressor by TIE1 C-t.
To further elucidate the mechanisms by which TIE1 regulates shoot branching, we performed RNA-seq transcriptome analysis of rosette leaf axil tissue (highly enriched in axillary buds) of 35S-GFP-TIE1 overexpression lines and wild-type controls. We found that 1503 genes were upregulated and 1151 genes downregulated in the TIE1 overexpression line (q value<0.05; fold change≥1.5 and ≤-1.5) (S1 Table). We compared these genes with a list of 307 BRC1-dependent genes (False Discover Rate <0.05) [20], and found a negative correlation between TIE1-responding genes and the BRC1-dependent genes: TIE1-upregulated genes appeared at a much higher frequency than expected for a random gene list among the BRC1-downregulated genes (30% of the BRC1-downregulated genes, p value = 2.1E-18 in a hypergeometric test) (Fig 6A and S2 Table). Likewise TIE1-downregulated genes were significantly enriched among the BRC1-upregulated genes (22% of the BRC1-upregulated genes, p value = 1.5E-27 in a hypergeometric test) (Fig 6A and S2 Table), supporting that TIE1 acts to antagonize BRC1 activity during bud development. These enrichments were remarkably higher than those found when the comparison was done between TIE1-dependent genes and the BRC1-independent genes obtained in the same experiment (i.e. genes that respond to a low Red:Far red light ratio, both in wild type and brc1 mutants) [20].
Then we further compared TIE1-dependent genes with a particular subset of BRC1-dependent genes that also responded significantly to decapitation [28]. Genes upregulated in response to BRC1 and downregulated 24 hours after decapitation were termed Bud dormancy genes. Genes downregulated in response to BRC1 and upregulated 24 hours after decapitation were termed Bud activation genes [20]. Again, TIE1-dependent upregulated genes appeared among Bud activation genes at a much higher frequency than that expected in a random gene list (33%, p value = 6.55E-15) and TIE1-dependent downregulated genes were significantly enriched among Bud dormancy genes (29%, p value = 5.29–29).
Interestingly, three BRC1 direct targets, HB21, HB40 and HB53 [18], were among the TIE1-downregulated genes (Fig 6C, S2 and S3 Tables). Quantitative RT-PCR analysis confirmed that the transcriptional levels of these genes were significantly lower in the TIE1 overexpression line and in brc1-2 mutants than in wild-type controls (Fig 6D and 6F). These data indicate that TIE1 modulates the expression of sets of BRC1-dependent, bud activation and dormancy genes.
Finally, we also found that in our RNA-seq data BRC1 was downregulated (S3A Fig), and qRT-PCR analyses confirmed that BRC1 mRNA levels were much lower in TIE1 overexpression lines than in the wild-type control (S3B Fig). Conversely, BRC1 expression levels were significantly higher in three 35S:TIE1mEAR-VP16 lines than in the wild-type control (S3C Fig). These results suggest that TIE1 may also directly or indirectly regulate BRC1 at the transcriptional level.
In this study, we discovered that the transcriptional repressor TIE1 is a regulator of shoot branching. The gain-of-function mutant tie1-D and transgenic plants overexpressing TIE1 produce more branches, whereas TIE1 loss-of function leads to lower bud activity and fewer branches. In addition, we demonstrated that TIE1 interacts, in vitro and in vivo, with BRC1, a transcription factor that plays an important role in the control of bud activity. Furthermore, TIE1 is expressed in young axillary buds and is regulated during bud development in patterns that overlap with those of BRC1 [6], further supporting the possibility that TIE1 interacts with BRC1 in planta. By binding to BRC1, TIE1 inhibits BRC1 activity and consequently represses the transcription of many BRC1 target genes (Fig 7A and 7B). Our data not only demonstrate that TIE1 is an important regulator in the control of shoot branching, but also provides evidence of a novel layer of regulation of BRC1 at the protein level.
TCP proteins are plant-specific transcription factors that group into class I and class II subclasses on the basis of sequence similarity [29,30]. The class II TCPs are further categorized into CINCINNATA-like TCPs and CYCLOIDEA/TB1 (CYC/TB1)-like TCPs [30]. The modulation of TCP activity at the protein level is important for plant development. Some proteins, including the SWI/SNF chromatin remodeling ATPase BRAHMA (BRM) and the ARMADILLO BTB ARABIDOPSIS PROTEIN1 (ABAP1) have been reported to interact with CIN-like TCPs and regulate their activity [31,32]. Recently, we found that the EAR-motif containing repressor TIE1 suppresses CIN-like TCP activity by recruiting the transcriptional co-repressors TOPLESS (TPL)/TOPLESS-RELATED (TPR) proteins during leaf development [25]. A yeast two-hybrid screening revealed that TIE1 also interacts with BRC1 (a TCP factor of the CYC/TB1 subclade), a prominent bud-specific regulator of shoot branching [6,10]. However, the role of TIE1 in the control of shoot branching had not been identified so far. In this paper, our genetic and biochemical data indicate that TIE1 is not only an important factor regulating CIN-like TCPs, but also regulates BRC1 at the protein level. TIE1 interacts with BRC1 and prevents the transcription of BRC1 target genes. Like in the case of CIN-like TCPs, TPL/TPRs could be recruited by TIE1 to repress BRC1 activity during bud development (Fig 7A and 7B). This is consistent with the observation that plants expressing a BRC1 fusion protein carrying the C-terminal of TIE1 produce more branches.
Interestingly, other TPL/TPR-interacting proteins have been previously implicated in the control of shoot branching through SL signaling. Indeed, the rice SL signaling repressor D53, and its orthologs in Arabidopsis, SMXL6, SMXL7 and SMXL8, interact with TPL/TPR proteins [33–36]. This interaction may promote TPL/TPR oligomerization and formation of a repressor-corepressor nucleosome complex [37]. This interaction has been proposed to be responsible for the transcriptional repression of OsTB1/BRC1 although this is yet unclear [38,39]. Our findings show that TPL/TPRs are also recruited by TIE1 to directly repress BRC1 at the protein level, suggesting that the TPL/TPRs use different molecular mechanisms to control shoot branching. Furthermore, rice D53 interacts with Ideal Plant Architecture 1 (IPA1), another negative regulator of shoot branching [40–42] that binds the OsTB1/FINE CULM1 promoter [22], and may affect its expression. This interaction, conserved in wheat, leads to suppression of the transcriptional activity of IPA1-like factors [42]. These observations indicate that TPL/TPRs-interacting proteins, such as TIE1-like and D53-like proteins, play important roles in the control of shoot branching both in dicots and monocots. The rice genome has six TIE1 homologs [43,44]. It will be very interesting to determine whether OsTIEs interact with OsTB1/FINE CULM1 and TPL/TPR corepressors.
Remarkably, our deletion and mutation analysis suggested that TIE1 interacts with the TCP domain of BRC1, responsible for DNA binding [30]. The TCP domain of BRC1 is necessary and sufficient for the TIE1 and BRC1 interaction, because point mutations in the TCP domain completely abolish this interaction. This raises the alternative possibility that TIE1 represses BRC1 activity by preventing BRC1 binding to DNA as it has been described for DELLA proteins, which repress the activity of TCP14 and TCP15 by interacting with their TCP domains [45]. Therefore, TIE1 is likely to inhibit BRC1 activity either by recruiting the transcriptional repressor machinery and/or by hindering the TCP domain of BRC1 from binding the promoters of target genes (Fig 7B and 7C). These two mechanisms may work together to precisely regulate BRC1 activity and shoot branching in response to internal and external cues. In addition, we found that BRC1 itself was down-regulated by TIE1, which indicates that TIE1 may also control directly or indirectly BRC1 at the transcriptional level. Investigating how this transcriptional and post-transcriptional regulation of BRC1 by TIE1 affects plant architecture remains to be determined.
Recently, ABA has been reported to negatively regulate axillary bud growth in Arabidopsis [46]. BRC1 is an important regulator of ABA signaling in buds partly through the regulation of three genes, HB21, HB40 and HB53, encoding HD-ZIP transcription factors [18]. Our results showed that TIE1 regulates about one third of the BRC1-dependent genes induced in dormant buds including HB21, HB40 and HB53, which raises the possibility that TIE1 helps BRC1 finely tune the transcriptional level of these branching control genes.
It is worth noting that the phenotype of TIE1 gain-of-function plants is not identical to that of brc1 mutants: in addition to an excess of branching, the former display many other phenotypes including epinastic leaves, dwarfism and early flowering [25]. Indeed, TIE1 expression in tissues other than axillary buds (e.g. leaf, sepal and stem vasculature) as well as the reported interaction of TIE1 with other transcription factors and TPL/TPRs (see above) may account for these phenotypes unrelated to shoot branching of TIE1 gain-of-function lines.
We have recently found that TIE1 is ubiquitinated by several E3-ligase proteins TEARs (TIE1-associated RING-type E3 ligases) for degradation [26]. Interestingly, disruption of TEARs using the dominant-negative strategy and sextuple tear mutants also cause excessive branching [26], this is consistent with our observation in this study that overexpression of TIE1 promotes shoot branching. The characterization of potential additional components of the molecular machinery that controls shoot branching via modulation of the activity of the BRC1 protein will help us further understand the complex regulatory mechanisms that determine plant shoot architecture in response to environmental cues.
The Arabidopsis thaliana ecotype Columbia-0 (Col-0) was used in this study. The mutants tie1-D and brc1-2 were described previously [6, 25]. Half-strength Murashige and Skoog medium with or without 20 μg/mL DL-phosphinothricin or 50 μg/mL kanamycin were used for growing or screening the plant seeds. The plates with seeds were placed at 4°C for 2 d synchronization before being incubated at 22°C under long-day conditions (16-h light and 8-h dark, 70% relative humidity). The seven-day-old seedlings were transferred to soil and were grown under the same conditions as described above.
To generate the TIE1 overexpression line, the TIE1 coding sequence was amplified from Arabidopsis seedling cDNA using the primer pairs TIE1-F/R (S4 Table). The PCR product was cloned into pENTR/D TOPO (Invitrogen) to generate pENTR-TIE1. Then, the overexpression construct 35S-GFP-TIE1 was generated by an LR reaction between pENTR-TIE1 and pB7GWF2 (Ghent University). To examine the temporal and spatial expression pattern of TIE1, the 2790-bp genomic fragment upstream of TIE1 start codon was amplified using the primers TIE1P-F and TIE1P-R and was cloned into pENTR/D-TOPO to generate pENTR/D-TIE1P. TIE1P-GUS was generated by LR reaction between pENTR/D-pTIE1 and pKGWFS7 (Ghent University). To generate 35S-BRC1-TIE1C construct, the coding region of BRC1 without a stop codon was amplified from Arabidopsis seedling cDNA with primers BRC1-F1/R1 and further was cloned into pENTRY/D-TOPO to generate pENTRY-BRC1N. The CaMV 35S promoter was amplified from vector pWM101 with primers p35S-F/R. The fragment was cloned into pDONRP4P1r (Invitrogen) to generate pENTRY-L4-35S-R1. The C-t of TIE1 sequence was amplified from pENTR-TIE1 using primers TIE1C-F/R and was cloned into pDONRP2rP3 (Invitrogen) to generate pENTR-R2-TIE1C-L3. The 35S-BRC1-TIE1C construct was generated by LR reaction from pENTRY-L4-35S-R1, pENTRY-BRC1N, and pENTR-R2-TIE1C-L3 and pK7m34GW (Ghent University). To generate 35S-TIE1mEAR-VP16, TIE1mEAR was amplified with primers TIE1m-F/R and the PCR product was cloned into NTRY/D-TOPO to generate pENTRY-TIE1mEAR. The coding region of VP16 was amplified from pTA7002 [47] and was cloned into pQDR2L3 with primers VP16-F/R to generate pENTR-R2-VP16-L3. The 35S-TIE1mEAR-VP16 construct was generated by LR reactions among plasmids pK7m34GW, pENTRY-L4-35S-R1, pENTRY-TIE1mEAR and pENTR-R2-VP16-L3. These constructs were transformed into Agrobacterium tumefaciens GV3101/pMP90 by electroporation method and then into Arabidopsis as described previously by floral dip method [48].
For GUS staining, tissues from TIE1pro-GUS lines were soaked in 90% acetone solution for 20 mins on the ice and washed by phosphate buffer twice. Then the samples were incubated in GUS staining buffer containing 0.5 mg/mL 5-bromo-4-chloro-3-indolyl glucuronide and vacuumed for 30 min before incubation overnight at 37°C. The staining buffer was then replaced by 70% ethanol for decolorizing before microscopy analysis. Plastic embedding and sectioning of GUS-stained stem fragments of adult plants was carried as described in Chevalier et al. [49].
For quantitative RT-PCR, total RNAs of the tissues around leaf axils from 25-day-old wild-type, 35S-GFP-TIE1 and brc1-2 were extracted using TRIzol reagent (Invitrogen). Reverse transcription was carried out using Superscript II Reverse Transcriptase Kit (Invitrogen). Quantitative RT-PCR was performed with three biological repeats using SYBR Green Realtime PCR Master Mix (Toyobo) and using the diluted cDNA as the template. The 2-ΔΔCT method was used to calculate the relative expression level of each gene [50]. Primers used were listed in S4 Table. AtUBQ10 gene was used as an internal control.
For RNA-seq, total RNAs of the tissue surrounding leaf axils enriched in axillary buds from 25-day-old wild-type and 35S-GFP-TIE1 plants were extracted using TRIzol reagent. The RNA-seq was performed on the Illumina HiSeq 2000 platform (Illumina) at the Biodynamic Optical Imaging Center (BIOPIC) of Peking University. The bioinformatic and statistical analysis of the RNA-seq data was performed according to the procedures described previously [51]. Genes with changes of more than 1.5-fold (Q-value ≤ 0.05) were defined as differentially expressed genes. The hypergeometric test was performed in the software environment R (CRAN) using the phyper function.
To test the interaction between TIE1 and BRC1/BRC2, the N-t of TIE1 (1–108) and the coding sequences of BRC1/BRC2 were amplified with primers TIE1N-F/R and BRC1-F/R or BRC2-F/R listed in S4 Table. The products were cloned into pENTR/D-TOPO to generate pENTRY-TIE1N and pENTRY-BRC1/BRC2. The bait construct DBD-TIE1N was generated by LR reaction between pDEST32 (Invitrogen) and pENTRY-TIE1N. The prey construct AD-BRC1/BRC2 were generated by LR reaction between pENTRY-BRC1/BRC2 and pDEST22 (Invitrogen). The bait construct and each prey one were co-transformed into the yeast strain AH109. Medium without Leu, Trp and His and with 5 mM 3-amino-1,2,4-triazole (3-AT) was used for selection.
To determine which region of BRC1 interacts with TIE1, fragments of BRC1 where amplified by PCR and cloned in pDONR207 by BP clonase (Thermofisher) and then inserted in pGADT7-GW by LR recombination using Gateway LR clonase II (Thermofisher). The TIE truncated in the C-t part (TIE1(1–108)) was also cloned in pDONR207 and inserted afterwards in pGBKT7-GW (YTH assays; Thermofisher). Vectors were transformed in yeast strain AH109 and medium without Leu, Trp and His and with 5 mM 3-amino-1,2,4-triazole (3-AT) was used for selection.
The MBP-BRC1 construct was generated by LR reaction between pENTR/D-BRC1 and pMAL-GW modified from pK2GW7 (Ghent University). The pET28-TIE1-His construct was generated by enzyme digestion reaction with the EcoR I and Sal I sites of pET-28a (+) (Novagen). The constructs were introduced into E. coli BL21 (DE3) competent cells for protein expression. The transformed cells were cultured in LB medium at 37°C until the OD600 reached 0.5 and then moved to 18°C condition for 12h in the presence of 0.5mM IPTG for the induction of protein expression. The proteins were extracted in buffer containing 20 mM Tris-HCl [pH7.4], 200 mM NaCl, 1 mM EDTA, 1 mM PMSF, and 1×C-complete protease inhibitor [Roche]. Bacterial lysates in extraction buffer contained 50 μg MBP-BRC1 or the control MBP proteins were mixed with lysates containing 50 μg TIE1-His fusion protein. The mixtures were incubated with Amylose resin (NEB) at 4°C for 3 h. Beads were washed six times with the column buffer (20 mM Tris-HCl, pH 7.4, 200 mM NaCl, 1mM EDTA, 1mM PMSF, and 1×C-complete protease inhibitor [Roche]). The supernatant in the first wash (elution 1) and sixth wash (elution 6) of the beads was boiled with 2×SDS buffer for further immunoblot analysis. After the sixth washes, the bound proteins (precipitate) were eluted with 2×SDS buffer and boiled for 5 min. Immunoblot analysis was then performed to detect the proteins with anti-His antibody (Sigma-Aldrich).
The constructs nYFP-TIE1 and cCFP-BRC1 were generated by LR reactions between pENTR-TIE1 and pnYFPXGW or between pENTR-BRC1 and pcCFPXGW [52]. The above constructs were first transformed into A. tumefaciens GV3101 and then the nYFP-TIE1 and cCFP-BRC1/BRC2 were co-infiltrated into the leaves of N. benthamiana. The plants were grown in the dark for 12 h followed by 48 h in a growth chamber under normal conditions. The fluorescence signal of GFP in N. benthamiana leaf cells was observed under a Leica SPE confocal microscope (Leica). A DAPI (Sigma) solution was used to stain the nuclei. The excitation laser was set at 488 nm for GFP and 405 nm for DAPI staining. For LCI assay, the constructs TIE1-nLUC and cLUC-BRC1 were generated by cloning the TIE1 gene into pCAMBIA-nLUC-GW and by cloning BRC1 gene into pCAMBIA-cLUC-GW [26]. The above constructs were first transformed into A. tumefaciens GV3101 and then the different combinations of the constructs, i.e. cLUC-BRC1 and TIE1-nLUC, cLUC and TIE1-nLUC, cLUC-BRC1 and nLUC, were co-infiltrated into the N. benthamiana leaves. The plants were placed in the dark for 12 h followed by 48 h in a growth chamber under normal condition. The infiltrated N. benthamiana leaves were sprayed with luciferin (100 mM) and kept in dark for 10 mins. The leaves were observed under a low-light cooled charge-coupled device (CCD) imaging apparatus Lumazone_1300B (Roper Bioscience).
The TIE1 and BRC1 full sequences without codon stop were cloned in pDONR207 and then inserted by Gateway cloning (Thermofisher) in pABindGFP, pABindmCherry and pABindFRET allowing production of the proteins fused to GFP, mCherry or GFP-mCherry, respectively. Vectors were Agro-infiltrated in N. benthamiana leaves and protein production was inducted 24h after infiltration and APB-FRET assays were performed 16-20h after induction. APB-FRET conditions and FRET efficiency were as described in Nicolas et al. [10].
To perform the transactivation assay, we used a 2-kb promoter of the BRC1 direct target gene HB53 cloned in pGWB435 [53] for fusion with the LUCIFERASE reporter gene as described in González-Grandío et al. [18]. TIE1 and BRC1 were cloned in the destination vector pGWB2 for their constitutive expression under the CaMV 35S promoter. The different constructs were co-infiltrated in tobacco leaves and the LUC activity was measured 16–20 h after infiltration in a LB 960 Microplate Luminometer (Berthold) as described in Nicolas et al. [9].
Sequence data from this article can be found in the Arabidopsis Genome Initiative under the following accession numbers: TIE1, At4g28840; TIE2, AT2g20080; TIE3, At1g29010; TIE4, At2g34010; BRC1, AT3g18550; HB21, At2g18550; HB40, AT4g36740; HB53, AT5g66700.
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10.1371/journal.pntd.0007532 | Clinical and histopathologic features of canine tegumentary leishmaniasis and the molecular characterization of Leishmania braziliensis in dogs | Cutaneous leishmaniasis (CL), caused by Leishmania braziliensis, is the most important presentation of tegumentary leishmaniasis (TL) in Latin American. While the role of dogs as reservoirs of Leishmania infantum, and the clinic features of canine visceral leishmanisis are well described, little is known about the importance of dogs in the transmission of L. braziliensis to humans. In the present study, we determine the frequency of L. braziliensis infection in dogs with cutaneous and mucosal ulcers in an endemic area of CL. We also describe the clinical manifestations and histopathologic features, and determine if the parasites isolated from dogs are genetically similar to those found in humans.
This is a cross sectional study in which 61 dogs living in an endemic area of CL and presenting ulcerated lesions were evaluated. Detection of L. braziliensis DNA by polymerase chain reaction (PCR) in skin biopsies, serology and leishmania skin test (LST) with soluble L. braziliensis antigen were performed. The clinical and histopathologic features were described, and we compared the genotypic characteristics of isolates obtained from dogs and humans.
The sensitivity of the three tests together to detect exposure was 89% and the concordance between the tests was high. The skin lesions were most frequent in the ears, followed by scrotal sac. The PCR was positive in 41 (67%) of animals, and the lesions in the snout, followed by the scrotal sac and ears were the sites where parasite DNA was most detected. There were genotype similarities between L.braziliensis isolates from dogs and humans.
The high frequency of L. braziliensis infection in dogs with ulcers and the similarities between the isolates of L. braziliensis and cutaneous leishmaniasis in dogs and humans in an endemic area of TL, raise the possibility of an important role of dogs in the transmission chain of L. braziliensis.
| Cutaneous Leishmaniasis (CL) is the major clinical forms of tegumentary leishmaniasis. More than 1 million of people develop cutaneous leishmaniasis worldwide and Leishmania braziliensis is the main causal agent of the disease in Latin American. Wild and domestic animals play an important role in the maintenance of the parasites that are transmitted to humans by infected sandflies. There is no vaccine for leishmaniasis and control measures are not effective in part because it is not known the reservoirs of L. braziliensis. Here, we show that dogs, living in houses in an area of L. braziliensis transmission presented cutaneous and mucosal ulcers similar to the one presented by humans. In all the houses with canine CL there were active or past history of cases of human CL, and L. braziliensis isolated from dogs were genetically similar to the one from humans. These data are in favor of the participation of dogs in the transmission of L. braziliensis and may have a great importance in the control of the expansion of L. braziliensis infection.
| Leishmaniasis is an antropozoonosis caused by the protozoa of the genus Leishmania, which is transmitted to humans by infected sandflies. Leishmaniasis is classified as visceral leishmaniasis (VL) and tegumentary leishmaniasis (TL). Approximately 20 Leishmania species may cause human disease. In the New World, VL is caused by Leishmania infantum and TL is mainly caused by Leishmania Viannia braziliensis, Leishmania Viannia guaynensis and Leishmania mexicana mexicana [1]. Foxes and the dogs are the most common wild and urban reservoir for L. infantum, and play a key role in the persistence of the disease [2]. Unlike VL, very little is known about the wild and urban reservoirs of parasites that cause TL in the New World. L. braziliensis is the most important causal agent of leishmaniasis in Latin America. Cutaneous leishmaniasis (CL) is the most frequent form of TL, occurring in more than 90% of patients. Additionally, about 3% of patients infected with L. braziliensis develop mucosal leishmaniasis and 2% disseminated leishmaniasis [3–5]. Amastigote forms of Leishmania have been documented in tissue from dogs, donkeys, horses and rats in endemic areas of L. braziliensis, raising the possibility of the role played by these animals in L. braziliensis transmission [6, 7]. In endemic areas of CL the exposure of dog to Leishmania sp have been documented by serologic tests, identification of amastigotes in biopsies of cutaneous lesions, or isolation of parasite in aspirated material from skin cutaneous ulcers [8]. However, the identification of the species of Leishmania was performed in only a few reports and there is a lack of studies describing the clinical and histopathologic features of the L. braziliensis infection in dogs. Unlike canine VL, a disease that is well characterized by different clinical forms, including asymptomatic, oligosymptomatic, and symptomatic clinic canine VL [9], there is no clinical and histopathologic characterization of TL in dogs.
Currently, the most studies included just a small number of animals evaluated and without identification of the parasite species [6,10]. Recently in the southern region of the state of Bahia, Brazil, a known endemic area for L. braziliensis, 560 dogs were analyzed for the presence of skin lesions of CL. Anti-Leishmania antibodies and a polymerase chain reaction (PCR) for detection of L.braziliensis DNA were performed. Specific primers were used to differentiate between L. braziliensis [11] and L. infantum [12]. In this study, 32 (6%) of the 560 dogs presented lesions suggestive of CL, and the PCR for L.braziliensis was positive in 54.7% of 413 skin biopsies obtained from dogs [13].
Genetic differences from isolates of Leishmania belonging to the same species have been widely documented [14–16]. Moreover, L. braziliensis is polymorphic and genotypic differences are associated with different clinical forms of the disease [17,18]. Thus, it is important in epidemiologic studies to determine if the species found in humans are similar to those found in animals, which may have the potential to participate in the transmission chain. Additionally, polymorphisms of L. braziliensis isolates from dogs have been detected [19]. However, no studies have been performed to evaluate if isolates of L. braziliensis from dogs are similar to the parasites isolated from humans. These data clearly indicate the need for studies that characterize canine cutaneous leishmaniasis and the role of dogs as a reservoir of L. braziliensis.
The aim of the present study was to determine the frequency of L. braziliensis infection in dogs with cutaneous and mucosal ulcers in an endemic area of human cutaneous leishmaniasis. Further, the study aimed to describe the clinical manifestations and the histopathology features of canine CL caused by L. braziliensis, and to determine if the parasites isolated from dogs are genetically similar to those found in humans.
This study was performed in strict accordance with the recommendations of Brazilian Federal Law on Animal Experimentation. The protocol was approved by the Ethics Committee for the Use of Animals in Research (CEUA license number: 015/2017) from the Gonçalo Moniz institute (IGM-FIOCRUZ). The owners of the dogs gave written informed consent prior to sample collection and the skin sampling procedures were performed under sedation. No adverse events were recorded during the sample collection.
The study was conducted in the village of Corte de Pedra, Bahia, Brazil, where TL due to L. braziliensis is endemic. The village is characterized by isolated sites of secondary forest, with agricultural activities providing the main source of income for the majority of the population. Agricultural work increases exposure to L. braziliensis through increased contact with Lutzomyia (Nyssomyia) whitmani and Lutzomyia (Nyssomyia) intermedia sandflies, which are the vectors of L. braziliensis in the region [20].
This is a cross sectional study to determine the frequency of canine leishmaniasis caused by L.braziliensis infection in dogs with ulcerated skin or mucosal lesions, to describe the clinical and histopathologic features of canine leishmaniasis caused by this species, and to determine the positivity of different diagnostic tests for L. brazilensis in these animals. Moreover, we compared genotypically the isolates of L. braziliensis from dogs with the isolates from human CL.
The 50 residences closest to the medical clinic of Corte de Pedra who had dogs with ulcerated lesions participated in the study. The selection of the dogs was based on the information by mouth about the presence of dogs with ulcerated lesions, initially in the houses close to the Health Post and later in four neighborhoods close to the Heath Post.
There were 61 dogs with lesions in the 50 houses. Initially, we explained to the dogs’ owners the objectives of the study and the informed consent form was read and signed by them. A questionnaire was answered about demographic characteristics of the animals, duration of the lesion and occurrence of previous or present cases of human disease in the house. The dogs were then immobilized, physical examination was performed and blood (8 mL) from the lateral saphenous vein was obtained.
After the area of the lesion was cleaned with alcohol and lidocaine for anesthesia was applied, a skin biopsy of the ulcer was obtained with a punch of 4 mm and added to Eppendorf tubes containing RNA Later Solution (Ambion, Life Technologies, Thermo Fisher Scientific, USA). In animals that had more than one lesion we choose the greatest ulcer to obtain the skin biopsy.
In about half of the animals, the biopsy was divided in two pieces being one added to formaldehyde and other to the RNA Later Solution.
Parasites were genotyped according to the haplotypes of polymorphic nucleotides in the locus CHR28/425451, previously shown to distinguish L. (V.) braziliensis strains in Corte de Pedra [27]. Primers 5´:TAAGGTGAACAAGAAGAATC and 5´:CTGCTCGCTTGCTTTC were used to amplify a 622 nucleotide-long segment in CHR28/425451 from parasite genomic DNA as previously described [27]. Amplicons were cloned using the Original TA Cloning Kit pCR 2.1 VECTOR (Invitrogen, Thermo Fisher Scientific Co., MA, USA), according to manufacturer’s instructions. Briefly, the amplicons were inserted by overnight ligation into PCR 2.1 plasmids, which were used for chemical transformation of competent DH5α Escherichia coli. Plasmid minipreps were generated from four recombinant bacteria colonies per study isolate [28]. Amplicon cloning was confirmed by digestion analysis, using Eco RI restriction endonuclease (Invitrogen). Plasmid inserts were sequenced by the Sanger method with primers complementary to the M13 vector sequences. Sequencing was performed at Macrogen Inc. (Seoul, South Korea). Mega 5.0 software [29] was used to align the sequences with the CHR28/425451 clones obtained from the panel of L. (V.) braziliensis parasites, in order to determine the SNP/indel haplotypes detectable in each study isolate.
The study consisted of a panel of two groups of samples, 29 L. braziliensis isolates obtained from the dogs included in this study and 113 L. braziliensis isolates obtained from human beings in 2008–2011 periods.
In order to check whether L. braziliensis strains circulating among dogs in Corte de Pedra might be shared by human CL patients in that region, the 600 base pairs long locus that starts at nucleotide position 425,451 on the parasite’s chromosome 28 (i.e. CHR28/425451) was PCR amplified, cloned, sequenced and compared across a panel of isolates of dog or human origin. For analysis of the nucleic acid sequences of the polymorphic locus CHR28/425451, the consensus sequence at the locus explored from the CL sample (i.e., 2008–2011) was first determined, which was used to compare the different L. braziliensis samples obtained from 29 animals. Then, the sequences were analyzed for the identification of the occurrence of SNPs and/or indels for the identification of polymorphism alleles. We defined polymorphism as a single difference between the sequences of the evaluated isolates and polymorphic allele as a linear DNA sequence of the locus studied, detected in more than one clone per parasite isolate, and in more than one isolate of L. braziliensis in our study sample. The frequency of distinct alleles was determined in each isolate of L. braziliensis obtained from the dog biopsy for the locus CHR28 / 425451. The alignment between the isolates from dogs and humans showing the polymorphisms is presented in S1 Fig.
The distribution of variables such as age, duration of disease (years), number of lesions and site of injury were expressed by mean and standard deviation and were analyzed by the student's T-test. The Chi-square and Fisher's exact tests were used for comparisons of proportions between groups. The comparative analyzes of the diagnostic tests were performed by the Kappa Index of concordance. A p value of <0.05 was considered statistically significant.
The 61 dogs with skin lesions enrolled in this study lives in 50 houses. Twenty animals (33%) presented more than one cutaneous lesion and 6 (9.8%) had also mucosal leishmaniasis. The demographic and clinical features according to the positivity of the PCR are presented in (Table 1). The percentage of males was higher in the group with positive PCR (p<0.008) but there was no difference between age, duration of illness and number of lesions with regard to the positivity of the PCR. The site of the largest lesion in those animals with a positive PCR was in the scrotal sac, followed by the ears and in six animals the lesions were in the muzzle. The PCR was positive in the lesion tissues of 41 (67%) of the 61 animals.
All the lesions were well limited ulcers with raised borders (Fig 1). The pictures are from six different animals with ulcers located in the scrotal sac (Fig 1A and 1B), in the nose (Fig 1C and 1D) and in the ears (Fig 1E and 1F). In all of these lesions, DNA of L. braziliensis was documented in the biopsies. The age of the animals presented in these pictures ranged from three years (Fig 1B) to 10 years (Fig 1C).
The sensitivity of all the three tests together to detect exposure to L. braziliensis was 89%, and the concordance between the tests was high. For this analysis, we used the PCR test as the gold standard. The concordance for PCR and LST and PCR and serology was considered substantial (κ = 0.597 and κ = 0.674, respectively). In the 41 animals with positive PCR, antibodies were detected in 37 (90%) and the LST was positive in 32 (78%). In the 41 animals with positive serology, the LST was positive in 32 (78%). The age of the 54 dogs with at least one positive test was 6 ± 3.2 years and among the seven animals without evidence of exposure was 3 ± 1.9 years (p = 0.02). The illness duration in the two groups was of 210 (90–547) and 75 (38–296) years respectively (p = 0.12).
The histopathology analysis of the cutaneous ulcer border was performed in 35 animals, (Table 2).
In 26 animals (74%), amastigotes were documented, and in nine (26%) parasites were not detected. However, the histopathologic features were similar in those with or without evidence of amastigotes. In all the animals, there was a chronic inflammation with lymphocytes, plasma cells, macrophages infiltration and granulation tissue. The inflammation was not associated either with the presence of parasites, necrosis and fibrosis were documented (Fig 2).
An inflammation predominantly moderate or mild was observed in 77% of the biopsies with the presence of amastigotes, and a similar finding was detected in 89% of the biopsies negative for parasites. Granuloma was observed in only three (8%) of the animals, two of which were PCR positive. Focal necrosis was observed in 42% and 55% in the biopsies with or without amastigotes, respectively. Presence of fibrosis was observed in 75% of the biopsies, and intense or moderate fibrosis was similar in animals with or without parasites.
In order to check whether L. braziliensis strains circulating among dogs in Corte de Pedra might be shared by human CL patients in that region, the 600 base pairs long locus that starts at the nucleotide position 425,451 on the parasite’s chromosome 28 (i.e. CHR28/425451) was PCR amplified, cloned, sequenced and compared across a panel of isolates. After alignment of CHR28/425451 sequences, a total of 20 different alleles could be identified in parasites isolated from dogs as determined by their SNP and indel contents, while seven could be identified in L. braziliensis obtained from human beings. Overall, 24 different alleles could be enlisted, since parasites from human beings and dogs shared three distinct alleles, which were based on the contents of positions 30, 286 and 545 within the locus that could be described as TT-, CCT and CC-. The frequency distribution of seven alleles in L. braziliensis locus CHR28/425451 among patients and dogs are shown on (Table 3).
The frequency of isolates presenting the alleles CCT was high in both dogs and humans, and alleles TT- were present similarly in isolates of both humans and dogs.
The knowledge of wild and domestic reservoirs of Leishmania are important to understanding the dynamic of the transmission of this protozoa and to the establishment of control measures, aimed to decrease transmission and consequently decrease the appearance of disease. While the role of dogs in the epidemiology of L. infantum infection is well known, little is known about the reservoirs of L. braziliensis. Here, we show that in a highly endemic area of L. braziliensis, dogs present ulcerated lesions typical of CL, and have evidences of L. brazilensis infection by documentation of DNA of L. braziliensis, documentation of parasites in histopathology examination, positive serologic test and or a positive leishmania skin test.
There are previous publications calling attention to the presence of dogs presenting ulcerated lesions with evidence of Leishmania infection or even documentation of parasites in bone marrow and spleen of asymptomatics dogs in areas of L. braziliensis transmission [6, 29, 30]. However, the majority of these studies lack documentation that L. braziliensis was the causal agent of the infection [8, 31–39]. Here, evaluating 61 dogs with ulcerated lesions, evidence of leishmaniasis infection was observed in 89%, and 67% of the animals had detection of DNA of L. braziliensis. The clinical characteristic of the lesion was a well limited ulcer with raised borders similar to the ulcers typically found in human CL due to L. braziliensis. The presence of ulcers mainly in the scrotal sac was likely due to this area being exposed to sandflies bites. The long duration of the disease in dogs differs from what has been observed in humans with L. braziliensis. The short illness duration in humans could be explained in part by the seek for medical attention and use of therapy for leishmaniasis. But, based in studies in which no therapy or use of placebos were administrated in clinical trials with human cutaneous ulcers caused by L. braziliensis, the lesions were healed within one year of illness duration [40–43]. In contrast, in this study, the majority of the dogs had illness duration for more than six months and some of them had the disease for more than three to five years. These data support the argument that dogs keep the illness for a long period of time, and the active disease may increase their ability to transmit the infection.
The histopathology features of the dogs with ulcerated lesions was characterized by lymphocytes, plasma cells and macrophages infiltration as it is observed in human CL [6,26]. Nevertheless, they differ from the lesions in humans because they have less inflammation and more vascularization than human lesions. The similarities between the histopathologic findings in animals with positive or negative PCR and in dogs with or without evidence of amastigotes support the hypothesis that likely all the animals in the present study may have been infected with Leishmania. The observation that the intensity of the inflammatory reactions was moderate or light in the majority of the animals may be explained by the long illness duration of the disease and the persistence of the lesions for many years.
We have previously shown that L. braziliensis in Corte de Pedra consists in a complex population made of several different strains of the parasite [18]. We further extended this understanding after cloning and comparing the sequences of several loci in different chromosomes of the L. braziliensis of Corte de Pedrda [27]. In that study, we detected six loci that presented two or more alleles in that protozoa population. In that and follow-up research, we successfully employed the 500 base-pairs locus that starts at nucleotide position 425,451 of L. braziliensis chromosome 28 to test association between parasite genotype and outcomes of leishmaniasis [27,44,45]. In the current study, we explored that same locus to determine if parasites that infect the dogs presented genotypes similar to those that infect humans.
The observation that the frequency of the alleles TT- and CCT was similar in L. braziliensis isolated from dogs and humans suggests that L. braziliensis from humans and dogs share, at least in part, similar genotype profile in Corte de Pedra. We are aware that in addition to these observations others studies need to be performed to better characterize the importance of dogs and canine TL in the transmission of L. braziliensis. However, it is relevant that additionally to these findings regarding the similarities between the genotypic characteristics of humans and dogs isolates, active disease or past history of CL was documented in all houses with dogs with TL. Specifically, there were active or past history of CL in 93 individuals living in the 50 houses where canine TL was observed.
This study shows that in an area of L. brazilensis transmission dogs presenting cutaneous ulcers are likely infected by L. brazilensis. The clinical and histologic features of canine CL were similar to the observed features in humans, but dogs may remain with the disease for a longer period of time. The occurrence of humans with CL or previous history of CL, and dogs with canine CL in the same house and the similarities between the parasites isolates from dogs and humans strongly argue in favor of the possibility that dogs participate in the transmission of L. braziliensis.
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10.1371/journal.pntd.0001993 | Field Evaluation and Impact on Clinical Management of a Rapid Diagnostic Kit That Detects Dengue NS1, IgM and IgG | Dengue diagnosis is complex and until recently only specialized laboratories were able to definitively confirm dengue infection. Rapid tests are now available commercially making biological diagnosis possible in the field. The aim of this study was to evaluate a combined dengue rapid test for the detection of NS1 and IgM/IgG antibodies. The evaluation was made prospectively in the field conditions and included the study of the impact of its use as a point-of-care test for case management as well as retrospectively against a panel of well-characterized samples in a reference laboratory.
During the prospective study, 157 patients hospitalized for a suspicion of dengue were enrolled. In the hospital laboratories, the overall sensitivity, specificity, PPV and NPV of the NS1/IgM/IgG combination tests were 85.7%, 83.9%, 95.6% and 59.1% respectively, whereas they were 94,4%, 90.0%, 97.5% and 77.1% respectively in the national reference laboratory at Institut Pasteur in Cambodia. These results demonstrate that optimal performances require adequate training and quality assurance. The retrospective study showed that the sensitivity of the combined kit did not vary significantly between the serotypes and was not affected by the immune status or by the interval of time between onset of fever and sample collection. The analysis of the medical records indicates that the physicians did not take into consideration the results obtained with the rapid test including for care management and use of antibiotic therapy.
In the context of our prospective field study, we demonstrated that if the SD Bioline Dengue Duo kit is correctly used, a positive result highly suggests a dengue case but a negative result doesn't rule out a dengue infection. Nevertheless, Cambodian pediatricians in their daily practice relied on their clinical diagnosis and thus the false negative results obtained did not directly impact on the clinical management.
| Dengue is a potentially life-threatening viral disease. Symptoms are often not specific hence the importance to confirm the diagnosis during the early stage of the disease. Nevertheless, until recently only specialized laboratories were able to confirm dengue diagnosis. The discovery of the NS1 protein as a marker of infection has allowed the development of point-of-care tests for a rapid diagnosis confirmation. These tests have previously been evaluated by laboratories, but their performances have never been assessed in field conditions. In this study we evaluated the performance of SD Bioline Dengue Duo kit when tests were performed by hospital laboratories staff in a dengue hyper-endemic country. We also assessed the impact of the test results on the clinical management decision. The combination of NS1 test with antibodies detection improved the performance, though discordances on IgM and IgG results were observed between the hospitals and the national reference laboratories. Physicians treated patients according to their clinical diagnosis and did not take negative results into consideration.
| The World Health Organization estimates that 50 million dengue infections occur annually and approximately 2.5 billion people live in area at high risk of infection. These areas are located in tropical and sub-tropical regions in South East Asia, Africa, Eastern Mediterranean, Western Pacific, Central and South America. The number of reported cases increased approximately 30 times over the last 50 years [1] and this could be in relation to many factors including population growth, urbanization, failure to control mosquito vectors, etc. [2].
Dengue is a viral disease transmitted by Aedes mosquitoes, principally Ae. aegypti. Dengue virus (DENV) belongs to the family Flaviviridae, genus Flavivirus. There are 4 antigenically and genetically distinct serotypes (DENV-1, -2, -3 and -4). In human, the virus can cause a spectrum of illness ranging from asymptomatic infection or self-limiting influenza-like illness (dengue fever or DF) to life-threatening disease associated with vascular leakage, hemorrhage (dengue hemorrhagic fever or DHF), potentially leading to vascular shock (dengue shock syndrome or DSS).
There is currently no specific treatment available for dengue. An early diagnosis is nevertheless very important for efficient clinical management in order to cure or prevent life-threatening complications. In addition, accurate and early diagnosis directs clinical attention to warning signs of an evolution to severe forms and avoids unnecessary use of antibiotics. A range of serological and virological diagnostic methods are available but most of them require specialized laboratory equipment, experienced personnel and are time consuming which is not adapted for a field and point-of-care use. Serological diagnosis by ELISA or rapid diagnostic tests (RDTs) is technically easy to perform and provides fast results but requires most of the time paired sera to definitively confirm the diagnosis [1], [3].
Detection of the NS1 antigen in the blood is a recent and very popular diagnostic method. This viral protein is secreted in the blood and can be detected by ELISA or immunochromatographic tests from the first day of fever and up to 14 days after infection [4]–[7].
The purpose of this study was to evaluate a commercial rapid dengue diagnostic kit, the SD Bioline Dengue Duo device (Standard Diagnostic Inc., Korea), in particular in point-of-care applications, and to evaluate the impact of the results of this combined test on the clinical management decision. The SD Bioline Dengue Duo kit is composed of 2 tests designed to detect DENV NS1 antigen (first test) and anti-DENV IgM/IgG (second test) in serum, plasma or whole blood. The kit evaluation was double. Firstly, the use of the test in the field was for the first time evaluated during a prospective study in 2 Cambodian provincial hospitals. The results obtained in the hospital's laboratories were then compared with those reported with the same samples by a national reference laboratory at Institut Pasteur in Cambodia (IPC). We also investigated how the results of this point-of-care test designed to assist clinical management were perceived and subsequently incorporated into the clinical management decision of physicians from 2 hospitals during a dengue epidemic. Secondly, a more usual retrospective case-control evaluation against reference methods was performed at IPC in order to assess the kit performances in the context of a dengue-endemic South-East Asian country.
Patients were enrolled in the pediatric wards of Kampong Cham and Takeo provincial hospitals during the 2011 dengue epidemic in Cambodia i.e. between June and October 2011. Patients presenting spontaneously to these hospitals or referred by health centers with a history of fever during the previous 7 days and at least one of the following symptoms: rash or severe headache or retro-orbital pain or myalgia or joint pain or bleeding, were examined by physicians who decided whether or not the child should be hospitalized. When the number of beds available was limited, priority was obviously given to the most severe cases. In each hospital, a maximum of 10 hospitalized patients, randomly selected, were enrolled weekly. Patient's information and clinical data were collected by physicians using a specific case report form and blood samples were taken at the time of hospital admission (early/acute specimen) and discharge (convalescent/late specimen). Patients with incomplete test kit results, missing blood samples and incomplete clinical records were excluded.
The panel used for the retrospective laboratory evaluation of the kit performances consisted of 157 samples collected in 2011 during the field prospective evaluation and tested negative or positive by the reference methods available at IPC completed with an additional 167 samples selected from IPC's dengue laboratory's biobank (samples collected between 2008 and 2010). Positive samples were selected in order to obtain an evaluation panel as balanced as possible in terms of DENV serotypes, day of collection after onset of fever (DAOF), anti-DENV antibodies titer and immune status (primary/secondary infections). Negative samples were selected from patients presenting with a non-dengue febrile illness and also from pregnant women.
For the field prospective evaluation, a written consent was signed by the children's legal representatives before enrolment. This study was approved by the Cambodian National Ethics Committee. The use of stored samples from IPC's biobank was also approved by the Cambodian National Ethics Committee.
The SD Bioline Dengue Duo kits were provided by Standard Diagnostics (Kyonggi-do, Korea) and tests were performed according to the manufacturer's instructions. For the prospective study, only acute blood samples were tested with the kit in hospitals as well as at IPC.
At IPC, laboratory diagnosis was based on RT-PCR, isolation of DENV after inoculation into mosquito cell lines, detection of anti-DENV IgM and measure of an increase of anti-DENV antibodies titer measured by hemagglutination inhibition assay (HIA) between acute and convalescent sera.
RT-PCR was performed after viral RNA extraction from acute serum samples using QIAmp Viral RNA Mini kit (Qiagen, Hilden, Germany). Either a conventional nested RT-PCR according to Lanciotti et al. [8] protocol and modified by Reynes et al. [9] or a real-time multiplex RT-PCR based on the technique developed by Hue et al. [10] was performed.
DENV was isolated on C6/36 cells and the virus serotype identified by immunofluorescence assay using monoclonal antibodies as described previously [11].
An in-house IgM capture Enzyme-Linked Immuno-Sorbent Assay (MAC-ELISA) was used to detect anti-DENV and anti-Japanese Encephalitis virus (JEV) IgM as describe previously [11]. A result was considered positive for dengue when the optical density (OD) was higher than 0.1 for the DENV IgM and when the OD of the anti-DENV ELISA was higher than the OD of the anti-JEV ELISA.
HIA followed the method described by Clark and Casals [12] adapted to 96-well microtiter plate. Primary or secondary acute dengue infection was determined by HI titer according to criteria established by WHO [13]. In brief, the patient was defined as having a primary infection when the convalescent serum had a HI titer ≤2560 associated with a fourfold rise of the titer between the acute and convalescent sera (collected with a time interval of at least 7 days). When the convalescent serum had an HI titer >2560, the patient was defined as having a secondary dengue infection.
All early samples were tested by PCR, viral isolation, IHA and MAC-ELISA whereas late samples were only tested by HIA and MAC-ELISA.
Confirmed and suspected dengue cases were defined according to WHO guidelines [1]. A confirmed case was defined by a RT-PCR and/or a culture positive result and/or an IgM seroconversion in paired sera and/or a fourfold antibodies titer increase measured by HIA in paired sera. A probable dengue infection was defined by an HI antibody titer >2560 in paired sera without a fourfold increase or IgM positive result in the acute serum [1].
At IPC, technicians were blinded for the results of the kit evaluated as well as for the results of gold standard tests. In hospitals, the staff performing rapid diagnostic tests was blinded for the results obtained with these tests as well as for the results of the gold standard assays.
Each clinical record contained the complete medical data recorded at the time of admission and the complete follow-up of the patient during the hospitalization (temperature, blood pressure, pulse, diuresis, medical prescriptions, etc.) until discharge. These data were anonymized by the physicians for the purpose of the analysis.
Statistical analysis was performed using STATA version 11.0 (StataCorp, College Station Texas, USA). Significance was assigned at P<0.05 for all parameters and were two-sided unless otherwise indicated. Uncertainty was expressed by 95% confidence intervals (CI95).
For the prospective study, agreement between hospital's laboratories and IPC laboratory's data was measured by agreement percentage and Kappa (κ) coefficient.
For the prospective study, sensitivity and specificity obtained when tests were performed at hospitals were compared with those obtained at IPC with McNemar test. Positive and negative predictive values (PPV and NPV) were compared with Fisher exact test. For the retrospective laboratory study and for the analysis of medical records Fisher exact test was used.
During the retrospective laboratory evaluation, sensitivity was calculated according to infecting serotype, DAOF, immune status and antibodies profiles. Four different antibodies profiles were arbitrarily defined according to HIA and MAC-ELISA results: profile 1, low HI titer (<640) and negative MAC-ELISA; profile 2, low HI titer and positive MAC-ELISA; profile 3, high HI titer (≥640) and negative MAC-ELISA; profile 4, high HI titer and positive MAC-ELISA.
The medical records of 129 patients (82.2% of all patients enrolled) were provided by the two hospitals and subsequently analyzed. All the 66 patients who tested positive for acute dengue infection using the IPC gold standard test were also clinically diagnosed by the physicians as dengue cases, with or without co-infection (63 and 3 patients, respectively). One patient with a laboratory-suspected DENV infection as well as two children who tested negative were clinically diagnosed as non-dengue febrile illness (Table 1).
All patients received a treatment based on WHO 2009 recommendations, i.e., intravenous fluid therapy with 0.9% saline, Ringer's lactate or Ringer's acetate with or without dextrose, paracetamol if fever and oral rehydration solution or other fluids containing electrolytes and sugar when possible. Patients in circulatory shock received dextran, O2 and blood transfusion when necessary. Twenty-nine patients (27.7%) also received antibiotics. The prescription of antibiotics was justified by the phisicians in the medical records of 11 patients because the following diagnoses: 4 dysenteric syndromes with suspicion of typhoid fever, 3 meningitis or meningo-encephalitis, 1 suspicion of nosocomial infection, 2 pharyngitis and 1 bronchiolitis. Among the 90 patients with a positive NS1 and/or IgM and/or IgG test, 17.8% (16/90) were treated with antibiotics. Out of 39 patients who tested negative by the RDT, 13 (33.3%) also received antibiotics. The comparison of antibiotic prescription between both groups was at the limit of significance (p-value = 0.067). There was no difference in the duration of antibiotic therapy between patients with a positive test and those with a negative test (p-value = 0.216).
Among the 16 positive patients who received antibiotics, only 7 (43.7%) had their antibiotic therapy stopped once the point-of-care kit tested positive for dengue. Among the 13 patients with a negative result who received antibiotics, 8 (61.5%) had their antibiotic therapy stopped once the test was performed. The decision to maintain or discontinue the antibiotic therapy was not affected by the result of the RDT (p-value = 0.338).
Early management of patients with dengue infection is essential to ensure a favorable evolution of the disease and prevent the occurrence of severe forms. Until recently an early confirmed diagnosis was only achievable in specialized laboratories. The discovery of the NS1 protein as an early marker for DENV infection, especially in RDT format, now allows dengue diagnosis during the early phase of the disease, even in laboratories with limited equipments and human resources. Evaluations are required to ensure that these tests are suitable for diagnosis and clinical management or epidemiological surveillance and outbreak investigations. Different methodologies can be used: laboratory-based evaluations (or retrospective evaluations) and field evaluations (or clinical-based/prospective evaluations) [14]. Retrospective evaluations are easy to perform but tend to overestimate tests accuracy. Prospective evaluations allow determination of PPV and NPV with tests performed on patients in the real clinical settings. However, accuracy of diagnostic tests estimated by prospective evaluations could be biased due to imperfect gold standard in the prospective clinical setting. In our study we combined both prospective and retrospective evaluations. The retrospective part was added in order to better understand the results obtained in the field during the prospective study.
Since the two test kits of the SD Bioline Dengue Duo combo test do not give exactly the same information, the NS1 assay was initially assessed alone in the prospective as well as in the retrospective study. If a positive NS1 test can confirm a dengue diagnosis, this is not the case for IgM and IgG tests as the antibodies remain detectable for months and thus a positive result obtained on a single blood specimen is only suggestive of a dengue infection. Indeed, to confirm an acute dengue infection by serology, an IgM seroconversion or a four-fold increase of IgG antibody titers in paired sera must be demonstrated (which cannot be done with the RDT kit as result is only qualitative) [1]. By evaluating separately, but in parallel, the NS1 test and the serological kit, we estimated the ability of the test to both suggest and confirm a dengue infection.
During the prospective study, the sensitivity of the SD Bioline Dengue Duo NS1 when performed at the hospitals was only 44.5% to confirm dengue infections in children hospitalized for dengue-like illness during the epidemic season. The tests were carried out in laboratories equipped for routine medical biology. Out of the 127 patients included in the prospective evaluation, 70 (54.7%) had an HI titer ≥640 which could probably explains such a poor sensitivity. The retrospective study helps to understand why the sensitivity was limited. It suggested that the presence of high level of anti-DENV HI antibodies in the sample was a major factor for sensitivity decrease. Indeed, while a sensitivity >80% was obtained with samples containing no or low HI antibodies titer (<640), the sensitivity dropped to 37% when the HI titer was ≥640. Almost 86% of the samples with a high HI titer issued from patients with a secondary infection. Since HI titer reflects mainly IgG response, the poor sensitivity observed during secondary infections is probably directly linked to the high IgG titer. Similar observations were already made by other authors. In Vietnam, the same NS1 test demonstrated a sensitivity of 24.6% for samples positive for IgG by GAC-ELISA and a sensitivity of 77.3% in sera negative for IgG [15]. In Colombia, Osario et al. reported an even lower sensitivity (IgG negative: 65.6%, IgG positive: 15.6%) [16]. Of note, the methods used for IgG detection in these evaluations were all different and rather than giving the real performance of the kit, the data indicate a global trend to a lower sensitivity when IgG titers increase.
As others [16], [17], we observed that the sensitivity of this test decreased when the window of time between onset of fever and sampling increased. This was expected since the IgG titer also increased with the time. Finally, a higher IgG titer also characterizes the secondary dengue infections and the better sensitivity of the NS1 in primary infections was also already reported [15]–[17].
The performances of the NS1 test reported here as well as by other retrospectives studies are close to those observed with other commercial NS1 RDTs [15], [18]. A major value of the kit marketed by SD is the combination of the NS1 test with an anti-DENV antibodies detection kit. Indeed, the serological results improved the sensitivity by compensating for the loss of sensitivity usually observed with the NS1 test when used alone in the presence of specific anti-DENV antibodies. During the prospective evaluation, we demonstrated that the addition of IgM and IgG results to the NS1 data was only associated with a slight non-significant decrease of the specificity. However, this result should be interpreted with caution as the number of negative patients included was relatively small. In addition, the relatively low overall performance of the IgM/IgG test could well be partially due to imperfect gold standard tests. In the retrospective study, we did not observed any cross-reactivity with Chikungunya virus, Orientia tsutsugamushi or Plasmodium sp.. However when evaluating the SD Bioline Dengue Duo kit, Blacksell et al. [18] reported 12.2% (10/82) of cross-reactivity with Chikungunya virus, 12.5% (1/8) with Orientia tsutsugamuhi and 100% (1/1) with Plasmodium sp. When evaluating only the IgM part of the kit, Hunsperger et al. [19] reported around 35% of IgM cross-reactivity with malaria as well as some false positive results with leptospirosis, tuberculosis and West-Nile infections.
During the prospective evaluation, the PPV value of the NS1 test was 98.2%, suggesting that the probability to correctly confirm a dengue infection was very high when the test was positive. When the test was used in combination, the PPV decreased only very slightly (NS1/IgM: 96.9%; NS1/IgM/IgG: 95.6%). Consequently, the NPV observed when the tests were performed in the hospitals was only 29% for the NS1 test alone and 56.8% for the combination test. In other words, the probability of truly exclude a dengue infection when the tests were negatives was low. These PPV and NPV results should be regarded with caution as they depend on the dengue disease prevalence that can be extremely different in other contexts and epidemiological situations. In this prospective study, the prevalence of dengue infection was very high (80.3%, 126/157) because the evaluation was performed during the peak epidemic season and only involved dengue suspect patients. Observing high prevalence of dengue infections in suspect patients hospitalized is common in Cambodia (87.8% of average between 2000 and 2008) and in neighboring countries like Vietnam (86.2% during a DENV-4 epidemic in 2002) [20], [21].
On the samples collected during the prospective study, the comparison of the results of the tests performed by technicians in hospital laboratories or by health workers who did not receive any specific training for the use of the kits with the results reported by the staff of the national reference laboratory at IPC demonstrated a moderate agreement with the serological tests and an excellent agreement with the NS1 test. Indeed, 49 discordant results between the hospitals and IPC were observed with the IgM/IgG test out of which 34 (69.3%) were positive at IPC but negative at the hospitals while 13 (26.5%) were negative at IPC but positive at the hospitals. These discrepancies could be explained if the reading was made before the recommended 15 minutes (leading to false negative results) or after the correct time (leading to apparition of unspecific bands) or because of problems with the interpretation of weak signals (faint bands). To evaluate if the issue was the interpretation of the faint bands, these data were removed from the analysis and a better agreement percentage and Kappa coefficient were obtained (82.0% vs 68.8% and 0.73 vs 0.55). A problem of reproducibility could also have accounted for some of the discrepancies observed. Nevertheless, in the case of bad reproducibility an equal number of discrepancies should have been observed in each laboratory which was not the case in our study. Moreover all tests were from the same manufacturing lot. During a malaria RDTs evaluation, misinterpretation of weak signal in the field had already been reported [22]. It was also reported that health workers in the field tend to read the results before the time recommended by the manufacturer [22], [23]. Despite its relative ease to use, the performances of the IgM/IgG RDT are obviously partially person-dependent, hence the importance of providing specific training or at least very clear pamphlets which could guide the health worker in its interpretations and expose the risks of false results when the recommendations are not strictly followed. On the contrary a very good agreement was observed with the NS1 test since the bands in this immunochromatographic device almost always appear very clearly. As the RDTs have a significant cost, promoting the use of these kit does only make sense if the health workers can perform the tests in good conditions, which seems to be sometimes challenging in intensive care units and pediatric wards that are often unable to cope during peak epidemics. Knowing these constraints and limitations, the manufacturer should be encouraged to correct, if possible, the reading issues of the serological test. The outcomes of the patients who were wrongly tested negative by the kit was a matter of concern as RDTs are designed for rapid diagnostic and to assist physicians in their decisions. Dengue is a life-threatening disease that requires specific clinical care. The analysis of the medical records demonstrated that physicians ignored the negative results and followed their clinical instinct as all patients who tested negative by RDT received an intravenous fluid therapy which is recommended in patients with warning signs [1] but which is also often administrated in mild cases to prevent complications. Similar observations were also made in the context of malaria RDTs use. Between 54% and 85% of the patients with negative malaria RDT results were treated with anti-malaria drugs in Nigeria, Tanzania, Burkina Faso, Philippines and Laos [22]–[25]. There are probably several reasons that could explain that physicians did not consider the negative results obtained with the RDT: the habit to rely mostly on clinical intuition explained by a frequent limited access to laboratory tests, some mistrust against a new test, the difficulties to understand the kinetic of the immune response during dengue infection and the significance of NS1, IgM and IgG test results, a high confidence in clinical diagnosis when children present to pediatric wards with dengue-like symptoms during the epidemic season (especially since the national virological surveillance confirms usually more than 80% of the dengue clinical diagnosis) [20], the fear that a misdiagnosed dengue infection evolves towards a DHF or a DSS while these complications are pretty easy to prevent with simple clinical management, etc. The SD Bioline dengue Duo test could have a better utility in smaller medical care structures, like health care centers and dispensary where the proportion of dengue among all febrile diseases is lower (e.g., 12% of all the febrile episodes in Kampong Cham province, 2006–2008) [11] and where routine hematology (e.g., hematocrit, platelet count) that could help to orientate the diagnosis are not often available.
One of the advantages to perform a rapid confirmatory diagnostic of dengue in the context of febrile illness is to avoid the unnecessary use of antibiotics. In the context of Cambodia, it seems the RDT results did not have a significant impact on the decision to start or discontinue an antibiotic therapy.
In an endemic country, especially in the context of an epidemic, it seems that the sensitivity of the NS1 RDT alone is too low and that only positive results should be taken into consideration. Nevertheless, the performances of the combined kits are good and these kits appear to be a useful tool for the clinicians as they can quickly confirm the diagnosis of dengue and therefore contribute to the an optimal clinical management of the cases and avoid an unnecessary use of antibiotics or other drugs which is important in the context of a developing country with limited resources.
In conclusion, we observed that for a patient presenting with dengue-like symptoms in a dengue-endemic/epidemic region, a NS1 positive result obtained with the SD Bioline Dengue Duo kit confirms a dengue diagnosis, an IgM and/or IgG positive result highly suggests dengue infection but a negative result doesn't rule out a dengue infection. We have also demonstrated that the performances of the test in the field were lower than the ones obtained in the more experienced hands of technicians working in a national reference laboratory. This suggest that even for a point of care test theoretically designed to be used by untrained staff, there is still a significant improvement of the performance of the test to expect if a proper training and a quality assurance program can be implemented. With the time, the trust of the physician will probably increase if the accuracy of the test improves. In general, manufacturers should always bear in mind that the ultimate goal of the RDTs is essentially to be used as a point-of-care test or in support of epidemiological investigation and as such should be easy to use, stable at room temperature but also not posing reading difficulties unless they can provide proper training and organize quality programs. More prospective field evaluations are still necessary now to better assess the interest to use such point-of-care tests in the real conditions that justified their development and to address some of the questions and concerns raised by this study.
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10.1371/journal.ppat.1004449 | Identification of the Microsporidian Encephalitozoon cuniculi as a New Target of the IFNγ-Inducible IRG Resistance System | The IRG system of IFNγ-inducible GTPases constitutes a powerful resistance mechanism in mice against Toxoplasma gondii and two Chlamydia strains but not against many other bacteria and protozoa. Why only T. gondii and Chlamydia? We hypothesized that unusual features of the entry mechanisms and intracellular replicative niches of these two organisms, neither of which resembles a phagosome, might hint at a common principle. We examined another unicellular parasitic organism of mammals, member of an early-diverging group of Fungi, that bypasses the phagocytic mechanism when it enters the host cell: the microsporidian Encephalitozoon cuniculi. Consistent with the known susceptibility of IFNγ-deficient mice to E. cuniculi infection, we found that IFNγ treatment suppresses meront development and spore formation in mouse fibroblasts in vitro, and that this effect is mediated by IRG proteins. The process resembles that previously described in T. gondii and Chlamydia resistance. Effector (GKS subfamily) IRG proteins accumulate at the parasitophorous vacuole of E. cuniculi and the meronts are eliminated. The suppression of E. cuniculi growth by IFNγ is completely reversed in cells lacking regulatory (GMS subfamily) IRG proteins, cells that effectively lack all IRG function. In addition IFNγ-induced cells infected with E. cuniculi die by necrosis as previously shown for IFNγ-induced cells resisting T. gondii infection. Thus the IRG resistance system provides cell-autonomous immunity to specific parasites from three kingdoms of life: protozoa, bacteria and fungi. The phylogenetic divergence of the three organisms whose vacuoles are now known to be involved in IRG-mediated immunity and the non-phagosomal character of the vacuoles themselves strongly suggests that the IRG system is triggered not by the presence of specific parasite components but rather by absence of specific host components on the vacuolar membrane.
| For some time we have studied an intracellular resistance system essential for mice to survive infection with the intracellular protozoan, Toxoplasma gondii, that is based on a family of proteins, immunity-related GTPases or IRG proteins. Immediately after the parasite enters a cell, IRG proteins accumulate on the membrane of the vacuole in which the organism resides. Within a few hours the vacuole membrane breaks down and the parasite dies. A puzzle is why this mechanism works on Toxoplasma, but only on one other organism among the many tested, namely the bacterial species, Chlamydia. What do these widely different parasites have in common that so many other bacteria and protozoa lack? Neither Toxoplasma nor Chlamydia is taken up by conventional phagocytosis. In this paper we suggest that this is an important clue by showing that a microsporidian, Encephalitozoon cuniculi, a highly-divergent fungal parasite, which also invades cells bypassing phagocytosis, is resisted by the IRG system. Therefore, we propose here the “missing self” principle: IRG proteins bind to vacuolar membranes only in the absence of a host derived inhibitor that is present on phagosomal membranes but excluded from the plasma membrane invaginated by IRG target organisms during non-phagosomal entry.
| The IFNγ-inducible, immunity-related GTPases (IRG proteins) are a family of proteins essential for innate resistance of mice against certain intracellular pathogens. Until now, the protozoon Toxoplasma gondii [1], [2], [3], [4], [5] and its very close relative, Neospora caninum [6], [7], as well as two strains of the bacterium Chlamydia [8], [9], [10], [11], [12] have been shown to be controlled by the IRG system. However, the IRG resistance system does not engage many other highly diverse organisms, including Salmonella, Listeria, Mycobacteria, Trypanosoma, Rhodococcus or Plasmodium, and the murine-specific strain of Chlamydia [reviewed in [13]].
From extended work in the Toxoplasma system, we and others have demonstrated that effector IRG proteins such as Irga6, Irgb6, Irgb10, Irgb2-b1 and Irgd relocalise from their cytosolic compartments to the cytosolic face of the parasitophorous vacuolar membrane (PVM) [4], [14], [15] in a GTP-dependent [16], [17] and cooperative manner [18]. In electron microscopy images, the PVM appears ruffled and vesiculated [3], [4], [19]. It is proposed that this action reduces the effective surface-to-volume ratio, putting the PVM under tension against the elastic cytoskeleton of the parasite and leading ultimately to its rupture [13]. Once exposed to the cytosol the parasite dies for unexplained reasons [3], [4], [20].
IRG proteins can be divided into the effector GKS subfamily, including the IRGA and IRGB proteins and Irgd (all carrying a canonical GxxxxGKS motif in the P-loop of the GTP-binding site) and the regulatory GMS subfamily, namely Irgm1, Irgm2 and Irgm3 (with a non-canonical GxxxxGMS motif) [21], [22]. GMS proteins populate the vacuoles of T. gondii or inclusions of Chlamydia either not at all (Irgm1) or only to a limited extent (Irgm2, Irgm3) [1], [4], [18], [23]; their function is to inhibit inappropriate GTP-dependent activation of the GKS proteins on host vesicular compartments in IFNγ-induced cells before the parasite enters [16].
It is not known why the action of IRG proteins is restricted to so few and so dissimilar parasitic organisms. We hypothesized that unusual features of the intracellular replicative niches of T. gondii and Chlamydia strains, neither of which resembles a phagosome, might hint at a common principle. To test this hypothesis we decided to examine cell-autonomous resistance in vitro to the microsporidian, Encephalitozoon cuniculi. The Microsporidia, recently re-classified as the earliest divergent group of the Fungi [24], [25], are abundant, obligate intracellular eukaryotic parasites of many diverse animal groups including mammals. E. cuniculi is a convenient representative since it is easily cultivated in vitro and its genome is fully sequenced, which is at 2.9 Mb one of the smallest known eukaryotic genomes [26]. E. cuniculi and its relatives E. hellem and E. intestinalis are common opportunistic pathogens for immunocompromised humans [27].
Microsporidia, including E. cuniculi, have a peculiar entry mechanism into host cells utterly unlike conventional phagocytosis. The thick-walled unicellular spore of E. cuniculi contains the organism itself (the sporoplasm) and a coiled proteinaceous tube, the polar tube, which can be suddenly extruded as a result of an osmotic stimulus and pushes a deep and narrow invagination in any adjacent host cell plasma membrane. The sporoplasm is then expelled through the polar tube and can be found deep in the host cytoplasm in an intracellular parasitophorous vacuole bounded by a membrane mainly derived from the host plasma membrane [reviewed in [28]]. The intracellular sporoplasm, now termed meront, divides repeatedly in its vacuole and eventually differentiates into large numbers of spores, finally lysing the host cell to release the mature environmentally-resistant spores [29]. By virtue of its remarkable non-phagocytic entry mechanism, this natural parasite of rodents and rabbits was an interesting potential target for the IRG resistance system, and it has been reported that IFNγ induces strong cell-autonomous immunity against this organism [30], [31], [32].
In the present study we show that the IRG system is indeed required for cell-autonomous resistance to E. cuniculi. We confirm the development of resistance following IFNγ treatment of fibroblasts and show that several IRG proteins localise to the PVM of intracellular E. cuniculi. Moreover, E. cuniculi infection triggered IFNγ-dependent reactive cell death, as seen earlier in T. gondii resistance [20]. To demonstrate the importance of IRG proteins in IFNγ-dependent growth restriction of E. cuniculi, we show that IFNγ-mediated resistance was completely lost in mouse cells that are deficient in the regulatory GMS proteins, Irgm1 and Irgm3, a double deficiency that inactivates the whole IRG system.
The phylogenetic range of the three classes of target organism of the IRG resistance system - bacteria, a fungus and protozoa - and their vastly dissimilar biology, strongly suggests that the specificity with which IRG proteins localise to parasitophorous vacuoles relates to a common characteristic of the host-derived membrane of such vacuoles rather than to a common ligand derived from the parasites themselves.
It was first reported in 1995 that IFNγ induction restricts microsporidial growth in mammalian cells in vitro using E. cuniculi infection of murine peritoneal macrophages [30]. Subsequent studies confirmed the suppressive effect of IFNγ on E. cuniculi growth as well as on E. intestinalis using murine peritoneal macrophages [31], [32], the murine enterocyte cell line CMT-93 and human enterocyte cell line Caco-2 [33] as well as primary human monocyte-derived macrophages [34]. Furthermore, IFNγ-deficient mice are susceptible to E. cuniculi and E. intestinalis infection [32], [35], [36], [37]. It is characteristic of the IRG resistance system that it is efficient in non-myeloid cells such as fibroblasts [1], [4], [38]. We therefore tested IFNγ-dependence of cell-autonomous resistance against E. cuniculi in primary mouse embryonic fibroblasts. IFNγ-induced and uninduced C57BL/6 mouse embryonic fibroblast (MEF) monolayers were infected with E. cuniculi spores and the replication of the parasites followed by immunofluorescence microscopy and Western Blot analysis. E. cuniculi was detected with an antibody directed against a cytoplasmic protein (mAB 6G2) of the earliest infectious stages (meronts), or an antiserum against the spore wall protein 1 (pAS anti-SWP1), which is synthesized later in infection [39].
A time series of 0.5 to 24 hours post infection showed continuous IFNγ-dependent loss of E. cuniculi meronts (determined by counting of 6G2-positive meronts per host nuclei). Meront numbers at the earliest time point measured (0.5 hours) were equivalent in IFNγ-treated and untreated cells showing that E. cuniculi invasion into the host cells was not significantly affected by prior IFNγ induction (Figure 1A). Next, we quantified not only single meronts, but also meronts that had replicated by binary fission (double meronts) and compared uninduced to IFNγ-induced MEF cells 24 hours post infection. Of the few surviving meronts at 24 h, very few had successfully divided in IFNγ-induced cells (Figure 1B).
In addition, Western Blot analysis of whole cell lysates from infected MEF cells showed that meront development as well as the formation of new spores was blocked by IFNγ (Figure 1C). In uninduced MEF cells, E. cuniculi-dependent protein bands were detected with the meront-specific antibody, indicative of replication, and with the spore-specific antiserum, indicative of maturation at 2 days post infection. The intensity of these parasite-specific bands further increased at 5 days post infection. In contrast, these bands could not be detected in E. cuniculi-infected IFNγ-induced cells either 2 or 5 days after infection. Taken together, IFNγ kills meronts, inhibits meront replication, and blocks spore formation of E. cuniculi in primary mouse embryonic fibroblasts.
When T. gondii infects IFNγ-treated mouse fibroblasts, the induced IRG proteins, especially the effector GKS proteins, accumulate on the PVM and lead to disruption of the vacuole [20]. To examine whether similar IRG-related processes might also occur on the microsporidian vacuole, we co-stained IFNγ-treated, E. cuniculi-infected, MEF cells with immunological reagents against individual GKS effector proteins (Irga6, Irgb6 and Irgd) as well against the GMS regulator proteins (Irgm1 and Irgm2) 24 h post infection (Figure 2). Some meronts were indeed coated with IRG proteins, but most were IRG-negative. Both Irga6-coated and uncoated vacuoles were found together in multiply infected host cells (Figure 2A). Irga6 and Irgb6 were found on vacuoles at higher frequencies, while Irgd and Irgm2 were found at lower but consistent frequencies (below 5%) (Figure 2B–E). Irgm1 was never found at the E. cuniculi PVM (more than 1000 meronts in three independent experiments were analysed). In IFNγ-induced cells, cytoplasmic Irga6 is predominantly in the GDP-bound form, but accumulates on the T. gondii PVM in the GTP-bound activated form, which can be specifically detected with the mouse antibody10D7 [17]. Because we could not conduct a co-staining with the mouse anti-meront antibody, 6G2, in combination with 10D7, we identified the meront via its enhanced DAPI signal to show that indeed Irga6 was accumulating on E. cuniculi vacuoles in the GTP-bound state (Figure 2F) as in T. gondii immunity. The number of vacuoles positive for Irga6 or Irgb6 was examined in more detail at different time points after infection (Figure 2G). The frequency of Irga6- and Irgb6-positive vacuoles varied between experiments (1–20%), but did not significantly increase or decrease between 0.5–24 hours post infection (Figure 2G) as the number of meronts progressively dropped, suggesting relatively fast clearance of the IRG-positive vacuoles.
A detailed view of IRG loading onto the T. gondii PVM has been established. IRG proteins accumulate on the PVM in a hierarchical order with Irgb6, Irgb10 and Irga6 as pioneers and demonstrate cooperative behaviour by stabilizing each other at the PVM [18]. In order to investigate cooperative loading on the E. cuniculi PVM, we conducted triple immunofluorescent stainings to identify the meront and two GKS proteins, Irga6 and Irgb6. Individual vacuoles positive for both IRG proteins were observed at early and late time points, such as 12 h post infection (Figure 3A) and 24 h post infection (Figure 3B, C). In most cases, Irga6 and Irgb6 co-localised to single vacuoles (Figure 3B), although often without accurate spatial coincidence on the PVM (Figure 3C). Notably, the number of E. cuniculi vacuoles accumulating both IRG proteins was higher than single-coated ones (Figure 3D). In view of the low frequencies of accumulation of individual IRG proteins, it is clear that the frequency of double-loaded vacuoles is highly non-random, suggesting that loading is cooperative between different IRG proteins, as seen on the T. gondii PVM.
To assess the importance of IRG proteins in the IFNγ-dependent restriction of E. cuniculi, we investigated the development of the parasite in cells derived from IRG knock-out mice. First, we examined E. cuniculi infection in IFNγ-induced primary wildtype and Irgm1/Irgm3−/− MEF cells, which lack the two regulator GMS proteins, Irgm1 and Irgm3, and also express reduced levels of GKS proteins [40]. The number of meronts observed in Irgm1/Irgm3−/− MEF cells 24 h after infection was the same whether the cells were induced with IFNγ or not (Figure 4A, B). In contrast, and as observed in Figure 1, the number of meronts in IFNγ-induced wildtype cells was drastically reduced at 24 h after infection compared with uninduced controls (Figure 4A, B).
We next assayed IFNγ-inducible resistance to E. cuniculi in transformed fibroblasts from mice deficient in single IRG genes as well as in the Irgm1/Irgm3−/− double knock-out cells. Parasite growth was assessed with the anti-meront antibody by Western blot, while the expression of Irgb6 (or Irga6) confirmed successful IFNγ induction (Figure 4C–D). In wildtype cells, IFNγ-induction resulted in complete loss of the meront marker at 2 and 5 days after infection, while in the Irgm1/Irgm3−/− double knock-out cells IFNγ-induction caused no inhibition of meront growth. Single mutants for either Irga6 or Irgd, two members of the GKS effector subfamily, showed no loss of resistance relative to wildtype cells. However, cells lacking one GMS protein, Irgm1 or Irgm3, both showed clear susceptibility phenotypes. Susceptibility of the Irgm1-deficient cells was incomplete, while Irgm3-deficient cells were apparently as susceptible as Irgm1/Irgm3 double-deficient cells. Interestingly, Irgm3 deficiency also has a stronger susceptibility phenotype than Irgm1 deficiency for T. gondii in IFNγ-induced MEFs (unpublished observations). The stronger phenotype from the GMS knock-outs is expected, because these deficiencies deregulate all the GKS effectors [41]. The undetectable effects of the two GKS effector knock-outs is consistent with the much weaker in vivo phenotypes of single Irga6 and Irgd deficiencies in T. gondii infection [2], [42]. A deficiency of several GKS effectors would be expected to show a stronger phenotype.
We and others have documented the direct disruption of the IRG protein-coated T. gondii PVM, followed by necrotic host cell death in mouse cells induced by IFNγ [4], [15], [20], [43]. It was of interest to find out whether this consequence of IRG protein action could also be observed in IFNγ-induced mouse cells infected with E. cuniculi. In the first experiments we stained infected primary MEF wildtype cells under live-cell conditions with the membrane-impermeable dye propidium iodide in order to stain nuclei of necrotic cells, and with the membrane–permeable dye Hoechst, which stains all nuclei. We found a significant excess of propidium iodide-positive nuclei in E. cuniculi infected and IFNγ-treated cells compared to untreated or single-treated control samples (Figure 5A, B). Next, we used a standard formazan-based colorimetric assay to measure viability of MEF wildtype cells with increasing multiplicity of infection with E. cuniculi. At one day post infection, viability of infected cells was significantly reduced in dependence of IFNγ-induction and this was even more pronounced at 2 days post infection (Figure 5C). Thus, in the presence of IFNγ, E. cuniculi infection seems to induce the same response as T. gondii, namely reactive death of the host cell itself.
Restricting nutrient acquisition is a common defence mechanism against intracellular parasites. Deprivation of tryptophan by the IFN-inducible indoleamine 2,3-dioxygenase (IDO) is often claimed to be the main inhibitor of T. gondii replication in IFNγ-induced human fibroblasts [reviewed in [44], [45]], following reports that replication can be rescued by supplementation of the medium with tryptophan [46], [47]. IDO-mediated growth restriction of E. intestinalis has been proposed following observations in a mouse enterocytic cell line CMT-93 [33]. However, another study in activated mouse peritoneal macrophages showed that L-tryptophan supplementation failed to rescue the infection [48]. In view of these apparently inconsistent results, we analysed E. cuniculi growth in IFNγ-induced mouse cells following tryptophan supplementation (Figure 6). In wildtype MEF cells, as well as in CMT-93 cells, IFNγ-mediated growth restriction on E. cuniculi could not be reversed by supplementation with excess tryptophan, arguing strongly against mediation of the inhibition via IDO. Taken together with the complete loss of resistance caused by IRG protein deficiencies, we conclude that the IFNγ-mediated restriction of E. cuniculi in non-myeloid cells is mediated exclusively by the IRG system in mice.
The IFN-inducible IRG proteins of the mouse are essential for resistance against some strains of the intracellular bacterium, Chlamydia, and against the intracellular protozoon, Toxoplasma gondii, but seem to play no role in resistance against a multitude of other intracellular bacterial and protozoal infections. The combination of selectivity and lack of phylogenetic consistency in IRG protein action calls for a mechanistic explanation that unifies the two widely disparate target species while excluding organisms that do not engage the IRG system. The purpose of this study was to test the hypothesis that the key lies in how different organisms enter the host cell. Most of the organisms that are ignored by the IRG system, such as Salmonella, Listeria, Leishmania, Mycobacteria, and Rhodococcus, engage the phagocytic mechanism and are taken up into and reside, whether temporarily or permanently, in more or less modified phagosomes. In contrast T. gondii typically enters actively using force generated by its own cortical cytoskeleton without engaging the phagocytic mechanism [reviewed in [49], [50]]. However, it has recently been described that T. gondii may enter macrophages, but not fibroblasts, by phagocytic uptake, but this is followed by active exit from the phagosome into a conventional parasitophorous vacuole and the loading of Irgb6 appears to be unaffected [51]. Chlamydia is taken up by an unknown process with some features of clathrin-mediated endocytosis but none of phagocytosis [52]. With this disparity in mind, the working hypothesis behind this paper was that organisms that enter cells without engaging the phagocytic mechanism may become preferential targets for IRG protein-mediated resistance, regardless of their taxonomic status. To generalise this idea, we tested another intracellular organism with an anomalous, non-phagocytic mode of cellular invasion and a wide taxonomic divergence from the other two known IRG protein targets: the microsporidian, Encephalitozoon cuniculi.
The unambiguous conclusion from our experiments is that infection by E. cuniculi is resisted in IFNγ-induced mouse fibroblasts by the action of the IRG proteins, and several aspects of the process closely resemble features that have been studied in detail in T. gondii infection. Resistance is associated with accumulation of IRG proteins onto at least a proportion of the intracellular organisms within the first 30 minutes after infection, a time at which IRG protein accumulation onto the T. gondii vacuolar membrane is already well-advanced [18]. At the light microscopical level it is not possible to say precisely where the IRG proteins are localised, but images from later time points after infection, when the vacuole is enlarged and the PVM is separated from the organism by an intravacuolar space, suggest that the IRG proteins are loaded onto the parasitophorous vacuole membrane.
The cooperative pattern of loading of the different IRG proteins is also familiar from T. gondii. The frequency of vacuoles loaded at any time is low, but the majority of vacuoles carry more than one IRG protein (Figure 3). This result could of course also arise if only a few vacuoles are receptive to IRG proteins at any time. However, data from T. gondii showed that the loading of Irgb6 was stabilised and enhanced by the loading of Irga6, and thus clearly cooperative [18]. There is also a tendency in the E. cuniculi infection, perhaps not so well marked as in T. gondii infection, for Irgb6 to load more vacuoles than Irga6, and Irgd to load fewer. Also, as in T. gondii [1], [4] and in C. trachomatis infection [8], [10], [23], the IRG regulatory protein, Irgm1, does not load onto any E. cuniculi vacuoles, while Irgm2 can be found on some. In another respect, however, the loading of E. cuniculi vacuoles with IRG proteins appears to be different from the loading of T. gondii vacuoles. With avirulent T. gondii, the number of vacuoles loaded with IRG proteins rises to as much as 90% of all vacuoles within 2 h after infection. With E. cuniculi, the number of vacuoles loaded reaches a plateau between 5 and 15% within 30 minutes of infection, and persists at that level for many hours while the number of live meronts progressively falls (Figure 2). These different loading behaviours can be reconciled with qualitatively similar processes operating on the vacuoles of both organisms, if the initiation of IRG protein loading onto individual E. cuniculi vacuoles takes on average longer than onto T. gondii vacuoles, and if E. cuniculi vacuoles subsequently disintegrate and are cleared with faster kinetics than T. gondii vacuoles. With increasing time after infection more and more parasites are cleared from the cells, accounting for the long, slow loss of detectable meronts (Figure 1) reaching about 90% only 24 h after infection (Figure 7).
Light microscopy does not allow us to see exactly what happens to E. cuniculi vacuoles after loading with IRG proteins. Clear-cut disruption typical of the IRG-loaded T. gondii vacuole [20] is not easy to register. Nevertheless the vacuoles and their included parasites disappear (see Figure S1). In T. gondii, the disruption of the vacuole is followed within about 20 minutes by the death of the parasite and after an hour or two by the necrotic death of the infected cell. We also observed an excess of dead, presumably necrotic, cells in IFNγ-induced fibroblasts infected with E. cuniculi.
Lastly, as with T. gondii resistance, the IRG system appears to be the only mechanism in IFNγ-induced mouse fibroblasts that is capable of restriction of E. cuniculi. In fibroblasts from mice double deficient for the regulator IRG proteins, Irgm1 and Irgm3, in which the whole IRG system is largely disabled, all IFNγ-inducible resistance against the growth and development of the parasite was lost, and the IFNγ-inducible catabolic enzyme for tryptophan played no role in resistance against E. cuniculi.
In summary, every property of the IRG-dependent resistance mechanism that has been analysed for T. gondii is probably also valid against E. cuniculi, and, to the extent that it is known, also against Chlamydia. Since effective resistance dependent on IRG proteins seems to be perfectly correlated with the accumulation of IRG proteins on the parasitophorous vacuole, the challenge is to determine the common factor that enables IRG proteins to accumulate on the vacuoles of these three organisms but not on the vacuoles of other organisms. These three organisms cover three kingdoms of life: protozoa, bacteria, and fungi. The broad phylogenetic distribution makes it unlikely a priori that the IRG proteins target a common ligand expressed by all restricted pathogens on their vacuolar membranes. Our preferred view builds on a hypothesis first formulated by Martens [53] to account for the targeting of IRG proteins to the T. gondii vacuole rather than to other cellular organelles. Martens proposed the existence of a self-derived factor X expressed on the membranes of cellular organelles that inhibits the accumulation and activation of IRG proteins on these sites, thereby protecting these organelles from IRG protein mediated damage. Parasitophorous vacuoles, lacking factor X, would be exposed to IRG accumulation and activation. This elegant “missing self” model was confirmed some time later and “factor X” was revealed to be the three GMS proteins, Irgm1, Irgm2 and Irgm3, which are bound to distinct subsets of organellar membranes and act as guanine nucleotide dissociation inhibitors of the effector GKS proteins at these sites [16]. In the absence of one or more GMS proteins, GKS proteins form activated, GTP-bound assemblies in the cytoplasm, probably associated with “unprotected” organellar membranes [23], [41]. The GMS IRG proteins seem to fulfil exactly the role of Martens' Factor X for the distinction between intracellular organelles and a parasitophorous vacuole. However, GKS effector IRG proteins do not accumulate or activate on the plasma membrane, which to the best of our knowledge is not protected by any GMS protein. We are therefore forced to introduce a new hypothetical inhibitor associated with the plasma membrane that inhibits GKS activation at that location (Figure 8). Parasitophorous vacuoles are formed by invagination of the plasma membrane, and as we know with some precision from experiments with T. gondii, the vacuoles are receptive to IRG loading and activation immediately after parasite entry [18]. Thus entry of the parasite and formation of the parasitophorous vacuole must entail loss of the hypothetical plasma membrane-bound inhibitor. We propose that this is the essential distinction between those organisms that do, and those that do not, engage the IRG system, and that loss of the plasma membrane inhibitor is due to the unusual, non-phagocytic entry mechanisms of all three parasites.
Recently, the Atg5-dependent module of the ubiquitin-like conjugation system of autophagy has been proposed to mediate IRG targeting to the vacuole of T. gondii [19], [54], [55]. But since IRG and GBP proteins in Atg5−/− cells form cytosolic GTP-bound aggregates [18], [19], [55] which are unable to target pathogens, loss of resistance appears rather to be caused by deregulated effector protein homeostasis. It has also been difficult to localise any autophagic component to T. gondii parasitophorous vacuoles [4]. Recent data from Choi et al. (2014) suggest occasional localisation of native LC3II at the PVM, but on only a small minority of vacuoles, uncorrelated with the presence of IRG proteins [54].
The entry of T. gondii into cells has been studied in considerable detail and probably provides the best system for establishing the identity of the plasma membrane inhibitor. Several categories of protein are depleted from the developing vacuolar membrane, presumably as a result of the sieving action of the parasite-derived RON protein complex formed at the moving junction through which the parasite enters the cell [56], [57], [58], [59]. Apart from transient activation of host actin at the moving junction [60], [61], there is also no evidence that components of the cortical cytoskeleton remain associated with the nascent vacuole.
In the case of Encephalitozoon cuniculi, electron microscopic studies suggested that the PV membrane is derived from the host cell [29]. Subsequent studies from Bohne and colleagues established that the early PVM is non-fusogenic and devoid of any endolysosomal markers immediately after invasion [39], and moreover that the lipids of the PVM are indeed host cell-derived and that the PVM also forms simultaneously with the extrusion of the sporoplasm. This is all consistent with the suggestion that the early PVM is an invagination of the host cell plasma membrane [28], [62]. Due to the high speed of host cell entry, we suggest that physical forces may determine the presence and composition of host cell surface proteins on the newly formed PVM, which would be a prerequisite of IRG protein recognition. Although E. cuniculi spores are actively phagocytosed by macrophages, this is unlikely to be a biologically significant entry route, because it does not contribute to the intracellular meront population in mouse macrophages [63] or mouse embryonic fibroblasts (unpublished results Springer-Frauenhoff).
This study was designed based on knowledge of the interaction of the mouse IRG protein system and T. gondii. We found four major similarities in the action of the IRG system in defense against E. cuniculi: (1) the relocalisation of multiple IRG proteins to the cytosolic face of the PVM; (2) cooperativity by double-loading; (3) IFNγ- and infection- dependent host cell death and (4) IDO-independent IFNγ-mediated restriction in mouse cells.
While the IRG system clearly plays an essential role in defending mice and probably other small rodents against certain infections, there is every reason to believe that the system is effectively completely absent from humans and higher primates, birds, cats and doubtless many other mammalian species too [21]. The sporadic occurrence of a developed IRG system among vertebrate groups suggests that its possession is costly and only justified when certain classes of parasite exert intense selection pressures [14].
All animal experiments were conducted under the regulations and protocols for animal experimentation according to the German “Tierschutzgesetz” (Animal Experimentation Law). The local government authorities, Landesamt für Natur- und Umweltschutz Nordrhein-Westfalen, and its ethics committee approved the work (LANUV Permit No. 84-02.05.40.14.004).
Primary C57BL/6 mouse embryonic fibroblasts (MEFs) were prepared from mice at day 14 post coitum. Irgm1−/−, Irgm3−/−, Irgm1/Irgm3−/−, Irgd−/− MEFs (kindly provided by Greg Taylor) or Irga6−/−MEFs [42] were immortalized by transfection of pSV3-neo plasmid [64] and pPur (Clontech, Saint-Germain-en-Laye, France) in a ratio 9∶1 using FuGENE HD (Roche, Mannheim, Germany) according to the manufacturer's instructions. After 24 h, cells were put under selection with 3 µg/ml puromycin (Clontech). Primary and transformed MEFs as well as mouse rectal carcinoma CMT-93 cells (ATCC CCL-223) were cultured in DMEM, high glucose (Invitrogen Life Technologies, Darmstadt, Germany) supplemented with 10% fetal calf serum (FCS, Biochrom AG, Berlin, Germany), 2 mM L-glutamine, 1 mM sodium pyruvate, 1× MEM non-essential amino acids, 100 U/ml penicillin and 100 mg/ml streptomycin (all PAA, Pasching, Austria). Human foreskin fibroblasts (Hs27; ATCC CRL-1634) were cultured in IMDM, high glucose (Invitrogen Life Technologies) supplemented with 5% FCS, 100 U/ml penicillin and 100 mg/ml streptomycin (PAA). Cells were stimulated with 200 U/ml of mouse IFNγ (PeproTech, Rocky Hill, NJ, USA) for 24 h. For IDO-inhibition, L-tryptophan (W) (Sigma-Aldrich Co., Saint Louis, MO, USA) was added 15 min prior to infection.
E. cuniculi spores were a generous gift from Prof. Peter Deplazes (University of Zürich, Switzerland). Spores were routinely propagated in Hs27 cells as described in [62]. Briefly, infected monolayers were scraped 7–12 days post infection and the suspension was passed through a 26G needle. The first centrifugation (10 min at 500 rpm) removed the host cell debris, whereas the second centrifugation (20 min at 2500 rpm) sedimented the spores. A stock solution with 4×107 spores/ml PBS was stored at 4°C for max. 3 month. For infection assays, 8–12×104 host cells were seeded in 6-well plates 48 h prior infection, optionally stimulated, and infected with a multiplicity of infection (MOI) of 10 parasites per host cell for microscopic assays and MOI 5 for Western Blot analysis. In order to obtain synchronous infection, spores were allowed to infect the cells for 2–4 h followed by one careful washing step with PBS and addition of fresh medium. Cells were fixed or harvested at the indicated time points post infection. For E. cuniculi genotyping, 60×107 E. cuniculi spores were centrifuged for 20 min at 2500 rpm, resuspended in 200 µl PBS and 20 µl of Proteinase K, DNA was isolated with the DNeasy Blood & Tissue DNA purification kit (Qiagen, Hilden, Germany) according to manufactures instructions. The rRNA gene region of large (LSU rRNA) and small ribosomal subunit (SSU rRNA) and ITS region were amplified as described in [65]. The E. cuniculi stain used in this study had the genotype I.
The following immunoreagents were used: rabbit anti-Irgm1 polyclonal antiserum (pAS) rbMAE15 [66], rabbit anti-Irgm2 pAS H53/3 [4], [18], rabbit anti-Irga6 pAS 165/3 [67], anti-Irga6 mouse monoclonal antibody (mAB) 10D7/10E7 [17], anti-Irgb6 mouse mAB B34 [68], anti-Irgb6 goat polyclonal antibody (pAB) A20 (sc-11079, Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA), anti-meront mouse mAB 6G2 [39], anti-SWP1 [69], anti-Calnexin rabbit (pAB) (Calbiochem Merck KGaA, Darmstadt, Germany). Secondary antibodies were Alexa Fluor 488/555/647-labeled donkey anti-mouse, -rabbit, and -goat antisera (all Molecular Probes, Invitrogen Life Technology), donkey anti-rabbit- (GE Healthcare, Freiburg, Germany), and goat anti-mouse-HRP (horseradish peroxidase) (Pierce, Thermo Fisher Scientific, Bonn, Germany) antisera. 4′, 6-Diamidino-2-phenylindole (DAPI, Roche, Mannheim, Germany) was used for nuclear staining at a final concentration of 0.5 mg/ml.
Immunocytochemistry was carried out on paraformaldehyde-fixed cells grown on glass cover slips as described earlier [4]. In brief, cells were permeabilized and blocked with 3% BSA and 0.1% saponin (both Roth, Karlsruhe, Germany) in PBS, stained with the primary antibodies diluted in blocking buffer for 1 h at room temperature or overnight at 4°C following incubation with the secondary antibody diluted in blocking buffer for 30 min at room temperature. Between all steps cells were triple washed with 0.1% saponin in PBS and then mounted on glass microscopic slides in ProLong Gold anti-fade reagent (Invitrogen Life Technology). The images were taken with an Axioplan II fluorescence microscope and AxioCam MRm camera and processed by Axiovision 4.7 (all Zeiss, Oberkochen, Germany). All samples were counted blind.
Live cell imaging was performed in μ-slide I chambers (Ibidi, Munich, Germany) as described earlier [20]. For live cell experiments, wt MEF cells were transiently transfected with pEGFP-N3-Irga6-ctag1 [20] using FuGENE HD (Roche) according to the manufacturer's instructions and induced with 200 U/ml IFNγ. After 24 hours cells were infected with E. cuniculi spores at a MOI 50 in phenol red-free RPMI 1640 (PAA). After infection with E. cuniculi, the cells were observed with a Zeiss Axiovert 200 M motorized microscope fitted with a wrap-around temperature-controlled chamber (Zeiss). The time-lapse images were obtained and processed by Axiovision 4.6 software (Zeiss).
At 2 or 5 days post infection, MEF or CMT-93 cells were washed with PBS once and directly lysed in 200 µl 2× SDS sample buffer (2% SDS, 100 mM Tris/HCl (pH 6.8), 10% Glycerol, 0.005% bromophenol blue, 1.4% β-mercaptoethanol). The lysates were transferred into Eppendorf tubes and boiled 5–10 min at 95°C. 15–20 µl were subjected to 10% SDS-PAGE and Western blot. Protein transfer was confirmed by staining the nitrocellulose membranes with Ponceau S solution [0.2% Ponceau S (Roth) and 3% acetic acid in dH2O]. Membranes were blocked in 5% non-fat dry milk in PBS and probed for the proteins of interest with the indicated primary and HRP-coupled secondary antibodies.
Primary MEF cells (5000 cells/96-well) were seeded and induced with IFNγ for 24 h or left untreated. The cells were then infected with E. cuniculi spores at the indicated MOI for 24 h or 48 h. Thereafter, viable cells were quantified by the CellTiter 96 AQueous non-radioactive cell proliferation assay (Promega, Mannheim, Germany) according to the manufacturer's instructions. Infection with avirulent T. gondii Me49 served as positive control [20].
MEF cells grown in 6 cm-dishes were induced with IFNγ for 24 h and infected with E. cuniculi spores at a MOI 10. At 24 h post infection, Bisbenzimide Hoechst 33342 and Propidium Iodide (both Sigma-Aldrich) were added to the medium (1 µg/ml final concentration for both) and incubated at 37°C for 15 min. 10 fluorescent pictures per sample were photographed with the Zeiss Axiovert 200 M microscope with a 10 fold magnification. Total cell number (Hoechst-positive nuclei) and dead cells (PI-positive nuclei) were automatically enumerated using the Volocity software (PerkinElmer, Santa Clara, CA, USA). At least 500 cells were counted per sample and percentage of dead cells per total cell number was calculated. In five independent experiments, a total of 10.000 cells or more was counted per sample.
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10.1371/journal.pgen.1007081 | Orienting the causal relationship between imprecisely measured traits using GWAS summary data | Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.
| Understanding the causal relationships between pairs of traits is crucial for unravelling the causes of disease. To this end, results from genome-wide association studies are valuable because if a trait is known to be influenced by a genetic variant then this knowledge can be used to test the trait’s causal influences on other traits and diseases. Here we discuss scenarios where the nature of the genetic association with the causal trait can lead existing causal inference methods to give the wrong direction of causality. We introduce a new method that can be applied to summary level data and is potentially less susceptible to problems such as measurement error, and apply it to evaluate the causal relationships between DNA methylation levels and gene expression. While our results show that DNA methylation is more likely to be the causal factor, we point out that is it crucial to acknowledge that systematic differences in measurement error between the platforms could influence such conclusions.
| Observational measures of the human phenome are growing ever more abundant, but using these data to make causal inference is notoriously susceptible to many pitfalls, with basic regression-based techniques unable to distinguish a true causal association from reverse causation or confounding [1–3]. In response to this, the use of genetic associations to instrument traits has emerged as a technique for improving the reliability of causal inference in observational data, and with the coincident rise in genome-wide association studies it is now a prominent tool that is applied in several different guises [3–6]. However, shifting from observational associations to instrumentation does require more (often untestable) assumptions, and potential pitfalls remain. One that is often neglected is the influence of non-differential measurement error on the reliability of causal inference.
Measurement error is the difference between the measured value of a quantity and its true value. This study focuses specifically on non-differential measurement error where all strata of a measured variable have the same error rate, which can manifest as changes in scale or measurement imprecision (noise). Such variability can arise through a whole plethora of mechanisms, which are often specific to the study design and difficult to avoid [7, 8]. Array technology is now commonly used to obtain high throughput phenotyping at low cost, but comes with the problem of having imperfect resolution, for instance methylation levels as measured by the Illumina450k chip are prone to have some amount of noise around the true value due to imperfect sensitivity [9, 10]. Relatedly, if the measurement of biological interest is the methylation level in a T cell, then measurement error of this value can be introduced by using methylation levels from whole blood samples because the measured value will be an assay of many cell types [11].
Measurement error will of course arise in other types of data too. For example when measuring BMI one is typically interested in using this as a proxy for adiposity, but it is clear that the correlation between BMI and underlying adiposity is not perfect [12], leading to the problem that phenotypes may be imprecisely defined. A similar problem of biological misspecification is unavoidable in disease diagnosis, and measuring behaviour such as smoking or diet is notoriously difficult to do accurately. Measurement error can also be introduced after the data have been collected, for example the transformation of non-normal data for the purpose of statistical analysis will lead to a new variable that will typically incur both changes in scale and imprecision (noise) compared to the original variable. The sources of measurement error are not limited to this list [8], and its impact has been explored in the epidemiological literature extensively [13, 14]. Given the near-ubiquitous presence of measurement error in phenomic data it is vital to understand its impact on the tools we use for causal inference.
An established study design that can provide information about causality is randomisation. Given the hypothesis that trait A (henceforth referred to as the exposure) is causally related to trait B (henceforth referred to as the outcome), randomisation can be employed to assess the causal nature of the association by randomly splitting the sample into two groups, subjecting one group to the exposure and treating the other as a control. The association between the exposure and the outcome in this setting provides a robust estimate of the causal relationship. This provides the theoretical basis behind randomised control trials, but in practice randomisation is often difficult or impossible to implement in an experimental context due to cost, scale or inability to manipulate the exposure. The principle, however, can be employed in extant observational data through the use of genetic variants associated with the exposure (instruments), where the inheritance of an allele serves as a random lifetime allocation of differential exposure levels [15, 16]. Two statistical approaches to exploiting the properties of genetic instruments are widely used: mediation-based approaches and Mendelian randomisation (MR).
Mediation-based approaches employ genetic instruments (typically single nucleotide polymorphisms, SNPs) to orient the causal direction between the exposure and the outcome. If a SNP is associated with an exposure, and the exposure is associated with some outcome, then it logically follows that in this simple three-variable scenario the estimated direct influence of the SNP on the outcome will be zero when conditioning on the exposure. Here, the exposure completely mediates the association between the SNP and the outcome, providing information about the causal influence of the exposure on the outcome. This forms the basis of a number of methods such as genetical genomics [17], the regression-based causal inference test (CIT) [4, 18], a structural equation modelling (SEM) implementation in the NEO software [5], and various other methods including Bayesian approaches [6]. They have been employed by a number of recent publications that make causal inferences in large scale ‘omics datasets [6, 19–23].
MR can be applied to the same data—phenotypic measures of the exposure and the outcome variables and a genetic instrument for the exposure—but the genetic instrument is employed in a subtly different manner. Here the SNP is used as a surrogate for the exposure. Assuming the SNP associates with the outcome only through the exposure, the causal effect of the exposure on the outcome can be estimated by scaling the association between the SNP and the outcome by the association between the SNP and the exposure. Though difficult to test empirically, this assumption can be relaxed in various ways when multiple instruments are available for a putative exposure [24, 25] and a number of sensitivity tests are now available to improve reliability [26]. Additionally, if valid genetic instruments are known for both traits of interest then MR can be performed in both directions (bi-directional MR), testing the influence of one trait on the other and vice versa, to infer the causal direction between the two phenotypes [27, 28].
By utilising genetic instruments in different ways, mediation-based analysis and MR models have properties that confer some advantages and some disadvantages for reliable causal inference. In the CIT framework (described fully in the Methods) for example, the test statistic is different if you test for the exposure causing the outcome or the outcome causing the exposure, allowing the researcher to infer the direction of causality between two variables by performing the test in both directions and choosing the model with the strongest evidence. The CIT also has the valuable property of being able to distinguish between several putative causal graphs that link the traits with the SNP (Fig 1). Such is not the case for MR, where in order to infer the direction of causality between two traits the instrument must have its most proximal link with the exposure, associating with the outcome only through the exposure.
Assuming biological knowledge of genetic associations can be problematic because if there exists a putative association between two variables, with the SNP being robustly associated with each, it can be difficult to determine which of the two variables is subject to the primary effect of the SNP (i.e. for which of the two variables is the SNP a valid instrument? Fig 1). By definition, we expect that if the association is causal then a SNP for the exposure will be associated with the outcome, such that if the researcher erroneously uses the SNP as an instrument for the outcome then they are likely to see an apparently robust causal association of outcome on exposure. Genome-wide association studies (GWASs) that identify genetic associations for complex traits are, by design, hypothesis free and agnostic of genomic function, and it often takes years of follow up studies to understand the biological nature of a putative GWAS hit [29]. If multiple instruments are available for an hypothesised exposure, which is increasingly typical for complex traits that are analysed in large GWAS consortia, then techniques can be applied to mitigate these issues [16]. But these techniques cannot always be applied in the case of determining causal directions between ’omic measures where typically only one cis-acting SNP is known. For example if a DNA methylation probe is associated with expression of an adjacent gene, then is a cis-acting SNP an instrument for the DNA methylation level, or the gene expression level (Fig 1)?
MR has some important advantages over the mediation-based approaches. First, the mediation-based approaches require that the exposure, outcome and instrumental variables are all measured in the same data, whereas recent extensions to MR circumvent this requirement, allowing causal inference to be drawn when exposure variables and outcome variables are measured in different samples [30]. This has the crucial advantage of improving statistical power by allowing analysis in much larger sample sizes, and dramatically expands the breadth of possible phenotypic relationships that can be evaluated [26]. Second, the mediation-based approach of adjusting the outcome for the exposure to nullify the association between the SNP and the outcome is affected by unmeasured confounding of the exposure and outcome. This is because adjusting the outcome by the exposure induces a collider effect between the SNP and outcome [31], and in order to fully abrogate this association one must also adjust for all (hidden or otherwise) confounders. MR does not suffer from this problem because it does not test for association through adjustment. Third, when MR assumptions are satisfied the method is robust to there being measurement error in the exposure variable [32]. Indeed instrumental variable (IV) analysis was in part initially introduced as a correction for measurement error in the exposure [33], whereas it has been noted that both classic mediation-based analyses [13, 14, 34, 35] and mediation-based methods that use instrumental variables [36, 37] are prone to be unreliable in its presence.
Using theory and simulations we show how non-differential measurement error in phenotypes can lead to unreliable causal inference in the mediation-based CIT method. Though we only examine the CIT method in detail, we believe that attempting to adjust for mediating variables to make causal inference is susceptible to problems, which can be generalised to other mediation-based methods. We then present an extension to MR that allows researchers to ascertain the causal direction of an association even when the biology of the instruments are not fully understood, and also a metric to evaluate the sensitivity of the result of this extension to measurement error. Finally, to demonstrate the potential impact of measurement error we apply this method to infer the direction of causation between DNA methylation levels and gene expression levels. Our analyses highlight that because these different causal inference techniques have varying strengths and weaknesses, triangulation of evidence from as many sources as possible should be practiced in causal inference [38].
We model a system whereby some exposure x has a causal influence βx on an outcome y such that
y = α x + β x x + ϵ x
In addition, the exposure is influenced by a SNP g with an effect of βg such that
x = α g + β g g + ϵ g
The α* terms represent intercepts, and henceforth can be ignored. The ϵ* terms denote random error, assumed independently and normally distributed with mean zero. Mediation-based analyses that test whether x causally relates to y rely on evaluating whether the influence of g on y can be accounted for by conditioning on x, such that
c o v ( g , y - y ^ ) = 0
where y ^ = β ^ x x and assuming no intercept y - y ^ = ϵ x. MR analysis estimates the causal influence of x on y by using the instrument as a proxy for x, such that
x ^ = β ^ g g y = β M R x ^ + ϵ M R
where βMR ≠ 0 denotes the existence of causality, and βMR is an estimate of the causal effect.
Measurement error of an exposure can be modeled as a transformation of the true value (x) that leads to the observed value, xo = f(x). For example, following Pierce and VanderWeele [32] we can define
f ( x ) = α m x + β m x x + ϵ m x
where αmx and βmx influence the error in the measurement of x by altering its scale, and ϵmx represents the imprecision (or noise) in the measurement of x. Measurement imprecision can represent imprecise measurement due to limits on sensitivity of measuring equipment, or arise because of phenotypes being imprecisely defined. The same model of measurement error can be applied to the outcome variable y.
In this study we assume there is no measurement error in the SNP. Common genetic variants are typically less susceptible to measurement error due to strict quality control procedures prior to genome wide association studies. Any non-differential measurement error that might be present (either because the SNP is poorly typed or because the SNP is not in complete linkage disequilibrium with the causal variant) will reduce power in MR but will not incur bias [3, 13, 32]. We also assume that measurement error in the exposure and the outcome are uncorrelated.
In the causal inference test (CIT), the 4th condition (see Methods) employs mediation for causal inference, and can be expressed as c o v ( g , y - y ^ ) = 0, where y ^ = α ^ x + β ^ x x o. When measurement error in scale and imprecision is introduced, such that yo is the measured value of y, it can be shown using basic covariance properties (S1 Text) that
c o v ( g , y - y ^ ) = c o v ( g , y o ) - c o v ( g , y ^ o ) = β m y β g β x v a r ( g ) - D β m y β g β x v a r ( g )
where
D = β m x 2 v a r ( x ) β m x 2 v a r ( x ) + v a r ( ϵ m x )
Thus an observational study will find c o v ( g , y o - y o ^ ) = 0 when the true model is causal only when D = 1. Therefore, if there is any measurement error that incurs imprecision in x (i.e. var(ϵmx) ≠ 0) then there will remain an association between g and yo|xo, which is in violation of the the 4th condition of the CIT. Note that scale transformation of x or y without any incurred imprecision is insufficient to lead to a violation of the test statistic assumptions, and henceforth mention of measurement error will relate to imprecision unless otherwise stated.
We performed simulations to verify that this problem does arise using the CIT method. Fig 2 shows that when there is no measurement error in the exposure or outcome variables (ρx,xo = ρy,yo = 1) the CIT is reliable in identifying the correct causal direction. However, as measurement error increases in the exposure variable, eventually the CIT is more likely to infer a robust causal association in the wrong direction. Also of concern here is that increasing sample size does not solve the issue, indeed it only strengthens the apparent evidence for the incorrect inference.
If we do not know whether the SNP g has a primary influence on x or y then CIT can attempt to infer the causal direction. Though bi-directional MR can be used to orient causal directions [27], this requires knowledge of a valid instrument for each trait, and we were motivated to develop the MR Steiger method that could operate on summary data to orient the direction of causality using the same conditions as the CIT, where the underlying biology of a single SNP is not fully understood. We go on to explore the scenarios in which the method is likely to return the correct or incorrect causal directions.
We performed simulations to compare the power and type 1 error rates of MR and CIT in detecting a causal association between simulated variables under different levels of imprecision simulated in the exposure. Comparing the performance of methods with different sets of assumptions can be difficult, but a basic comparison is shown in Fig 3. We observe that the CIT is more conservative under the null model of no association owing to the omnibus test statistic comprising several statistical tests. The FDR using a p-value threshold of 0.05 appears to be close to zero, whereas for the MR Steiger method the FDR is around 0.05. Using the same p-value thresholds to declare significance in the non-null simulations, the general trend appears to be that the CIT power reduces as measurement error in the exposure increases more steeply than that of the MR Steiger method.
For a particular association, it is of interest to identify the range of possible measurement error values for which the method will give results that agree or disagree with the empirically inferred causal direction (Fig 4a, S2 Text). This metric can be used to evaluate the reliability of MR Steiger test.
We show that in the presence of measurement imprecision, d = ρx,xo − ρx,yρy,yo (S2 Text) determines the range of parameters around which the MR Steiger test is liable to provide the wrong direction of causality (i.e. if d > 0 then the MR Steiger test is likely to be correct about the causal direction). Fig 4b shows that when there is no measurement error in x, the MR Steiger test is unlikely to infer the wrong direction of causality even if there is measurement error in y. It also shows that in most cases where x is measured with error, especially when the causal effect between x and y is not very large, the sensitivity of the MR Steiger test to measurement error is relatively low.
Unmeasured confounding between the exposure and outcome can also give rise to problems with the MR Steiger approach (S3 Text). The relationship between unmeasured confounding and causal orientation is complex across the parameter space of possible confounding values (S2 Fig). Based on the range of parameter values that we explored, when the magnitude of the observational variance explained between the exposure and the outcome is below 0.2 the MR Steiger method is unlikely to return the incorrect causal direction due to unmeasured confounding.
We used simulations to explore the performance of the MR Steiger approach in comparison to CIT for different levels of measurement error. The performance was compared in terms of the rate at which evidence of a causal relationship is obtained for the correct direction of causality, and the rate at which evidence of a causal relationship is obtained where the reported direction of causality is incorrect. Simulations were performed for two models, one for a “causal model” where there was a causal relationship between x and y; and one for a “non-causal model” where x and y were not causally related, but had a confounded association induced by the SNP g influencing x and y independently.
Fig 5a shows that, for the “causal model”, the MR analysis is indeed liable to infer the wrong direction of causality when d < 0, and that this erroneous result is more likely to occur with increasing sample size. However, the CIT is in general more fallible to reporting a robust causal association for the wrong direction of causality. When d > 0 we find that in most cases the MR Steiger method has greater power to obtain evidence for causality than CIT, and always obtains the correct direction of causality. The CIT, unlike the MR Steiger test, is able to distinguish the “non-causal model” from the “causal model” (Methods, Fig 5b), but it is evident that measurement error will often lead the CIT to identify the causal model as true, when in fact the underlying model is this non-causal model.
We used the MR Steiger test to infer the direction of causality between DNA methylation and gene expression levels between 458 putative associations. We found that the causal direction commonly goes in both directions (Fig 6a), but assuming no or equal measurement error, DNA methylation levels were the predominant causal factor (p = 1.3 × 10−5). The median reliability (R) of the 458 tests was 3.92 (5%-95% quantiles 1.08–37.11). We then went on to predict the causal directions of the associations for varying levels of systematic measurement error for the different platforms. Fig 6a shows that the conclusions about the direction of causality between DNA methylation and gene expression are very sensitive to measurement error. We made a strong assumption that either methylation influenced gene expression or vice versa, but it is certainly possible that the SNP is solely or additionally influencing some other trait that confounds the association between gene expression and DNA methylation.
We performed two sample MR [30] for each association in the direction of causality inferred by the Stieger test. We observed that the sign of the MR estimate was generally in the same direction as the Pearson correlation coefficient reported by Shakhbazov et al [39] (Fig 6b). There was a moderate correlation between the absolute magnitudes of the causal correlation and the observational Pearson correlation (r = 0.45). Together these inferences suggest that even in estimating associations between ‘omic’ variables, which are considered to be low level phenotypes, it is important to use causal inference methods over observational associations to infer causal effect sizes.
We also observed that for associations where methylation caused gene expression the causal effect was more likely to be negative than for the associations where gene expression caused methylation (OR = 0.61 (95% CI 0.36–1.03), Fig 6c), suggesting that reducing methylation levels at a controlling CpG typically leads to increased gene expression levels, consistent with expectation [40].
Researchers are often confronted with the problem of making causal inferences using a statistical framework on observational data. In the epidemiological literature issues of measurement error in mediation analysis are relatively well explored [41]. Our analysis extends this to related methods such as CIT that are used in predominantly ’omic data. These methods are indeed susceptible to the same problem as standard mediation based analysis, and specifically we show that as measurement error in the (true) exposure variable increases, CIT is likely to have reduced statistical power, and liable to infer the wrong direction of causality. We also demonstrate that, though unintuitive, increasing sample size does not resolve the issue, rather it leads to more extreme p-values for the model that predicts the wrong direction of causality.
Under many circumstances a practical solution to this problem is to use Mendelian randomisation instead of methods such as the CIT or similar that are based on mediation. Inferring the existence of causality using Mendelian randomisation is robust in the face of measurement error and, if the researcher has knowledge about the biology of the instrument being used in the analysis, can offer a direct solution to the issues that the CIT faces. This assumption is often reasonable, for example SNPs are commonly used as instruments when they are found in genes with known biological relevance for the trait of interest. But on many occasions, especially in the realm of ’omic data, this is not the case, and methods based on mediation have been valuable in order to be able to both ascertain if there is a causal association and to infer the direction of causality. Here we have described a simple extension to MR which can be used as an alternative to or in conjunction with mediation based methods. We show that this method is still liable to measurement error, but because it has different properties to the CIT it offers several main advantages. First, it uses a formal statistical framework to test for the reliability of the assumed direction of causality. Second, after testing in a comprehensive range of scenarios the MR based approach is less likely to infer the wrong direction of causality compared to CIT, while substantially improving power over CIT in the cases where d > 0.
We demonstrate this new method by evaluating the causal relationships of 458 known associations between DNA methylation and gene expression levels using summary level data. The inferred causal direction is heavily influenced by how much measurement error is present in the different assaying platforms. For example, if DNA methylation measures typically have lower or equal measurement error compared to gene expression measures then our analysis suggests that DNA methylation levels would be more often the causal factor in the association. Indeed, previous studies which have evaluated measurement error in these platforms do support this position [42, 43], though making strong conclusions for this analysis is difficult because measurement error is likely to be study specific. We also haven’t accounted for the influence of winner’s curse, which can inflate estimates of the variance explained by SNPs, with higher inflation expected amongst lower powered studies. Using p-values for genetic associations from replication studies will mitigate this problem.
In our simulations we focused on the simple case of a single instrument in a single sample setting with a view to making a fair comparison between MR and the various mediation-based methods available. However, if there is only a single instrument it is difficult to separate between the two competing models of g instrumenting a trait which causes another trait, and g having pleiotropic effects on both traits independently [44]. Under certain conditions of measurement error the CIT test can distinguish these models. We also note that it is straightforward to extend the MR Steiger approach to multiple instruments, requiring only that the total variance explained by all instruments be calculated under the assumption that they are independent. Multiple instruments can indeed help to distinguish between the causal and pleiotropic models, for example by evaluating the proportionality of the SNP-exposure and SNP-outcome effects [16]. Additionally, if there is at least one instrument for each trait then bi-directional MR can offer solutions to inferring the causal direction [16, 28, 45]. We restricted the simulations to evaluating the causal inference between quantitative traits, but it is possible that the analysis could be extended to binary traits by using the genetic variance explained on the liability scale, taking into account the population prevalence [46]. However, our analysis goes beyond many previous explorations of measurement error by assessing the impacts of both imprecision (noise) and linear transformations of the true variable on causal inference.
Our new method attempts to infer causal directions under the assumption that horizontal pleiotropy (the influence of the instrument on the outcome through a mechanism other than the exposure) is not present. Recent method developments in MR [24, 25] have focused on accounting for the issues that horizontal pleiotropy can introduce when multiple instruments are available, but how they perform in the presence of measurement error remains to be explored. An important advantage that MR confers over most mediation based analysis is that it can be performed in two samples, which can considerably improve power and expand the scope of analysis. However, whether there is a substantive difference in two sample MR versus one sample MR in how measurement error has an effect is not yet fully understood. We have also assumed no measurement error in the genetic instrument, which is not unreasonable given the strict QC protocols that ensure high quality genotype data is available to most studies. We have restricted the scope to only exploring non-differential measurement error and avoided the complications incurred if measurement error in the exposure and outcome is correlated. We have also not addressed other issues pertaining to instrumental variables which are relevant to the question of instrument-exposure specification. One such problem is exposure misspecification, for example an instrument could associate with several closely related putative outcomes, with only one of them actually having a causal effect on the outcome. This problem has shown to be the case for SNPs influencing different lipid fractions, for example [47, 48].
Mediation based network approaches, that go beyond analyses of two variables, are very well established [37] and have a number of extensions that make them valuable tools, including for example network construction. But because they are predicated on the basic underlying principles of mediation they are liable to suffer from the same issues of measurement error. Recent advances in MR methodology, for example applying MR to genetical genomics [49], multivariate MR [48] and mediation through MR [50–52] may offer more robust alternatives for these more complicated problems.
The overarching result from our simulations is that, regardless of the method used, inferring the causal direction using an instrument of unknown biology is highly sensitive to measurement error. With the presence of measurement error near ubiquitous in most observational data, and our ability to measure it limited, we argue that it needs to be central to any consideration of approaches which are used in attempt to strengthen causal inference, and any putative results should be accompanied with appropriate sensitivity analysis that assesses their robustness under varying levels of measurement error.
Here we describe how the CIT method [4] is implemented in the R package R/cit [18]. Assume an exposure x is instrumented by a SNP g, and the exposure x causes an outcome y, as described above. The following tests are then performed:
The term in the 4th test can be rewritten as c o v ( g , y | x ) = c o v ( g , y - y ^ ) where y - y ^ = y - (α ^ g + β ^ g x) is the residual of y after adjusting for x, and x is assumed to mediate the association between the SNP and the outcome. The condition in the 4th test is formulated as an equivalence testing problem that is estimated using simulations, comparing the estimate from the data against empirically obtained estimates for simulated variables where the independence model is true (full details are given in [4]). We note here that this approach is liable to fail, even when there is a true causal relationship, when confounders of the exposure and outcome are present, as these will induce collider bias.
If all four tests reject the null hypothesis then it is inferred that x causes y. The CIT measures the strength of causality by generating an omnibus p-value, pCIT, which is simply the largest (least extreme) p-value of the four tests, the intuition being that causal inference is only as strong as the weakest link in the chain of tests.
Now we describe how we used the CIT method in our simulations. The cit.cp function was used to obtain an omnibus p-value. To infer the direction of causality using the CIT method, an omnibus p-value generated by CIT for each of two tests—pCIT,x→y, was estimated for the direction of x causing y (Model 1), and for the direction of y causing x, pCIT,y→x (Model 2). The results from each of these methods can then be used in combination to infer the existance and direction of causality. For some significance threshold α there are four possible outcomes from these two tests, and their interpretations are as follows:
For the purposes of compiling simulation results we use an arbitrary α = 0.05 value, though we stress that for real analyses it is not good practice to rely on p-values for making causal inference, nor is it reliable to depend on arbitrary significance thresholds [53].
Two stage least squares (2SLS) is a commonly used technique for performing MR when the exposure, outcome and instrument data are all available in the same sample. A p-value for this test, pMR, was obtained using the systemfit function in the R package R/systemfit [54]. Note that the value of pMR is identical when using the same genetic variant to instrument the influence of the exposure x on the outcome y, or erroneously, instrumenting the outcome y on the exposure x.
The method that we will now describe is designed to distinguish between two models, x → y or y → x. Unlike the CIT framework, this approach cannot infer if the true model is x ← g → y. We also assume all genetic effects are additive.
To infer the direction of causality it is desirable to know which of the variables, x or y, is being directly influenced by the instrument g. This can be achieved by assessing which of the two variables has the biggest absolute correlation with g (S2 Text), formalised by testing for a difference in the correlations ρgx and ρgy using Steiger’s Z-test for correlated correlations within a population [55]. It is calculated as
Z = ( Z g x - Z g y ) N - 3 2 ( 1 - ρ x y ) h
where Fisher’s z-transformation is used to obtain Z g * = 1 2 ln ( 1 + ρ g * 1 - ρ g * ),
h = 1 - ( f r m 2 ) 1 - r m 2
where
f = 1 - ρ x y 2 ( 1 - r m 2 )
and
r m 2 = 1 2 ( ρ g x 2 + ρ g y 2 ) .
The Z value is interpreted such that
Z { > 0 , x → y < 0 , y → x = 0 , x ⊥ ⊥ y
and a p-value, pSteiger is generated from the Z value to indicate the probability of obtaining a difference between correlations ρgx and ρgy at least as large as the one observed, under the null hypothesis that both correlations are identical.
The existence of causality and its direction is inferred based on combining information from the MR analysis and the Steiger test. The MR analysis indicates whether there is a potential causal relationship (pMR), and the Steiger test indicates the direction (sign(Z)) of the causal relationship and the confidence of the direction (pSteiger). For the purposes of compiling simulation results, these can be combined using an arbitrary α = 0.05 value:
Note that the same correlation test approach can be applied to a two-sample MR setting. Two-sample MR refers to the case where the SNP-exposure association and SNP-outcome association are calculated in different samples (e.g. from publicly available summary statistics [26, 30]). Here the Steiger test of two independent correlations can be applied where.
Z = Z g x - Z g y 1 / ( N 1 - 3 ) + 1 / ( N 2 - 3 )
An advantage of using the Steiger test in the two sample context is that it can compare correlations in independent samples where sample sizes are different. Steiger test statistics were calculated using the r.test function in the R package R/psych [56].
The Steiger test assumes that there is a causal relationship between the two variables, and that the SNP is a valid instrument for one of them. However it is liable to give incorrect causal directions under some other circumstances. First, some levels of horizontal pleiotropy, where the SNP influences the outcome through some pathway other than the exposure, could induce problems because this is a means by which the instrument is invalid. Second, some differential values of measurement error between the exposure and the outcome could lead to incorrect inference of the causal direction (S2 Text). Third, some levels of unmeasured confounding between the exposure and the outcome could lead to inference of the wrong causal direction (S3 Text).
The Steiger test for inferring if x → y is based on evaluating ρgx > ρgy. However, ρgx (or ρgy) are underestimated if x (or y) are measured imprecisely. If, for example, x has lower measurement precision than y then we might empirically obtain ρg,xo < ρg,yo because ρg,xo could be underestimated more than ρg,yo.
As we show in S2 Text it is possible to infer the bounds of measurement error on xo or yo given known genetic associations. The maximum measurement imprecision of xo is ρg,xo, because it is known that at least that much of the variance has been explained in xo by g. The minimum is 0, denoting perfectly measured trait values (the same logic applies to yo). It is possible to simulate what the inferred causal direction would be for all values within these bounds.
To evaluate how reliable, R, the inference of the causal direction is to potential measurement error in x and y we need to predict the values of ρgy − ρgx for those values of measurement error. We offer two tools in which to do this. First, the user can provide values of measurement error for x and y and obtain a revised inference of the causal direction. Second, we integrate over the entire range of ρgy − ρgx values for possible measurement error values, assuming that any measurement error value is equally likely. Across all possible values of measurement error in x and y we find the volume that agrees with the inferred direction of causality and the volume that disagrees with the inferred direction of causality, and take the ratio of these two values. A ratio R = 1 indicates that the inferred causal direction is highly sensitive to measurement error, because equal weight of the measurement error parameter space supports each direction of causality. In general, the R value denotes that the inferred direction of causality is R times more likely to be the empirical result than the opposite direction (S2 Text).
Simulations were conducted by creating variables of sample size n for the exposure x, the measured values of the exposure xo, the outcome y, the measured values of the outcome yo and the instrument g. One of two models are simulated, the “causal model” where x causes y and g is an instrument for x; or the “non-causal model” where g influences a confounder u which in turn causes both x and y. Here x and y are correlated but not causally related. Each variable in the causal model was simulated such that:
g ∼ B i n o m ( 2 , 0 . 5 ) x = α g + β g g + ϵ g x o = α m x + β m x x + ϵ m x y = α x + β x x + ϵ x y o = α m y + β m y y + ϵ m y
where non-differential measurement error is represented by a noise (measurement imprecision) term ϵ m * ∼ N ( 0 , σ m * 2 ), and measurement bias terms αm* and βm* for the exposure variable x and the outcome variable y. Note that following the first section of the Results we no longer include the bias terms for simplicity. We have formulated the non-causal model as:
y = α g y + β g y g + ϵ g y
All α values were set to 0, and β values set to 1. Normally distributed values of ϵ* were generated such that
c o r ( g , x ) 2 = 0 . 1 c o r ( x , y ) 2 = { 0 . 2 , 0 . 4 , 0 . 6 , 0 . 8 } σ m x 2 = { 0 , 0 . 2 , 0 . 4 , 0 . 6 , 0 . 8 , 1 } σ m y 2 = { 0 , 0 . 2 , 0 . 4 , 0 . 6 , 0 . 8 , 1 } n = { 100 , 1000 , 10000 }
giving a total of 432 combinations of parameters. Simulations using each of these sets of variables were performed 100 times, and the CIT and MR methods were applied to each in order to evaluate the causal association of the simulated variables. Similar patterns of results were obtained for different values of cor(g, x).
Two sample MR [30] was performed using summary statistics for genetic influences on gene expression and DNA methylation. To do this we obtained a list of 458 gene expression—DNA methylation associations as reported in Shakhbazov et al [39]. These were filtered to be located on the same chromosome, have robust correlations after correcting for multiple testing, and to share a SNP that had a robust cis-acting effect on both the DNA methylation probe and the gene expression probe. Because only summary statistics were available (effect, standard error, effect allele, sample size, p-values) for the instrumental SNP on the methylation and gene expression levels, the Steiger test of two independent correlations was used to infer the direction of causality for each of the associations. The Wald ratio test was then used to estimate the causal effect size for the estimated direction for each association.
All analysis was performed using the R programming language [57] and code is made available at https://github.com/explodecomputer/causal-directions and implemented in the MR-Base (http://wwww.mrbase.org) platform [26].
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10.1371/journal.ppat.1005710 | The Actin Filament-Binding Protein Coronin Regulates Motility in Plasmodium Sporozoites | Parasites causing malaria need to migrate in order to penetrate tissue barriers and enter host cells. Here we show that the actin filament-binding protein coronin regulates gliding motility in Plasmodium berghei sporozoites, the highly motile forms of a rodent malaria-causing parasite transmitted by mosquitoes. Parasites lacking coronin show motility defects that impair colonization of the mosquito salivary glands but not migration in the skin, yet result in decreased transmission efficiency. In non-motile sporozoites low calcium concentrations mediate actin-independent coronin localization to the periphery. Engagement of extracellular ligands triggers an intracellular calcium release followed by the actin-dependent relocalization of coronin to the rear and initiation of motility. Mutational analysis and imaging suggest that coronin organizes actin filaments for productive motility. Using coronin-mCherry as a marker for the presence of actin filaments we found that protein kinase A contributes to actin filament disassembly. We finally speculate that calcium and cAMP-mediated signaling regulate a switch from rapid parasite motility to host cell invasion by differentially influencing actin dynamics.
| Parasites causing malaria are transmitted by mosquitoes and need to migrate to cross tissue barriers. The form of the parasite transmitted by the mosquito, the so-called sporozoite, needs motility to enter the salivary glands, to migrate within the skin and to enter into blood capillaries and eventually hepatocytes, where the parasites differentiate into thousands of merozoites that invade red blood cells. Sporozoite motility is based on an actin-myosin motor, as is the case in many other eukaryotic cells. However, most eukaryotic cells move much slower than sporozoites. How these parasites reach their high speed is not clear but current evidence suggests that actin filaments need to be organized by either actin-binding proteins or membrane proteins that link the filaments to an extracellular substrate.
The present study explores the role of the actin filament-binding protein coronin in the motility of sporozoites of the rodent model parasite Plasmodium berghei. We found that the deletion of P. berghei coronin leads to defects in parasite motility and thus lower infection of mosquito salivary glands, which translates into less efficient transmission of the parasites. Our experiments suggest that coronin organizes actin filaments to achieve rapid and directional motility. We also identify two signaling pathways that converge to regulate actin filament dynamics and suggest that they play a role in switching the parasite from its motility mode to a cell invasion mode.
| Malaria-causing parasites need to actively migrate at several steps in their complex life cycle [1]. Without motility they would fail, for example, to enter red blood cells or to penetrate the mosquito midgut. The stage with the most formidable motility is the sporozoite, which migrates at average speeds exceeding 1 μm/s through the skin [2,3]. Plasmodium sporozoites are formed in parasitic oocysts at the midgut wall of Anopheles mosquitoes and, after successful transmission from the mosquito to the mammalian host, ultimately differentiate in hepatocytes to generate red blood cell infecting merozoites. Sporozoites first need to emerge from the oocysts, float through the circulatory fluid of the insect, attach to and actively invade the salivary glands [1]. After ejection with the saliva during the mosquito bite, sporozoites are deposited into the dermis, where they migrate actively at high speed to attach to and enter into blood vessels [1,4]. Taken away with the blood stream they again attach to the liver endothelium and pass through this barrier to finally enter hepatocytes [4,5]. Sporozoites are crescent shaped chiral cells that can also move on diverse substrates without changing their shape at average speeds of 1–2 μm/s [6–8]. The motor driving this gliding motility is located underneath the plasma membrane in a narrow space delimited by a membrane organelle called the inner membrane complex (IMC) that subtends the plasma membrane at a distance of approximately 30 nm. Within this space, it is thought that myosin, anchored in the IMC, drives actin filaments rearwards in what resembles retrograde flow [9,10]. Actin filaments themselves are likely linked to transmembrane proteins that contain adhesive domains including an integrin-like A-domain [9,10]. This linkage thus drives parasite motility upon attachment to a substrate although it is not clear how the different transmembrane proteins transmit force [11–13]. Actin filaments are extremely short in Plasmodium as well as in related parasites and cannot be routinely visualized [14–16]. This is at least partly due to a number of differences in the actin monomer structure that prevent the formation of long filaments [17–19]. In addition, actin-binding proteins might play a role in regulating actin filament dynamics. For example, the deletion of actin depolymerizing factor in the related parasite Toxoplasma gondii leads to formation of long filaments and stalls motility [15,20]. The Plasmodium genome only encodes a small set of canonical actin-binding proteins [21–23]. The only bona-fide actin filament-binding protein in the Plasmodium genome is coronin, which shares only 31% sequence identity to Dictyostelium coronin [24]. Coronin is conserved among the different species of Plasmodium and shows 57% identity between the major human malaria-causing parasite P. falciparum and the rodent model parasite P. berghei (S1 Fig).
Coronins are a family of actin filament-binding proteins with the first identified coronin described to be important for cell motility and differentiation in Dictyostelium discoidum [25,26]. All coronins have one or two WD40 repeat-containing ß-propellers that mediate actin filament-binding [25,27]. Coronins can harbor 2 independent actin-binding sites in their ß-propellers and can also bind membranes [28,29]. Furthermore, coronins can contain a number of additional domains and regions that allow dimer formation and interaction with a range of different proteins including actin-binding proteins and microtubules [27,30]. In alveolata, a major superphylum of protists, coronins belong to the orphan class of short coronins containing one ß-propeller [27,31,32]. Coronin has been examined in several protozoan parasites such as the human malaria-causing parasite Plasmodium falciparum, the related apicomplexan parasite Toxoplasma gondii and in Leishmania, unicellular parasites from the excavata branch. Yet the coronins in each of these organisms display divergent functions. While in Leishmania coronin is essential for microtubule organization, in Toxoplasma gondii it only binds actin filaments weakly and plays a minor, uncharacterized role during host cell invasion [31,32]. In P. falciparum coronin was identified and characterized as an actin filament-binding protein [24] and shown to be able to bind to membranes and bundle actin filaments [33]. These functional differences might be explained by the divergence of coronin among these organisms (S1 Fig).
Here we generated a series of parasite lines that either lack coronin, contain mutations of important residues in the ß-propeller or express coronin fused to mCherry in P. berghei, a rodent model parasite. These lines show that P. berghei coronin localizes to the sporozoite periphery in a calcium dependent but actin independent manner and actively contributes to parasite motility through actin filament-binding. During the rapid onset of migration extracellular ligands first trigger intracellular calcium release prior to actin filament formation and coronin relocalization to the rear of the gliding sporozoite. Importantly, coronin is required for efficient transmission of the parasite from mosquitoes to mice as coronin(-) parasites and parasites expressing mutated coronin show defects in salivary gland invasion. Lastly, we suggest a switch in signaling pathways linking calcium- and cAMP- mediated signaling during migration and invasion.
To investigate the functions of coronin, we first generated a P. berghei parasite line lacking the coronin gene by transfecting a vector containing a resistance cassette that integrated via double homologous recombination across the coronin open reading frame (S2A Fig). A previous study on P. falciparum coronin localized the protein in blood stages suggesting a potential role in red blood cell invasion [33]. Remarkably, we readily obtained three different coronin(-) parasite lines from two independent transfections suggesting that there was no detrimental effect of the lack of coronin in the blood stage of P. berghei, where transfection and selection was performed (S2B Fig). Indeed, quantitative analysis of parasite growth in the blood of infected mice revealed no difference to wild type parasites. Coronin(-) parasites were transmitted normally to the mosquito vector and resulted in similar oocyst and midgut sporozoite numbers than infection with wild type parasites (Table 1), suggesting that coronin also plays no detectable role in parasite infection of the mosquito midgut. However, we found significantly lower numbers of coronin(-) sporozoites accumulating in the salivary glands of mosquitoes (Table 1), with a reduction of the infection rate by 60–70%, suggesting that coronin plays a significant role in invasion of this organ.
Upon intravenous injection of 10.000 salivary gland-derived sporozoites, the parasitemia of mice receiving coronin(-) parasites increased from 0.06% to 1.8% between day 4 to day 6. This was comparable to the increase in mice receiving wild type sporozoites (from 0.14–2.6%). Similarly, during natural transmission by the bites of 10 mosquitoes coronin(-) parasites showed an increase from <0.01 to 1.5% parasitemia over the same period, while WT parasites showed an increase from 0.2 to 3.0%, again suggesting no effect of coronin ablation on blood stage growth. However, these data also showed that coronin(-) infected mice had consistently lower parasite burdens early during infection. In addition, only 17 of 20 mice were infected with coronin(-) parasites after mosquito bites, while all mice bitten by mosquitoes carrying WT sporozoites became infected (Table 2). These, with coronin(-)infected mice also showed a slight delay in the onset of blood stage development compared with the wild type controls (Table 2). In contrast, when sporozoites were injected directly into the blood stream of mice there was no difference in the number of infected mice compared to the mosquito bite experiments yet, the onset of a blood stage infection was also slightly delayed (Table 2). The slight delay in blood stage infection went along with fewer animals dying from symptoms of experimental cerebral malaria (ECM), a condition where mice suffer brain hemorrhages early during infection (Table 2). Survival from ECM is often associated with delayed liver stage development [34,35] and was accompanied by a change in the interferon-γ and interleukin-10 levels at the critical time of cerebral malaria symptoms development (S2C Fig). Together these in vivo data suggest that coronin(-) sporozoites might have a defect in migration in the skin where they are deposited by a mosquito bite and/or during liver stage development. To investigate liver stage growth we infected hepatocytes with wild type and mutant sporozoites and indeed found coronin(-) parasites developing slower within liver cells as they form smaller parasites at 48 hours post infection when compared to the wild type (Table 2). We also infected C57BL/6 mice with 10.000 sporozoites and analyzed the parasite load in the liver 42 hours post infection. This showed an overall slightly reduced liver burden in mice infected with coronin(-) parasites compared to WT infected mice (S3G Fig).
To investigate sporozoite motility, we analyzed salivary gland derived coronin(-) sporozoites by video microscopy on a 2D glass substrate, where wild type parasites usually move in near-perfect circles (Fig 1A(i)). In contrast to the wild type, coronin(-) sporozoites rarely managed to move persistently on circular paths, but often detached from the substrate, frequently moving just back and forth over a single adhesion site (Fig 1A(ii)). This motility form was similar but not identical to patch-gliding [7], which has so far only been observed in sporozoites isolated from the hemolymph. This aberrant motility suggests that the motor or the actin filaments are not properly oriented [7,36]. In addition, coronin(-) sporozoites frequently undergo bending and flexing movements without moving forward, a phenotype so far also not described for Plasmodium sporozoites (Fig 1Aiii and 1Aiv). As a consequence of these motility defects tracks of the motile coronin(-) sporozoites on glass appear different than those of wild type sporozoites (Fig 1B).
Like with metazoan cells, sporozoite motility defects measured on a flat substrate often do not translate directly into motility defects in a 3-dimensional environment such as the skin [38–40], where sporozoites need to pass through dermal and epithelial skin cells to gain access to the blood [41]. To investigate motility in the skin we thus generated a coronin(-) parasite line that strongly expresses mCherry in the sporozoite stage (S3A Fig). These parasites showed the same salivary gland invasion and migration defects as non-fluorescent coronin(-) sporozoites (S3B and S3C Fig). To image sporozoites in the skin we let mosquitoes bite the ear of an anesthetized mouse and transferred the mouse to a wide-field fluorescence microscope [3]. Imaging of several bite sites showed that coronin(-) sporozoites migrated with similar speed and at similar numbers as did control parasites expressing the same fluorescent proteins (S3D–S3F Fig). This suggests that the main in vivo migration defect of coronin ablation is a reduced entry into the salivary gland of mosquitoes and not after ejection into the skin (Tables 1 and 2).
To get further insights into coronin function we next investigated coronin localization in the parasite. To this end we engineered an additional parasite line that expresses a coronin-mCherry fusion protein under the control of the endogenous promoter (S4A and S4B Fig). The mCherry signal could not be visualized in asexual blood stages, gametocytes and ookinetes but was evident in midgut and salivary gland sporozoites. In sporozoites isolated from oocysts and in those isolated from salivary glands, the weak fluorescent signal localized to the periphery of the parasite (Fig 1C). These sporozoites were slightly slower than wild type sporozoites (Table 1). Also the liver stage development and blood stage appearance of coronin-mCherry parasites was somewhat delayed (Table 2) suggesting a minor but measureable impact of the mCherry tag on the function of the fusion protein. During liver stage development coronin continued to be weakly expressed, but the protein appeared cytoplasmic (S4C Fig). Curiously, when bovine serum albumin, which stimulates motility [6,42,43], was added to the salivary gland derived sporozoites, coronin-mCherry relocalized to the rear of the now migrating parasite (Fig 1C and S1 Movie).
To probe if coronin interacts with actin filaments in the sporozoite, we added increasing concentrations of the actin filament modulating compounds cytochalasin D and jasplakinolide to the medium and monitored the distribution of the fusion protein. Both compounds are known to affect sporozoite motility [7,36,44]. To quantify the outcome we measured the intensity profile of the signal across the sporozoite at the front and rear end (Fig 1D) and plotted the difference in maximum fluorescent signal as the front versus rear ratio (Fig 1E). At high concentrations of both compounds coronin-mCherry was no longer localized to the rear end but redistributed to the periphery of the entire parasite (Fig 1D). This showed that already at a low concentration of 30 nM cytochalasin D a relocalization effect was apparent, while for the actin filament stabilizing jasplakinolide this only became apparent above 200 nM (Fig 1E). These observations suggest that coronin is localized to a membrane at the periphery of the parasite, either the plasma membrane or the IMC, and upon activation is moved rearwards with actin filaments.
To further investigate the molecular mechanism of this change in localization and its effect on motility we sought to generate parasite lines expressing mutated versions of coronin. Multiple sequence alignment of both P. berghei and P. falciparum coronin with the mouse and yeast coronin showed conservation of the 4 suggested actin-binding sites (Fig 2A) [45,46]. Due to the low expression of endogenous coronin, we generated another coronin-mCherry parasite line that overexpresses the fusion protein as an additional copy from the stronger sporozoite and liver stage specific uis3 promoter (S5A and S5B Fig). The coronin-mCherry overexpressed from the uis3 promoter was detected by western blotting using an antibody against mCherry (S5C Fig). As with the parasite expressing endogenous coronin-mCherry, this parasite line showed clear localization of the protein to the periphery in immature midgut sporozoites and non-activated salivary gland sporozoites, while it relocalized to the rear upon activation (Fig 2B). These coronin-mCherry overexpressing sporozoites were also more motile in medium lacking BSA (18%) compared with the wild type (4%) suggesting that additional coronin leads to stabilization or increased activity of the motility machinery and hence more robust motility.
We next chose to generate a parasite line expressing a mutant coronin-mCherry where 8 conserved residues at four different locations suggested to play a role in actin filament-binding and possible membrane association were replaced by alanines (Fig 2C and S5D Fig). This parasite line showed only cytoplasmic localization of coronin-mCherry (Fig 2C). We next made 6 more parasite lines with three of the amino acid pairs changed into either alanines or amino acids of opposite charge (Fig 2C and S5E Fig). These lines showed remarkable differences in localization. While the R24A, R28A mutant showed a similar localization as the wild type in activated sporozoites, the R24E, R28E mutant localized only to the periphery and did not relocalize to the rear upon activation (Fig 2C). In contrast, the other two mutants showed similar phenotypic localizations independent of their substituted amino acids. Mutations of residues 283 and 285 led to a cytoplasmic localization of the fusion protein, suggesting that these residues are important for membrane association (Fig 2C). Mutation of residues 349 and 350 led to a peripheral localization similar to the R24E, R28E mutant suggesting that these amino acids mediate actin filament-binding as has been suggested for coronins in yeast [45]. A quantitative assessment of the front versus rear ratios further showed that the localization was most disturbed in the R349E, K350E mutant, while the R349A, K350A mutant was similar to R24E, R28E (Fig 2D). If there was however still actin-mediated localization of the mutants to the periphery we anticipated that jasplakinolide or cytochalasin D should lead to a change in localization. This was not the case for either 100 nM or 1 μM of both drugs (Fig 2E), which suggests that indeed coronin is localizing in an actin filament independent fashion to the periphery prior to sporozoite activation. Similarly, activation of secretion by adding ionomycin [43], did not lead to a change in localization of mutant coronin-mCherry in this line (Fig 2E).
To investigate the effect of these coronin mutations on motility we generated four additional parasite lines where the endogenous coronin was replaced by four selected mutated forms fused to mCherry (S6 Fig). For these experiments we chose the R24A, R28A mutant, which we expected to behave like wild type coronin, K283A, K285A; R349E, K350E as well as the coronin harboring all 8 amino acid changes, which we expected to show a phenotype different from wild type sporozoites. Like coronin(-) parasites, we could not observe any difference in growth of these parasite lines in the blood and early mosquito stages but found different effects on the numbers of sporozoites in the salivary gland, their migration behavior and animal infection capacity (Fig 3, Tables 1 and 2). As expected, the parasite carrying the R24A, R28A mutation showed no difference to wild type control parasites (i.e. endogenous coronin-mCherry expressing parasites). All other parasites however showed severe motility defects similar to those observed in coronin(-) sporozoites (Fig 3, Tables 1 and 3).
Of these, the parasite line containing all mutations showed the strongest phenotype. In mosquitoes we found that all but R24A, R28A showed lower numbers of sporozoites in the salivary gland, albeit the ratio of midgut versus salivary gland sporozoites was also low (Table 1). In transmission assays we found that R24A, R28A showed no delay in blood stage appearance although not all animals became infected (Table 2). Of the other mutants only R349E, K350E showed a further delay in blood stage appearance of half a day compared to coronin-mCherry parasites (Table 2). Curiously the size of the liver stages of all mutants was closer to those of wild type parasites as those of coronin(-) parasites suggesting that the liver stage phenotype might not be due to the interaction of coronin with actin filaments. Yet, there is a delay of about half a day in the appearance of blood stage parasites of these mutants (Table 2). This hints at a role of coronin prior to hepatocyte invasion, which could be due to a decreased invasion of blood vessels in the skin or decreased extravasation in the liver. Taken together, these data suggest that coronin binds to a peripheral membrane in non-activated sporozoites and following activation binds to actin filaments that move the protein to the rear end. This might also suggest that the role of coronin in sporozoite entry into salivary glands and liver stage development might be due to different mechanisms.
Intracellular calcium release precedes the relocalization of coronin and motility Little is known about the nature of the extracellular activating ligand and the sequence of events leading to sporozoite activation upon ligand binding. Given the different localization patterns of coronin-mCherry in activated versus non-activated sporozoites, we sought to use this parasite line as a visual tool to dissect activating signaling events. Activation of sporozoites with BSA has been shown to include a raise in cytoplasmic calcium [43]. We thus investigated if coronin relocalization appears prior to or after the increase in intracellular calcium following external ligand stimulation. To this end we stimulated sporozoites overexpressing coronin-mCherry with BSA and then chelated intracellular calcium ions with BAPTA-AM. Consistent with previous results [43], BAPTA-AM slowed sporozoites down (Fig 4A). This also resulted in a redistribution of coronin-mCherry to the cytosol (Fig 4A). This redistribution was surprising as we hypothesized that intracellular calcium contributes or leads to actin dependent posterior localization of coronin. We next treated non-activated salivary gland and midgut sporozoites with BAPTA-AM. This also resulted in the cytoplasmic localization of coronin (Fig 4A). We next incubated sporozoites in non-activating medium and added either ionomycin or ethanol, which lead to an increase in intracellular calcium and secretion of vesicles in sporozoites and T. gondii tachyzoites [47,48]. When treated at low concentrations (100 nM) with these stimulants parasites moved actively for a few minutes with a polarized coronin-mCherry localization (Fig 4B). However, ionomycin treatment did not rescue motility of coronin(-) parasites (S7 Fig). This suggests that ligand-dependent calcium elevation appears to be upstream of coronin relocalization but that in the absence of coronin, forced exocytosis does not rescue motility. We also noticed that during treatment with BAPTA-AM, coronin was distributed throughout the cytoplasm. These data suggest that basal levels of intracellular calcium are needed for coronin localization to the periphery in non-activated sporozoites. Moreover, upon ligand stimulation an increase in calcium leads to microneme secretion and coronin relocalization (Fig 4C). Others have shown that increased intracellular calcium also leads to massive exocytosis that mediates invasion in the liver [49] and hence we speculate that different levels of calcium mediate different functions at subsequent stages of the sporozoite journey (Fig 4C).
We finally probed a potential involvement of kinases and cAMP signaling in activation of gliding motility using two different cell permeable inhibitors, H89 (inhibits protein kinase A) and SQ22536 (inhibits adenylyl cyclase). All these inhibitors stopped sporozoite motility to some degree as was shown previously [50]. Conversely, when we stimulated cAMP production in the absence of BSA with forskolin, some sporozoites still moved also confirming previous results [50] (Fig 5A and 5B). Remarkably, when sporozoites halted their movement during inhibition by PKA inhibitors, the coronin-mCherry signal remained polarized at the rear (Fig 5C and 5D). This is remarkable as in all our previous experiments the inhibition of motility always altered the localization of coronin from the rear to the periphery (Fig 1D and 1E). This suggests that the inhibition of the cAMP-protein kinase A (PKA) pathway interferes with actin dynamics in a different way to jasplakinolide or cytochalasin D. PKA could phosphorylate coronin and thus lead to altered actin filament-binding characteristics, while the compounds clearly act on actin filaments themselves. To test this, we added cytochalasin D to sporozoites pre-incubated with BSA and H89. This resulted in the peripheral localization of coronin (Fig 5C). Hence, these data suggest that actin filament turnover is halted by PKA inhibition and implies that PKA and coronin are involved in the turnover of actin filaments at the rear. To further probe this we investigated the fluorescence recovery after photo-bleaching (FRAP) of coronin-mCherry overexpressing parasites (Fig 5E). FRAP of motile sporozoites in medium containing BSA recovered a bleached signal within 2.4±0.2 seconds (range: 0.6–4.6 s) to half its original intensity (Fig 5E and 5F). The bleach spot did not move rearwards and there was no directionality in recovery suggesting that the coronin bound actin filaments are stable and that coronin recovers by diffusion (Fig 5E). In medium lacking BSA we bleached motile and non-motile sporozoites. The motile sporozoites recovered within the same time independent of the presence of BSA, while non-motile sporozoites recovered in just 1.7±0.6 seconds. This suggests that binding to actin filaments slows the mobility of coronin-mCherry. Yet, neither cytochalasin D nor jasplakinolide caused a different recovery rate after FRAP compared to controls suggesting that actin filaments play no role in slowing coronin-mCherry mobility (Fig 5F). Curiously, however, incubation with H89 caused a slower recovery (3.1±1.3 s) compared with control parasites and parasites treated with 1 μM cytochalasin D or 100 nM jasplakinolide (Fig 5F). These data suggest a complex level of actin filament regulation by coronin and cAMP mediated signaling that we can currently not resolve. Together, however, these data show a rapid steady-state dissociation of coronin from actin filaments and that coronin mobility is decreased upon sporozoite activation, possibly (but likely only partially) due to its association with actin filaments. The slight increase of recovery under H89 but the absence of an effect of cytochalasin D and jasplakinolide might point to a difference of actin filament dynamics or coronin binding to actin filaments in the presence of these drugs as already suggested by the experiments described in Fig 5C and 5D.
Taking together, these data and data by other groups [49,51] suggest complex signaling events with feedback loops during activation for gliding. We hypothesize that calcium signaling is upstream of cAMP signaling during gliding, while during invasion these signaling cascades appear reversed (Fig 6).
Our data showed that deletion of coronin diminished sporozoite motility and causes a decrease in salivary gland invasion but does not impair gliding within the dermis. While we cannot rule out that sporozoites lacking coronin also have a deficiency in entering the blood circulation, these results suggest that a decrease in gliding affects salivary gland infection more than gliding in the skin. This is in contrast to findings with hsp20(-) sporozoites, which also show a severe (but different) decrease in gliding motility but enter salivary glands at wild type levels while moving only very slowly through the skin [7,40]. hsp20(-) sporozoites glide very slowly in vitro and in vivo and appear to attach well to substrates, while coronin(-) sporozoites move fast in vivo but have problems to stay attached on the surface. Thus it might be that coronin(-) sporozoites fail to properly attach to the surface of salivary glands or fail to produce enough force to cross the basal lamina surrounding this tissue, while the 3D environment in the skin allows them to move as efficiently as wild type sporozoites. A similar disconnection of motility in 2D and 3D migration is not uncommon in higher eukaryotic cells [39].
None of the coronin isoforms from higher eukaryotes studied so far have been found to be essential for cellular survival. However, several coronins have clearly important functions. For example, coronin assists in organismal survival by mediating proper B- and T-cell functions [52,53]. Coronin function can also be subverted by mycobacteria, which are protected in its presence once engulfed by macrophages [52,53]. In other single-celled eukaryotes such as yeast and Dictyostelium, coronin can be ablated without a detrimental effect although it clearly plays a role in actin reorganization in these organisms and might indeed be essential if studied in an environmental rather than tissue culture setting [54,55]. The complex life cycle of Plasmodium allowed uncovering of an essential function of coronin in efficient life cycle progression in a rodent model parasite. P. berghei is frequently used to study mosquito and liver stages instead of the human infecting P. falciparum. We believe that basic cell biological functions between these two parasites are largely conserved. Along these lines we would expect that coronin will also play a role in sporozoite motility in other Plasmodium species. However, the difference in expression of coronin in blood stages of P. falciparum versus no expression in P. berghei is interesting and requires further study. While P. berghei coronin(-) parasites can be transmitted, the observed decrease in their transmission efficiency to the salivary glands by about two thirds (Table 1) would probably rapidly eliminate coronin(-) parasites in an environmental setting. Thus the malaria-causing parasite, in addition to being a medically important cell, provides an intriguing model system for studying subtle effects of proteins on motility in the context of their overall biological role. Such correlations are often missed in higher eukaryotes, where effects such as coronin mediated debranching of actin filaments [56] cannot be correlated with gene deletion studies in mice where no phenotype was observed [53,57]. Here we show that the only bona-fide actin filament-binding protein coronin of Plasmodium localizes to the periphery of sporozoites, participates in sporozoite motility (Fig 1) and is essential for efficient transmission of malaria (Table 1).
At the periphery of sporozoites coronin could localize at the subpellicular network, a cytoskeletal structure underlying the inner membrane complex (IMC), the IMC itself or the plasma membrane. As these structures are only 30 nm apart [8] it is not possible to distinguish them using a light microscope. However, the observation that coronin relocalizes upon sporozoite activation in an actin filament-dependent manner (Figs 1C–1E and 2B) suggests that it is recruited either to the plasma membrane or the IMC. It has been shown that coronin can localize to membranes in macrophages through cholesterol [58] and in neurons via interactions with trans-membrane proteins [59]. Also PIP2 can regulate coronin function at the leading edge of motile cells by inhibiting coronin mediated actin filament disassembly [60]. The N-terminal part of P. falciparum coronin (i.e. the ß-propeller) was also shown recently to bind to PIP2 [33]. Our work with P. berghei coronin mutated in the ß-propeller surface revealed that amino acids K283 and D285 might be involved in membrane association. Coronin mutated at these sites did not associate with membranes and parasites expressing a K283A, D285A mutation failed to migrate properly (Figs 2C and 3). This suggests the possibility that coronin could be a linker between the membrane and actin filaments, either directly or through transmembrane proteins, in the same way coronin in neurons can link acetylcholine receptors with the actin cytoskeleton [59]. However, this is clearly too speculative with the current set of data and would require additional biochemical and mutational evidence. We currently prefer a model where coronin is membrane associated in non-activated sporozoites and translocates to actin filaments upon their formation (see below and Fig 6).
Actin filament-binding is likely mediated by residues R24, R28, R349 and K350 as mutating these amino acids abrogated relocalization of coronin upon sporozoite activation (Fig 2C and 2D) and mutations in R349 and K350 also lead to aberrant motility (Fig 3). These data also suggest that coronin binds to actin filaments with higher affinity than to membranes. As sporozoites lacking coronin or containing mutations with actin filament-binding defects undergo aberrant motility, this suggests that coronin plays a role in organizing actin filaments that allows for continuous motility. This could be achieved by bundling filaments, if coronin forms dimers as has been shown for other coronins [37,61] including T. gondii coronin [32]. Alternatively, a single coronin could bundle filaments as observed with the N-terminal half of P. falciparum coronin [33]. The suggestion of coronin as an actin filament organizer is particularly tempting considering that aberrant movement of the types seen in coronin(-) sporozoites can also be observed after adding jasplakinolide or in parasites isolated from the hemolymph [7]. Also parasites lacking the transmembrane protein thrombospondin related anonymous protein as well as parasites lacking the beta subunit of the actin capping protein undergo similar aberrant movement, although these parasites do not enter salivary glands [7,62,63]. Hence a coordination of transmembrane adhesins, actin filaments and actin-binding proteins is clearly needed to achieve continuous motility, which is first needed for salivary gland invasion, a process during which the sporozoites need to cross the basal lamina surrounding the salivary gland. As forced actin polymerization through small doses of jasplakinolide could partially compensate for the loss of the TRAP related adhesin TRAP-like protein (TLP), we previously speculated that actin filament organization is important for motility [11,36]. Jasplakinolide did not rescue the defects of coronin(-) sporozoites over a range of concentrations. This suggests that in tlp(-) sporozoites actin filaments are organized but the missing link to the substrate causes motility defects that longer filaments can compensate. In contrast, addition of jasplakinolide in coronin(-) parasites where actin filaments are disordered would not compensate. Thus, we speculate that coronin is organizing actin filaments such that the stroke of myosins pulls them rearwards, while in coronin(-) parasites the filaments are pulled in different directions leading to non-continuous movement (Fig 6). Curiously, this defect is compensated in vivo, where coronin(-) parasites migrate similarly as wild type parasites in the skin (S3D and S3F Fig). One explanation for this compensation might be that in vivo (in 3D) more TRAP-family adhesins link the substrate to the actin filaments than in vitro (in 2D).
Sporozoite motility is regulated by the formation and turnover of distinct adhesion sites with the substrate [7,40]. Actin filaments and members of the TRAP- family of adhesins also play a role in adhesion formation and turnover [7,11–13]. As parasites lacking coronin frequently detach from the substrate and struggle to re-attach (Fig 1), this also suggests a function for coronin in adhesion formation. Due to the lack of actin filament visibility in sporozoites it remains speculative on how they are organized. Yet, the data collected here further suggest a crucial interplay between TRAP-family adhesins, actin filaments and actin regulating proteins for adhesion formation as a prerequisite for sporozoite migration. The fact that coronin(-) sporozoites can migrate at similar rates to wild type parasites in 3D (S3 Fig) further shows that adhesion formation is the limiting factor in motility. This observation has implications for our understanding of how force is transmitted across the plasma membrane in sporozoites and other apicomplexans such as Toxoplasma gondii, where most motility studies have been performed in 2D and only recently quantitative studies in 2D and 3D challenge the accepted model for parasite motility [64–66]. It is likely that the loss in motility in 2D is due to a defect in cell adhesion, which can be compensated for in 3D. A similar finding was shown for parasites lacking the TRAP- family adhesin TLP, where tlp(-) parasites migrated as well under flow as wild type parasites in the absence of flow [12]. And indeed tlp(-) sporozoites are less capable of force generation than WT sporozoites [11].
In macrophages, T- and B-cells coronin is activated during outside-in signaling to further signal for calcium release from intracellular stores [53,67–69]. Our data suggest an opposite signaling pathway. During outside-in signaling, calcium is first released leading to the activation of either coronin itself and/or of the activation of actin filament formation that concomitantly leads to the recruitment of coronin to filaments. Coronin activation could be modulated in different ways, one being through phosphorylation. Indeed coronin was found to be phosphorylated in proteomic screens in P. falciparum and T. gondii [70]. One calcium-dependent kinase has already been shown to play a role in parasite motility, although for an earlier stage during mosquito infection [71]. Intriguingly, staurosporine also arrests red blood cell invading parasites in a similar fashion as cytochalasin D [72].
cAMP-mediated signaling plays a role in many motility and invasion processes including in sperm [73]. cAMP signaling also plays a role in invasion of red blood cells by merozoites [51] and of liver cells by sporozoites [49]. When we altered cAMP levels by the application of drugs we found that a number of sporozoites stopped their movement but the coronin signal in these parasites stayed at the rear (Fig 5A–5C). This was in contrast to the relocalization with actin dynamics inhibiting drugs (Fig 1D and 1E). We thus assume that actin filaments are still assembled in parasites treated with the protein kinase A-inhibitor H89 and the adenyl cyclase inhibitor SQ22536. This suggests that cAMP signaling is important for disassembly of actin filaments at the rear, which appears crucial for continuous motility. Also, it suggests that cAMP signaling acts downstream from calcium release, which leads to coronin relocalization. During motility these processes are likely interlinked and they possibly switch during invasion of sporozoites into liver cells, where cAMP signaling was suggested to occur upstream from calcium signaling mediated by a change in the potassium concentration surrounding the sporozoites during transmigration [49] or of pH as in the case of red blood cell invading merozoites [51]. During sporozoite migration we speculate that the two different cAMP signaling pathways initiated by membrane bound adenyl cyclase alpha and cytoplasmic adenyl cyclase beta might be activated differentially during transmigration through cells and extracellular gliding, respectively (Fig 6). Importantly, PKA is likely to phosphorylate coronin as 6 of the 21 phosphorylation sites on coronin show the hallmark of PKA substrates [70,74–76]. Their role could be addressed using the same point-mutagenesis approach as described in Figs 3 and 4.
Taking our results together and placing it within the context of other work [49–51] the following picture emerges for the activation of Plasmodium sporozoites (Fig 6): Upon sporozoite contact with the salivary gland basal membrane and again upon transmission into the skin parasites can be activated by a range of ligands from the extracellular matrix or by serum albumin, which signal through a transmembrane protein via phospholipase C to increase intracellular calcium [42,43,50]. This leads to exocytosis of micronemes that bring additional receptors onto the surface leading to increased or sustained signaling. This in turn leads to the polymerization of actin filaments, which are not yet organized. Upon their binding to coronin actin filaments bundle and get translocated by myosin to the rear end, where they accumulate (hence no movement of the bleach spot in Fig 5E) and disassemble to obtain persistent motility (Fig 6). A second signaling pathway through activation of PKA contributes to actin filament disassembly, possibly through coronin, and can be activated by high extracellular potassium ions to switch from a migration to an invasion mode [49]. The two signaling modules in our model appear to be connected such that gliding is occurring when calcium signaling is upstream from cAMP signaling, while invasion occurs upon the switch of these pathways.
All animal experiments were performed according to FELASA B and GV-SOLAS standard guidelines. Animal experiments were approved by the German authorities (Regierungspräsidium Karlsruhe, Germany). The project license numbers are G134/14 and G283/14 with a short grant names AAPZ and FAZP, respectively, as assigned by the ethics committee that approved our study.
Transfection vectors were generated with standard cloning techniques and transfection was performed according to standard protocols [77]. For generation of coronin(-) parasites, the vector pB3D+ containing the Toxoplasma gondii dihydrofolate reductase-thymidylate synthase (tgdhfr/ts) under regulation of the P. berghei ef1α promoter was used [63]. For generation of mCherry tagged coronin, the vector p262 containing the human dihydrofolate reductase (hDHFR) and the mCherry gene was used. The vector p262 is derived from p236 [78]. The overexpressing mCherry tagged coronin mutants were generated in the B3D+ vector [79]. To generate replacements of the endogenous coronin with mCherry tagged coronin mutants the previously generated p262-endogenous coronin vector (p262-eCRN) was used. For more details please see supplementary material.
Anopheles stephensi mosquitoes (Sda500 strain) were raised at 28°C, 75% humidity, under a 13/11 h light/dark cycle and maintained on 10% sucrose solution containing 0.05% para-aminobenzoic acid. NMRI mice (Charles River) were infected by intraperitoneal injection of 200 μL frozen parasite stocks. After 3 to 5 days post infection the number of exflagellation events were determined with a Zeiss light microscope and a counting grid. If >3 exflagellation events per field of view were observed, mice were anaesthetized with a mixture of 10% ketamine and 2% xylazin in PBS (100 μL per 20 g mouse body weight i.p.) and fed to Anopheles stephensi mosquitoes (3–5 days after hatching). Post infection mosquitoes were maintained at 20°C and 80% humidity. Mosquitoes were used 10–23 days post infection for further experiments.
Salivary glands of 20–25 mosquitos were dissected in 100 μL RPMI medium without phenol red (GIBCO) in a plastic reaction tube (Eppendorff). Isolated salivary glands were grounded with a pestle and released sporozoites were purified with an Accudenz density gradient [80]. Purified sporozoites were resuspended in 100 μL RPMI containing 3% BSA, transferred to a 96-well plate and centrifuged for 3 min at 1000 rpm. Sporozoites were imaged with differential interference contrast (DIC) and fluorescence using a 25x (NA: 0.8) objective on an inverted light microscope (Axiovert 200M, Zeiss). Video microscopy was performed with taking an image every 3 seconds (unless otherwise stated) for 5 min. When using drugs, we added the drugs after spinning the sporozoites. In some experiments (forskolin and some of the BAPTA-AM experiments), sporozoites were only spun in RPMI without BSA as indicated in the figures. While testing the activation of gliding motility with ionomycin, sporozoites were spun in RPMI medium (R). Ionomycin was added to reach a final concentration of 100 nM without BSA (R + I) or with 3% BSA (R + I + B); [n > 100].
Ears of naïve mice were unhaired by treatment with Veet 24 hours prior to intravital imaging to limit autofluorescence. Mosquitos were separated in cups to 10–12 each and starved overnight to increase their biting rate. On the day of imaging, mice were anaesthetized as described above and the ears were placed on the cups containing the infected mosquitoes. Ears were exposed to mosquitoes for 10–15 minutes and formed hematomas were lightly marked with a permanent marker. Bitten mice were placed on the microscope table in such a way that ears could be scanned for hematomas [3]. Upon finding a bite site, sporozoite migration was imaged for 10–15 minutes with an image every three seconds using the Zeiss Axiovert 200M widefield fluorescence microscope.
HepG2 cells were seeded into 24-well plates with a density of 5x104 and cultivated in DMEM under standard conditions for three days. Salivary glands were isolated 17 days post infection; homogenized with a pestle in 100 μL DMEM and released sporozoites were counted using a Neubauer haemocytometer. 50.000 sporozoites were seeded to each well of a 24-well plate with confluent HepG2 cells. After infection wells were filled up with DMEM to a total volume of 200 μL and incubated for 30 minutes at room temperature. After incubation cells were cultured for further 2 hours under standard cell culture conditions. The medium was removed and wells were washed twice with PBS to remove non-invaded sporozoites. Afterwards, cells were treated with 200 μL (0.05% trypsin) for 10 minutes. To each well 800 μL of DMEM supplemented with Antibiotic-Antimycotic (GIBCO, Life technologies) was added and cells were split in fresh 24-well plates containing cover slips (12x12 mm2, thickness 1 mm) for 2 time points. Medium was refreshed every 24 hours.
4 C57BL/6 mice per parasite line were infected with 10.000 sporozoites by intravenous injection. Livers were removed 42 hours and homogenized in 4 ml of Qiazol (QIAGEN). RNA was isolated from 0.5 ml suspension according to the manufacturer`s protocol. DNase treatment to remove contaminating gDNA was performed using the TURBO DNA-free Kit (AMBION) followed by cDNA synthesis using the First Strand cDNA Synthesis Kit (ThermoFisher). RT-PCR was performed on samples obtained from the cDNA synthesis with and without reverse transcriptase to exclude gDNA contaminations. Quantitative RT-PCR was conducted on a CFX96 Touch Real-Time PCR Detection System (BIO-RAD) with SYBR Green PCR Master Mix (Applied Biosystems). Gene-specific primers for mouse GAPDH and Plasmodium berghei 18S rRNA were used.
Naive mice were infected by salivary gland sporozoites isolated 17 days post infection either by mosquito bite or by intravenous (i.v.) injection into the tail vein. To infect mice by bite, C57BL/6 mice (n = 4 per group per experiment) were anesthetized as described above and individually exposed to cups containing 10–12 mosquitoes. Mosquitoes were starved 24 hours prior to the experiment to increase their biting rate. Mice were exposed to mosquitoes for 10–15 minutes, turning them every 4 minutes. After the experiment blood fed mosquitoes were put on ice and salivary glands isolated to quantify the number of sporozoites. The time between the infection and the first emergence of parasites in the blood, in the following referred to as prepatency, was monitored microscopically by Giemsa stained blood smears. Smears were performed daily from day 3 to day 20 (unless otherwise stated). For intravenous infections, salivary glands of infected mosquitos (17 days post infection) were dissected in 100 μL PBS and homogenized. The number of sporozoites was counted in a Neubauer counting chamber. Sporozoite solutions were diluted with PBS to 10.000 sporozoites per 100 μL and injected i.v. into the tail vein. The prepatency was determined as above. A mouse was considered to be positive if >1 parasite was observed during 5–10 minutes of observations. The survival of infected mice and the appearance of experimental cerebral malaria was monitored for 20 days.
30μL of tail blood were taken (every alternate day for 20 days or until mice suffered ECM symptoms) and incubated for 24 hours at room temperature to allow separation of serum and haematocrit. The serum was transferred into new reaction tubes and stored at -20°C until further use. To determine cytokine concentrations 15 μL of serum was analyzed using a multiplex bead array kit (TH1/ TH2/ TH17 BD Biosciences) according to the manufacturer’s protocol. Measurements were done by flow cytometry (BD FACSCalibur) and computational analysis was conducted using FCAP Array Analysis for Mac by Softflow Inc. as described before [35].
Imaging was performed on an inverted Zeiss Axiovert 200M microscope using a GFP or rhodamine filter set at room temperature. Images were collected with a CoolSnap HQ2 camera at 1 or 0.5 Hz using Axiovision 4.8 software and 10X (NA: 0.5), 25X (NA: 0.8) or 63X (NA: 1.4) objectives depending on the experimental setting. Image processing was done using ImageJ.
For FRAP experiments 2–3 infected salivary glands 17–23 days post mosquito infection were isolated in 100 μL RPMI or RPMI + 3% BSA and grounded with a pestle. Sporozoites expressing mCherry tagged coronin were transferred to a glass-bottom culture dish (35 mm Petri dish; 14 mm microwell with No. 1.0 coverglass (MatTek) and mixed with small molecule inhibitors or activators. Imaging was performed on a spinning disc Nikon TE2000 inverted microscope and a 100X (NA: 1.4) objective. The region of interest (ROI) (10x10 pixel size or 1.76 μm2) was bleached on each sporozoite followed by imaging for 300 frames with 5 frames per second. The fluorescence intensity of the bleached sporozoite surface was measured using a fixed-size ROI moved by hand to ensure that it is always centered over the moving sporozoites. The fluorescence intensity in a fixed ROI immediately adjacent to the sporozoite was measured and a value for every frame was subtracted from the frapped signal to correct for background fluorescence. As the area imaged was small and sporozoites were moving, it was not possible to correct for photo-damage or sporozoites losing focus. The resulting background-subtracted data was then normalized to the highest intensity of the pre-bleached image. The maximum recovery intensity was determined by averaging the intensities from frame 90 to 140 after bleaching, when recovery was complete for all experiments. The half recovery was calculated as T1/2 = (Intensity of Max recovery + Intensity of 1st frame after frap)/ 2 and the time required to reach this intensity determined as half time recovery. Similar results were obtained by fitting the dose response curve in Prism software (GraphPad).
Sporozoites expressing mCherry tagged coronin were isolated from infected salivary glands in RPMI. After centrifugation (7.000 rpm, 5 min) sporozoites were resuspended in RIPA buffer (5 M NaCl, 0.5M EDTA pH 8, 1 M Tris pH 8, 1% NP-40, 10% Na-deoxycholate, 10% SDS, dH2O) with protease inhibitors cOmplete, Mini, EDTA-free (Roche) and incubated on ice for 2 hours. 100.000 sporozoites were mixed with loading buffer and loaded onto a Mini-Protean TGX Gel, 4–15% (BioRad). After electrophoresis proteins were transferred onto a Trans-Blot Turbo Mini nitrocellulose membrane using the Trans-Blot Turbo Transfer System (BioRad) followed by blocking of the membrane with 5% milk in Tris (pH 7.5) supplemented with 1% Tween 20 (TBST) for 2 hours at RT. The membrane was probed with the respective primary antibodies diluted in 5% milk in TBST overnight at 4°C, washed 3x with TBST and secondary antibody was incubated in 5% milk in TBST for another hour at room temperature. Proteins were detected using ECL (Pierce). Dilutions of antibody combinations used: rabbit anti-mCherry 1:5.000 (Clontech Laboratories)/ goat anti-rabbit 1:10.000 (GE Healthcare); mouse anti-PbCSP 1:1.000 (Malaria research and reference reagent center: Mr4 mAb3D11)/goat anti-mouse 1:10.000 (GE Healthcare).
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10.1371/journal.pcbi.1005307 | Cell Sorting and Noise-Induced Cell Plasticity Coordinate to Sharpen Boundaries between Gene Expression Domains | A fundamental question in biology is how sharp boundaries of gene expression form precisely in spite of biological variation/noise. Numerous mechanisms position gene expression domains across fields of cells (e.g. morphogens), but how these domains are refined remains unclear. In some cases, domain boundaries sharpen through differential adhesion-mediated cell sorting. However, boundaries can also sharpen through cellular plasticity, with cell fate changes driven by up- or down-regulation of gene expression. In this context, we have argued that noise in gene expression can help cells transition to the correct fate. Here we investigate the efficacy of cell sorting, gene expression plasticity, and their combination in boundary sharpening using multi-scale, stochastic models. We focus on the formation of hindbrain segments (rhombomeres) in the developing zebrafish as an example, but the mechanisms investigated apply broadly to many tissues. Our results indicate that neither sorting nor plasticity is sufficient on its own to sharpen transition regions between different rhombomeres. Rather the two have complementary strengths and weaknesses, which synergize when combined to sharpen gene expression boundaries.
| In many developing systems, chemical gradients control the formation of segmental domains of gene expression, specifying distinct domains that go on to form different tissues and structures, in a concentration-dependent manner. These gradients are noisy however, raising the question of how sharply delineated boundaries between distinct segments form. It is crucial that developing systems be able to cope with stochasticity and generate well-defined boundaries between different segmented domains. Previous work suggests that cell sorting and cellular plasticity help sharpen boundaries between segments. However, it remains unclear how effective each of these mechanisms is and what their role in sharpening may be. Motivated by recent experimental observations, we construct a hybrid stochastic model to investigate these questions. We find that neither mechanism is sufficient on its own to sharpen boundaries between different segments. Rather, results indicate each has its own strengths and weaknesses, and that they work together synergistically to promote the development of precise, well defined segment boundaries. Formation of segmented rhombomeres in the zebrafish hindbrain, which later form different components of the central nervous system, is a motivating case for this study.
| The specification of segmental domains of gene expression is a fundamental aspect of animal development and a critical first step in bilaterian body plan organization [1, 2]. Within these domains, differential gene expression determines the functional properties of cells. For example, alternating domains of pair rule gene (e.g. fushi tarazu, even skipped [eve]) expression in the early Drosophila embryo organize the segmented body plan [3, 4]. In vertebrates, segmentally-organized somites form muscle segments and the vertebrae of the backbone [5–7]. In both cases, cells acquire their segmental identities along the anterior-posterior (A-P) axis through the functions of Hox genes. Further anteriorly, Hox paralogue groups 1–5 specify segments of the hindbrain (rhombomeres) [8–10]. How these segmented domains form has been the subject of intense investigation.
Morphogen gradients control the formation of segmental domains of gene expression, specifying distinct domains in a concentration-dependent manner. In the Drosophila embryo, maternal gradients of bicoid and caudal promote expression of different gap genes [11–15]. In vertebrates, secreted signaling molecules such as Fibroblast growth factor 8 (FGF), Wnt3a, and retinoic acid (RA) form gradients that influence somite formation [16–20]. Similarly, in the developing hindbrain, a network of FGF, Wnt and RA induce differential expression of Hox genes and Krox20 in adjacent rhombomeres [20–28]. However, morphogens are unlikely to be the only mechanism controlling segmentation in each of these cases. In particular, cell rearrangements are known to play a role. Steinberg’s differential adhesion (DA) hypothesis (DAH) predicts that cell sorting can generate distinct cell aggregates [29]. This mechanism works particularly well if cells of adjacent segments differ in the number or type of adhesion proteins they express, such as E-cadherin [30]. Similarly, contact-mediated repulsion can promote sorting. Repulsion between cells expressing Eph and Ephrin receptors is required for proper boundary formation between segments in both somites [31, 32] and rhombomeres [33–36]. When these two surface receptors come into contact, they elicit bi-directional signaling that causes cells to repel each other [37].
Wolpert’s classic French flag model posits that morphogens form well-defined gradients and that cells can precisely read the level of the signal at their location [38]. However, like any biochemical signal, morphogens are inherently noisy and the process of transducing the signal is stochastic. Noise can lead to mis-specification of cells, which will in turn produce a rough transition region between segments where multiple cell types co-exist in a salt-and-pepper arrangement. Actin cables or other physical barriers form at the interface between tissues in systems such as developing germ layers in early embryos [39], the Drosophila embryonic epidermis [40], or the zebrafish hindbrain [41] (reviewed in [42]). Thus, it is paramount that transition regions sharpen prior to the formation of these structures. The question thus becomes, how can a morphogen-organized system cope with stochasticity and generate refined, segmented zones of different cell types.
Cells may physically sort but the effectiveness of sorting is unclear, particularly in cases involving relatively small numbers of cells. For example, in the embryonic zebrafish hindbrain, rhombomeres are comprised of tens to a few hundred cells, depending on the stage [33, 34]. Very few cells occupy the local region near the interface between segments. The DAH assumes that tissues are liquid-like cell aggregates and that a sorted state is achieved as the system minimizes a tension/adhesion free energy. This however is primarily valid at macroscopic scales with large cell numbers [29, 30]. Furthermore, tissues do not necessarily behave as immiscible fluids [43]. Thus it is important to determine the effectiveness of cell sorting at smaller scales where the macroscopic assumptions of the DAH are not necessarily valid.
Previous work in the zebrafish hindbrain suggests that while cell sorting is important [33, 34, 36, 40, 44], cellular plasticity (e.g. transcription of target genes–hoxb1a and krox20) in response to morphogens (e.g. RA, Fgf and Wnt) also promotes sharpening of segment boundaries [45, 46]. Here, we use computational modeling to investigate the influences of these two different mechanisms. Since it involves both mechanical (e.g. adhesion, repulsion) and biochemical (e.g. gene transcription) processes, we develop a multi-scale model that accounts for both. Using this framework, we investigate each mechanism individually as well as in combination with others to determine effectiveness and potential interactions. Agent-based models treat each cell as a discrete entity with dynamically evolving properties [47, 48], while the Potts/Glazier-Graner-Hogeweg (GGH) model [49, 50] uses a lattice-based approach to account for dynamically evolving cell shapes [51] and cell-cell interactions. We use a sub-cellular element method (SCEM), which is similar to GGH, but allows more explicit descriptions of forces arising from cell-cell interactions [52, 53]. Each cell is treated as a collection of elements that interact according to user-defined forces. This has been used successfully to study the dynamics of epithelia [52, 53], the influence of Notch signaling on cell division [54], and homeostatic regulation in intestinal crypts [55]. We use SCEM to build a multi-scale, stochastic model of rhombomere boundary sharpening and investigate the effectiveness of cell sorting and plasticity (based on a stochastic description of hoxb1a and krox20 in cells). We show that adhesion, repulsion, and plasticity all have a role in this process, none of which sharpens boundaries efficiently on its own. Instead, each has benefits and weaknesses, which are complementary and appear to work synergistically to accomplish this goal.
How do distinct gene expression domains form in response to noisy positional information (Fig 1)? To address this question, we developed a set of hybrid computational models to investigate the effectiveness of different mechanisms at refining gene expression boundaries. Three possible mechanisms were considered: 1) differential adhesion, 2) cellular repulsion, and 3) cellular plasticity in gene expression.
We constructed three models–Sorting (S), Plasticity (P), and Sorting + Plasticity (SP). Model S includes only mechanical interactions such as cell-cell adhesion and repulsion. Model P assumes cells are stationary but allows for plasticity-mediated changes in cell fate. Model SP combines both. We used a discrete stochastic model formulation to account for low cell numbers. An SCEM framework endows each cell with a size, stiffness, and deformability (Fig A in S1 Text). The foundations of this method have been explained previously [52, 56]. To describe the influence of morphogens and gene regulation on cell identity, we constructed a spatial stochastic model of cell fate regulation. For each model, we utilized similar computational domains and initial conditions to aid direct comparison of results produced by each set of analyses. Motivated by rhombomere formation in the zebrafish, we consider a simplified computational domain consisting of a rectangular array of 6 cells in height with varying widths (Fig 1). We note that while this is a simplification, this dimension is on a similar scale in the horizontal direction; in the vertical direction, increasing cell numbers is computationally intensive yet does not give further valuable results, and thus we considered this simplified scenario. In most of our simulation, we compute our simulations in a time window corresponding to 10.7 to 12.7 hours post fertilization (hpf), during which the zebrafish rombomere 3/4 (r3/4) and 4/5 (r4/5) boundaries are sharpened [45].
The models discussed herein are necessarily complex. We thus focus on their aspects that are most relevant to this discussion. Specifically, we will consider how effective each is at sharpening gene expression boundaries, and where there are deficiencies, we will assess the source of that deficiency. Where possible, we will consider the sensitivity of results to model details. However, we note that given the complexity of these models, an exhaustive sensitivity analysis is not possible. We thus leave a detailed discussion of the sub-cellular element model that is the basis of Model S and the gene expression model that is the bases of Model P for references provided herein. Instead, we focus on the qualitative properties of these mechanisms and how they operate individually and in combination.
Model S was simulated under a range of conditions including varied levels of adhesion and repulsion between cells. With a rectangular array of cells, we considered multiple initial conditions in which we varied initial transition width (ITW = 2, 3, or 4-cell wide transition regions). We manually populated the transition region with a random array of the two cell types with precisely a half-half mixture, and performed an ensemble of simulations for each condition. By comparing either the number of boundary formed and boundary nearly formed simulations under different conditions, we found (Table 1) that mechanical cell sorting was effective when the initial transition region was narrow, especially in cases with stronger cell adhesion strength (see Morse potentials in Fig B in S1 Text). A substantial fraction of these simulations ended with boundary nearly formed rather than formed. However, most outlying cells were at the top/bottom edges of the boundary where they have fewer neighbors (due to the structure of the domain) and are subjected to weaker sorting influences. For wider initial transition regions (ITW = 3), sharpening was reduced and strongly dependent on the strength of sorting. For ITW = 4 and wider, mechanical cell sorting was ineffective at boundary sharpening, no matter how strong the sorting forces.
Since both differential adhesion and repulsive interactions between cells can lead to sorting independently [29], we next assessed the relative influence of each (Fig 2). Simulations were performed starting with identical initial conditions and adhesion or repulsion was either attenuated or strengthened. Inclusion of both adhesion and repulsion led to effective boundary sharpening (Fig 2A, bottom; S1 Movie). Removal of repulsion disrupted sorting, leading to a transition region that not only did not sharpen, but in many cases actually expanded (Fig 2A, top; S2 Movie). In contrast, removal of adhesion led to contiguous boundaries between regions of cells, though the resulting boundaries were far from straight (Fig 2A, middle; S3 Movie).
In cases where boundary sharpening failed, individual cells (Fig 2B) or cell groups (Fig 2C, top) were isolated from their preferred zone, mainly at the top or bottom edges of boundaries, as discussed previously. Since the only sorting interactions in this model were physical cell-cell interactions, once cells became isolated they encountered an isotropic environment with nothing biasing their direction of motion. Increasing the strength of cell-cell interactions reduced the frequency of these events (Table 1; Fig 2C, bottom). However, since there is a significant random component to the isolation of these cells, optimizing the properties of cell-cell interactions only marginally improved the outcome. Additionally, the likelihood of cells becoming isolated strongly depends on initial transition region width (ITW) and its noisiness. This is the primary reason that Model S became increasingly ineffective as the ITW increased (Fig 2D, S4 Movie). These results show that both adhesion and repulsion are required for proper sorting, and that these mechanical processes are only effective in boundary sharpening if the ITW is relatively narrow.
With model P we asked how effective plasticity alone is at sharpening boundaries between cellular zones (Fig 3). If morphogen signals are noise free and gene regulation is deterministic, morphogens will always form precisely placed, sharp boundaries. In reality, however, this regulatory system is stochastic at every level. We have hypothesized that “noise-induced switching” helps sharpen rhombomeres in the zebrafish hindbrain [41]. This is based on the idea that while morphogen stochasticity introduces disorder near the boundary between cellular zones (i.e. a transition region), stochasticity in gene regulatory processes can also help cells to transition to the correct gene expression state [41]. To test this in our model, we omitted cellular motion so that sharpening relied solely on this mechanism.
In this model, initial cell fates were determined by a single morphogen, which was assumed to direct fate specification by influencing transcription levels of A and B (which for r3-5 of the hindbrain was modeled as hoxb1a and krox20). Upon application of the morphogen M, two expression domains formed with an intervening transition region (Fig 3B). Ensemble simulation results confirmed that the transition region partially sharpened after initial cell specification. When relatively little stochasticity (noise) in gene regulation was included in the simulations (gene expression noise strength ηA = ηB = 0.03, see equations S2.4, S2.5 in S1 Text), the transition region narrowed but did not sharpen (Fig 3C). Too much noise (ηA = ηB = 0.09) overwhelmed the system (Fig 3E). When moderate noise (ηA = ηB = 0.06) was included, however, sharpening was more effective (Fig 3D; S5 Movie). Fig 3A shows the average (across simulations) locations of the anterior (red) and posterior (blue) ends of transition regions as a function of time under different conditions. To ensure cell distributions have reached a steady state, we double the simulation time. Inspection of each of 16 replicate simulations shows that after T = 12.03 hpf cell fate transition rates drop and the system achieves a steady distribution of A and B cells (Fig M in S1 Text). These results confirmed that, for the medium noise case, the width of the transition region was reduced (but not completely) to about 2 cell diameters in width. This indicates that noise-driven sharpening can narrow an initially wide transition region, but is less effective at sharpening it completely.
It is also instructive to consider how this refinement occurs. Fig 3A shows that with moderate or weak stochasticity, sharpening in Model P occurred with the posterior edge of the transition region steadily moving toward a fixed anterior edge over time, reducing the region’s width. This results from an asymmetry in the underlying gene regulatory network that generates noise driven red → blue transitions (with the reverse much more rare). When noise levels are even higher, this red → blue transition was so prominent throughout the domain, that the posterior edge converged to the anterior edge and blue zone overtook the red zone over time (Fig 3E). This contrasted with mechanical sharpening (Model S), where red cells tended to move anteriorly and blue posteriorly, leaving a border mostly in the middle of the original transition region (at least when sharpening occurs)–though it also depended on the numbers of red and blue cells. This suggests that the cell switching from type A to B that occurs in Model P, but not S, leads to a fundamental difference in the directionality of boundary sharpening.
We make a final note about the role of stochasticity in promoting sharpening. The idea underlying the theory of noise-induced plasticity is that cell states (A and B) are represented by stable wells in an energy landscape (see Fig 3B for a schematic). Depending on the cells local environment (determined by position in our case), the relative depth (e.g. stability) of those wells may be different. In this context, for plasticity to aid sharpening, the “correct” state should be a deeper well and the incorrect a shallower well. In this way, an incorrect → correct (e.g. shallow to deep, see Fig 3B) transition would be more likely than the reverse. If the two wells are of roughly equal stability, both transitions would occur with equal likelihood, which would provide no benefit. Thus a sufficient level of asymmetry is required for noise to aid sharpening. Of course, if both wells were either too deep or too shallow relative to noise strength, stochasticity would either have no effect or overwhelm the system (illustrated in Fig 3E). Thus while stochasticity can provide a benefit, the system must be in an appropriate operating regime to take advantage of it.
Our simulations with Models S and P show that mechanical sorting is effective at sharpening narrow transition regions while plasticity effectively narrows wider transition regions. How effective are these two mechanisms when combined? We hypothesized that plasticity narrows a transition region sufficiently to allow subsequent cell movements to complete sharpening. To test this, we considered the model SP, which essentially adds local cell-cell interactions that drive sorting to model P (Table 2; Figs 4 and 5D).
Fig 4C–4F shows temporal snapshots of sorting, where each simulation begins from the same initial state, which is generated by the morphogen regulatory system (Fig 4C). These results provide a direct comparison of sorting resulting from plasticity alone (Model P, Fig 4F), mechanical sorting alone (Model S, Fig 4E), and the two combined (Model SP, Fig 4D, S6 Movie). In Model S, after the initial state is specified by the morphogen, the gene regulation system is turned off, and all cells are unable to alter their gene expression levels. Thus the morphogen system serves only to generate the initial condition for Model S. Results indicate that boundaries sharpen more effectively with SP than either S or P individually, based on tracking the average (over 16 simulations) position of the transition region borders and the transition region width (Fig 4A and 4B). With SP all simulations led to formed or nearly formed boundaries and a larger fraction formed completely (Table 2). Tracking the SI changes over time (Fig 5D) showing that model SP is the best among the three (end SI = 0.26), while model S is the worst that only reduces SI a little (end SI = 0.95), and model P sits in between (end SI = 0.66). The standard deviation of SI of the model S, P and SP is shown in Fig K in S1 Text. Tracking the boundaries of the transition region over time also revealed that rather than sharpening to either the center or one side of the transition region, the final boundaries were within the initial transition region but biased toward the anterior (Fig 4A). This is consistent with a combination of the two mechanisms ultimately driving sharpening. Additionally, simulation results indicated that the final location of the boundary was precise when the sharpening was driven by cell sorting and/or plasticity (see S1 Text section S7 for further details).
We next sought to determine if the order of action or duration of these different sharpening mechanisms influence the outcome. To do so, we performed numerical simulations (results in Table 2) where 1) plasticity was only active early, up to a pre-determined time point (T = 11.37 or 11.7 hpf) after which sorting became active, 2) the reverse, sorting was followed by plasticity, and 3) the two mechanisms occurred simultaneously and for the full duration of the simulations (i.e. the SP model discussed previously).
When sorting was active early and plasticity occurred later, outcomes (Table 2, “S followed by P”) were better than with sorting alone and comparable to plasticity alone, but still ineffective. Sorting followed by plasticity (“P followed by S’) on the other hand yields a substantial effect (Fig L in S1 Text). This indicates that the early action of plasticity followed by later action of sorting improves outcomes over either mechanism alone. The combination of the two (model SP) acting in concert for the full sharpening window however yields yet further improvement (Fig L in S1 Text). Combined, these results suggest that the two mechanisms, mechanical sorting and plasticity, can work synergistically with plasticity serving to narrow transition regions to a manageable width and sorting serving to finalize the sharpening process.
The zebrafish hindbrain consists of 7 rhombomeres. While these segments utilize different signals and potentially different mechanisms to form and sharpen, the RA morphogen along with the Hoxb1/Krox20 regulatory system are vital to the formation of rhombomeres 3–5 (r3-r5) (Fig 1). Up to this point, we have modeled sharpening between two domains, but we now consider how effectively cell movements and plasticity (Models S, P, and SP) are at forming and sharpening three cellular zones.
To initialize the domain, the simulated RA morphogen generates a 20x6 domain of cells (Fig 7), which is similar in scale to the horizontal dimension of the r3-r5 zones at the onset of hindbrain specification in zebrafish [45]. We scaled the morphogen system such that the readout of the Hoxb1/Krox20 gene regulatory system in response to the RA gradient generates three zones of roughly the same size with transition regions in between. All three models (S, P, and SP) were simulated and the dynamics of the two transition regions were tracked over time. The combined model (SP) was highly effective at sharpening the r4/5 boundary (Table 3, Fig 7A and 7B, S8 Movie). When comparing the three models, models S or P individually were not as effective as SP, as was the case earlier in the 2-domain models. However, compared to 2-domain models, all three models (S, P, and SP) appeared to be more effective in the 3-domain scenario (Tables 2 and 3, Fig 7E). This results from the fact that the length scale of the RA morphogen was reduced to generate a sufficiently steep gradient to produce three cellular zones. Since the width of transition regions depends on the relationship between noise in the morphogen and steepness of the gradient, the transition widths in all 3-domain models were narrower than in the previous 2-domain simulations.
The dynamics of the r3/4 boundary were however significantly different than r4/5. At this boundary, plasticity was completely ineffective at sharpening in any simulations (Table 3; Fig 7). This reflects the fact that the interplay between stochasticity and the underlying gene regulatory network depends on morphogen levels. At the r3/4 boundary, morphogen levels are too low for plasticity to induce any state transitions. Models S and SP at this boundary performed nearly identically–especially when we compare the SI changes (Fig 7F). Additionally, simulations of model S at each of these two boundaries performed nearly identically. These results suggest that either mechanical sorting is the only manner of sharpening at the r3/4 boundary or some alternative type of cellular plasticity (e.g. other morphogens, other gene regulatory networks) is required to sharpen this boundary, unlike r4/5.
As we discussed above, contact-based cell sorting only appears to be effective at forming a sharp boundary when transition regions are narrow. This could of course be the result of sub-optimal cell-cell interaction parameters. However modulating the strength of inter-cellular forces does not improve the situation much (Fig J in S1 Text). Furthermore, simulations of r3-r5 formation, where two boundaries must form and sharpen, suggest that while sorting may be effective at one boundary, it is unlikely to be as effective at adjacent boundaries. Recent experimental studies suggest that long ranged cell sorting or chemotaxis may be very effective at forming a clear and sharp boundary [59, 60]. To investigate the effect of inter-cellular force range on boundary formation, we perform a similar simulation study with the same parameter values as in the mild adhesion and repulsion combination, but increase the effective range of cell-cell interactions to about 3 cell diameters (see S1 Text section S1) to mimic longer range chemotactic effects. With this addition, boundary formation becomes much more effective (Fig J in S1 Text): starting with ITW = 3, all 16 simulations ended up with a clear boundary formed, while with ITW = 4, 13 out of 16 simulations formed a clear boundary, the other 3 failing as a result of isolated cells that are pushed to corners of the domain.
The key observation from these and results above is that local, cell-cell contact based sorting forces appear to only be effective at sharpening domain boundaries if the initial imperfections are confined to a narrow band. Our prior results indicate that plasticity based effects could improve this process by narrowing initially broad transition regions. This is not however the only possibility. Results here indicate longer range chemotactic forces between cells could play a similar role. Alternatively, chemotaxis towards or away from an organizing center could provide the same benefits. While we cannot rule out any of these on the basis of the data available, we do note that the plasticity based mechanism discussed above would yield qualitatively different predictions than these chemotaxis based mechanisms. The essential failure of the simple, contact based sorting process is its locality. Each of these additions provides a separate means of providing more global information to the cells. In the case of chemotactic mechanisms, that global information would drive longer length scale motions of cells. With the plasticity based mechanism on the other hand, narrowing of transition zones would arise from fate transitions rather than longer length scale motions. Thus the qualitative movement patterns of cells could provide a means to delineate these mechanisms in future experimentation.
Embryonic segments are fundamental building blocks of the body plan of many animals. While intense research has been directed at elucidating how segmentation occurs [1–10], it remains unclear how the borders between different segments sharpen. For example, in any morphogen signaling system that controls segmentation (such as bicoid or RA), noise in both the morphogen itself and in the transduction of the morphogen signal may reduce precision in the ability of responding cells to form sharp boundaries between segmental domains, which could have long lasting effects on development.
To date, multiple theories for boundary sharpening and maintenance have been proposed. Mechanical structures such as actin cables have been posited to generate tension that robustly separates segments [40, 42]. These structures however could be a double-edged sword. While they might help maintain a boundary once it is established, the presence of noise could impair their development, potentially leading to a permanent inability to sharpen further. Thus, a relatively well-defined boundary should be present before the formation of these structures. The “Differential Adhesion” (DA) hypothesis [29] argues that mechanical cell-cell interactions promote segment formation and sharpening. This hypothesis is however most often discussed in the context of systems containing thousands of cells that can be thought of as an active fluid. It is thus unclear how effective DA may be when smaller numbers of cells are present, such as in the zebrafish hindbrain.
Our modeling results suggest that local, contact based adhesion/repulsion mediated sorting is only effective at sharpening narrow (approximately ≤3 cell diameters) transition regions between segments. This is because once individual cells or cell groups become isolated from their respective aggregate, the local nature of contact-mediated cell-cell interactions cannot provide sufficient information to direct cells to the correct location. In this sense, an initially wide transition region is meta-stable state and can only resolve through random movements over time. Thus, when small numbers of cells are present, purely local, contact based sorting (e.g. differential adhesion sorting) appears to be insufficient to guarantee robust organization.
There are a number of possible processes that could be layered on top of contact based sorting to improve sharpening. Motility based processes such as chemo-attraction or chemo-repulsion of like or different cells could provide additional, longer range information to coax cells toward the appropriate region of the domain [60]. Our results show that this is an effective mechanism. Chemotaxis toward or away from a pre-existing organizing center could serve a similar purpose (as suggested in [59]). Here we investigate an alternative possibility, the “cellular plasticity” theory, which argues cells can change their fate by up- or down-regulating the expression of critical genes [45]. This is fundamentally distinct from the motility based processes discussed above in that long range taxis information is not required. Here we show that this mechanism has its strengths and weaknesses, but that when combined with local, cell contact based sorting, the two work together synergistically to promote sharpening.
Plasticity-mediated sharpening can be explained from the standpoint of an energy landscape view (Fig 3B) of cell fate regulation [61, 62]. This theory posits that cell fates can be thought of as, loosely speaking, minima (or wells) in an underlying energy landscape. Each valley in the landscape is associated with a particular fate and thus multiple wells indicate multi-potency. However, cells do not prefer all of these local energy minima equally (i.e. they differ in stability). Different depths are associated with relative stability and the greatest minimum is typically associated with the “correct” fate. In this view, a cell in the wrong fate (based on its position) simply resides in a different, shallower energy well and stochasticity can drive the cell to the correct fate. The shallower the minimum, the more likely noise can lead to fate switching. Thus, cells at a significant distance from the presumptive boundary are quickly driven to the correct fate in our model by differences in morphogen concentration, narrowing the transition region. Once it is narrow, however, morphogen differences from cell to cell near the boundary become small and cells have a nearly equal preference for the two possible states (i.e. the two fates are represented by wells that have roughly the same depth). This process of course requires the landscape to have the appropriate structure, which can vary with the state of the system (represented by parameters). Our sensitivity results however suggest that the qualitative structure of the underlying landscape does not change dramatically as parameters are changed.
For this reason, plasticity-mediated sharpening is effective at rapidly narrowing a wide transition region, but is very slow at completing the sharpening process and in many cases fails to do so. Our results for cell-contact based sorting show the opposite. This mechanism is effective at sharpening narrow boundaries, but performs poorly for wider transition regions where cells are intermingled. Thus the two mechanisms have complementary strengths and weaknesses. When combined however, their strengths synergistically promote sharpening, plasticity narrows the transition region while cell contact based sorting completes the process.
Given that many segmented tissues (e.g. the zebrafish hindbrain) are comprised of more than two cellular zones, we further investigated the effectiveness of this joint mechanism at sharpening the boundaries between multiple zones of different cell types. We found that the two mechanisms have different roles at different boundaries. Specifically, while plasticity-induced sorting is effective at reducing the width of transition regions at the r4/5 boundary, it is ineffective at the r3/4 boundary, a result of different morphogen levels placing the system in a different dynamical regime of the underlying gene regulatory network.
Given the distinct strengths of plasticity versus sorting, plasticity is of most value early in the sharpening process while the primary value of sorting becomes apparent later in the process (though a model where they are jointly active for the entire sharpening time frame is most effective). We are not aware of quantitative, time-dependent gene expression data here for the hindbrain, but in the developing mammalian embryo, cell fate decisions have been found to be gradual with gene expression diverging over the course of hours and multiple cell divisions [63]. This gradual fate specification (if present here) could have two effects. First, the relative ease and frequency of stochastically driven cell fate transitions is related to the level of gene expression distinctiveness (or in other terms, the level of specialization) of the two fates. That is, the more distinct two cell fates are, the more difficult it becomes to stochastically drive transitions between them. Second, it is possible that the difference in adhesion / repulsion properties between multiple cell types is directly related to distinctiveness of those cell types (e.g. more distinctive cells exhibit stronger sorting). Combined, these would lead to a scenario where plasticity would be more active early while sorting would become active at later times. We note that Calzolari et al. [40] did not observe cell fate transitions during rhombomere formation. However, they focused primarily on stages when actin cables form between segments, which occurs near the end of the boundary sharpening process when plasticity effects would be less likely to be observed. Zhang et al [45] on the other hand report co-expression of hoxb1a and krox20 posterior to the future r4/5 boundary, consistent with cellular plasticity contributing to sharpening of this rhombomere boundary.
The most direct test of the hypothesis that plasticity and sorting both contribute to this process would be to simultaneously track cell positions and gene expressions from the earliest stage of rhombomere formation. An alternative, more indirect test of how the sharpening process occurs would be to track the spatial location of the resulting boundary as a function of time. If sorting alone is at work, the final location of a sharpened boundary will be near the center of the transition region. Alternatively, if plasticity plays a major role, that final boundary will consistently form near one end of the preceding transition region. Thus, these two mechanisms could potentially be distinguished with a smaller number of experimental observations and without tracking of individual cells.
We make one final note about the potential differences between these mechanisms. It is reasonable to expect that in addition to forming well-delineated boundaries, developmental systems should strive to ensure different segmented domains are the appropriate size. For example, the size of one rhombomere relative to its neighbors should be consistent (e.g. reproducible) across different embryos at the same developmental stage. A sharpening process that relies solely on sorting (whether it be contact based and / or chemo-repulsion / chemo-attraction / chemo-taxis based) of cells will have an inherent level of imprecision since the relative number of cells initially allocated to each of the cell lineages, which is stochastic, determines the final location of a boundary. A plasticity-based mechanism however provides a built-in control that could improve precision (see Fig G in S1 Text) since the final location of the boundary will be primarily determined by the morphogen itself.
While our results provide a general framework for explaining how initially noisy boundaries generated by a morphogen sharpen to form distinct segments, they also raise new questions. How do multiple segments (e.g. the zebrafish hindbrain or the neural tube) refine all zones jointly? While plasticity is vital to the refinement of noisy boundaries, the dynamics of morphogen signal transduction and the influence of stochasticity are highly dependent on the local levels of morphogen, which depend on position. Furthermore, morphogens have a spatial range of action limited by rates of diffusion, receptor binding, and the fidelity of signal transduction. In light of this, it is important to ask, can the same mechanisms and machinery be used to sharpen [1] unwanted transition regions. In large tissues beyond the length scales over which morphogens can act, other signals must be involved. But what about on smaller scales (e.g. multiple rhombomeres composed of only tens of cells)? Addressing these questions and gaining a more comprehensive understanding of how more complex systems organize will require moving beyond this setting and considering the presence of multiple morphogens (e.g. RA and Fgf in the hindbrain for example [26, 64–67]), how they function in parallel, and how they potentially interact in signaling pathways.
The modeling framework was based on a hybrid approach incorporating both noise-driven plasticity and sorting-driven mechanics. Plasticity was modeled by stochastic PDEs for the morphogens and several ODEs for the interaction among gene expression and the morphogen evaluated in each cell [45]. The equations were solved using an explicit finite difference scheme. The mechanics were modeled using SCEM, with forces included to describe both cell-cell adhesion and repulsion. For model and simulation details, please see supplementary material S1 Text.
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10.1371/journal.pbio.1000439 | Structural and Biochemical Characterization of the Human Cyclophilin Family of Peptidyl-Prolyl Isomerases | Peptidyl-prolyl isomerases catalyze the conversion between cis and trans isomers of proline. The cyclophilin family of peptidyl-prolyl isomerases is well known for being the target of the immunosuppressive drug cyclosporin, used to combat organ transplant rejection. There is great interest in both the substrate specificity of these enzymes and the design of isoform-selective ligands for them. However, the dearth of available data for individual family members inhibits attempts to design drug specificity; additionally, in order to define physiological functions for the cyclophilins, definitive isoform characterization is required. In the current study, enzymatic activity was assayed for 15 of the 17 human cyclophilin isomerase domains, and binding to the cyclosporin scaffold was tested. In order to rationalize the observed isoform diversity, the high-resolution crystallographic structures of seven cyclophilin domains were determined. These models, combined with seven previously solved cyclophilin isoforms, provide the basis for a family-wide structure∶function analysis. Detailed structural analysis of the human cyclophilin isomerase explains why cyclophilin activity against short peptides is correlated with an ability to ligate cyclosporin and why certain isoforms are not competent for either activity. In addition, we find that regions of the isomerase domain outside the proline-binding surface impart isoform specificity for both in vivo substrates and drug design. We hypothesize that there is a well-defined molecular surface corresponding to the substrate-binding S2 position that is a site of diversity in the cyclophilin family. Computational simulations of substrate binding in this region support our observations. Our data indicate that unique isoform determinants exist that may be exploited for development of selective ligands and suggest that the currently available small-molecule and peptide-based ligands for this class of enzyme are insufficient for isoform specificity.
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| Cyclophilins are proteins that catalyze the isomerization of prolines, interconverting this structurally important amino acid between cis and trans isomers. Although there are 17 cyclophilins in the human genome, the function of most cyclophilin isoforms is unknown. At least some members of this protein family are of interest for clinically relevant drug design, as they are targets of the drug cyclosporin, which is used as an immunosuppressant to treat patients following organ transplantation. The absence of a comprehensive picture of the similarities and differences between the different members of this protein family precludes effective and specific drug design, however. In the current study we undertake such a global structure∶function analysis. Using biochemical, structural, and computational methods we characterize the human cyclophilin family in detail and suggest that there is a previously overlooked region of these enzymes that contributes significantly to isoform diversity. We propose that this region may represent an important target for isoform-specific drug design.
| Cyclophilins are peptidyl-prolyl isomerases (PPIases: EC 5.2.1.8) and are characterized by their ability to catalyze the interconversion of cis and trans isomers of proline [1]. Cyclophilins and the structurally unrelated FK506 binding proteins were initially described as the in vivo receptors for the natural products cyclosporin, FK506/tacrolimus, and rapamycin/sirolimus [2],[3]. The immunosuppressant effect of these natural products, while revolutionizing the field of organ transplantation, were eventually determined to be unrelated to the inherent isomerase activity of the PPIases [4]. However, these small molecules bind to the active site of PPIases with high affinity and are capable of blocking isomerase activity against peptide substrates, making them a useful tool for biochemical and cellular assays of PPIase function [5].
The physiological function of cyclophilin PPIase activity has been for many years described as a chaperone or foldase [6],[7]. Certainly this functionality is well documented, for instance in the maturation of steroid receptor complexes (along with Hsp90/Hsc70) [8] or in the interplay between NinaA and rhodopsin in Drosophila [9]. In addition, the isomerase activity of at least two cyclophilin isoforms is crucial for host∶virus interactions and for viral maturation processes, and this activity seems to be mediated through the PPIase active site [10],[11]. However, it has become increasingly apparent that isomerization of proline is not the sole function of the PPIases, with the first example being the nonimmunophilin Pin1, a PPIase of the parvulin type. Pin1 is able to catalyze isomerization of the proline bond for target substrates only when a serine or threonine preceding the target proline is phosphorylated [12]. This phosphorylation-dependent isomerization places Pin1 directly in the context of traditional signal transduction pathways, including those involved in cell proliferation and tumorigenesis [13]. The identification of Pin1 substrates revitalized the search for additional functions of the immunophilin-type PPIases; although there is no example of phosphorylation-dependent isomerization for either FK506 binding proteins or for cyclophilins, a subset of substrates for these types of PPIases are certainly also dependent on nonchaperone functions. PPIA, along with classical functions in the chaperone-mediated processes outlined above, interacts with the receptor tyrosine kinase Itk post-translationally and modulates the activity state of the already folded protein in vivo [14]. PPIA also is known to modulate HIV infectivity by interacting with a proline-containing sequence in the capsid protein Gag, also in the context of a well-folded protein module [15]. More recently, PPIA has been shown to interact with CD147 in a manner that is proline-dependent and mediated through the active site of the isomerase, but does not contribute to CD147 folding per se [16],[17]. In addition, both PPIA and the highly similar PPIB have been shown to interact with NS5B, an RNA-dependent RNA polymerase necessary for hepatitis C viral replication [10],[18]. The three other single-domain PPIases—which encode only the PPIase domain and, in the case of PPIB and PPIC, a signal sequence—and the 13 multidomain PPIases are less well characterized; most of what is known for these cyclophilins centers not on the isomerase active site but on distinct regions with no known enzymatic function. For instance, the single domain PPIase PPIH (SnuCyp20) participates in the spliceosome through interactions with the 60K component of the tri-snRNP, also known as hPRP4; however, the co-crystal structure of PPIH with a peptide derived from hPRP4 showed that this interaction was mediated exclusively through a face opposite that of the active site [19]. A similar situation was found in another spliceosomal cyclophilin, PPIL1, which interacts with the protein SKIP; NMR data indicate that the chemical shift perturbations in PPIL1 upon SKIP binding did not involve residues involved in proline turnover, and that binding to SKIP occurred even when PPIL1 was bound to cyclosporin A [20]. Finally, PPIE has an RNA-recognition motif (RRM) and has been reported to have RNA-specific isomerase activity [21].
Cyclophilins have been implicated in diverse signaling pathways, including mitochondrial apoptosis [22],[23], RNA splicing [24],[25], and adaptive immunity [26]. However, the proteins that are substrates for cyclophilins in these pathways have not been identified. Moreover, even basic questions concerning the biochemical properties of these enzymes have not been fully addressed. For instance, of the 17 annotated human cyclophilins only seven have been tested for isomerase activity or for the ability to bind cyclosporin [20],[27]–[32]. In vitro techniques aimed at delineating substrate specificity for the canonical family member PPIA have been only moderately successful; mutational analysis of short proline-containing motifs has found that PPIA is a very broadly specific enzyme [33],[34], despite the relatively small number of in vivo–validated substrates. In the case of phage display, the optimized binding sequence does not correspond to the substrate determinants that have been found in vivo for this isoform, and this sort of randomized screening has not been accomplished for any of the less ubiquitous isoforms [35]. Generally, the issue of in vitro versus in vivo substrate selectivity for the isomerases is problematic: for a given isomerase for which there is no knowledge of in vitro substrate specificity, it is difficult to find and validate in vivo substrates. Even for the isoforms that have been tested in vitro for their substrate preferences, there has been little or no correlation with later discovery of in vivo substrate sequences. Clues in some cases may be derived from the identity of other domains expressed in tandem with the cyclophilin domain; for instance, the RRM domain previously mentioned implies an RNA targeting function for PPIE and PPIL4, and likewise the U-box motif of PPIL2 implies involvement in ubiquitin conjugation pathways [36]. The WD-40 repeat of PPWD1 most likely confers a protein∶protein interaction function, as this is its main function in other systems; the same holds true for the TPR motifs of RanBP2 and PPID. However, useful comparisons of in vitro activity with in vivo physiology must wait until the cyclophilin family is more fully characterized with data from either or both lines of research.
In this study, we have screened 15 of the 17 human cyclophilins for their ability to catalyze proline isomerization against standard tetrapeptide proline motifs. We also have determined binding affinities for each cyclophilin family member for the natural product cyclosporin, and have determined the structures of seven PPIase domains to high resolution using X-ray crystallography. These extensive studies reveal interesting biochemical and enzymatic diversity that is consistent with structural data. The structures also provide an opportunity to assess the cyclophilin family for regions of diversity among all family members. In addition, in silico methods based on a family-wide structural analysis were used to characterize a molecular feature contiguous with the canonical active site that may account for substrate specificity. This new description of the cyclophilin peptidyl-prolyl isomerase family highlights regions of diversity that may prove crucial for future physiologically relevant substrate identification and chemical probe development.
In order to elucidate the function of residues in the extended active site of the PPIase domain of the human cyclophilins, we probed the binding and catalytic function of these domains against either substrate or small-molecule inhibitors (see Figure 1 and Datapack S1 for graphical and tabular depictions of the active site). Three assays were utilized to explore these functions. In the first assay, changes in thermal stability were used to assess cyclosporin binding. This assay has been shown in several studies to be a reliable readout of small molecule binding for kinase and other enzyme families [37],[38]. Cyclosporin A (CsA) and the derivatives cyclosporin C, D, and H were screened against all PPIase domains except for PPIL3 and PPIL4, for which all constructs were insoluble or unstable in our hands (Table 1). Because of the inherent thermal stability characteristics of PPID and RanBP2, this technique was unable to distinguish between apo and cyclosporin-bound forms of those domains. However, data were collected for the remaining 13 isoforms, and binding to CsA, CsC, or CsD was noted for six isoforms published previously (PPIA, PPIB, PPIC, PPIE, PPIL1, and PPWD1) [20],[27]–[30],[32]. In addition, binding of CsA or derivatives was seen for PPIF, PPIG, PPIH, and NKTR. In the case of PPIG and PPIH, this explains previous data describing cyclosporin binding to the tri-snRNP complex that contains PPIH [25] and verifies the finding from a homolog that PPIG is capable of binding cyclosporin [39]. No binding was detected for PPIL2, PPIL6, or SDCCAG-10, making these, to our knowledge, the first set of human cyclophilins that have been found incompetent to ligate cyclosporin (Table 1). In order to quantify cyclosporin affinity we undertook isothermal calorimetry (ITC) analysis of all soluble cyclophilin isoforms; we found that a complete family-wide screen led to a range of binding affinities for CsA, expressed as the dissociation constant Kd, from low nanomolar to near micromolar values. We were also able to confirm that under the experimental conditions we tested there was no evidence of CsA binding to PPIL2, PPIL6, or SDCCAG-10 (Table 1).
A two-dimensional NMR experiment (1H/1H TOCSY) described previously [30],[40], the only in vitro protease-free method available to probe for both substrate binding and catalytic activity of cyclophilins, was used to assess the commercially available tetrapeptides of sequence AAPF, AFPF, and AGPF [34]. The NMR-based assay confers the advantage of being a highly sensitive assay for the detection of substrate binding in addition to catalytic activity; the standard chymotrypsin-coupled assay can detect only catalysis and does not provide any direct measurement of binding [40]–[42]. A number of articles have documented the drawbacks of the protease-coupled assay [33],[42]–[44], an obvious example being that the addition of protease to the reaction mixture in the chymotrypsin-coupled assay requires additional testing to ensure that the enzymes and substrates being screened are not proteolytic targets [43]. Additionally, the NMR-based assay does not require substrates to contain chemical modifications, and can be used to measure effects of amino acid substitutions at regions distal to the target proline not measurable by other methods [42]. We detected binding and turnover for at least one of the tetrapeptide substrates tested for PPIA, PPIB, PPIC, PPID, PPIE, PPIF, PPIG, PPIH, PPIL1, PPWD1, and NKTR (Table 1; see Figure S1 for representative data showing binding and activity). This correlated well with previously determined activities [2],[20],[21],[28],[30],[45],[46], and established activity measurements for PPIF, PPIG, and NKTR. For all isoforms tested there was a strict correlation between the ability to bind cyclosporin and activity against the tetrapeptide substrates (Table 1).
In order to understand the molecular basis of these results, we sought structural coverage of the entire human cyclophilin enzymatic class. We determined crystal structures of seven human PPIase domains—PPIC, PPIE, PPIG, PPWD1, PPIL2, NKTR, and SDCCAG-10 (Figure 2 and Datapack S1). There are six previously determined structures (PPIA, PPIB, PPIF, PPIH, PPIL1, and PPIL3). This leaves four structurally uncharacterized human PPIase domains of cyclophilins (PPID, PPIL4, PPIL6, and RanBP2) (Figure 2 and Table S1). However, if we include the highly homologous bovine structure for PPID (three amino acid substitutions compared to human) and compare the set of 14 isoforms for which we have experimental data, we find that they have very similar secondary structural elements (Figure 2). We can therefore use this dataset to provide excellent homology models for the remaining three isoforms (PPIL4, PPIL6, and the PPIase domain of RanBP2) (Figure 2). Models for these three isoforms were generated using the Phyre algorithm [47], and for all further discussions of the cyclophilin family the structures of all 17 PPIase domains will be considered.
All cyclophilins share a common fold architecture consisting of eight antiparallel β sheets and two α-helices that pack against the sheets (Figures 1 and 2). In addition, there is a short α-helical turn containing the active site residue Trp121 found in the β6-β7 loop region (Figure 1; all residue identities and numbers correspond to PPIA except where noted). RMSD across all atoms for all PPIase domains is less than 2 Å, and sequence identity over the same region varies from 61% to 86% (Figures 1, S2, and S3). The most divergent structures in this set are PPIL1, which is an NMR-derived structure (RMSD 1.7 Å), and the previously described PPWD1 (RMSD 1.4 Å) [30]. Excepting PPIL1 and PPWD1, the remaining experimental PPIase domains align over all atoms with RMSD ranging from 0.4 Å to 1.0 Å (see Figure 2 and also Figure S5 for a more detailed structural alignment). An overlay of the Phyre-derived modeled structures leads to an RMSD over all atoms of 1 Å or less compared to PPIA.
The active site of the cyclophilin family includes the invariant catalytic arginine (Arg55) and a highly conserved mixture of hydrophobic, aromatic, and polar residues including Phe60, Met61, Gln63, Ala101, Phe113, Trp121, Leu122, and His126 [48]–[50]. All of these sidechains contribute to an extensive binding surface along one face of the PPIase domain measuring roughly 10 Å along the Arg55–His126 axis and 15 Å along the Trp121–Ala101 axis (Figure 1). Many of these residues are well conserved across all PPIase domains and are thought to serve functions in either catalysis or substrate/inhibitor binding [48],[50],[51] (Figures 2, S2, and S3). Although there are sites of minor diversity among the family members at the Phe60, Met61, and His126 positions, the most striking correlation between cyclosporin binding, tetrapeptide identity, and active site residues is found at the Trp121 position. Our results clearly show that a tryptophan (as found in PPIA, PPIB, PPIC, PPIE, PPIF, PPIH, PPIL1, and PPWD1) or histidine (as found in PPID, PPIG, PPIL3, RANBP2, and NKTR) at this position is permissive for cyclosporin binding whilst other naturally occurring residues at this position (tyrosine in PPIL2, PPIL4, and PPIL6, and glutamic acid in SDCCAG10) abrogate cyclosporin binding under our experimental conditions (Table 1 and Figure 3). It has been shown that mutating Trp121 in PPIA to alanine or phenylalanine has a negative impact on cyclosporin affinity [51]–[53]. Mutation of the naturally occurring histidine in PPID to a tryptophan increases cyclosporin affinity dramatically, altering IC50 for cyclosporin from 1.9 mM to 28 nM and the Kdapp to 12 nM [31],[54]. There are no mutational or computational data for the human cyclophilins that have a tyrosine or glutamic acid substitution at the Trp121 position; we therefore made a set of mutants to both PPIA (mutating Trp121 to either tyrosine or glutamic acid) and to PPIL2 (mutating Tyr389 to either tryptophan or histidine). As expected, mutation of Trp121 in PPIA to glutamic acid abolished activity of this protein; however, the tyrosine mutant retained the ability to catalyze proline isomerization, a novel result. More importantly, the single mutation of Tyr389 to tryptophan converted PPIL2 to an active isomerase, thereby illustrating the fundamental importance of this residue in conferring activity to the cyclophilin family (Figure S1B). However, the Tyr389 mutation to histidine did not lead to activity as measured by NMR under the experimental conditions assayed. For this reason, both the Tyr389 mutants were tested for CsA binding using ITC, and both the Tyr389Trp and Tyr389His mutants were found to bind CsA with micromolar affinity (1.6 µM and 6.6 µM for Trp and His respectively). Taken together, it is clear that there is some flexibility in the active site with regard to the Trp121 position: a tryptophan is clearly optimal at this position but tyrosine is somewhat permissive for activity, as is histidine. Glutamic acid at this position seems to be incompatible with isomerase activity.
Previous computational work with PPIA indicates that the function of Trp121 is mainly to serve to build a hydrophobic pocket for the substrate proline to insert (along with Phe60, Met61, Phe113, and Leu126) [55],[56]. However, our experimental data do not fully support this notion. To explain these results we modeled the interaction of CsA with the active site of cyclophilins, as the macrocyclic ring of cyclosporin structurally mimics the placement of the substrate residues N terminal and C terminal to the target proline (where the sequence Xaa-Pro-Yaa is denoted P1, P1′, and P2′ respectively) within the active site [48],[57]–[59]. Modeling of either CsA into the active site of a histidine containing isoform (like NKTR) or computational mutation of the Trp121 in a PPIA∶CsA complex structure indicated that similar hydrogen bond distances can exist between the indole moiety of tryptophan or the imidazole ring of histidine and the carbonyl of methylleucine 9 (MLE9) in CsA (Figure S4). Therefore either residue would be competent for binding, as we have shown experimentally. Conversely, a tyrosine modeled in the conformation to coordinate with CsA created a steric clash with the carbonyl of MLE9 (1.75 Å); in addition, there was a close steric conflict with the modeled Tyr residue and Cζ of the highly conserved Phe60 residue that helps form the proline-binding pocket (Figure S4). Perhaps this is why in our apo PPIL2 structure the tyrosine at this position pointed away from the active surface (Figure 2). Consistent with this, electron density for Phe71 residue in NKTR indicated that alternative conformations are possible for this residue, which may also explain why the PPIA Trp121Tyr mutant was still capable of coordinating substrate in vitro (Figure 2). We propose that the function of the residue at this position is to make a specific polar interaction with either the carbonyl of MLE9 in CsA or the carbonyl of a substrate peptide at the P2′ position (C terminal to the target proline).
Three cyclophilins neither bound cyclosporin nor tetrapeptide: PPIL2, PPIL6, and SDCCAG-10 (Table 1). It is clear that these three proteins are quite divergent in the active site compared to PPIA (Figure 1C). Perhaps more importantly they are, along with PPIL4, the only isoforms that substitute the residue Trp121 with a non-histidine residue. Additionally, PPIL4 does not possess the otherwise strictly conserved Arg55 (there is an asparagine at the equivalent position), so it is not surprising that this isoform does not show activity against standard substrates. The molecular function of the PPIase domain for these isoforms is unknown, but our structures suggest that these isoforms could still serve as proline-binding domains. Indeed, our assays show binding to the standard substrate suc-AGPF-pNA even where we do not detect isomerase activity (Figure S1A).
PPIL2, PPIL6, and SDCCAG-10 are clearly divergent from the rest of the family in terms of in vitro activity. Next, a structural analysis of all family members was undertaken in order to probe for further isoform diversity. Examination of the surface of the PPIase domains near the active site revealed two pockets that potentially contribute to substrate specificity, binding, and turnover. The first pocket is the proline interaction surface (or S1′ pocket, where the target proline in substrate is again denoted as P1′) and is defined by the PPIA residues Phe113 at the base of the pocket and Phe60, Met61, Leu122, and His126 that form the sides of the pocket (Figure 4). As previously described, these residues are highly conserved across all PPIase isoforms and orthologs, consistent with minor discrimination against commercial substrates or cyclosporin [60]. The second pocket forms a surface that likely interacts with substrate residue P2 or P3 relative to the substrate proline, and so will be named the S2 pocket hereafter. Since the main-chain atoms of the β5-β6 loop define the base of the S2 pocket, the chemical identities of residues found in this region do not have much influence on the size and shape of the S2 pocket (Figure 4). Indeed, the S2 pocket is extremely uniform across cyclophilins; it is deep and relatively nonspecific, so it can accommodate long, short, polar, or hydrophobic sidechains without penalty. However, the S2 pocket surface is guarded by a set of “gatekeeper” residues whose sidechains are in a position to control access to this pocket. In PPIA, these residues are Thr73, Glu81, Lys82, Ala103, Thr107, Ser110, and Gln111 (Figure 4C). These gatekeeper residues at positions 81, 82, and 103 and the secondary gatekeeper at position 73 (so named because its position in most PPIase structures is pointed away from the S2 pocket) show major chemical and size variance. For instance, the residue that is at position 103 in PPIA varies from alanine in about half of the cyclophilin isoforms to a serine in PPIE, PPIH, and PPIL2; an arginine in PPIG and NKTR; lysine in PPIL6; asparagine in PPIL3 and PPIL4; and glutamine in RANBP2 (Figure 4C). The identities of the amino acids at positions 73, 81, and 82 are equally diverse across the cyclophilin family. The practical effect of this variance can be visualized by examining the surface properties of the cyclophilin family (Figure 5 and Datapack S1). These surfaces are clearly unique to the individual cyclophilin members, but can generally be classified into gatekeeper surfaces with mixed or neutral charges (see for example PPIA and several others); gatekeeper surfaces with overall acidic character (SDCCAG-10, PPIC, and PPWD1); and gatekeeper surfaces that occlude access to the S2 pocket (several; see Figure 5). The occluded set consists of the cyclophilin isoforms with bulky sidechains at the gatekeeper positions; for instance, NKTR has Lys84, Tyr93, and Arg114 compared to PPIA residues Thr73, Lys82, and Ala103 (Figures 4 and 5). Finally, residues within this region of PPIA, including Lys82, have previously been shown to be important for substrate binding as shown by NMR relaxation studies [61], consistent with a gatekeeper function.
The S2 pocket is where conformational divergence throughout the cyclophilin family is greatest (Figure 2 and Datapack S1). Most of the remaining structural diversity is found in three of the loop regions connecting secondary structural elements. A subset of cyclophilins have a deletion in the β1-β2 loop region (residues Ala11-Pro16 in PPIA) that significantly alters the β sheet lengths in this region along with the loop between them. The division between “deleted” β1-β2 loops and “full-length” β1-β2 loops follows a phylogram distribution of PPIase domains, with the more conserved isoforms relative to PPIA (PPIB, PPIC, PPID, PPIE, PPIF, PPIG, PPIH, PPIL6, NKTR, and RanBP2) encoding full-length loops and the more divergent members by sequence (PPIL1, PPIL2, PPIL3, PPIL4, SDCCAG-10, and PPWD1) encoding deleted β1-β2 loops (Figure 2 and Figure S5). The α1-β3 loop (Thr41-Gly50) is also a region of structural diversity. There are three distinct classes of conformations adopted by this loop: the PPIA α1-β3 loop family, which includes PPIA, PPIB, PPIC, PPIE, and PPIF; a shorter version of the loop represented by the structures of PPIL1, PPIL2, PPIL3, PPIL4, SDCCAG-10, and PPWD1; and a longer version found in PPID, PPIG, PPIH, PPIL6, and NKTR. The short version of the α1-β3 loop changes the orientation of the α1 helix and the β3 sheet, and causes a ∼2 Å displacement of α1 relative to PPIA (Figure S5). Finally, the α2-β8 loop (Gly146-Lys155) has two distinct groups: the standard conformation found in PPIA, PPIE, PPIF, PPIL6, and RANBP2, and the conformation adopted by all other isoforms (Figure S5). Interestingly, two regions found to have structural divergence (the β1-β2 and α2-β8 loops) form a contiguous surface on the “back” face of the cyclophilin fold relative to the active site. Sequence and structural diversity in this region could indicate a preference for different potential binding partners, as the back face of cyclophilins has previously been shown to mediate protein∶protein interactions [19],[20]. However, it seems that for substrate interactions mediated by the proline-binding pocket isoform selectivity is likely to be determined by the S2 pocket region rather than these distal regions. Thus, the functional significance of the S2 pocket will be further explored with regard to its effect on substrate binding and specificity.
Our biochemical data are the latest evidence that molecular determinants for tetrapeptide substrate or cyclosporin binding may not be identical to molecular determinants for physiologically relevant substrates, and supplements other recent publications along these lines [62],[63]. Additionally, structural analysis suggests that the region surrounding the S2 pocket is an attractive target to design isoform specificity. As commercially available ligands and substrates are unable to effectively probe this region of the cyclophilin family, we turned to in silico techniques to obtain insight into isoform gatekeeper identity and its relationship to accessibility to the S2 pocket. Four hundred test peptides of the general form Xaa-Zaa-Gly-Pro (corresponding to substrate positions P3-P2-P1-P1′) were docked into a subset of cyclophilin family members (PPIA, PPIL2, PPIC, PPWD1, and NKTR). These proteins were chosen because of the diversity of the amino acids in the gatekeeper and S2 pocket regions (Figure 5). Monte Carlo simulations were performed to sample conformational space for each combination of cyclophilin isoform and test peptide, allowing flexibility of the P2 and P3 residues of the potential substrate and of the sidechains of the gatekeepers at positions comparable to PPIA Thr73 (gatekeeper 1), Lys82 (gatekeeper 2), and Ala103 (gatekeeper 3) while keeping the rest of the protein rigid [64]. The sidechain of Arg377 in PPIL2, which is a glycine in the other cyclophilins investigated, was also allowed flexibility as it contributes a unique chemistry to the S2 region. Throughout the Monte Carlo simulations (200,000 iterations) tethers were imposed on the Gly and Pro residues to ensure that the tetrapeptides would remain bound to the active site. We made an assumption, based on a number of previous crystallographic and NMR-based studies of the cyclophilins, that the position and coordination of the Gly-Pro sequence of substrate is relatively fixed within the active site of the PPIase. Several structural studies with both synthetic and natural substrate data bound to PPIA support this assumption [30],[50],[59]. It was computationally necessary to fix the P1 and P1′ positions upon the enzyme in order to allow for more degrees of freedom at the P2 and P3 positions in our simulations; without these tethers we would have been testing the contribution of these two residues to the overall ability of substrate to bind the entire active site. While this is a very interesting line of study the interaction of proline in the proline binding or P1′ pocket was not the focus of the current work. For each combination of cyclophilin isoform and tetrapeptide, the lowest-energy complex was chosen as the preferred conformation of the bound complex, and an estimate of the binding energy was calculated using ICM [65]. Additionally, since low-energy complexes may or may not include significant interactions at the S2 pocket, the distance between the tetrapeptide and the Cα of the gatekeeper equivalent to PPIA Lys82 was calculated. This metric was designed to query for tetrapeptides that both bind with favorable energy in the S2 pocket, and also fill the S2 pocket if possible.
An energetic preference for aromatics interacting with the S2 pocket was found for PPIA, in particular tryptophan or tyrosine (Figure 6; for scatter plot representation see Figure S6). In addition, there were a few peptides containing methionine, lysine, or arginine at the P2 position that extended deeply into the S2 pocket, albeit with poor predicted binding energies. Peptides with isoleucine, leucine, valine, proline, alanine, glycine, cysteine, threonine, or serine at the P2 position were disfavored, with poor predicted binding energies. We observed much less discrimination for the identity of the P3 position, although there is a clear selection against basic chemistries (Figure 6). Visual inspection of the top 10 model complexes predicted for PPIA based on the energy metric (EFGP, EWGP, DYGP, DEGP, DDGP, YWGP, PYGP, EDGP, YFGP, and PWGP) showed that all of the residues at the P2 position are well positioned to fill the S2 pocket of PPIA, while inspection of some models that scored poorly (RFGP, ERGP, DFGP) showed incomplete entry into the S2 pocket. In addition, these models indicated interactions between the residue at the P3 position and the gatekeeper 1 residue, or with the P1′ pocket and the key active site residue Arg55.
The published data on specificity for PPIA are consistent with our findings. Previous in vitro phage display experiments with PPIA (designed to probe substrate preferences at the P1 to P8′ positions) found a strong preference for phenylalanine at the P2 and glutamic acid at the P3 position; these residues were provided by the expression vector used in the phage display and therefore biased the pool of samples available for initial selection [35]. Substitution of this glutamic acid/phenylalanine series with any other residues, however, lessened the signal on an array, thereby confirming a preference for these chemistries in solution. Our simulations support this chemical preference for acidic residues at P3 followed by aromatic residues at P2 (Figure 6). A well-characterized substrate in vivo for PPIA is the HIV capsid; there are several sequence variants that have been studied both in solution and in crystallographic experiments, and all sequences have either methionine or alanine at the P2 position and histidine or alanine at the P3 position [50],[66]. In the structures of PPIA with these peptides, the alanine does not fill the S2 pocket, and this is likely the reason why it does not score well in our modeling trials. Neither histidine nor alanine at the P3 position is predicted to score highly by our modeling trials, and in the co-crystal structures these residues are not making any significant contacts to the gatekeeper 1 region of PPIA. The validated in vivo substrate CD147 was also investigated. The natural sequence that is acted upon by PPIA is ALWP, which was not predicted to bind tightly to PPIA based on either the phage display data or our simulations, and experimentally was found to have rather weak affinity [17]. Finally, the PPIA substrate Itk contains the targeted sequence ENNP, which is a relatively high-scoring P3 and P2 sequence combination based on our models [14]. Our simulations recapitulate the experimental data that is available, but imply that none of the in vitro or in vivo substrates studied to date for PPIA interact with the S2 pocket with optimized space-filling or energetic properties.
In order to begin experimental validation of our in silico predictions, a peptide “test set” composed of the following sequences was synthesized: DEGPF, DFGPF, DYGPF, YGGPF, and VRGPF. We then monitored catalysis of all of these potential substrates using our NMR-based assay (Figure S1). These peptides were selected in order to allow us to discriminate between cyclophilin isoforms; initial studies were conducted with PPIA in order to optimize experimental conditions for the detection of binding and catalysis. Our data indicated that, although PPIA was competent to bind all five peptides, only those predicted to have significant scores on the binding energy metric were substrates for proline isomerization (DEGPF, DFGPF, and DYGPF; see Figure 6 and Figure S1). The two peptides that were not efficient substrates for catalysis (YGGPF and VRGPF) both yielded poor predicted binding energies in our docking study to PPIA. That there was little discrimination with our NMR assay between DEGPF, DFGPF, and DYGPF was somewhat inconsistent with our simulations, as the model peptide for DFGP did not extend fully into the S2 pocket. It is possible that while tethering the P1 and P1′ Gly-Pro sequence allowed us to obtain a large number of reasonable structures at the P2 and P3 positions, it may have artificially increased our in silico binding affinity in a way that we cannot recapitulate in vitro. It is also possible that this spatial constraint upon our simulations biased our results towards substrates with the key interacting residue at the P2 position. Perhaps in vitro it is the P3 position that contributes significantly to binding energy; therefore the binding contributed by the aspartic acid in the current test set was the significant determinant for binding to PPIA in addition to the identity of the residue at the P2 position. Regardless, these experimental results will allow us to next analyze the capacity of our test set to discriminate among cyclophilin isoforms. Additionally, as all of our test peptides are identical at the P1, P1′, and P2′ positions, we can see for the first time that substitutions at amino acids in the P2 and P3 positions have measurable effects on the ability of the broad specificity enzyme PPIA to bind and catalyze proline containing sequences.
Distinct patterns of chemical preference were noted for PPIC, PPIL2, NKTR, and PPWD1 (Figure 6; for scatter plot representation see Figure S6). Much like PPIA, the PPIase domains of PPIC and PPIL2 showed an energetic preference for tryptophan at the P2 position; and for PPIL2 and NKTR isoleucine, leucine, valine, proline, alanine, glycine, cysteine, threonine, and serine at the P2 position resulted in poor predicted binding energies and little penetration into the S2 pocket (Figure 6). Indeed, for NKTR there were relatively few tetrapeptide combinations with both favorable predicted binding energy and penetration into the S2 pocket; this is easily rationalized by the extremely narrow gap between the gatekeeper 1 and gatekeeper 3 regions in the NKTR structure, which occlude the S2 pocket and restrict the types of residues that can stably associate with the pocket without steric or charge clashes (Figures 5, 6). PPIC showed a distinct preference pattern for aromatic residues at P2 preceded by basic or aromatic residues at P3 (Figure 6). This is most likely due to the substitution of gatekeeper 2 and the overall acidic character of this region of PPIC relative to PPIA (Figure 5).
In the case of PPIL2, there was near equivalency between the aromatics at position P2, with perhaps a slight energetic preference for tryptophan but strong affinities for tyrosine and phenylalanine as well. Likewise there was little discrimination at the P3 position (Figure 6). Compared to PPIL2 simulations, the results for PPWD1 were striking: the acidic surface characteristics of this isoform selected strongly for an arginine at the P2 position, while lysine and aromatic residues also yielded good predicted binding energies (Figures 5 and 6). Of the surfaces tested, only PPWD1 provided a surface where strong energy scores were measured for basic residues at this position. Experimentally, the construct used initially for crystallization of PPWD1 contained a sequence AEGP found N-terminal to the PPIase domain, and this sequence was found associated with a neighboring PPIase domain in the crystal structure. NMR-based assays showed that AEGP bound PPWD1 but was not a good substrate for the enzyme, which correlates well with the poor binding energy predicted for the AEGP tetrapeptide in our simulations [30]. Again, the scarcity of experimental data for cyclophilin isoforms limits the ability to validate the simulations; but to the extent that such information exists, it correlates well with our in silico findings. Current efforts are underway to measure binding and/or proline isomerization of our test set peptides with NKTR, PPIC, PPIL2, and PPWD1; we predict based on our above analysis that several of our test set peptides would bind well to most or all of our test cyclophilins (see DYGP and DFGP in Figure 6), while others could be selective for some isoforms over others (VRGP, which has good energy metrics for PPWD1 but not for any other isoform in the current study). Although in vitro validation of our in silico results are still ongoing, we believe that the initial data we present here provide the basis for a renewed study of the S2 pocket of the human cyclophilins as a potential locus of chemical and substrate diversity.
In conclusion, there are cyclophilin family members that, while sharing overall conservation with active members of the family, do not possess isomerase activity in our assays. For PPIL2 and SDCCAG-10, both of which have been found associated with spliceosomal complexes, it may be that it is the non-active surface of the PPIase domain that performs the major function as in the cases of PPIH and PPIL1. Additionally, it may well be that the function of the PPIase domain in these cyclophilins is to simply bind proline-containing motifs. Our NMR data suggest this option, as binding without measurable catalysis to proline sequences is observed for all isoforms we were able to test.
Chemical probes such as cyclosporin are unselective with regard to the cyclophilin family (Table 1) [67]. Although a recent report focusing on aryl 1-indanylketones showed binding to PPIA, PPIF, and PPIL1 while not binding to PPIB, PPIC, or PPIH [67], it seems that any ligand that coordinates exclusively with the S1′ pocket and/or Trp121 region is unlikely to be selective with respect to the entire cyclophilin family. Potentially, the S2′ or S3′ region of the isomerase domain could be a site of selectivity; it is clear from our surface representations (Figure 5) that this is a variable part of the cyclophilin domain. However, our results indicate that a clear virtual chemical fingerprint exists for the S2 and S3 positions of the isomerase domain. For instance, PPIA and PPWD1 seem to have restricted sets of sidechains that are preferred at the P2 position (and the P3 position in the case of PPIA), while PPIC appears to be more promiscuous. The highly occluded nature for the S2 pocket exhibited by NKTR results in a restrictive set of allowed tetrapeptide sequences for this isoform; several other isoforms in the cyclophilin family also exhibit this type of gatekeeper restriction. Because of the very distinct molecular features of the S2 region, both in terms of the highly “druggable” S2 pocket and the chemical diversity seen for the gatekeeper residues, targeting this region of the cyclophilins for pharmacophore design and selection is more likely to result in tight binders with greater specificity for particular isoforms in the family.
Detailed materials and methods for cloning, expression, purification, and crystallization of all novel isomerase domain structures solved as part of the Structural Genomics Consortium are freely available at the Web site http://www.sgc.utoronto.ca/; where methods differ significantly from the following they are noted for each isoform in Text S2. In general, full-length cDNA clones were obtained from the Mammalian Gene Collection (accession numbers noted below). Constructs based around the predicted isomerase domain boundaries were cloned into pET28a using ligation-independent cloning methods (LIC) (BD Biosciences, San Jose, CA, USA) and transformed into BL21 Gold DE3 cells (Stratagene, La Jolla, CA, USA). The resulting vectors encode an N-terminal His6 tag with a thrombin cleavage site. Mutants of cyclophilin constructs were created either using standard Quickchange protocols (Stratagene) or by LIC-based methods on PCR fused gene products. Cultures were grown in Terrific Broth medium at 37°C to OD600 of 6 and induced at 15°C overnight with the addition of 50–100 µm isopropyl thio-β-D-galactoside (IPTG). Pellets were resuspended in 20 mL of lysis buffer (50 mm Tris, pH 8.0, 500 mm NaCl, 1 mm phenylmethanesulfonyl fluoride and 0.1 mL of general protease inhibitor (P2714, Sigma, St. Louis, MO, USA) and lysed by sonication; lysates were then centrifuged for 20 min at 69,673g. The supernatant was loaded onto nickel nitrilotriacetic acid resin (Qiagen, Valencia, CA, USA), washed with five column volumes of lysis buffer and five column volumes of low imidazole buffer (lysis buffer+10 mm imidazole, pH 8), and eluted in 10 mL of elution buffer (lysis buffer+250 mm imidazole, pH 8, and 10% glycerol). If the His6 tag was cleaved for crystallization purposes, then one unit of thrombin (Sigma) per milligram of protein was added to remove the tag overnight at 4°C. For gel filtration, a column packed with HiLoad Superdex 200 resin (GE Healthcare, Piscataway, NJ, USA) was pre-equilibrated with gel filtration buffer (lysis buffer+5 mM β-mercaptoethanol and 1 mM ethylenediaminetetraacetic acid). Peak fractions were pooled and concentrated using Amicon concentrators (10,000 molecular mass cut-off; Millipore, Danvers, MA, USA). The protein was generally used at 250–500 µM for crystallization screening.
Generally, crystal hits were initially prepared in sitting drop 96-well format. Proteins were set up as 1 µL protein+1 µL reservoir solution and incubated at 18°C for 24 h to 1 mo. If crystal optimization was required it was performed in 24-well hanging drop format with 1 µL protein+1 µL reservoir solution. Crystals were cryoprotected with mother liquor with 10%–15% glycerol. Datasets were collected on an in-house FR-E SuperBright Cu rotating anode/Raxis IV++ detector (Rigaku Americas, The Woodlands, TX, USA); except for PPIC, which was collected at APS 19-BM. Data was integrated and scaled using the HKL2000 program package [68],[69]. The program PHASER [70] was used as part of the CCP4 suite [71] to find the molecular replacement solution. Manual rebuilding was performed using either O [72] or COOT [73], and refined using REFMAC [74] in the CCP4I program suite [75]. In most cases ARP/wARP was utilized to assist in model building and iterative refinement of starting phases [76]. Final models were evaluated using PROCHECK [77] and MOLPROBITY [78], with all models judged to have excellent stereochemistry and no residues in disallowed regions of Ramachandran space.
All protein samples used for static light scattering (StarGazer) trials were assessed for purity utilizing SDS-PAGE and verified for mass accuracy using mass spectrometry. Methods were generally as described as in [38]; protein at approximately 20 µM concentration was heated from room temperature to 80°C in the presence or absence of small molecules, including cyclosporins A, C, D, or H (LKT Labs, MN, USA). The cyclophilins were originally prepared in 100% DMSO at 50–100 mM concentration, then diluted to 50 µM for screening, thereby ensuring the final DMSO concentration was less than 5% during the experiment. Ligand binding was detected by monitoring the increase in Tagg in the presence of the ligand; and any compound that caused a >2°C increase in Tagg were observed to be outside of the range of experimental error. Each compound was tested at least twice.
All experiments were performed using a VP-ITC microcalorimeter (Microcal, MA, USA), and data analysis was performed utilizing the Origin 7 software. All experiments were conducted at 25°C. Methods were roughly based on those in [67], with modifications as described. Highly pure proteins were dialyzed into ITC buffer (50 mM Hepes pH 8, 0.2 M NaCl), which was also used to dilute ligand stock to the concentrations used for ITC. In order to obtain strong signal for binding isotherms, proteins were used at concentrations ranging from 50 to 300 µM, with 100 µM being standard for most cyclophilins tested. The proteins were loaded into the syringe, with the ligand (cyclosporin A, LKT Labs, MN, USA) in the cell at 5 µM concentration. Generally 5–10 µL injections of protein were made; optimal volumes were determined experimentally to obtain reasonable data for single-site fitting. Ligands were described as not binding protein under these conditions if, at high concentrations of protein (∼300 µM), no change in isotherm deflection was noted after 10–20 injections (275 µL of protein).
Most protein samples aimed at assessing binding and/or catalysis of tetrapeptide substrates were diluted to 500 µL with 10% D2O and placed into a Shigemi microcell (Allison Park, PA, USA). Typical samples contained 0.075 mM protein and 2 mM of suc-AAPF-pNA, suc-AFPF-pNA, or suc-AGPF-pNA (Bachem), along with 100 mM phosphate buffer pH 7 and 100 mM NaCl. Spectra were collected at 25°C on a Varian 600 or 900 MHz spectrometer (Palo Alto, CA, USA). Spectra were acquired using standard Varian BioPack sequences, processed using NMRpipe software [79] and visualized using CCPN software [80]. For samples used to assess binding of PPIA to peptides DEGPF, DFGPF, DYGPF, YGGPF, or VRGPF, samples were as above except protein concentration was 0.3 mM and spectra were collected at 10°C.
A set of 400 test peptides of the general form X-Z-Gly-Pro were docked to a subset of cyclophilin isoforms (Protein Data Bank [PDB] codes: PPIA, 1AK4: PPIL2, 1ZKC; PPIC, 2ESL; PPWD1, 2A2N; and NKTR, 2HE9) using ICM software (Molsoft LLC). Monte Carlo simulations were performed to sample conformational space for each combination of cyclophilin isoform and test peptide, allowing flexibility of the tetrapeptide and the sidechains of the gatekeepers at positions comparable to PPIA Thr73, Lys82, and Ala103, and keeping the rest of the protein receptor rigid [64]. The crystal structure of PPWD1 (PDB: 2A2N) was used to determine the initial position of each tetrapeptide in the various cyclophilin isoforms by superimposing the Gly and Pro residues onto the corresponding residues bound to the active site of PPWD1, and the catalytic arginine was repositioned to align with Arg535 of PPWD1. Throughout the Monte Carlo simulations (200,000 iterations), tethers were imposed on the C-terminal Gly and Pro residues, to ensure that the tetrapeptides would remain bound to the active site. For each combination of cyclophilin isoform and tetrapeptide, the lowest-energy complex was chosen as the predicted conformation of the bound complex, and an estimate of the binding energy was calculated using ICM (Molsoft, LLC) [65]. Additionally, the distance between the tetrapeptide and the Cα of the gatekeeper equivalent to PPIA Lys82 was calculated (this residue is located at the far end of the S2 pocket; see Figure 4), to determine how well the docked peptide was predicted to fill the S2 pocket. Peptides derived from simulation data were synthesized without modification by the Core Facility at Tufts University (http://tucf.org/).
PDB codes for the novel cyclophilin structures presented within this manuscript are as follows: 2R99 (PPIE), 2ESL (PPIC), 2HE9 (NKTR), 2GW2 (PPIG), 2HQ6 (SDCCAG-10), 1ZKC (PPIL2), and 2A2N (PPWD1). PDB codes for the previously deposited set of structures used to generate figures and analyzed in the text are: 2CPL (PPIA), 2BIT (PPIF), 1CYN (PPIB), 1QOI (PPIH), 1XWN (PPIL1), and 2OK3 (PPIL3). GenBank accession numbers for the cyclophilins noted in the methods are: BC003026 (PPIA), BC020800 (PPIB), BC002678 (PPIC), BC030707 (PPID), BC008451 (PPIE), BC005020 (PPIF), BC001555 (PPIG), BC003412 (PPIH), BC003048 (PPIL1), BC000022 (PPIL2), BC007693 (PPIL3), BC020986 (PPIL4), BC038716 (PPIL6), NM006267 (RANBP2 - synthetic template), BC015385 (PPWD1), BC167775 (NKTR), and BC012117 (SDCCAG-10).
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10.1371/journal.ppat.1006865 | Conditional mutagenesis in vivo reveals cell type- and infection stage-specific requirements for LANA in chronic MHV68 infection | Gammaherpesvirus (GHV) pathogenesis is a complex process that involves productive viral replication, dissemination to tissues that harbor lifelong latent infection, and reactivation from latency back into a productive replication cycle. Traditional loss-of-function mutagenesis approaches in mice using murine gammaherpesvirus 68 (MHV68), a model that allows for examination of GHV pathogenesis in vivo, have been invaluable for defining requirements for specific viral gene products in GHV infection. But these approaches are insufficient to fully reveal how viral gene products contribute when the encoded protein facilitates multiple processes in the infectious cycle and when these functions vary over time and from one host tissue to another. To address this complexity, we developed an MHV68 genetic platform that enables cell-type-specific and inducible viral gene deletion in vivo. We employed this system to re-evaluate functions of the MHV68 latency-associated nuclear antigen (mLANA), a protein with roles in both viral replication and latency. Cre-mediated deletion in mice of loxP-flanked ORF73 demonstrated the necessity of mLANA in B cells for MHV68 latency establishment. Impaired latency during the transition from draining lymph nodes to blood following mLANA deletion also was observed, supporting the hypothesis that B cells are a major conduit for viral dissemination. Ablation of mLANA in infected germinal center (GC) B cells severely impaired viral latency, indicating the importance of viral passage through the GC for latency establishment. Finally, induced ablation of mLANA during latency resulted in complete loss of affected viral genomes, indicating that mLANA is critically important for maintenance of viral genomes during stable latency. Collectively, these experiments provide new insights into LANA homolog functions in GHV colonization of the host and highlight the potential of a new MHV68 genetic platform to foster a more complete understanding of viral gene functions at discrete stages of GHV pathogenesis.
| Gammaherpesviruses (GHVs), including the human pathogens Epstein-Barr virus and Kaposi sarcoma-associated herpesvirus, establish lifelong infections that can lead to cancer. Defining the functions of viral gene products in acute replication and chronic infection is important for understanding how these viruses cause disease. Infection of mice with the related GHV, murine gammaherpesvirus 68 (MHV68), provides a tractable small animal model for defining how viral gene products function in chronic infection and understanding how host factors limit disease. Here we describe the development of a new viral genetic platform that enables the targeted deletion of specific genes from the viral genome in discrete host cells or at distinct times during infection. We utilize this system to better define requirements for the conserved latency-associated nuclear antigen in MHV68 lytic replication and latency in mice. This work highlights the utility of this MHV68 genetic platform for defining mechanisms of GHV infection and disease.
| Gammaherpesviruses (GHVs) are large, enveloped, double-strand DNA viruses that include the human pathogens Epstein-Barr virus (EBV) and Kaposi sarcoma-associated herpesvirus (KSHV). GHVs establish lifelong, chronic infections in their hosts and are responsible for causing lymphoproliferative disorders (LPD) and cancer [1]. Like other herpesvirus subfamilies, infections by GHVs are characterized by two distinct stages: the productive or lytic replication cycle and latency. The lytic phase of infection involves expression of all kinetic classes (immediate-early, early, and late) of viral genes, production of infectious progeny, and viral dissemination, provoking inflammation and a potent antiviral immune response [2–4]. In contrast, latency is characterized by the absence of readily detectable infectious virion production and minimal viral gene expression, allowing infected cells to evade the immune system. Establishment and maintenance of latency enables GHVs to persist indefinitely within the host [1,2,4]. Given a proper stimulus, latent GHVs can undergo reactivation, a re-entry into the lytic cycle, which likely serves as a mechanism for re-seeding latent reservoirs and infecting new hosts [3,4]. Understanding the intricacies of GHV infection is imperative to deciphering the mechanisms that drive GHV disease.
Murine gammaherpesvirus 68 (MHV68) is a rodent GHV that is genetically and phenotypically related to EBV and KSHV [1,3,4]. MHV68 replicates efficiently in cell culture and readily infects and establishes latency in inbred and outbred strains of mice. MHV68 tropism for cells in the infected animal, such as B cells during latency, is similar to human GHVs. Since both the virus and host are amenable to targeted genetic manipulation, MHV68 infection of mice provides a versatile small animal model for studying the functions of viral gene products in GHV infection and for defining host responses to infection [1,3].
Though the sequence of events is not completely defined, MHV68 is thought to follow a complex route of dissemination in order to colonize a host. Acute replication in airway alveolar epithelial cells is thought to seed CD11c+ dendritic cells, which traffic virus from the primary site of infection to draining lymph nodes [5]. Studies in which the MHV68 non-coding RNA TMER4 was disrupted suggest that trafficking from draining lymph nodes to the spleen occurs hematogenously [6]. Although latency is established in splenic macrophages after IP inoculation of B cell-deficient MuMT mice, latency is not established in the spleens of these mice after IN inoculation [7,8]. Given that adoptive transfer of B cells into MuMT mice overcomes this defect [9], B cells appear to be required for establishment of splenic latency following IN inoculation and one model posits that B cells facilitate viral transit from draining LNs to the spleen via the blood. However, this has not been directly demonstrated. Considerably less is known about specific viral determinants that control viral mobility and tropism across different tissues during infection. One viral gene that potentially facilitates dissemination is the latency-associated nuclear antigen (LANA).
LANA is a conserved protein encoded by the ORF73 gene in members of the Rhadinovirus genus of GHVs [10,11]. LANA is expressed as an immediate-early gene product during the lytic phase of infection and is also one of the few viral genes expressed during latency [12–15]. Numerous functions are described for LANA homologs in tissue culture systems, including transcriptional regulation of viral and host genes, inhibition of tumor suppressors and cell-cycle dysregulation, regulation of viral DNA replication during latency, and maintenance of the latent viral episome for long-term persistence [4,10,16]. Studies using an mLANA-null MHV68 (73.STOP) defined functions for LANA in vivo in promoting efficient acute replication, establishment of latency, and reactivation from latency [17–23]. However, an inherent problem with this approach is that the absence of mLANA at early stages of infection may indirectly influence phenotypes observed at subsequent stages. For example, productive replication of mLANA-null MHV68 in the lungs of mice is significantly impaired, which may reduce the number of virus-seeded B cells capable of trafficking to the spleen [19]. Consequently, this may indirectly reduce the number of latently infected splenocytes and thereby obfuscate identification of the function of mLANA in facilitating splenic latency.
To address this limitation of prior studies, we have developed a genetic platform that enables dissection of mLANA functions at discrete steps in the MHV68 infectious cycle. We engineered a recombinant MHV68 that contains loxP sites flanking ORF73 (O73.loxP MHV68) to enable cell-type specific and inducible ablation of mLANA. We use this system to define requirements for mLANA in B cells, and in particular germinal center B cells, in colonization of the host by MHV68. We further test, by inducible deletion after the establishment of latency, the requirement of mLANA for maintenance of viral genomes during chronic MHV68 infection. Through these studies we delineate cell-type specific and temporal functions of mLANA in the establishment and maintenance of MHV68 latency and establish a more nuanced virus-host genetic approach to more precisely understand chronic GHV infection.
LANA homologs are multi-functional proteins that play roles in lytic replication, latency, and reactivation from latency [17–24]. In order to define roles for MHV68 LANA at discrete steps in the infection cycle, we developed a genetic platform to enable inducible deletion of viral genes in vivo in infected cells that express Cre recombinase. We first generated a new MHV68 BAC (termed FRT BAC) in which the loxP sites that flank the non-viral BAC vector sequences [25] were replaced with FRT sites (S1 Fig). This allowed us to make use of loxP sequences elsewhere in the viral genome while preserving the ability to excise the non-viral BAC vector sequences using Flp recombinase (S2 Fig). MHV68 produced using the newly derived BAC exhibits WT levels of acute replication, latency establishment, and reactivation from latency when compared directly to virus derived from the original MHV68 BAC (S3 Fig).
To enable conditional deletion of the mLANA-encoding ORF73 gene, we made an MHV68 recombinant in which loxP sites were inserted flanking ORF73 (O73.loxP MHV68, Fig 1A). For comparative purposes, we also re-derived the previously characterized mLANA-null MHV68 [73.STOP, [19]] in the newly generated FRT BAC. O73.loxP MHV68 replicated equivalently to WT MHV68 in tissue culture in both low- and high-MOI growth analyses (Fig 1B). To verify that the loxP-flanked (floxed) ORF73 gene could be deleted from O73.loxP MHV68, we infected Cre-ERT2 3T3 fibroblasts, cells that constitutively express Cre recombinase fused to a modified estrogen receptor [26], with WT MHV68, 73.STOP, or O73.loxP and then evaluated several MHV68 genomic loci by PCR. Though the ORF73 locus was predominately intact in O73.loxP-infected cells treated with vehicle, only the ORF73 deletion product was detected following activation of Cre by treatment of the cells with 4-hydroxytamoxifen (4-OHT) (Fig 1C). The spurious deletion observed in untreated cells is most likely the result of “leaky” Cre activity or homologous recombination between loxP sites [5]. ORF73 of WT MHV68 and 73.STOP were unaffected by Cre activity. The adjacent ORF72 gene and the distal ORF59 gene were examined by PCR for off-target effects and found to be unaffected in all viruses tested (S4 Fig and Fig 1C, respectively). As expected, deletion of floxed ORF73 by Cre induction resulted in an absence of detectable orf73 transcripts in RT-PCR analyses (S4 Fig) and mLANA protein in immunoblot analyses (Fig 1D). Importantly, mLANA was readily detected in vehicle-treated cells, demonstrating that the presence of loxP sites did not prevent mLANA expression. As with PCR analyses, non-target viral protein expression by O73.loxP remained unaffected by Cre induction. Importantly, transcription of the adjacent M11 gene was not affected by loxP insertion at the 3’ end of ORF73 (S4 Fig). These results confirm that Cre recombinase targeted to the ORF73 locus efficiently deleted ORF73 resulting in loss of mLANA production.
Given the critical role of mLANA in MHV68 chronic infection in mice, we tested the possibility that insertion of loxP sites flanking ORF73 would subtly disrupt mLANA function and attenuate O73.loxP in vivo. To test for such a possibility, we evaluated mice at various stages of infection following inoculation with O73.loxP compared to WT MHV68 and mLANA-null 73.STOP control viruses. With respect to acute replication, seven days after IN inoculation of WT C57BL/6 mice mean titers of WT and O73.loxP MHV68 in lungs were similar (Fig 2A), while 73.STOP titers were modestly attenuated relative to WT MHV68, which is consistent with previous studies [17,19]. Latency establishment by O73.loxP also was equivalent to WT MHV68 16–18 days after IN inoculation of WT mice; as expected, 73.STOP was minimally detected (Fig 3A, Table 1). Equivalent frequencies of WT MHV68 and O73.loxP genome-positive cells were detected in splenocytes following IP inoculation (Fig 3C), which is a more permissive route of infection that presumably allows the virus direct access to target cells for latency in the spleen. 73.STOP was detected at a ca. 50-fold reduced level (Fig 3C), consistent with previous observations [20,21]. The efficiency of O73.loxP reactivation from spleens, measured using an explant cytopathic effect assay [27], was impaired relative to WT virus following IN inoculation of WT mice (Fig 3B, Table 2), but a defect was not observed following IP infection (Fig 3D). As expected, O73.STOP was undetected in both assays. Since mLANA is necessary for latency establishment [18,19], these findings indicate that mLANA function is not impaired by the presence of loxP sites flanking ORF73 in the MHV68 genome. Because mLANA also is necessary for MHV68 reactivation from the spleen following IP inoculation [20,21], these data also demonstrate that O73.loxP is not generally impaired for reactivation. However, the partial impairment of O73.loxP in reactivation from the spleen following IN inoculation suggests the possibility that homologous recombination between loxP sites may have occurred during latency establishment, or perhaps ex vivo during reactivation [5]. Together these findings indicate that the presence of loxP sites flanking ORF73 in O73.loxP does not impair MHV68 latency establishment or reactivation from the spleen following direct IP inoculation; however, reactivation of O73.loxP from the spleen following IN inoculation should be considered in light of a possible defect.
LANA-null 73.STOP MHV68 is attenuated in acute replication in the lungs and fails to establish latency in spleens of mice after IN inoculation [18,19]. RT-PCR analyses and experiments that employed an MHV68 recombinant virus encoding an mLANA-beta lactamase fusion protein to “mark” mLANA-expressing cells indicate that mLANA is expressed in B cells during MHV68 latency establishment and maintenance [14,15]. However, whether mLANA functions in B cells to facilitate MHV68 lytic replication or latency establishment in the spleen after IN inoculation is not known.
To define the relationship between mLANA expression in B cells and acute and latent infection, we evaluated O73.loxP and WT MHV68 infection in mice that express Cre recombinase under the control of the B cell-specific CD19 promoter [CD19Cre/+ [28]] allowing deletion of floxed ORF73 in B cells. Seven days after IN inoculation of CD19Cre/+ mice, titers of O73.loxP and WT MHV68 were equivalent in lungs (Fig 2B). In contrast, while WT MHV68 established latency in CD19Cre/+ spleen at a frequency similar to that observed in WT mice (1 in 150 cells viral-genome positive), O73.loxP latency establishment was severely attenuated in the spleen following IN inoculation, with frequencies below the limit of detection of 1 in 10,000 cells (Fig 4A). This approximates the latency establishment defect of mLANA-null 73.STOP after IN infection of WT mice (see Fig 3A). As expected, given the results in Fig 4A, O73.loxP reactivation was severely impaired and below the limit of detection of 1 in 100,000 cells (Fig 4B). PCR analyses of the ORF73 and ORF59 loci performed on DNA isolated from spleens of infected CD19Cre/+ mice demonstrated that floxed ORF73 was deleted in vivo, while ORF59 remained intact (Fig 4C). These results indicate that functional deletion in vivo of ORF73 in B cells is critical for latency establishment in the spleen following IN inoculation. Furthermore, the absence of a phenotype during acute infection suggests that mLANA expression in B cells is not a major contributor to acute viral replication in the lung.
After IP inoculation of CD19Cre/+ mice, O73.loxP MHV68 exhibited reduced latency establishment as measured by viral genome-positive cells in the spleen compared to WT virus, but no defect was observed in peritoneal exudate cells (PECs, Fig 4D and 4G). Accordingly, in a non-quantitative analysis of viral gene deletion, detection of ORF73 by PCR was reduced in DNA isolated from spleens, but not PECs (Fig 4F and 4I). O73.loxP reactivation was reduced for splenocytes in a manner that correlated directly with latent viral loads, but was not diminished for PECs (Fig 4E and 4H). These data indicate that deletion of ORF73 from CD19+ cells reduces MHV68 latency in the spleen, even after IP inoculation, but has no effect on latency in PECs. The latter interpretation is consistent with macrophages, and not B cells, serving as the primary latency reservoir for MHV68 in the peritoneal cavity [8]. Together, these results demonstrate that mLANA function in B cells plays a critical role in MHV68 latency establishment, especially following IN inoculation.
MHV68 disseminates systemically via lymphatic and hematogenous routes after IN inoculation. Viral transit occurs through the stepwise infection of various cell types, including epithelial, myeloid, and lymphoid cells, at different anatomical sites [5,6,29–31]. A working model of MHV68 systemic dissemination is shown in Fig 5A. Studies in MuMT mice suggest that B cells are necessary for systemic dissemination of MHV68 after IN infection [7,9], but this has not been directly tested. Given the cell-type-specific loss-of-function phenotype exhibited by O73.loxP in CD19Cre/+ mice, we reasoned that these infections could be harnessed to better define B cell roles and restriction points in the process of MHV68 dissemination following IN inoculation.
We therefore evaluated O73.loxP, 73.STOP, and WT MHV68 in kinetic analyses of infection in draining LNs (mediastinal LNs, MLN), blood leukocytes, and spleens following IN inoculation of CD19Cre/+ mice. In MLNs, all three viruses were detected on day 10 post-infection, with O73.loxP and 73.STOP present at approximately 5-fold lower frequencies than WT MHV68 (Fig 5B). By day 16 post-infection, the frequencies of cells harboring WT MHV68 or O73.loxP increased to similar levels (1 in 200 and 1 in 800, respectively). mLANA-null 73.STOP was slightly diminished compared to day 10, remaining close to the limit of detection of 1 infected cell in 10,000 total cells (Fig 5E). A minimal amount of preformed infectious virus indicative of ongoing lytic viral replication was present in MLNs of mice day 10 post-infection (S5 Fig). It is possible that lytic replication occurring in the MLN contributes to seeding infection in this organ, but it is unlikely that this minimal amount of preformed infectious virus influenced frequency determinations. PCR analyses performed on DNA isolated from infected MLNs demonstrated that ORF73 was deleted in MLNs (S6 Fig). Though mLANA is not an absolute requirement for transit from lungs to spleens in severely immunocompromised mice lacking the type I interferon receptor [20], the failure of 73.STOP to expand in MLNs demonstrates the importance of mLANA in initial steps of dissemination. By extension, the minimal reduction in MLN latency observed for O73.loxP relative to WT virus, suggests that B cells are not critically involved in viral deposition in the MLN and initial expansion. Alternatively, it is possible that B cells mediate initial latency expansion in MLNs in a manner that is not dependent on mLANA.
In the blood, the number of genome positive cells was generally low for all three viruses on day 10 post-infection (Fig 5C). Extrapolated frequencies of genome-positive cells of 1 in 16,000 and 1 in 41,000 were determined for WT virus and O73.loxP, respectively, while 73.STOP was not definable. Though the frequency of cells latently-infected with WT MHV68 increased to ca. 1 in 2800 by day 16 post-infection, latency in the blood for O73.loxP and 73.STOP was negligible (< 1 in 100,000 genome-positive cells; Fig 5F). Likewise, though WT MHV68 was present in the spleen at an extrapolated frequency of ca. 1 in 15,000 cells on day 10 post-inoculation, O73.loxP and 73.STOP were minimally detected (< 1 in 100,000 and ca. 1 in 81,000 cells, respectively). Since O73.loxP established latency in MLNs but failed to efficiently spread through the blood, these data support a model in which B cells that express mLANA serve as a critical conduit for viral hematogenous dissemination and invasion of the spleen. By extension, these data indicate that MHV68 passage through B cells represents a critical bottleneck to hematogenous viral dissemination.
MHV68 infects and exploits germinal center (GC) B cells to expand the number of latently-infected cells in the spleen [32–35]. In addition, a high percentage of mLANA-expressing splenocytes are GC B cells [15], and mLANA stabilizes c-myc, which may facilitate GC responses in infected cells [36]. LANA homologs facilitate maintenance of the viral episome during cell division, which appears to be a fundamental requirement for establishing and maintaining viral latency during the rapid cellular proliferation that characterizes GC reactions. As a direct test of the need for mLANA in GC reactions as a mechanism to establish latency, we evaluated O73.loxP latency after IN inoculation of AIDcre/+ mice, which encode Cre under control of the activation-induced cytidine deaminase (AID) gene promoter [37]. AID is expressed in GC B cells to mediate class switch recombination and somatic hypermutation in the immunoglobulin gene locus [38,39]. While WT MHV68 established latency normally, neither O73.loxP nor mLANA-null 73.STOP were detected in the spleens of AIDCre/+ mice on day 16 post-infection (Fig 6). These data indicate that mLANA is necessary in AID-expressing GC B cells for latency establishment in the spleen.
Given that mLANA must be present in B cells for latency establishment following IN inoculation (see Fig 3) and that MHV68 genomes fail to form circular episomes after IP inoculation with 73.STOP [20], it has not been possible to separate potentially distinct roles for mLANA in latency establishment and long-term maintenance of the viral genome in vivo. To test the hypothesis that mLANA is required for latency maintenance in vivo, we intranasally inoculated Cre-ERT2 transgenic mice, which encode a tamoxifen-inducible Cre gene in all tissues [26], with O73.loxP. WT MHV68 and WT mice that do not encode Cre-ERT2 were infected as controls and to detect potential effects on latency resulting from tamoxifen treatment. Beginning on day 23 post-infection, after latency had been established in the spleen, tamoxifen was administered for five consecutive days to induce Cre activity and the resultant deletion of floxed ORF73. The schematic in Fig 7A outlines the timeline followed for this experiment. Two weeks after completing tamoxifen treatments (day 42 post-infection), spleens were harvested and the frequencies of latently infected cells were quantified by LD-PCR. For these analyses we used two different primer sets: one that amplifies ORF50, which should not be affected by ORF73 deletion, and another that spans the 5’ end of ORF73, the 5’ loxP site, and adjacent sequence upstream of ORF73. This amplicon should be absent upon ORF73 deletion, enabling a direct evaluation of cells that retain viral genomes (ORF50+) despite loss of mLANA-encoding ORF73. Validation experiments in cultured cells confirmed that ORF50- and ORF73-specific primers were equally sensitive for detecting viral genomes and that ORF73 primers did not amplify, while ORF50 primers did amplify, a product upon ORF73 deletion by Cre (S7 Fig).
In both Cre-ERT2 mice infected with WT MHV68 and WT mice infected with O73.loxP, tamoxifen treatment resulted in a slight increase in genome-positive cells compared to mice treated with vehicle control (Fig 7B, Table 3). Genome frequencies were nearly identical as measured by ORF50 and ORF73 primer sets. However, relative to vehicle control-treated mice, tamoxifen treatment resulted in an 8-fold decrease in the number of splenocytes harboring MHV68 genomes in Cre-ERT2 mice infected with O73.loxP (Fig 7B). Notably, a similar reduction was observed using both ORF50- and ORF73-directed primer sets. These findings indicate that Cre-mediated deletion of ORF73 following establishment of latency promoted a loss of MHV68 genomes from latently infected spleens and demonstrate that mLANA is critical for maintenance of long-term MHV68 latency.
In this study, we describe the generation and characterization of O73.loxP, a recombinant MHV68 that permits the Cre-dependent, conditional deletion of ORF73 in vivo. This system differs from traditional viral mutagenesis approaches in that it allows for cell-type specific and inducible deletion of a gene of interest rather than complete ablation. Since mLANA is a multifunctional viral protein that facilitates both lytic replication and latency establishment, we utilized this new reagent to better define mLANA’s functions in specific cell types and at certain stages of viral infection.
There are potential caveats to Cre-lox approaches to control the timing of viral gene deletion. Insertion of loxP sites within a viral genome may inadvertently impact transcription efficiency, impact splicing, or directly alter the function of overlapping noncoding RNAs. With regard to O73.loxP, the loxP sites flanking ORF73 did not overtly influence viral replication or latency establishment in mice lacking Cre. Given the potent attenuation of mLANA-null and other ORF73-mutant viruses [19–21], this shows that the loxP insertions did not disrupt mLANA expression. Although viral reactivation efficiency after IN inoculation was reduced for O73.loxP, reactivation occurred normally after IP inoculation. Given the necessity for mLANA in reactivation after IP infection [20,21], this indicates that mLANA in O73.loxP is capable of normal function during reactivation. In light of findings by Stevenson and colleagues indicating that homologous recombination between loxP sites within the MHV68 genome is possible [5], we reason that a similar phenomenon may be at work in our experiments following IN inoculation and after viral dissemination to the spleen.
Although it appears from our data that Cre-mediated excision of floxed viral genes is very efficient for cell-type-specific Cre expression, it remains possible that not every Cre-encoding cell will delete the targeted locus. And, while viral genomes were reduced in mice following tamoxifen induction of Cre, a proportion of cells infected with O73.loxP did not delete ORF73. Whether this represents an issue specifically in B cells, difficulty in targeting viral genomes that are potentially epigenetically modified, or an intrinsic inefficiency in the inducible Cre mice is not yet clear. Nonetheless, the dramatic phenotypes observed in our studies demonstrate the utility of this approach to better define the necessity of specific viral genes in a time- and tissue-specific fashion in a small animal model of GHV pathogenesis.
Previous studies using traditional mutagenesis approaches demonstrated that both the absence of mLANA and point mutations in the DNA binding domain of mLANA lead to a profound defect in latency establishment in the spleen after IN inoculation [18,19,21]. However, these mutations also result in defects in lytic viral replication [17,19,21,22]. Since lytic replication in the lung is important for MHV68 dissemination to the spleen after IN inoculation [40], it remained possible that defects in latency establishment observed with mLANA-null or mutant virus were indirect, the result of upstream defects in acute replication and dissemination. This idea was further supported by the observation that mLANA-null and mutant viruses establish and maintain splenic latency after IP inoculation [20,21]. The finding that viral titers in the lungs of CD19Cre/+ mice infected with WT or O73.loxP MHV68 were equivalent, yet O73.loxP latency in the same mice was strongly attenuated, indicates that defective lytic replication in the lung in the absence of ORF73 is not the sole explanation for defective latency observed with 73.STOP.
Provided ORF73 deletion occurs rapidly upon B cell infection, our results also suggest that B cells, which are infected by MHV68 in mouse lungs [40], are not major contributors to productive viral replication in the lungs. Consistent with these observations, a study utilizing a recombinant MHV68 that encodes a switchable, floxed fluorescent marker confirmed that B cell-derived virus was undetectable in the lungs during the first 7–10 d post IN infection of CD19Cre/+ mice [41]. It therefore is likely that mLANA functions in mucosal epithelial cells of the lung to promote efficient lytic replication.
Although it is clear from our studies that mLANA expression in B cells is necessary for dissemination from the lung (or MLN) to the spleen, it remains plausible that lytic replication and/or reactivation from B cells facilitates systemic infection. A direct test of this hypothesis is possible using an extension of the genetic platform described above. Conditional deletion of ORF50 or ORF57 in a virus in which ORF73 is intact, with resultant defects in the establishment of latency in the spleen following IN infection of CD19Cre/+ mice, would provide strong evidence that lytic replication in B cells is necessary for systemic MHV68 infection.
Based on studies from the Stevenson laboratory demonstrating that floxed fluorescent loci in the MHV68 genome were recombined in mice expressing Cre in myeloid cells, indicating passage of MHV68 through that compartment, our working model depicts MHV68 trafficking from epithelial barriers to draining lymphoid organs via dendritic cells and/or macrophages [5,31]. Reduced latent infection of MLNs by mLANA-null virus, but only minimal impact on O73.loxP infection of CD19Cre/+ mice, suggests the possibility that mLANA functions in non-B cells to permit efficient trafficking to the lung, or is necessary for seeding and expansion of B cells in the MLN after trafficking. Our data are consistent with ORF73 deletion not occurring until B cell expansion is underway in MLNs, or indicate that mLANA is not required for latency amplification in MLN B cells once a virus encoding mLANA is deposited in the lymph node via a non-B cell route. The latter point seems less likely given the importance of mLANA in maintaining viral genomes during cell division.
Since O73.loxP expanded in MLNs after IN inoculation of CD19Cre/+ mice, but failed to attain WT levels in blood, we propose that mLANA functions in B cells to permit efficient hematogenous dissemination and viral colonization of the spleen. A failure of O73.loxP MHV68-infected B cells to proliferate or a gradual loss or dilution of viral genomes resulting from a lack of mLANA-mediated episome maintenance during B cell expansion are possible mechanisms. Failed latency establishment by O73.loxP in AIDCre/+ mice demonstrates the requirement for mLANA in GC B cells. Moreover, mLANA stabilizes c-myc to promote cellular proliferation [36], and, although not evaluated in B cells per se, ectopically expressed mLANA induces promoters for genes that drive cell cycle progression [36,42]. Use of the H2b-YFP MHV68 recombinant [33] coupled with floxed ORF73 could directly evaluate proliferation and gene expression in peripheral B cells upon ORF73 deletion.
Maintenance of the viral episome is perhaps the most extensively studied function of the KSHV LANA protein [10,43], and mLANA also supports the replication of plasmids containing a terminal repeat in vitro [24]. However, whether episome maintenance, primarily defined in cell culture, is required for maintenance of latency in vivo is unknown. The KSHV genome contains an autonomous replication element that is LANA-independent [44], and shRNA-mediated depletion of kLANA does not “cure” PEL cell lines of KSHV [45]. Likewise, LANA-null and LANA DNA-binding mutant MHV68 are maintained long-term in splenic B cells after IP inoculation, despite a modest defect in early establishment [20,21]. However, the severe latency establishment defect observed following infection of AIDcre/+ mice is most consistent with a model in which mLANA facilitates viral genome maintenance in rapidly proliferating GC B cells. Moreover, the reduction in O73.loxP infected splenocytes following induction of Cre activity after latency establishment supports the long-standing hypothesis that mLANA is necessary for genome maintenance during long-term chronic infection, although it is not yet clear whether infection is primarily depleted in the subset of GC B cells that remain infected long-term or in memory B cells [15,32–35].
MHV68 spreads from epithelial barriers to draining lymph nodes via myeloid cells [5,29,31]. However, cell types that mediate viral trafficking to the spleen and systemic infection are less clear. The observation that CD19+ B cells constitute a much greater proportion of MHV68-infected cells in MLNs than do CD11c+ and CD11b+ myeloid cells (~100 to 1000 fold higher) suggests that B cell expansion in the LN precedes hematogenous spread [6]. This is in concordance with the necessity for B cells in splenic latency establishment after IN inoculation of MuMT mice mentioned above [7,9]. The deficit in O73.loxP latency in blood-borne leukocytes of CD19Cre/+ mice adds new support to the hypothesis that B cells are the key vehicles for transport of MHV68 to the spleen. Once in the spleen, it is likely that MHV68 usurps GC reactions in order to facilitate latency establishment. Indeed, GC reactions supported by T follicular helper (TFH) cells and IL-21 are critical for the expansion of MHV68 latency in the spleen [46,47]. The latency defect exhibited by O73.loxP MHV68 in AIDcre/+ mice emphasizes the importance of MHV68 passage through GC B cells in order to successfully colonize the host. Together, the defects in latency establishment by O73.loxP in CD19Cre/+ and AIDCre/+ mice add a viral-genetic component to models suggesting that MHV68 makes use of B cells for blood-borne dissemination and that infection of GC B cells is necessary for latency in the spleen.
Mouse experiments performed for this study were carried out in accordance with National Institutes of Health, United States Dept. of Agriculture, and UAMS Division of Laboratory Animal Medicine and Institutional Animal Care and Use Committee (IACUC) guidelines. The protocol supporting this study was approved by the UAMS IACUC (animal use protocol 3587). Mice were anesthetized prior to inoculations and sacrificed humanely at the end of experiments.
NIH 3T12 (ATCC CCL-164) and Swiss albino 3T3 fibroblasts (ATCC CCL-92) were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin, 100 μg/ml streptomycin, and 2 mM L-glutamine (cMEM). Cells were cultured at 37°C with 5% CO2 and ~99% humidity. Murine embryonic fibroblasts (MEFs) were harvested from C57BL/6 mouse embryos and immortalized as previously described [27]. 3T3 fibroblasts encoding tamoxifen-inducible Cre recombinase were a gift from Dr. Eric J. Brown. Vero-Cre cells were originally obtained from the laboratory of Dr. David Leib. To generate 3T12 cells stably expressing Flp recombinase, BOSC23 (ATCC CRL-11270) cells first were transfected with pMSCV-Flp, a retroviral plasmid encoding Flp recombinase. Retrovirus containing supernatants were harvested at 24 and 48 h post-transfection, filtered through a 0.45 μm filter (Millipore), and used to transduce 3T12 cells. Transduced cells were selected with puromycin and expanded. Previously described viruses used in this study include BAC-derived wildtype MHV68 [25] and mLANA-null MHV68 (73.STOP) [19]. Derivation of a new WT MHV68 BAC that contains frt sites flanking BAC vector sequences, O73.loxP, and 73.STOP in the FRT BAC is described below. Viruses derived from MHV68 with frt sites flanking the BAC cassette were passaged in Flp-expressing 3T12 fibroblasts to remove the BAC cassette.
Male and female C57BL/6, CD19Cre/+ (B6.129P2(C)-Cd19tm1(cre)Cgn/J), AID Cre/+ (B6.129P2-Aicdatm1(cre)Mnz/J), and inducible Cre-ERT2 (B6.Cg-Tg(UBC-cre/ERT2)1Ejb/J) mice were purchased from Jackson Laboratories. Mice were bred and maintained according to all local, state, and federal guidelines under the supervision of the Division of Laboratory Animal Medicine at the University of Arkansas for Medical Sciences. Eight-to-ten week old mice were anesthetized using isoflurane and inoculated with 1000 PFU of virus diluted in incomplete DMEM (20 μl) for IN inoculations or injected with 1000 PFU of virus diluted in incomplete DMEM (100 μl) for IP inoculations. Splenocytes and peritoneal exudate cells (PECs) were harvested as described previously [20]. Cells from mediastinal lymph nodes (MLNs) were harvested by homogenizing pooled MLNs on cell strainers and resuspending in DMEM. Blood was extracted by cardiac puncture and deposited into conical tubes pre-coated with EDTA and containing 200 μl of heparin sulfate solution (1000 U/ml). Buffy coats were prepared using Lympholyte-M solution for cell separation (Cedarlane #cI5030).
Tamoxifen treatment of Cre-ERT2 transgenic mice was performed as described previously [26,48]. Briefly, 8 to 10 week old, male and female Cre-ERT2 mice or their wild-type littermates were infected intranasally with 1000 PFU of either FRT or O73.loxP MHV68. Twenty-three days after infection, mice were injected IP with either 2 mg of tamoxifen (Sigma Aldrich #T5648) dissolved in 98% corn oil and 2% ethanol or vehicle control once a day for 5 consecutive days.
Limiting-dilution (LD) analyses to quantify the frequency of latently infected cells from spleen, MLNs, blood or peritoneum were performed as described previously [8]. Briefly, cells from infected mice were plated in three-fold serial dilutions for a total of 6 dilutions (12 wells per dilution) in a background of 104 uninfected 3T12 fibroblasts per well. Cells were digested by proteinase K treatment at 56°C overnight. Cell extracts were then subjected to two rounds of nested PCR using gene-specific primers to either an ORF50 target [8] or to ORF73 (73USoutF and 73USoutR as the outer primer pair and 73USnestF and 73nestR as the nested primer pair; see S1 Table). PCR products were resolved in a 1.5% agarose gel.
PCR for the detection of the full-length ORF73 gene was performed utilizing primers 73_IG_DS and 73_IG_US (S1 Table) docking at genomic coordinates 103881–103907 and 104900–104924, respectively, proximal to ORF73 using Taq polymerase (Sydlabs) and reaction conditions: 94°C for 2 min; 94°C for 30 sec, 55°C sec, 72°C for 1 min 30 sec for 45 cycles; 72°C for 10 min; 4°C for indefinite time. ORF59 and GAPDH genes were detected by PCR utilizing primers 59PCR1 and 59PCR2 (S1 Table) for ORF59 and GAPDHF and GAPDHR for GAPDH, as described previously [49].
To determine the frequency of reactivating cells [27], splenocytes or PECs harvested from infected mice were resuspended in cMEM (106 cells/ml) and were plated in two-fold serial dilutions on 96 well tissue culture plates containing an indicator monolayer of MEFs. Separate samples of mechanically disrupted cells also were plated on MEF monolayers to detect preformed infectious virus. Cell monolayers were evaluated for the presence of CPE 14 and 21 days post-plating.
Immunoblot analyses were performed as previously described [49]. Briefly, cells were lysed with radio immunoprecipitation (RIPA) buffer (150 mM NaCl, 20 mM Tris, 2 mM EDTA, 1% NP-40, 0.25% deoxycholate supplemented with phosphatase and protease inhibitors), and protein samples were centrifuged at 16,000 xg to remove insoluble debris. Protein content for each sample was quantified using the BioRad DC Protein Assay (BioRad). Samples were diluted in 2X Laemmli sample buffer and resolved by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to nitrocellulose membranes (Thermo Scientific). Blots were probed with the indicated primary antibodies and with horseradish peroxidase (HRP) conjugated secondary antibodies (Jackson ImmunoResearch). Chemiluminescent signal was detected using a ChemiDoc MP Imaging System (Bio-Rad) on blots treated with SuperSignal Pico West ECL reagent (Thermo Scientific) or Clarity ECL reagent (Bio-Rad).
Live cells were imaged by fluorescence microscopy at the indicated time points using 20X magnification on an Eclipse Ti-U fluorescent microscope (Nikon). Images were acquired with a D5-QilMc digital camera and analyzed using NIS-Elements software (Nikon).
Plaque assays were performed as previously described [49]. Briefly, infected cells were lysed by two freeze-thaw cycles. Ten-fold serial dilutions of lysates were added to monolayers of NIH 3T12 cells (2 x 105 cells/well) that were plated the previous day. For determining viral titers from infected organs, lungs were homogenized by mechanical disruption (5 cycles) in a Mini-Beadbeater-16 (Biospec Products), and homogenates were freeze-thawed twice and serially diluted (10-fold) in cMEM. For all plaque assays, plates were rocked every 15 min for 1 h at 37°C. Infected cells were overlaid with 1.5% methylcellulose in cMEM supplemented with 5% FBS and incubated at 37°C for 7–8 days. Methylcellulose media was then aspirated, and cell monolayers were stained with a solution of crystal violet (0.1%) in formalin to facilitate the identification and quantification of plaques.
The WT MHV68 BAC was created by en passant mutagenesis [50]. To replace the loxP sites with frt sites on the original WT MHV68 BAC [25], primers containing a 40 bp homologous BAC cassette sequence adjacent to one of the loxP sites, the 34 bp frt site sequence, and a sequence homologous to a kanamycin (Kan) selection cassette (KanFRT1_Fwd and KanFRT1_Rev, S1 Table) were used to amplify the kanamycin selection cassette and IsceI recognition site from the plasmid pEPKanS2 by PCR [50]. The resulting amplicon was digested with DpnI to remove template DNA, excised and purified from an agarose gel, and electroporated into a competent E. coli strain GS1783.5 harboring the original WT MHV68 BAC. After recovery, the electroporated cells were plated on Luria broth (LB) agar plates containing chloramphenicol (30 μg/ml) and kanamycin (25 μg/ml) and allowed to grow at 30°C for 48 h. Transformants were screened by colony PCR using primers specific to the Kan resistance cassette. Positive colonies were inoculated into LB broth containing chloramphenicol and 2% arabinose to induce IsceI expression and excision of the Kan marker, followed by substitution of the target loxP site for a frt site by Red-mediated recombination. Recombined clones were screened for the presence of frt by PCR and verified by sequencing. The second frt site was introduced in a second round of en passant mutagenesis utilizing primers KanFRT2_Fwd and KanFRT2_Rev (S1 Table) for amplification of the Kan cassette from pEPKanS2 by PCR.
To generate the O73.loxP BAC, loxP sites were inserted adjacent to the 5’ and 3’ ends of ORF73 in a FRT BAC template by two successive rounds of en passant mutagenesis utilizing primers 73loxpR1_fwd and 73loxpR1_rev for the first round and primers 73loxpR2_fwd and 73loxpR2_rev (S1 Table) for the second round of mutagenesis, following the procedure outlined for the generation of the FRT BAC. The 3’ end of M11 was not disrupted by insertion of loxP sites. The 3’ end of M11 was regenerated when inserting the loxP site at the 3’ end of ORF73 in a manner that maintained the natural coding and transcription termination sequence of M11. Similar approaches were previously used to maintain M11 sequence when manipulating the 3’ end of ORF73 [15,51]. 73.STOP FRT was generated by introducing a premature stop codon and frameshift mutation into ORF73 on a FRT BAC template by en passant mutagenesis utilizing primers 73stopFRT_fwd and 73stopFRT_rev (S1 Table). Viruses were passaged in Flp-expressing 3T12 fibroblasts to remove the BAC cassette, and titers were quantified as described previously [49].
Antibodies used in this study include goat polyclonal anti-GFP (Rockland Immunochemicals, Inc, #600-101-215), rabbit polyclonal mLANA anti-serum [21], mouse polyclonal MHV68 anti-serum [49], chicken anti-ORF59 IgY (Gallus Immunotech), and mouse monoclonal anti-β-actin (Sigma Aldrich, #A2228). Fluorophore-conjugated secondary antibodies used in this study include AlexaFluor donkey anti-goat 488, AlexaFluor goat anti-mouse 568, and AlexaFluor goat anti-chicken 568 (Life Technologies). For drug treatments, the 17β-estradiol agonist Z-4-hydroxytamoxifen (4-OHT; Alexis Biochemicals; #ALX-550-361-M001) was dissolved at a stock concentration of 2 mM. Inducible-Cre 3T3 fibroblasts plated the previous day were treated with either 0.2 μM 4-OHT to induce nuclear translocation of Cre or ethanol as a vehicle control for 24 h prior to infection.
pMSCV-Flp was generated by first amplifying the Flp recombinase ORF from pCMV14-Flp utilizing Flp-specific primers that also encode overhangs for restriction enzymes BamHI and EcoRI (forward primer FlpR_BglII and reverse primer FlpR_EcoRI, S1 Table). The resulting amplicon was digested with BamHI and EcoRI along with pMSCV. Both the PCR product and pMSCV were resolved by gel electrophoresis, gel-purified, and ligated using T4 DNA ligase (New England Biolabs; #M0202). Competent DH5α E. coli were transformed with the ligated product, and after recovery, a small inoculum was plated on LB ampicillin plates. Positive clones were screened by restriction digest and sequenced to ensure proper insertion of the Flp ORF into pMSCV. All transfections were performed using Lipofectamine and Plus Reagent (Life Technologies) according to the manufacturer’s instructions.
All statistical analyses were performed using GraphPad Prism software (GraphPad Software, San Diego, CA). Statistical significance was determined using two-way ANOVA with Bonferroni correction or by a two-tailed unpaired Student's t test with a 95% confidence.
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10.1371/journal.pntd.0006728 | Shorter-course treatment for Mycobacterium ulcerans disease with high-dose rifamycins and clofazimine in a mouse model of Buruli ulcer | Starting in 2004, the standard regimen for treatment of Buruli ulcer (BU) recommended by the World Health Organization has been daily treatment for eight weeks with rifampin (RIF) and streptomycin. Based on recent clinical trials, treatment with an all-oral regimen of RIF and clarithromycin (CLR) may be an effective alternative. With the achievement of an all-oral regimen, a new goal is to find a regimen that can shorten the duration of treatment without compromising efficacy. We recently observed that increasing the dose of RIF from the standard 10 mg/kg dose to 20 or 40 mg/kg, or replacing RIF with the more potent long-acting rifamycin, rifapentine (RPT) at 10 mg/kg or 20 mg/kg increased the bactericidal activity of the RIF+CLR regimen in a mouse model of BU. We also recently showed that replacing CLR with clofazimine(CFZ) at 25 mg/kg may have greater sterilizing activity than the RIF+CLR regimen. Here, we demonstrate that combining high-dose rifamycins with CFZ at a lower dose of 12.5 mg/kg results in similar reductions in swelling, bacterial burden and mycolactone concentrations in mouse footpads compared to the standard regimens and more rapid sterilization of footpads as determined by the proportions of footpads harboring viable bacteria three months after completion of treatment. The potential of these high-dose rifamycin and CFZ combinations to shorten BU treatment to four weeks warrants evaluation in a clinical trial.
ClinicalTrials.gov NCT03474198, NCT01659437
| Buruli ulcer, a neglected tropical skin disease caused by Mycobacterium ulcerans, is treatable since 2004 with antibiotics instead of surgery. Treatment with either rifampin plus streptomycin or, more recently, rifampin plus clarithromycin requires taking the drugs daily for 8 weeks. Streptomycin is administered by injection and may result in hearing loss. Clarithromycin often causes gastrointestinal discomfort. Our goal is to identify a regimen that is both shorter and associated with fewer side effects. Rifampin, previously an expensive drug, is well tolerated not only at the standard dose of 10 mg/kg but at doses of 20 and 40 mg/kg. The related rifamycin, rifapentine, has a longer half-life and is also well tolerated. We tested in a mouse model of Buruli ulcer whether higher doses of these rifamycins together with clofazimine, a drug that has transient skin pigmentation side effects but no toxicities, could effectively reduce lesion size, the number of bacteria, and production of the mycolactone toxin, in a shorter time than that for the existing drug regimens. We found that treatment for 4 weeks with a high dose rifamycin plus clofazimine is as effective as 8 weeks of the current standard regimens of rifampin plus streptomycin or rifampin plus clarithromycin.
| Buruli ulcer (BU), or Mycobacterium ulcerans disease, is a necrotizing skin disease driven by production of the immunosuppressive and cytotoxic macrolide-like toxin, mycolactone. Treatment for BU shifted from surgery and skin grafting to antibiotic therapy following pre-clinical and clinical evidence that combination, as opposed to single drug, therapy could be highly efficacious in killing M. ulcerans, stopping disease progression, reversing tissue damage and preventing relapse after treatment [1–3]. The original regimen of rifampin (RIF, 10 mg/kg) and streptomycin (STR, 15 mg/kg) given daily has both the benefit and the drawback of the inclusion of an injectable drug: patient and provider adherence is better assured but daily injections disrupt work, study, and recreation and burden the healthcare system. Although ototoxicity and other complications of STR treatment were not initially observed in West African patients based on self-report, more objective audiometric testing showed that hearing loss does indeed occur [4]. In Australia, which also has a large number of cases, all-oral antibiotic therapy combined with surgery has been in practice for many years [5]. In March of 2017, the BU Technical Advisory Group for the World Health Organization Global BU Initiative, due to difficulties in obtaining STR and preliminary findings of non-inferiority in a clinical trial (NCT01659437) in West Africa, recommended replacing STR with oral clarithromycin (CLR, 15–30 mg/kg) [6]. Compared to other chronic mycobacterial diseases such as leprosy and tuberculosis (TB) requiring antibiotic treatment for 6 to 24 months, depending on the form of the disease in each case, the treatment of BU with either regimen is relatively short at only 2 months. With the achievement of an all-oral regimen in hand, the next goal is to find ways to shorten treatment to one month or less.
Previous studies in our laboratory established that clofazimine (CFZ) at a dose of 25 mg/kg, when used in combination with RIF, is as effective as either standard regimen (i.e., RIF+STR or RIF+CLR) in reducing footpad swelling, production of mycolactone, and the M. ulcerans burden in a mouse footpad model of BU [7]. The regimen was comparable to RIF+STR and was significantly superior to RIF+CLR in preventing relapse after treatment for 6 weeks. Subsequently, research in our group with a mouse model of TB has shown that the dose of CFZ can be halved from 25 to 12.5 mg/kg with no loss of efficacy and a reduction in the transient skin discoloration associated with the drug [8, 9]. The MIC for CFZ against both M. tuberculosis and M. ulcerans is approximately 0.25–0.5 μg/ml [7]. Recent studies in our group have shown that increasing the dose of RIF and the long half-life rifamycin, rifapentine (RPT) shortens the treatment duration needed to prevent relapse in mouse models of TB [10, 11]. More recently, we found that replacing RIF with high-dose RIF or RPT resulted in greater bactericidal activity compared to RIF+CLR in a mouse footpad model of BU (T. Omansen et al., 2017, P1621, ECCMID, p. 191, WHO Meeting on BU, p. 128). Likewise, Chauffour et al. [12] showed that the regimen of RPT 10 mg/kg together with CLR achieved superior bactericidal effects and sterilizing efficacy at least comparable to that of RIF+STR in a mouse footpad model of BU.
One complication in the treatment of BU in humans is a paradoxical worsening of the clinical appearance of lesions [13]. Given that CFZ has both antimicrobial and anti-inflammatory properties and treats erythema nodosum reactions in leprosy, it has the additional potential advantage over STR and, possibly, CLR to reduce the risk of such paradoxical worsening.
Here, we evaluated the bactericidal and sterilizing efficacy of a rifamycin plus CFZ at the lower CFZ dose of 12.5 mg/kg and at escalating doses of RIF from 10 to 20 or 40 mg/kg and of RPT from 10 to 20 mg/kg. The results indicate that increasing the rifamycin dose can significantly shorten the treatment duration necessary to achieve culture negativity and prevent relapse when combined with CFZ, which may be a superior alternative to CLR or STR as a companion drug of the rifamycin.
M. ulcerans 1059 (Mu1059), originally obtained from a patient in Ghana, was generously provided by Dr. Pamela Small, University of Tennessee. Autoluminescent Mu1059 (Mu1059AL) was generated in our laboratory [14, 15]. These strains both produce mycolactone A/B, and this toxin kills macrophages and fibroblasts in vitro [16, 17]. The Mu1059AL strain was passaged in mouse footpads before use in these studies. The bacilli were harvested from footpads with grade 2 level swelling, i.e., swelling with inflammation[18].
All animal procedures were conducted according to relevant national and international guidelines. The study was conducted adhering to the Johns Hopkins University guidelines for animal husbandry and was approved by the Johns Hopkins Animal Care and Use Committee, #MO17M13. Johns Hopkins University is in compliance with the Animal Welfare Act regulations and Public Health Service Policy and also maintains accreditation of its program by the private Association for the Assessment and Accreditation of Laboratory Animal Care International.
RIF, CFZ, and STR were purchased from Sigma (St. Louis, MO). RPT and CLR were prepared from Priftin (Sanofi) and generic CLR (Aurobindo Pharma, Hyderabad, India, Dayton, NJ, USA) tablets, respectively, purchased at a pharmacy. Stock RIF and RPT (with brief sonication) suspensions were prepared every two weeks in distilled water; STR and CLR solutions were prepared weekly in water; and CFZ was suspended weekly in an 0.05% (w/v) agarose solution in distilled water. All drugs were given 5 days per week in 0.2 ml. RIF (10, 20, and 40 mg/kg), RPT (10 and 20 mg/kg), CFZ (25 mg/kg and 12.5 mg/kg), and CLR (100 mg/kg) were administered by gavage. STR (150 mg/kg) was administered by subcutaneous injection (S1 Table). Doses for CLR and STR were chosen based on mean plasma exposures (i.e., area under the concentration-time curve over 24 hours post-dose) compared to human doses.
BALB/c mice (N = 292), age 4–6 weeks (Charles River, Wilmington, MA), were inoculated in both hind footpads with approximately 4.42 log10 (2.65 x104) CFU of Mu1059AL in 0.03 ml PBS, resulting in a mean (±S.D.) CFU count of 3.53±0.37 log10 M. ulcerans per footpad three days after infection. Treatment began 38 days after infection when footpad swelling increased to approximately grade 2 [18], and there were 5.31±0.28 log10 CFU/footpad. Treatment with RIF+STR, RIF+CLR, RIF+CFZ, RPT+CFZ and RIF or RPT alone was administered for up to 6 weeks for the combination regimens and up to 4 weeks for the monotherapy regimens. Footpads were harvested before treatment initiation (Day 0) and after 1, 2, and 4 weeks of treatment from mice (6 footpads from 3 mice) for CFU and relative light unit RLU counts to assess luminescence and mycolactone detection. For relapse determinations, 10 mice (20 footpads) were held without treatment for approximately 12 weeks after completing a 4- or 6-week combination regimen treatment (See the overall experiment scheme, including each regimen evaluated, in S1 Table). Mice were euthanized if they reached grade 3 swelling on a scale of 0–4, as described [18]. Footpad tissue was harvested, minced with fine scissors, suspended in 1.5 ml PBS, serially diluted, and plated on Middlebrook selective 7H11 plates (Becton-Dickinson, Sparks, MD). Plates were incubated at 32°C and colonies were counted after 8–12 weeks of incubation.
Autoluminescence was assessed using a Turner Designs (TD 20/20) luminometer in both intact footpads and in suspensions of minced footpads in PBS. Values in the latter tended to be approximately 5 times higher than those obtained in intact footpads and only the suspension values are reported here. The values are reported as relative light units (RLU).
Samples of footpad tissue were stored in PBS at -20°C prior to mycolactone quantification. Mycolactone was extracted from 50 μl of tissue homogenate with 0.2 ml of acetonitrile containing 100 ng/ml of the internal standard, itraconazole. The standard curve and quality controls were prepared in blank mouse EDTA plasma. After centrifugation, the supernatant was transferred into an autosampler vial for LC-MS/MS analysis. Separation was achieved with a Thermo Betasil Phenyl (50 × 2.1 mm, 3 μm) column at 40°C with a gradient. Mobile phase A was water containing 0.1% formic acid and mobile phase B was acetonitrile containing 0.1% formic acid. The gradient started with mobile phase B was held at 20% for 0.5 minutes and increased to 100% over 0.5 minutes; 100% mobile phase B was held for 2 minutes and then returned back to 20% mobile phase B and allowed to equilibrate for 2 minutes. Total run time was 5 minutes with a flow rate of 0.3 ml/min. The column effluent was monitored using a Sciex triple quadrupole 4500 mass spectrometry detector (Sciex, Foster City, CA, USA) using electrospray ionization operating in positive mode. The spectrometer was programmed to monitor the following Multiple Reaction Monitoring transitions: 765.4 → 429.3 for mycolactone and 705.3 → 392.0 for itraconazole. Calibration curves for mycolactone were computed using the area ratio peak of the analysis to the internal standard using a quadratic equation with a 1/x2 weighting function over the range of 0.5 to 100 ng/ml.
DNA from each individual colony was extracted by boiling in 1X TE (Tris-EDTA, pH 8.0) buffer for 5 minutes; 5 μl of the supernatant was then used for PCR. Specific primers, forward primer MU_rpoF 5’ CGACGACATCGACCACTTC 3’ and reverse primer MU_rpoR 5’ CGACAGTGAACCGATCAGAC 3’, were used to amplify a 400 bp region encompassing the rifampin resistance-determining region. The PCR product was then sequenced to identify the presence of any mutation. Colonies from untreated control groups were used as a negative control.
GraphPad Prism 6 was used to compare group means by student’s T test and analysis of variance and group proportions by Fisher’s exact test, and to determine Spearman’s correlation coefficients to evaluate RLU and CFU correlations.
Treatment was initiated five weeks after inoculation of M. ulcerans strain Mu1059AL, when the mean footpad swelling index was approximately 1.75 on a scale of 0–4 [21]. The mean CFU count on treatment initiation (Day 0) was 5.31 ± 0.28 log10, and the mean RLU count was 239.3 ± 84 (Fig 1).
Treatment for BU was revolutionized in the last 20 years after work in a mouse model like that employed here demonstrated that a combination of RIF10 plus either STR or amikacin could prevent the development of swelling and treat established lesions [18, 20, 21]. In humans, RIF+STR is efficacious but the inclusion of STR has the drawbacks of ototoxicity and a requirement for daily injections for 8 weeks [4]. In March, 2017, a WHO technical advisory group recommended the adoption of an all-oral regimen of RIF+CLR on the basis of a series of clinical trials [6, 22–24]. While better tolerated, the recommended regimen still requires 8 weeks of treatment. Therefore, shortening the duration of treatment required to safely eradicate M. ulcerans and its mycolactone toxin is now a major goal of research to improve BU treatment.
Increasing the rifamycin exposure by using higher doses of RIF or RPT increase the sterilizing activity of combination therapy in mouse models of TB [10, 11]. The treatment-shortening potential of anti-TB regimens containing daily RIF doses as high as 35 mg/kg [25] and RPT doses as high as 20 mg/kg [26] are now being evaluated in phase 2/3 trials (NCT02581527, NCT03474198, NCT02410772). We hypothesized that similar dose increases would shorten the duration of all-oral treatments for BU. Indeed, we found that, while monotherapy with the standard dose of RIF10 had a modest impact on swelling, mycolactone production, and bacterial burden, increasing the rifamycin dose had a significant impact on all three parameters, particularly on mycolactone levels and bacterial burden. Whereas RIF20 alone may have lagged behind the other high-dose rifamycins as monotherapy, they all had efficacy similar to that of the control regimens after 2–4 weeks of treatment. Combining high-dose RIF or RPT together with CFZ resulted in superior reduction of CFU burden and footpad swelling compared to rifamycin monotherapy, particularly after week 2. More importantly, these combinations significantly reduced the proportion of mice relapsing after 4 weeks of treatment compared to both RIF10+STR and RIF10+CLR. Combining CFZ with RIF20, RIF40 or RPT20 completely prevented relapse after 4 weeks of treatment, whereas RIF10+STR and RIF10+CLR treatment for 2 additional weeks was still associated with relapse in 15% and 65% of footpads, respectively. These results suggest that all-oral high-dose rifamycin and CFZ regimens have the potential to reduce the treatment duration from 8 weeks to 4 weeks without reducing efficacy. This would represent a substantial advance over the current standard of care.
CFZ was recently repurposed for treatment of multidrug-resistant TB as a component of a short-course regimen now endorsed by WHO [27]. There may be additional significant advantages to replacing CLR with CFZ in all-oral regimens for BU. RIF significantly induces human metabolism of CLR, lowering average CLR concentrations by 73–87% and compromising its activity as a companion agent [28, 29]. While CLR concentrations remain above the MIC when CLR is administered with standard doses of RIF to BU patients [30], the inductive effect of high-dose rifamycins is expected to be even greater and more consistent from person-to-person. The inductive effect of high-dose rifamycins is expected to be even greater and more consistent from person-to-person. CFZ, on the other hand, has no known unfavorable interactions with RIF. At the 100 mg human dose providing plasma exposures similar to the 12.5 mg/kg dose in mice, CFZ has better gastrointestinal tolerability than CLR. Given the anti-inflammatory activity of CFZ, treatment with this drug may reduce the possibility of paradoxical reactions [5, 13]. While the precise mechanisms underlying the induction of paradoxical reactions remain uncertain, accumulations of macrophages/giant cells have been observed. CFZ has been shown to accumulate in macrophages and to inhibit TNF production and boost anti-inflammatory IL-1RA production, including in dermal macrophages [31]. Giant cells form in response to both CD4-mediated and innate immune mechanisms [32]. Accordingly, we speculate that CFZ may modulate at least some of the inflammatory signals that may be involved in the induction of paradoxical reactions. Finally, CFZ is expected to become more readily available now due to its recommended use in the treatment of multi-drug resistant TB. Clinical trials are also underway (NCT03474198) or in the planning stages for combining rifamycins with CFZ in treatment of drug-susceptible TB based on treatment-shortening effects in a mouse model [8, 33, 34].
The main side effect of CFZ is skin discoloration. However, it should be emphasized that the discoloration is dose- and duration-dependent, is less noticeable in pigmented skin, and resolves completely after treatment completion [35–38]. We believe it is unlikely that 4 weeks of treatment producing plasma exposures observed with the 12.5 mg/kg dose used here in mice would produce noticeable or disconcerting skin discoloration. Any skin discoloration is expected to be a minor, short-lasting effect, particularly in comparison to the gastrointestinal intolerance associated with CLR [5, 39] and the toxicity and discomfort with STR [4]. CFZ may also be safer in BU patients with other co-morbidities. Caution in treating with CLR has been urged in patients with coronary heart disease in whom an increase in death has been observed after a two-week course of CLR. Deaths were apparent after patients were followed for one year or longer (NCT00121550) [40]. O’Brien et al. [5] noted that severe antibiotic complications developed at a median time of 4 weeks in Australian patients treated with the currently used oral regimens for BU, either RIF+CLR or RIF+fluoroquinolone, and were associated with reduced renal function.
Relapse of BU after antimicrobial treatment is thought to be rare, unlike in the preceding era when surgery without antibiotic treatment inevitably missed covert areas of infection. These observations suggest that there are new opportunities to shorten treatment durations with more potent drug combinations. To test the new regimens studied here, we used the stringent outcome measure of relapse prevention and found that high-dose rifamycins together with CFZ prevented relapse more effectively than RIF+STR and RIF+CLR despite shorter durations of treatment. Based on these promising results with drugs that are already in clinical use, these regimens warrant evaluation in clinical trials seeking to shorten the treatment of BU.
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10.1371/journal.ppat.1007207 | S‐acylation mediates Mungbean yellow mosaic virus AC4 localization to the plasma membrane and in turns gene silencing suppression | RNA silencing plays a critical role in plant resistance against viruses. To counteract host defense, plant viruses encode viral suppressors of RNA silencing (VSRs) that interfere with the cellular silencing machinery through various mechanisms not always well understood. We examined the role of Mungbean yellow mosaic virus (MYMV) AC4 and showed that it is essential for infectivity but not for virus replication. It acts as a determinant of pathogenicity and counteracts virus induced gene silencing by strongly suppressing the systemic phase of silencing whereas it does not interfere with local production of siRNA. We demonstrate the ability of AC4 to bind native 21–25 nt siRNAs in vitro by electrophoretic mobility shift assay. While most of the known VSRs have cytoplasmic localization, we observed that despite its hydrophilic nature and the absence of trans-membrane domain, MYMV AC4 specifically accumulates to the plasma membrane (PM). We show that AC4 binds to PM via S-palmitoylation, a process of post-translational modification regulating membrane–protein interactions, not known for plant viral protein before. When localized to the PM, AC4 strongly suppresses systemic silencing whereas its delocalization impairs VSR activity of the protein. We also show that AC4 interacts with the receptor-like kinase (RLK) BARELY ANY MERISTEM 1 (BAM1), a positive regulator of the cell-to-cell movement of RNAi. The absolute requirement of PM localization for direct silencing suppression activity of AC4 is novel and intriguing. We discuss a possible model of action: palmitoylated AC4 anchors to the PM by means of palmitate to acquire the optimal conformation to bind siRNAs, hinder their systemic movement and hence suppress the spread of the PTGS signal in the plant.
| Plants have developed small RNA (siRNA)-mediated post-transcriptional gene silencing as a defense mechanism against viruses. In response, plant viruses encode viral suppressors of RNA silencing (VSRs) that can interfere with various steps of the silencing pathway. Mungbean yellow mosaic virus (MYMV) is a plant bipartite geminivirus responsible for a devastating disease in some areas of tropics and sub-tropics where its natural host, Vigna mungo, is a staple food crop. We discovered that the MYMV-encoded AC4 protein is a determinant of pathogenicity, binds native 21–25 nt siRNAs in vitro, and counteracts virus induced gene silencing by strongly suppressing the systemic phase of silencing but not the local production of siRNA. MYMV AC4 undergoes palmitoylation, a post-translational modification never reported before for plant viral proteins that mediates specific localization of the protein to the plasma membrane (PM). Interestingly, palmitoylation and in turns PM localization is indispensable for direct VSR function of AC4. AC4 also binds the PM-located BAM1, a trigger of cell-to-cell spread of RNAi. Taken together our results suggest that AC4 has synergic mechanisms of action, based on the specific PM localization, to prevent spreading of antiviral RNAi silencing in not yet infected cells.
| Viruses are obligate intracellular parasites that exploit host machineries to propagate and spread in the host. Their presence and activity deploy diverse plant mechanisms to combat viral infections at both the cellular and the whole-organism levels. Double-stranded (ds)RNA forming during virus replication and self-complementary foldback RNAs from single-stranded viral RNAs or aberrant RNAs can trigger host defence responses via a mechanism of RNA interference (RNAi) that results in inhibition of target RNA expression [1, 2]. The RNase III-type DICER enzymes process these viral RNAs into small-interfering (si)RNAs (21–24 nucleotides) that accumulate in the infected cells and guide the RNA-induced silencing complex (RISC) to degradation of complementary viral RNA sequences [3, 4]. RNA silencing is a non-cell autonomous process thus, silencing signals spread from the site of induction to neighbouring cells and systemically to confer silencing of homologous targets in distant tissues of the host plant [5, 6]. However, the evidence that virus infection often induces symptom and damage in the host highlights the presence of a counter defence strategy that suppresses the host surveillance [7]. Viruses encode one or more proteins that can inhibit initiation (viral RNA recognition and the subsequent degradation), maintenance, or systemic spreading of silencing thus allowing efficient viral replication in single cells and spread of the infection. These virulence factors, called viral suppressors of RNAi (VSRs), share no obvious sequence homology with each other and follow distinct mechanisms of suppression by targeting different points of the RNA silencing pathway, such as viral RNA recognition, dicing, RISC assembly, RNA targeting, and amplification [2, 8]. To overcome the host silencing machinery, several virus species have developed a siRNA sequestration strategy that the different VSR apply in various manners by preventing the assembly of the RISC effector [8]. As siRNA duplexes act as mobile silencing signals moving ahead of the virus to activate antiviral silencing in not yet infected cells, by sequestering and inactivating siRNA VSRs can counter react this defense strategy and allow spreading of the viral infection in the plant [9].
Members of the family Geminiviridae are small, circular, single-stranded DNA viruses composed of one or two genomic segments of 2500–3100 nucleotides which are encapsidated within small twinned icosahedral particle that replicate in the nucleus of an infected cell via double-stranded intermediates that also serve as templates for bidirectional transcription [10].
Geminivirus host different suppressor proteins encoded by open reading frame (ORF) AC2, V2, ORF β C1, ORFs AC4 and AC5 [11–15]. The transcriptional activator protein (TrAP) encoded by the ORF AC2 of African cassava mosaic virus (ACMV), Tomato golden mosaic virus and Mungbean yellow mosaic virus (MYMV), the C2 of Tomato leaf curl virus and the β C1 of the Tomato yellow leaf curl China virus (TYLCCNV) share sequence nonspecific DNA binding activity and localization in the nucleus where they act by a mechanism depending on interaction with DNA and transcriptional activation or with key components of the RNA silencing pathway. On the other side, the V2 protein of Tomato yellow leaf curl virus (TYLCV)-Is has specific cytoplasmic localization and exerts its VSR activity by targeting a step after siRNA production thus representing a different example of VSR in geminivirus [16]. The MYMV AC5, a protein encoded by some begomoviruses, suppresses post-transcriptional gene silencing (PTGS) and can reverse methylation-mediated TGS [14].
The AC4/C4 gene lies entirely within the Rep coding region, but in a different reading frame, and is one of the least conserved among members of the Geminiviridae family. Its function is very controversial: mutagenesis and/or transgenic expression of some AC4/C4 genes results in no phenotype or phenotypes consistent with movement protein or symptom determinant activity [17]. This puzzling information has been enriched with the discovery of a role of AC4/C4 in the suppression of RNA silencing in different strains of ACMV [13, 18], in MYMV [19], in the monopartite TYLCV and in Bhendi yellow vein mosaic virus [20, 21]. These proteins block cytoplasmic RNA silencing by a mechanism that involves binding of single-stranded siRNA and miRNA and possibly facilitates their degradation. This suggests that the severe developmental defects observed upon transgenic expression of some AC4/C4 might be due to suppression of overlapping steps in the siRNA and miRNA pathways [22, 23].
Interestingly, AC2s and AC4s of cassava viruses behave differently in regulating silencing suppression exerting strong or weak activity depending on the viral strain, and apparently compensating each-other function [13]. Consequently, mixed-strain infections can be responsible of unusually severe cassava mosaic disease in the field [24].
S-acylation or palmitoylation, is a reversible posttranslational modification of a protein covalently attaching through a cysteine residue(s) to long chain fatty acid, usually the 16-carbon palmitate via a thioester bond. This modification increases protein membrane affinity and provides an important mechanism for regulating cellular functions including subcellular localization, stability, trafficking, stress response, disease resistance, hormone signaling, cell polarisation, cell expansion and cytoskeletal organization [25, 26].
Unique among lipid modifications of proteins, this attachment is reversible, thus offering dynamic control over the cellular processes and protein function in response to stimuli. Our understanding of S-acylation function in plants is quite limited compared with other organisms and mainly comes from targeted studies on the functional characterization of individual proteins that happen to be S-acylated [27].
Several examples both in plant and animal systems describe palmitoylation as a modification used by proteins to switch subcellular localization between nucleus and plasma membrane (PM) and to accomplish their tasks. For example, specific functions regulated by transcription factor (TF) in the nucleus are triggered or hindered by palmitoylation-mediated protein localization to the nucleus or to the PM, respectively [28, 29].
In plants, differential subcellular localization of TF induced upon palmitoylation, are associated to plant response to abiotic stresses such as salt and drought increase [28, 30]. A large number of S-acylated proteins are also involved in plant–microbe interactions. Among them, a proteomic approach identified proteins involved in pathogen perception and response, mitogen-activated protein kinases (MAPKs), leucine-rich repeat receptor-like kinases (LRR-RLKs) and RLK superfamily members, ATPases, integral membrane transporters, soluble N-ethylmaleimide-sensitive factor-activating protein receptors (SNAREs) and heterotrimeric G-proteins [31]. PM is a critical subcellular compartment for the actors of a pathosystem. In fact, plants use the covalent addition of fatty acids to target an array of sensor/receptor proteins to the PM and detect invading pathogens whereas pathogens (except for viruses that do not penetrate plant cells actively) secrete effector proteins into the plant cell, particularly the internal face of the host PM, to threaten this surveillance and induce plant susceptibility to infection [32]. Animal viral proteins such as glycoproteins from Vesicular stomatitis virus [33], the Influenza virus hemagglutinin as well as the transmembrane Matrix-2 (M2) [34, 35] can also undergo S-acylation that is essential for virus replication or infection. However, palmitoylation of plant viral proteins has not been reported so far. Interestingly, C4 protein from Tomato yellow leaf curl virus (TYLCV) targets, through a yet unknown mechanism, PM and plasmodesmata (PD) where inhibits the intercellular spread of RNAi by interacting with receptor-like kinase (RLK) BARELY ANY MERISTEM 1 (BAM1) [36].
In this study, we examined the role of MYMV AC4 in viral infectivity. We revealed that AC4 undergoes post-translational palmitoylation that mediates protein targeting to the PM. When localized to the PM, AC4 strongly suppresses systemic silencing whereas delocalization from such subcellular compartment impairs VSR activity.
As a first step to gain more insights into the function of MYMV AC4, we determined whether AC4 is essential for successful MYMV infection. To this aim, we modified the three in frame start codons of the ORF AC4 within the infectious clone pGA1.3A [37] to obtain a MYMV-ΔAC4 DNA A mutant. Mutations were designed to be silent in the overlapping AC1 ORF and did not produce any change in the amino acid sequence of AC1. Vigna mungo plants were biolistically inoculated with recombinant and wild type (wt) MYMV DNA A, each together with the infectious MYMV DNA B clone pGA1.3B [37], and monitored for symptom appearance for two months.
Typical yellow mosaic and leaf curling symptoms in the trifoliate leaves became evident 18 (+/- 3) days post inoculation (dpi) on plants infected with wt MYMV whereas no symptom were observed on plants inoculated with a MYMV-ΔAC4 for the entire time of observation (Fig 1A). PCR analysis of total DNA extract confirmed the absence of viral DNA in systemic leaves (Fig 1B) and highlighted the requirement of AC4 for systemic spread of MYMV in the host.
The absence of systemic symptoms in plants inoculated with MYMVΔAC4 might be either consequence of a local event (such as virus inability to replicate/accumulate in the initially inoculated cells or to move out of them) or reflect long distance movement incompetence and, similar to homologue geminivirus VSR, MYMV AC4 could suppress very early host antiviral defence [13].
To investigate in more detail the role of AC4 in the early stage of infection, we conducted quantitative real-time (qPCR) experiments to analyse and monitor the accumulation of MYMV in V. mungo biolistically-inoculated leaves during the initial five days of infection. To this aim, we constructed two control virus mutants: MYMVΔAC1 and MYMVΔBC1 expressing null replicase and movement protein functions, respectively. To evaluate the genome replication and accumulation of MYMVΔAC4, MYMVΔAC1 and MYMVΔBC1 virus mutants relatively to MYMV WT, we compared the quantification of the MYMV AC2 gene, unrelated to the mutated genes, at four time points (1-2-3-5 dpi) and used contrast statistical analysis to compare the value recorded at 5 dpi with those obtained at the previous time points for each couple of virus constructs inoculated.
Statistically equivalent trends of viral DNA accumulation were observed in plants inoculated with MYMV WT and MYMVΔAC4, both of which reached the highest and statistically most distinct value at 5 dpi (Fig 1C).
Due to the lack of viral replicase, the concentration of MYMVΔAC1 dropped within the first two days and continued to decrease slowly but constantly accordingly to the continuous degradation of the input DNA (Fig 1C, red line).
Accumulation of the MYMVΔBC1 mutant, deprived of the movement protein function, also followed a negative trend statistically not different from MYMVΔAC1 (Fig 1C). However, differently from MYMVΔAC1, the concentration of MYMVΔBC1 decreased more slowly than MYMVΔAC1, probably reflecting the occurrence of MYMVΔBC1 replication in single cells, and remained statistically not different from MYMVΔAC4 until 3dpi (S1 Fig). From this time point, the inability to exit infected cells probably triggered MYMVΔBC1 DNA degradation or infected-cell death.
Taken together, these results, obtained from three independent replications of the experiment, indicate that, in the absence of AC4, MYMV can replicate and move from cell to cell, and rule out the absolute requirement of AC4 for virus replication and cell-to-cell movement. However, the lower efficiency of MYMVΔAC4 compared to wt (S1 Fig), and the relatively low, albeit statistically significant difference with the movement-impaired MYMVΔBC1 mutant, suggest that a possible indirect contribution to the mechanism of virus transport in plant cannot be excluded.
Many VSRs are pathogenic determinants of their virus host. They can interfere, independently or synergistically with other VSRs, to enhance the severity of symptoms caused by related or unrelated viruses [38, 39]. As the knock out of AC4 in the viral genome hinders systemic plant infection, to investigate the involvement of this VSR in viral pathogenicity, we tested the effect of MYMV AC4 expression on the symptom onset induced by heterologous Potato virus X (PVX) [40] infection in Nicotiana benthamiana.
All inoculated plants developed systemic leaf puckering and those expressing VSRs showed additional severe stunting (Fig 2). PVX-induced symptoms were significantly worsened by the simultaneous expression of AC4; however, the necrotic phenotype typically induced by Carnation Italian ringspot virus P19, which we used as positive control [41, 42], was not observed in plants agroinfiltrated with PVX-AC4 (Fig 2). This result provides support for the notion that MYMV AC4 is a determinant of viral pathogenicity and suggests that, as for other VSR, the enhancement of PVX-induced symptoms might be related to the capacity of AC4 to interfere with components of the endogenous RNAi pathway.
Aiming at understanding the molecular basis of the RNAi suppression activity of AC4, we used GFP-transgenic N. benthamiana plants (line 16c) overexpressing GFP constitutively [39]. These plants show green fluorescence under UV light. Upon transient expression of GFP, inducing silencing of the transgenically expressed GFP, they display only chlorophyll autofluorescence and appear red under UV light. In the presence of a suppressor of RNA silencing GFP silencing is blocked and plants continue to exhibit green fluorescence.
To induce silencing, we used a PVX-GFP plasmid [40] to agroinfiltrate N. benthamiana 16c plants and monitored the progress of silencing on the upper leaves for 30 days. Upon infiltration of PVX-GFP, ectopic GFP expression was observed in the inoculated leaves and in the veins of new leaves from 2 dpi under UV light (Fig 3A). The intensity of the green fluorescence signal increased until 5 dpi but was followed by a rapid replacement of the green fluorescence with chlorophyll red autofluorescence, due to GFP silencing, at 6–7 dpi (Fig 3A). By 12 dpi these plants appeared completely red fluorescent (Fig 3A).
Plants inoculated with equal amounts of PVX-GFP, PVX-AC4 (PVX-GFP+PVX-AC4) and PVX-P19 (PVX-GFP+PVX-P19) showed green fluorescence on newly emerging leaves starting from 5 dpi, simultaneously to the onset of silencing in PVX-GFP agroinfiltrated plants (Fig 3A).
At 15 dpi, when PVX-GFP plants were completely red fluorescent, plants infiltrated with PVX-GFP+PVX-AC4, similar to the control and PVX-P19 (PVX-GFP+PVX-P19) [42], were still green fluorescent, and most of the leaves continued to show silencing suppression at 30 dpi, very strongly in the youngest emerging leaves (Fig 3A).
RNA gel blot analysis of GFP mRNA and 21–25 nt RNA confirmed these observations. 15 dpi, high levels of GFP mRNA were found in young leaves of plants inoculated with both PVX-GFP+PVX-AC4 and with PVX-GFP+PVX-P19 (used as positive control) whereas GFP mRNA was undetectable in plants inoculated with PVX-GFP (Figs 3B and 6C). Conversely, GFP siRNA where detected only in plants inoculated with PVX-GFP alone (Fig 3B).
Collectively, these results demonstrate that co-delivery of GFP and MYMV AC4 onto GFP-expressing N. benthamiana strongly suppresses the onset of VIGS compared with the progress of gene silencing obtained with PVX-GFP alone.
A Northern blot analysis was also conducted on the same samples with a PVX coat protein (CP) probe. Consistent with the extent of silencing suppression, very high levels of PVX chimera were observed in plants inoculated with PVX-GFP+PVX-AC4 and with the PVX-GFP+PVX-P19 control (Fig 3C). The fact that the PVX CP probe detected much lower levels of viral RNA in plants inoculated with PVX-GFP alone (Fig 3C) suggests that the spread of GFP silencing observed in young leaves of plants inoculated also with PVX-GFP+PVX-AC4 was due more to the spread of the silencing signal than to a de novo silencing by PVX-GFP.
These results demonstrate that MYMV AC4 can suppress silencing-related defence responses in transgenic N. benthamiana plants. Nevertheless, the loss of virus accumulation observed in systemic leaves of V. mungo infected with MYMV-ΔAC4 (Fig 1B) suggests that this may be the result of systemic VIGS also in the virus natural host.
The VSR activity of AC4 was further investigated in PTGS experiments. To this aim, AC4 and P19 were cloned under the control of the 35S promoter and each co-infiltrated to line 16c plants, together with the silencing inducer (full-length GFP) under the control of the same promoter. Two dpi, infiltrated leaf patches appeared green fluorescent under UV light and, consistently with higher accumulation of the GFP transcript, those co-infiltrated with the P19 VSR control appeared brighter than the others (Fig 4A and 4B). By 7 dpi, when red fluorescence had completely replaced green fluorescence in 35S-GFP infiltrated patches, GFP mRNA was still present in AC4 and P19 co-agroinfiltrated plants but almost undetectable in plants infiltrated with 35S-GFP alone (Fig 4A and 4B).
Interestingly, GFP siRNAs started to accumulate in 35S-AC4 co-infiltrated patches at 2 dpi, and at 7 dpi they were in a concentration similar to 35S-GFP but much higher than the 35S-P19 control (Fig 4B). Remarkably, despite a red fluorescent front developed around the infiltrated area, systemic GFP silencing as well as accumulation of 21–25 nt RNA were not observed in the upper leaves of plants agro-infiltrated with 35S-GFP+35S-AC4 (Fig 4A and 4B).
The evidence that siRNAs accumulate in 35S-GFP+35S-AC4 patches at a concentration similar to 35S-GFP (Fig 4B) reveals that MYMV AC4 does not interfere with production of transgene-induced gene silencing whereas the absence of siRNAs in the upper leaves indicates a possible involvement in long-distance spreading of the silencing signal.
To identify the major site(s) of subcellular localization of AC4, we fused the recombinant AC4 with an influenza virus hemagglutinin epitope (HA) tag, and used it for protoplast transfection. Protoplasts transfected with pCKAC4HA were lysed at 24 h post transfection (hpt) and the lysate was submitted to differential centrifugations: low speed centrifugation (500 g) to collect nuclei and residual intact cells, and high speed centrifugation (30000 g) to separate the soluble membrane fraction from the crude part. Equivalent amounts of each fraction were analysed by immunoblotting with an anti-HA antibody. AC4 was detected in the pellets from both low- and high-speed centrifugations (Fig 5A) but was absent in the supernatants. To further investigate the association of AC4 with the membrane fraction, we treated the pellet obtained from high-speed centrifugation with Na2CO3, urea or KCl that remove proteins weakly bound to membranes. After a second high-speed centrifugation, a band corresponding to AC4 was detected in every pellet regardless of the different treatment applied (Fig 5A). The evidence that none of the treatments could dislodge AC4 from the membrane fraction indicates a very strong interaction of the protein with cellular membranes whereas the presence in the low-speed pellet suggests that AC4 could also be present in the cytosol and possibly in the nucleus.
The subcellular localization of AC4 was further investigated by expressing the protein in fusion with GFP in N. benthamiana protoplasts. The fluorescence signal was monitored at different time points between 4 and 48 hpt. Between 4 and 6 hpt, a fluorescence signal localized to the PM was visible in most of the transfected protoplasts whereas only in few of them GFP-AC4 was also visible in the nucleus. Starting from 8 hpt the majority of fluorescent protoplasts showed a double localization to the PM and the nucleus (Fig 5B) that did not change in the course of the experiment. GFPAC4 was also expressed in V. mungo leaf mesophyll by means of biolistic particle delivery. Consistently with observation in protoplasts, GFPAC4 localizes in the nucleus and at the cell periphery of single mesophyll cells (S2 Fig), and accumulates at PD (S3 Fig).
In silico analysis of the physical-chemical properties of AC4, performed by using the web-interface SeqWeb of GCG Wisconsin Package (version 2) [43], predicted that AC4 is an hydrophilic protein except for a region comprised between aminoacids 6 to 12 (S4 Fig). The core of this region is characterized by two hydrophobic phenylalanines flanking a polar cysteine (position 11), which, the CSS-Palm 4.0 software [44] predicted might be palmitoylated (S4 Fig). The AC4 sequence following this hydrophobic part is expected to have a high surface probability (S2 Fig). The pick of this region is occupied by the KRR amino acid sequence that was predicted to be a potential nuclear localization signal (NLS) by the NucPred software [45].
To gain more insight into the involvement of the two in silico-identified domains in the subcellular localization of AC4, we replaced the amino acid C11 by an alanine to produce the GFPAC4(C-A) mutant. The amino acids K19, R20 and R21 were also replaced together by alanines to obtain the mutant GFPAC4(KRR-AAA). These single mutants and a double mutant comprising both mutations GFPAC4(C-A/KRR-AAA), were transiently expressed in N. benthamiana protoplasts and fluorescence signal was observed at 24 hpt.
Upon alanine-substitution of C11, GFPAC4(C-A) accumulated only in the nucleus and didn’t show PM localization at any time (Fig 5B). On the other side, mutation of the hypothetical NLS delocalized AC4(KRR-AAA) from the nucleus and the protein accumulated only at PM (Fig 5B). Mutation of both domains resulted in cytoplasmic diffusion of AC4(C-A/KRR-AAA) with subcellular localization indistinguishable from free GFP (Fig 5B). These results, further supported by similar results obtained upon expression in single mesophyll cells of bombarded V. mungo leaves (S2 Fig), indicate that the in-silico predictions were correct and that the two predicted domains are indeed responsible for the subcellular localization AC4.
AC4 mutagenesis indicates that C11 is a critical amino acid for protein targeting to the PM and strongly supports the in silico prediction of post-translational palmitoylation of the protein. S-acylation (palmitoylation) is the reversible post-translational addition of a saturated fatty acids (palmitate or stearate) through thioester linkages to cysteine residues of proteins [46]. While no specific consensus domain exist for palmitoylation, the cysteine involved in the thioester bond should be localized inside the protein in a favourable context to allow insertion of the fatty acid and docking to the PM [47]. To confirm that C11 in AC4 can direct protein localization to the PM, we inserted the AC4 DNA sequence encoding aminoacids 1 through 12 upstream of egfp (Fig 5C). The corresponding fusion protein, AC4(1–12)GFP was transiently expressed in N. benthamiana protoplasts and observed at 24 hpt by video confocal microscopy. The addition of the N-terminal 12 amino acids of AC4 displaces GFP from te cytosol and AC4(1–12)GFP is relocated to the PM (Fig 5C).
To get a definitive proof of AC4 palmitoylation, we performed a biotin-switch assay, a biochemical test using hydroxylamine for specific cleavage of thioester bonds. Therefore, this assay allows only palmitoylated proteins to be cleaved and biotinylated in a cellular lysate and, upon biotin affinity purification, their detection by immunoblotting. To this aim, we transfected protoplasts with the pCKAC4HA or pCKAC4(C-A)HA mutant plasmids expressing recombinant proteins in fusion with the HA tag. The protoplast lysate was subjected to the biotin-switch assay and the presence of AC4-HA in the precipitate, detected by western blotting using an anti-HA antibody (Fig 5D) indicates that AC4 is S-acylated in planta. On the other hand, the evidence that AC4(C-A)HA was not recovered from the neutravidin beads (Fig 5D) reveals that the mutant protein was not originally S-acylated and that C11 is essential for AC4 palmitoylation.
Even though the site of accumulation of a protein does not necessarily correspond to its site of biological action, the evidence that AC4 is post-translationally modified to specifically target the cellular plasma membrane suggests that a protein function could be connected to palmitoylation. As AC4 acts as a VSR, we investigated the relation of silencing suppression function with PM localization. To this aim, we agroinfiltrated the PVX-GFP vector in combination with PVX-AC4, PVX-AC4(C-A), PVX-AC4(KRR-AAA) or PVX- AC4(C-A/KRR-AAA) in N. benthamiana 16c plants.
Starting from the onset of silencing, plants infiltrated with PVX-GFP in combination with the PVX-AC4(C-A) and PVX- AC4(C-A/KRR-AAA) showed the same systemic pattern as those infected with PVX-GFP alone (Fig 6A, compare with Fig 3A). On the other hand, similarly to PVX-GFP/PVX-AC4 (Fig 3A), plants inoculated with PVX-GFP/PVX-AC4(KRR-AAA) appeared fluorescent consistently with absence of GFP silencing (Fig 6A). From 12 dpi, only plants co-infected with PVX-AC4 or AC4(KRR-AAA) showed suppression of systemic silencing whereas plants inoculated with the C-mutated proteins, appeared red-fluorescent under UV light (Fig 6A). The GFP pattern remained unchanged even one month after agroinfiltration (S5 Fig).
RNA gel blot analysis confirmed that the persisting systemic expression of GFP in plants agroinfiltrated with PVX-GFP/PVX-AC4(KRR-AAA) was the result of inhibition of VIGS, and in turns that the AC4 NLS is dispensable for silencing suppression (Fig 6B and 6C). In fact, at 15 dpi the GFP siRNAs were detected in total RNA extracted from new leaves of plants agroinfiltrated with PVX-AC4(C-A) and PVX- AC4(C-A/KRR-AAA) but were absent in those infiltrated with PVX-AC4(KRR-AAA) (Fig 6B and 6C).
Consistently, GFP mRNA levels observed in plants co-inoculated with PVX-AC4 and PVX-AC4(KRR-AAA) were comparable with those in mock-inoculated plants (Fig 6B and 6C) confirming that the VSR function of AC4 is inhibited by mutation of C11.
These results strongly indicate that post-translational palmitoylation of AC4 and, consequent, PM localization are essential for efficient silencing suppression.
Based on the evidence that the geminivirus TYLCV C4 targets PM and PD where interacts with BAM1 to inhibit intercellular spread of RNAi [36], we investigated whether MYMV AC4 could also interact with BAM1 in N. benthamiana leaves. Indeed, we observed that BAM1 and AC4 co-localize at PM and in PD (S6 Fig), and demonstrated their interaction by Bimolecular fluorescence complementation (BiFC) (Fig 7A). The interaction between AC4 and BAM1 was further confirmed using Fӧrster resonance energy transfer–fluorescence lifetime imaging (FRET-FLIM) (Fig 7B). These results convincingly support the evidence that AC4 requires PM localization for silencing suppression function.
Collectively, our results indicate that AC4 undergoes a post-translational modification that mediates protein targeting to the PM. When localized to the PM, AC4 strongly suppresses systemic silencing whereas delocalization from such subcellular compartment impairs VSR activity. Furthermore, AC4 does not interfere with siRNA production and local PTGS and VIGS are not affected by the presence of the protein.
To investigate whether MYMV VSR might interfere with the transport of silencing signal by sequestering siRNA, we tested the ability of MYMV AC4 to bind small RNAs in vitro by electrophoretic mobility shift assay. For this assay, we used purified viral protein expressed in fusion with the glutathione S-transferase (GSTAC4) and gel purified GFP siRNAs produced upon PVX-GFP induced gene silencing in N. benthamiana 16c line. Upon combination with different concentration of GSTAC4, we observed slower migration of siRNAs indicating the formation of a protein-siRNA complex which confirmed the ability of MYMV AC4 to bind native 21–25 nt siRNAs (Fig 8). Such ability is not lost upon mutation of the palmitoylated C in A. In fact, the GSTAC4(C-A) mutant also binds siRNA, albeit probably less efficiently, as suggested by comparing the intensity of the siRNA fraction bound to equal amount of GSTAC4 and GSTAC4(C-A) (Fig 8, lanes 3 and 5 second gel).
AC4 is among the least conserved proteins of all geminiviruses and appears to have divergent biological functions among species of the family, being mostly involved in virus-plant interactions and in pathogenesis [48–51]. It was shown to play a role in the regulation of cell division [52] whereas, in other species, mutagenesis and/or transgenic expression of AC4 has no consequence on infection of several host plant [53].
Aiming at gaining insights into the mechanism of pathogenesis of MYMV and, more in detail, into the way of action of AC4, first and foremost, we have shown that infection of V. mungo with an AC4-deficient MYMV mutant develops asymptomatic phenotype, which reveals the essential role of AC4 for virus viability. Interestingly, this viral mutant replicates in inoculated leaves, albeit not at the same rate as the wild type MYMV and, opposite to MYMVΔBC1, lacking movement function, it moves short distance cell–to-cell whereas systemic transport is fully hindered.
Expression of MYMV AC4 increases severity of symptom induced by PVX in N. benthamiana, and suppresses systemic but not local GFP silencing in transgenic line 16c.
Interestingly, we observed that AC4 targets the PM few hours post inoculation but shortly after, it starts to accumulate also into the nucleus via a typical NLS, and such specific double localization is maintained in the course of infection.
The subcellular localization of AC4 is intriguing because normally, among plant viral proteins, only movement proteins localize to the PM. AC4 hosts no transmembrane domain but is post-translationally covalently modified by attachment of a lipid to the Cys11 that allows protein targeting and attachment to the PM. Such modification, known as S-acylation or palmitoylation and primarily meant to anchor otherwise soluble proteins to membranes, is now considered an important dynamic regulatory mechanism in signaling pathways in plants [25].
We found that the VSR activity of AC4 depends on protein binding to PM and is impaired upon mutation of Cys11. MYMV hosts another strong silencing suppressor: the transactivator AC2, which accumulates predominantly in the nucleus, excluding the nucleolus [12]. Opposite to AC2 that suppresses silencing in the nucleus, nuclear localization of AC4 is not related to this protein activity suggesting that the two MYMV VSR act in different cellular compartments and with different modalities. Therefore, the significance of AC4 targeting the nucleus remains to be investigated. Interestingly, we observed that PM is the first localization of MYMV AC4 while nuclear accumulation is visible later after transfection. This suggests that the protein could target the nucleus when accumulation to the PM attains saturation in a cycle of natural turnover for palmitoylated proteins [54, 55]. Alternatively, activation/inhibition of palmitoylation could be a strategy to switch between different protein functions requiring distinct subcellular localization [30]. In fact, considering that some viral proteins evolved silencing suppression activity after or concomitantly with other functions essential for virus viability [9], another role distinct from silencing suppression, which we proved to be uncoupled from nuclear localization, cannot be ruled out for AC4.
The absolute requirement of the Cys11 for silencing suppression activity reflects the need of such specific localization and justifies the highly stable association of AC4 to PM. The VSR function of East African cassava mosaic virus (EACMV) AC4 is also dependent on localization to the PM [18]. This protein is predicted to be N-myristoylated, and this modification is correlated to the VSR function. Proteins with the potential to become S-acylated often undergo myristoylation to interact with membranes and, subsequently, they become S-acylated and fixed to them [56]. Interestingly, EACMV AC4 hosts also a Cys in a favorable context for palmitoylation, whose mutation partially restricts the protein on perinuclear vesicles [18]. While palmitoylation of MYMV AC4 helps both binding and docking of the protein to the PM, in EACMV AC4, the two actions could be mediated by palmitoylation and myristoylation, respectively. However, as the authors did not confirm experimentally the post-translational modification of the protein, it remains to be demonstrated the role of palmitoylation for the AC4 of this begomovirus.
In Begomovirus infecting cassava, such as EACMV, African cassava mosaic virus (ACMV) and Indian cassava mosaic virus (ICMV), both AC2 and AC4 VSR are functional and matched in a way that when AC2 is a strong suppressor its correspondent AC4 is a mild suppressor, and vice-versa [38].
Conversely, MYMV AC2 and AC4 are both strong VSR with distinct subcellular localization, and apparently both essential. The evidence that, despite the presence of functional AC2, MYMV-ΔAC4 failed to establish systemic infection in V. mungo confirms that AC2 cannot compensate AC4 VSR function, and vice-versa [12].
While several examples of siRNA-sequestering VSRs as well as some movement proteins also acting as silencing suppressors are described in the literature [9], to our best knowledge this is the first report of a siRNAs-binding VSR that absolutely requires PM localization to perform its function.
AC4 is not a movement protein and the absolute requirement of PM localization for its silencing suppression activity is very interesting. TYLCV C4 also targets the PM and binds to BAM1 to hinder the spread of silencing signal triggered by this receptor-like kinase [36]. We demonstrate that MYMV AC4 also binds BAM1 and similar to C4 might hinder the silencing-related function of BAM1. However, based on the experimental evidences collected in this study and particularly the capacity of MYMV AC4 (unknown for TYLCV C4) to bind siRNAs and its much stronger VSR ability compared to C4 [57], we hypothesize a different or additional mechanism of action for this protein. Upon targeting to the PM and particularly to PD, MYMV AC4 could bind siRNAs and stop their passage to the neighbouring cell thus suppressing the spread of the PTGS signal through the plant. However, further experimental in vivo evidence required to confirm this working hypothesis.
The two nonpolar phenylalanines flanking Cys11, might have the important role of creating the required hydrophobic environment to allow association of the hydrophilic AC4 to the PM and the insertion of palmitate into the double lipid layer of PM [47]. This specific amino acid context (Phe-Cys-Phe) suggests that AC4 could dock to the PM folded in shape of “V” where the bottom tip is occupied by the cysteine bond to palmitic acid inserted into PM. The two phenylalanines would provide hydrophobic stability to the bond whereas the hydrophilic tails of AC4 would be kept on the cytoplasmic side, away from the membrane and available for interactions with siRNAs. The evidence that the AC4(C-A) mutant, missing the palmitoylated C, binds siRNA less stronger than AC4 WT, supports the hypothesis that the “Phe-Cys-Phe” hydrophobic domain might be important for conformational stability of the protein.
Whether the AC4 interaction with BAM1 is functional in regulation of RNAi cell-to-cell spreading, and if this strategy is complementary or synergic with the siRNA sequestering capacity of AC4 is yet to be elucidated.
Antiviral systemic signaling is a still unknown aspect of host defense and further validation is required to prove that plant immunity can be reached by systemic movement of vsiRNA. However, taken together the evidence here provided, it is tempting to speculate that MYMV AC4 would hijack the host lipidation machinery to target PD and, by binding vsiRNA, block the signal of “plant immunity”.
The existence of different types of geminiviral VSRs, suggests that these proteins (co)-evolved to target different steps of the silencing pathway in a temporal and/or spatial manner. In the case of MYMV for example, AC4 might strategically be localized to PD to pose a physical barrier to the spread of silencing signal that could have escaped the suppression control of AC2. As several suppressor proteins have multiple roles, including non-silencing functions critical for virus viability, and their synchronized action is essential in order to fulfill the multiple tasks, post translational modification could be an efficient strategy to reach this goal. In fact, S-acylation and/or N-myristoylation is predicted in AC4/C4 of the Geminiviridae species (S2 Table), and phosphorylation, another mechanisms of post-translational modification, has been recently reported to regulate subcellular localization and in turn VSR activity of cucumber mosaic virus 2b protein [58]. Therefore, post-translational modification and its correlation to VSR function should be considered and investigated, particularly for those proteins with multifunctional behavior and potential localization to membrane compartment.
In this study, we present the first report of palmitoylation and more in general of lipidation of a plant viral protein. The critical role of this post-translational modification on the function of MYMV AC4 suggests that lipidation is a very reliable way to target viral proteins to the membrane compartment and that more viral proteins might use these modifications for regulating their function at membranes.
All plasmids used in transient expression experiments are based on pCKGFP [59], modified by replacing GFP with the EGFP coding sequence and by the addition of two restriction sites (MluI and XbaI) at the 3' end.
The AC4 wt ORF was amplified by PCR from the pGA1.3A clone [37] with AC4-F and AC4-R primers (S1 Table) and the product was cloned into the MluI-XbaI sites of pCKEGFP in frame with EGFP. AC4 (C11-A) and AC4 (KRR-AAA) mutants were derived from pCKEGFPAC4wt using the QuickChange XL Site-Directed Mutagenesis Kit (Agilent Technologies) with AC4(C11-A)F/AC4(C11-A)R and AC4(KRR-AAA)F/AC4(KRR-AAA)R pairs of primers covering the aminoacids mutated, according to the manufacturer’s instructions. Plasmids containing mutated C and KRR were used as a template for site-directed mutagenesis to obtain the pCKEGFPAC4 (C11-A /KRR-AAA).
The AC4 sequence encoding aminoacids 1 through 12 was fused in frame with the 5’ of EGFP by amplifying EGFP with a forward primer containing the AC4 sequence fragment.
Infectious clones based on the PVX genome, were obtained from the pGR107 plasmid. AC4 wt and mutant ORFs were amplified from the pCKEGFP clones with AC4(SmaI)F and AC4(SalI)R (S1 Table) whereas the p19 and the mGFP5 sequences were amplified from the pGA482p19 clone [41] with p19SmaI and p19SalI primers (S1 Table) and from DNA extracted from N. benthamiana 16c plants with mGFP5_F(SmaI) and mGFP5_R(SalI) primers (S1 Table), respectively. PCR products were cloned in the SmaI-SalI sites of the multiple coning site of the pGR107 plasmid and the obtained plasmids introduced into Agrobacterium tumefaciens strain C58C1 by a freeze–thaw method.
AC1, AC4 and BC1 genes were knocked-out within the virus infectious clones pGA1.3A (DNA A wt) and pGA1.3B (DNA B wt) [37] using the QuickChange XL Site-Directed Mutagenesis Kit (Agilent Technologies) and the MYMV-AC1koF/ MYMV-AC1koR, MYMV-AC4koF/MYMV-AC4koR, and MYMV-BC1koF/MYMV-BC1koR pairs of primers (S1 Table) covering the aminoacids mutated, respectively.
For expression and suppression of GFP RNA silencing, the binary plasmid pGA482p19 and a pCAMBIAAC4 plasmid based on pCAMBIA32 modified by cloning an expression cassette containing AC4 under the control of the 35S promoter in PstI restriction site, were introduced into Agrobacterium tumefaciens strain C58C1 by a freeze–thaw method.
For the electrophoretic mobility shift assay, MYMV AC4 was amplified with the MYMVAC4 F/ MYMVAC4-HA R pair of primers (S1 Table) and cloned into the EcoRI/SalI sites of pGEX-6p-1 (GE Healthcare) in frame with the glutathione S-transferase (GST) coding sequence. The MYMVAC4-HA R reverse primer (S1 Table) contained the sequence encoding the 9 aminoacids of the HA epitope (TAC CCA TAT GAC GTC CCA GAT TAC GCT encoding YPYDVPDYA).
The third stop codon following the SalI site in the pGEX6p1 plasmid in frame with AC4 sequence was used for termination of translation. The HA (human influenza hemagglutinin) epitope tag was engineered onto the C- terminus of AC4 sequence so that the tagged protein could be analyzed and visualized using immunochemical methods.
For BiFC and FRET-FLIM analysis, AC4 was cloned in pENTRD/TOPO (Invitrogen) using primers CACCATGAAGATGGAGAACCTCATCT and GTATATTGAGGGCCTGTAACTTG. Gateway cloning (Invitrogen) was used to fuse AC4 to GFP in pGWB505 [60], to RFP in pB7RWG2.0 [61], and nYFP/cYFP in pGTQL1211YN/pGTQL1221YC [62].
Construct to express C4-GFP, BAM1-RFP, PM-RFP (Plasma Membrane protein NCBI number NP_564431), C4-cYFP, BAM1-cYFP and BAM1-nYFP are described in Rosas-Diaz et al., 2018.
Protoplasts were isolated from N. benthamiana and transfected as described [63]. V. mungo plants were biolistically transfected with clones to express AC4 WT and mutants and stained with DAPI. Fluorescent proteins were examined with a Nikon Eclipse 80i microscope equipped with video confocal technology (VICO). For GFP, DsRed and DAPI images, the ET-GFP filter set (Chroma 49002, Nikon), the G-2A filter (Nikon) and DAPI filter were used, respectively.
N. benthamiana plants were agroinfiltrated with clones to express BAM1-GFP and AC4-RFP and stained with aniline blue. Imaging was performed as described in Rosas-Diaz et al., 2018.
Bimolecular fluorescent complementation (BiFC) assays were performed as described previously [36]. In brief, N. benthamiana plants were agroinfiltrated with clones to express the corresponding proteins, and samples were imaged two days later on a Leica TCS SMD FLCS confocal microscope, using the pre-set settings for YFP with Ex:514 nm, Em: 525–575 nm.
FRET-FLIM experiments were performed as described previously [36].
N. benthamiana plants were grown at 25°C. At six-leaves stage plants were infiltrated with A. tumefaciens C58C1 harboring the appropriate constructs. A. tumefaciens carrying each construct was grown on selective media overnight, resuspended in the infiltration buffer (10 mM MES, 0.15 mM acetosyringone, 10 mM MgCl), kept at 25°C for 2-3h, and subsequently infiltrated into wt or 16c plant leaves at OD = 1. In co-infiltration experiments, equal volumes/concentration of each suspension were mixed prior to infiltration. GFP fluorescence was observed under long-wavelength UV light (Black Ray model B 100A, UV Products) and photographed with a yellow filter.
Leaf samples from MYMV bombarded V. mungo plants were collected and analysed in triplicates. DNA was extracted by Dellaporta method [64] with slight modification. 50 mg fresh leaf tissue was ground in liquid nitrogen, mixed with extraction buffer (50 mM Tris-HCl pH 8.0, 20 mM EDTA pH 8, 350 mM NaCl, 8 M Urea, 2% N-Lauril-Sarcosine) and equal volume of phenol and incubated at 70°C for 5 min. DNA was extracted from the supernatant upon centrifugation by volume of phenol: chloroform (1:1), isopropanol precipitation and RNase treatment.
Two different couples of primers were designed: VrACtfor/ VrACtrev to amplify an endogenous actin gene (Vigna radiata actin, accession number AF143208) and AC2-RT_F/ AC2-RT_R to amplify the target MYMV AC2 gene (S1 Table).
Relative qPCR was performed using C1000 thermal cycler (Bio-Rad). The cycling profile consisted of 95°C for 20 s, 40 cycles of 3 s at 95°C and 30 s at 60°C, one cycle of 10 s at 95°C, as recommended by the manufacturer, using 2X Fast SYBR Green PCR Master Mix (Applied Biosystems), 400 nM forward and reverse primers, 4 ng of V. mungo DNA and nuclease-free water in a total volume of 12.5 μL.
Each DNA sample was amplified in duplicate for each primer pair and immediately after the final PCR cycle, a melting curve analysis was performed to determine the specificity of the reaction. Relative quantification was calculated using the comparative cycle threshold (Ct) method (RQ = 2–ΔΔCt) [65], in which the change in the amount of the target viral RNA was normalized in relation to the endogenous control.
Data were log-transformed [log(x+1)] before statistical analysis in order to fulfil the assumptions for parametric statistics. Transformed data were analyzed in a repeated measure factorial design using the MIXED procedure of SAS (SAS/Stat Inc.), in which the variables ‘Experiment’, ‘Treatment’ and ‘Time’ were considered as fixed effects. Contrasts were performed to test the experiment reproducibility, viz. the hypothesis of no difference between the two independent experiments made, and the difference among treatments.
Total RNA was extracted from 100 mg of leaf tissue. Plant materials homogenized in liquid nitrogen was resuspended in 600 μl of extraction buffer (0.1 M Glycine-NaOH, pH 9.0, 100 mM NaCl, 10 mM EDTA, 2% SDS) and mixed with an equal volume of phenol. The aqueous phase was treated with equal volumes of phenol-chloroform, precipitated with ethanol, and finally resuspended in sterile water. RNA gel blot analysis of higher molecular weight RNAs was performed as previously described [41]. For analysis of siRNAs, low-molecular-weight RNAs (LMW-RNAs) were enriched from total RNAs extract by removing high-molecular-mass RNAs with 10 % polyethylene glycol (PEG8000) and 1M NaCl. Approximately 5 μg of LMW RNAs were separated by 17 % PAGE with 7 M urea and then blotted onto Hybond-N+ membranes. After UV cross-linking, the membranes were hybridized at 42°C and the detection was carried out with DIG non-radioactive system (Roche Applied Science) according to the manufacturer’s instructions using a probe covering the entire mGFP5 sequence (GenBank: U87973). The blots were incubated in antibody solution, anti-DIG-AP Conjugate (Roche) and CDP-STAR (Roche) for chemiluminescence detection.
Cell fractionation was performed as previously described with some modifications [66] [67]. Protoplasts transfected with pCKAC4HA plasmid were pelleted, resuspended in buffer (1X PBS (pH 7.4), protease inhibitors, 0.5% Tween) and lysed by 5 freeze-and-thaw cycles. Cell debris (P0.5) was isolated by 3 min centrifugation at 500 x g, 4°C. The supernatant (S0.5) was further centrifuged at 30,000 x g and 4°C for 30 min to yield supernatant (S30) and pellet fractions (P30).
For analysis of the membrane part, the P30 fraction was incubated for 30 min on ice in the presence of one of the following reagents: 100 mM Na2CO3 (pH 11.5), 4 M urea, or 1 M KCl [68]. After centrifugation at 30,000 x g for 30 min at 4°C, pellets and supernatants were resolved on 12% SDS-PAGE, transferred to Hybond PVDF membrane (Millipore) and subjected to Western blot analysis. Proteins were detected with anti HA antibody (1:4000, Santa Cruz Technology), visualized with SuperSignal West Pico Chemiluminescent Substrate (Pierce) according to the manufacturer’s instructions and scanned by Chemidoc Touch Imaging System (BioRad).
For biotin switch assay, collected protoplasts transfected with pCKAC4HA and pCKAC4(C-A)HA were resuspended in 500 μL lysis buffer (1X PBS pH 7.4, protease inhibitors, 1 mM EDTA, 1% Triton X-100, 25 mM N-ethylmaleimide) and treated following the method described [69]. Eluted proteins were analyzed by SDS-PAGE and Western blotting as described above.
Recombinant GSTAC4, GSTAC4(C-A), and GST proteins were produced by overexpression in Escherichia coli BL21 codon plus cells (Agilent Technologies). Cells were grown to OD600 ≈ 0.7 and IPTG was added to a final concentration of 0.4 mM for AC4 and of 0.2 mM for GST for induction (3 h at 37°C). GST fusion protein supernatants obtained after bacterial lysis and centrifugation were purified with Glutathione Sepharose 4B beads (GE Healthcare) following the manufacturer’s instructions.
For preparation of 21-25-nucleotide siRNA, 45 to 50 μg of total RNA were electrophoresed on 17% PAGE with 8 M urea followed by ethidium bromide staining in 1 × Tris-borate-EDTA. 21–25 nt siRNA fraction was cut and incubated in buffer 0.3 M NaCl, 0.1% SDS, overnight at 4°C with rocking. After a gentle centrifugation for 5 min at 2000g, the supernatant was transferred to a 50 mL tube. The crushed gel slice was incubated for a second elution in the same buffer with rocking. The gel residues were pelleted by centrifugation, and the two supernatants were precipitated together with ethanol.
For binding assays, increasing amount of purified GSTAC4 and of GSTAC4(C-A), and siRNA (1.5 ug) were mixed and incubated for 20 min at room temperature in binding buffer (20 mM Tris–HCl pH 8, 5 mM MgCl2, 50 mM KCl, 25 mM NaCl and 2.5 mM DTT, 0.02% Tween, 10% glycerol). Each sample contained 40U RNasin. The reaction was stopped by adding dyes, and loaded onto 8% native PAGE. The gel was transferred to Hybond-N+ membrane and after UV cross-linking, the membranes were hybridized as described above.
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10.1371/journal.pgen.1004518 | Requirements for Pseudomonas aeruginosa Acute Burn and Chronic Surgical Wound Infection | Opportunistic infections caused by Pseudomonas aeruginosa can be acute or chronic. While acute infections often spread rapidly and can cause tissue damage and sepsis with high mortality rates, chronic infections can persist for weeks, months, or years in the face of intensive clinical intervention. Remarkably, this diverse infectious capability is not accompanied by extensive variation in genomic content, suggesting that the genetic capacity to be an acute or a chronic pathogen is present in most P. aeruginosa strains. To investigate the genetic requirements for acute and chronic pathogenesis in P. aeruginosa infections, we combined high-throughput sequencing-mediated transcriptome profiling (RNA-seq) and genome-wide insertion mutant fitness profiling (Tn-seq) to characterize gene expression and fitness determinants in murine models of burn and non-diabetic chronic wound infection. Generally we discovered that expression of a gene in vivo is not correlated with its importance for fitness, with the exception of metabolic genes. By combining metabolic models generated from in vivo gene expression data with mutant fitness profiles, we determined the nutritional requirements for colonization and persistence in these infections. Specifically, we found that long-chain fatty acids represent a major carbon source in both chronic and acute wounds, and P. aeruginosa must biosynthesize purines, several amino acids, and most cofactors during infection. In addition, we determined that P. aeruginosa requires chemotactic flagellar motility for fitness and virulence in acute burn wound infections, but not in non-diabetic chronic wound infections. Our results provide novel insight into the genetic requirements for acute and chronic P. aeruginosa wound infections and demonstrate the power of using both gene expression and fitness profiling for probing bacterial virulence.
| Soft tissue infections, such as those in burns, bed sores, and diabetic ulcers, are a significant healthcare and economic burden in the developed and developing world. The opportunistic pathogen P. aeruginosa can cause both acute and chronic infections, and the trajectory of these two types of infections is vastly different. We used high-throughput sequencing to profile P. aeruginosa genome-wide gene expression and mutant fitness during mouse model acute and non-diabetic chronic wound infections. Using these data, we show that wounds are nutrient-rich growth environments in which long-chain fatty acids are a primary source of carbon and energy. We also show that the ability to travel along spatio-chemical gradients by chemotaxis is critical for bacterial fitness and virulence in acute but not chronic infections. Our results demonstrate the utility of simultaneous mutant fitness and gene expression profiling to discover critical functions in complex growth environments.
| Infections caused by opportunistic bacterial pathogens are a primary cause of morbidity and mortality in both the developed and developing world. These infections are often characterized by robust growth of the pathogen in the infection site and increasingly high resistance to antibiotic treatment. The opportunistic pathogen Pseudomonas aeruginosa is responsible for a wide range of infections in immunocompromised hosts [1]. Among the most significant of these infections are those localized to soft tissues, including chronic and burn wounds [1], [2]. Chronic wounds are defined as wounds that have “failed to proceed through an orderly and timely process to produce anatomic and functional integrity, or proceeded through the repair process without establishing a sustained anatomic and functional result” [3]. Chronic wounds include pressure ulcers (bed sores), diabetic ulcers, venous ulcers, and arterial ulcers, and affect approximately 5–7 million people per year in the US at a cost of $10–20 billion per year [4]. Infections in burn wounds also carry a heavy medical and economic burden not only in the developed world, but also in the developing world, where 70% of burns affect children, and mortality in patients with burns covering >40% total body surface area approaches 100% [5], [6]. Interestingly, burn infections caused by P. aeruginosa often deteriorate rapidly and lead to systemic spread and death within days or weeks, yet P. aeruginosa chronic wound infections persist for much longer with little associated mortality [5]. As this difference in infection trajectory is not thought to be a result of colonization with specific P. aeruginosa strains, the mechanisms underlying this difference remain a mystery. One possibility is that the type of injury impacts key features of the host environment, such as the immune response, and that this dictates disease progression [7]. A second, non-mutually exclusive possibility is that P. aeruginosa physiology and gene expression is different in chronic and acute wounds. In this work, we set out to address this second possibility using genomic methods.
Phenotypes thought to be associated with acute or chronic P. aeruginosa infections have been extensively studied in vitro, and much is known about the genes responsible for these phenotypes and their regulation. For example, the Gac/Rsm and cyclic-di-GMP signaling networks both control expression of “acute” virulence determinants (e.g., type III secretion) and “chronic” virulence determinants (e.g., exopolysaccharides) [8], [9]. Yet the genetics and physiology of acute and chronic infections have not been directly compared in vivo using open-ended methods such as those enabled by recent advances in high-throughput genomics. Wounds represent an excellent system to study chronic and acute infections since both occur in soft tissues. Furthermore, mouse models of these infections recapitulate key features of infections in humans, such as rapid sepsis and mortality in acute infections, and prolonged healing times in both diabetic and non-diabetic chronic infections [10], [11].
In this study, we chose to study acute and chronic wound infections in mice using two complementary genomic technologies: RNA sequencing (RNA-seq) and transposon-junction sequencing (Tn-seq). Sequencing RNA-derived cDNA with high-throughput methods has proven to be a highly sensitive and comprehensive method for profiling bacterial transcriptomes [12]. Transcriptome-based approaches essentially use the infecting bacterium as a “biosensor”, using differential gene expression as a measure of the molecular and physiological cues sensed by the organism. Performing RNA-seq on RNA isolated from infected tissue from both model hosts and human patients has yielded genome-scale insights into differential gene expression by numerous bacteria during infection [13]–[15]. The requirement of individual genes for growth in an environment can be assessed genome-wide using Tn-seq. This method involves the quantitative sequencing of genomic DNA adjacent to a transposon insertion site to measure the abundance of an insertion mutant in a complex library containing tens or hundreds of thousands of individual mutants [16]–[18]. By subjecting this library to growth in a particular condition (such as infection of a model host) and subsequently profiling the abundance of each mutant by high-throughput sequencing, mutations that affect fitness in that condition can be identified upon comparison with an appropriate control condition. This approach has proven successful in identifying determinants of antibiotic resistance, carbon and energy utilization, and in vivo fitness [19]–[21]. However, despite the fundamentally distinct insights that can be gained by examining genome-wide gene expression and mutant fitness, few studies have performed these analyses on the same set of conditions, and none have done so during infection.
The goal of this work was to compare P. aeruginosa metabolism and virulence during models of acute and chronic infection. To this end, we performed RNA-seq and Tn-seq in P. aeruginosa grown in vitro and in two non-diabetic murine wound infection models, an acute infection model resulting in high levels of mortality and a non-lethal chronic infection model. Our results reveal that gene expression and mutant fitness are not correlated for most genes with the exception of metabolic genes, where differential expression is more predictive of a gene's role in fitness. By comparing gene expression and mutant fitness in vivo to growth in a defined medium, we reconstructed metabolism of P. aeruginosa during wound colonization and identified several metabolic pathways, including long-chain fatty acid catabolism, that are required for colonization and persistence. Additionally, we discovered that the ability to chemotax is required for P. aeruginosa fitness in burn but not chronic wound infections. These findings identify key features that are required for P. aeruginosa fitness in wound infections and demonstrate the utility of simultaneous gene expression and knockout fitness profiling for the study of bacterial metabolism, virulence, and physiology during infection.
P. aeruginosa causes both acute and chronic wound infections, and we hypothesize that both bacterial and host factors mediate the outcome of wound infections. Here, we investigated P. aeruginosa wound infections from the perspective of the infecting bacterium to uncover similarities and differences between bacterial physiology in acute and chronic infections. Investigation of genetic requirements for P. aeruginosa colonization and persistence during infection requires animal models that encapsulate many of the key characteristics of human infections. In this study, two non-diabetic murine models of wound infection were used, one acute and one chronic. In the acute model, a dorsal full-thickness (third degree) burn is induced by scalding and infected subcutaneously with 102–106 P. aeruginosa. This infection is highly virulent, rapidly causing sepsis that leads to ∼100% mortality within 48 hours [11]. The chronic model involves infection of a surgically created full-thickness dorsal excision wound with 105 P. aeruginosa that is covered by an adhesive dressing. This prevents contractile healing and ensures that these wounds heal by deposition of granulation tissue, much like human chronic wounds [22]. This infection can persist for weeks and is highly resistant to antibiotic treatment, and underlying conditions such as diabetes can extend the persistence time of these infections [10], [23]. Importantly for our purpose, these two infections can be initiated at approximately the same infecting dose with the same strain of P. aeruginosa.
To examine the physiology of P. aeruginosa during growth in these two model infections, we initially used RNA-seq (Table S1). The rationale for these experiments was that when compared to an appropriate control, transcriptomic methods such as RNA-seq can provide a genome-wide view of differential gene expression during infection, essentially using the infecting bacterium as a “biosensor” to report signals and cues sensed by the bacterium in vivo. As P. aeruginosa exhibits robust growth and persistence in these two infection models, much like in clinical infections, we were particularly interested in its primary metabolism during infection. Therefore, to interpret our RNA-seq results with a focus on central metabolism, we chose to compare the transcriptome of P. aeruginosa grown in vivo to growth in a defined minimal medium, specifically, growth to mid-logarithmic phase in a MOPS-buffered medium containing succinate as the sole carbon source (MOPS-succinate). This allowed comparison of metabolic gene expression in an unknown environment (in vivo) to an environment in which metabolism is largely understood (defined medium). We chose to profile the acute infection 40 hours post inoculation and the chronic infection 4 days post inoculation because these two timepoints represent midpoints of the trajectory of these respective infections. We reasoned that P. aeruginosa would have sufficient time to adapt to the infection environment and the host would have sufficient time to mount any immune response it was capable of raising at these timepoints. While no single timepoint can capture the dynamics of gene expression throughout the course of an infection, the timepoints chosen reflect a similar degree of progression in both wounds.
We found that P. aeruginosa differentially regulates 14% and 19% of its genome during growth in murine burn and chronic wounds, respectively, as compared to MOPS-succinate (P<0.01, negative binomial test, fold change ≥4) (Table S2). The transcriptional responses of P. aeruginosa during growth in these two wound types as compared to MOPS-succinate are highly correlated (Spearman rank correlation coefficient = 0.840), suggesting that the cues sensed by P. aeruginosa in acute and chronic wound infections are largely similar (Figure 1A). Notably, 7.3% of the genome is commonly up- or down-regulated in both wound infections, which is a significant overlap (P<4.72×10−110, Fisher's exact test). The P. aeruginosa genome encodes numerous virulence factors, and our data provides a genome-wide perspective on the expression of these virulence genes (Table S3). We saw that many genes in the PA3160-PA3141 cluster, which encodes genes required for lipopolysaccharide O antigen biosynthesis [24], were down-regulated in vivo, and to a greater extent in chronic wounds. This suggests either that P. aeruginosa may alter its outer surface during infection, or that O antigen biosynthesis is regulated as a consequence of more static growth in vivo. We also saw that genes responsible for the biosynthesis of the siderophores pyochelin and pyoverdine were greatly up-regulated in vivo. Iron is known to be a limited resource in numerous infections, and our results suggest that iron acquisition is important in P. aeruginosa soft tissue infections as well [25]. Many type II and type III secretion system genes were up-regulated in both acute and chronic wounds as well, indicating that P. aeruginosa may be modulating host cellular physiology and extracellular environment through these well-characterized secretion systems [26], [27]. Finally, we saw down-regulation of many genes in the psl cluster, which is responsible for synthesis of the Psl exopolysaccharide. In strain PAO1, Psl is the primary exopolysaccharide involved in biofilm formation on abiotic surfaces [28]. Thus, P. aeruginosa differentially regulates much of its virulence repertoire upon wound infection, further underscoring the multifaceted nature of its virulence.
To determine what general features of P. aeruginosa physiology are altered in vivo, we performed COG enrichment analyses of genes differentially expressed in wounds as compared to MOPS-succinate (Figure 1B). As expected, genes involved in transport of inorganic ions, such as those encoding predicted ferric and ferrous iron transport systems, are enriched in the set of genes up-regulated in vivo. We also noted that amino acid biosynthetic genes are significantly enriched in the set of down-regulated genes in both wound types as compared to MOPS-succinate, suggesting that many amino acids are available in both chronic and acute wounds. Finally, the most extensive regulation in vivo was seen in COG category C, which includes genes involved in energy production and conversion, suggesting that the primary metabolism of P. aeruginosa is extensively remodeled during infection relative to growth in minimal media.
Our transcriptomic results suggest that bacterial gene expression is extensively regulated during infection. Yet it is unclear whether those genes that are differentially regulated play a role in in vivo fitness. To address this question, we chose to complement our in vivo transcriptomic studies with Tn-seq to identify the genetic determinants of bacterial fitness in acute and chronic wound infections. Briefly, a library of ∼100,000 P. aeruginosa transposon mutants [20] was grown in MOPS-succinate and in both acute and chronic wound models, and mutant abundance was profiled by Tn-seq either 24 hours or 3 days post inoculation for the acute and chronic infections, respectively (Table S4). As the abundance of a particular mutant in the library will be influenced by its relative fitness throughout the history of the library, these timepoints are sufficient to query genes required for both initial colonization and subsequent growth in these infections. We found that 11% and 16% of the genome contributes to fitness in murine burn and chronic wounds as compared to growth in MOPS-succinate (P<0.05, negative binomial test, fold change ≥4), respectively, and that 3% of the genome contributes to fitness in both wounds, which is a significant overlap (P<1.66×10−25, Fisher's exact test) (Table S5). We first examined the fitness contribution of known virulence factors (Table S6). We saw that the flagellum is required only in burn wounds, confirming previous studies and the validity of our Tn-seq approach [29]. Many genes in the type III and the type VI secretion systems contribute to fitness in chronic wounds, further suggesting that inter-cellular delivery of effector proteins may be important in these wounds. Interestingly, despite their down-regulation, the psl exopolysaccharide genes contribute to fitness in both acute and chronic wounds. Finally, some genes involved in producing type IV pili, another motility system [30], appear to be required in both acute and chronic wounds. Taken together, our results emphasize that virulence in P. aeruginosa is multifactorial, involving the coordinated action of motility, biofilm formation, and secretion systems.
Since transcriptomics has been used in the past to identify bacterial genes potentially important for in vivo fitness [14], [15], [31], we hypothesized that genes identified as important for fitness using Tn-seq would display increased expression in vivo. If this hypothesis is true, one would expect that a correlation coefficient (which expresses correlation between two variables on a scale from −1, or perfectly anticorrelated, to 1, or perfectly correlated) would be closer to −1. However, we found that mutant fitness and differential expression are uncorrelated, suggesting that in this case RNA-seq is not a good predictor of genes important for fitness in wounds (Figures 2A and S1, Table 1). One should keep in mind that Tn-seq is a competitive infection since most strains are wild-type for a given genetic locus, and RNA-seq may be more predictive if individual mutants are examined.
Although it is clear that Tn-seq and RNA-seq results are not correlated when all genes are examined, we hypothesized that particular subsets of genes may show a stronger correlation. If this is the case, identifying these subsets of genes would have the potential to guide hypotheses regarding genes important for fitness in bacteria with poor genetic tools, or in natural microbial populations such as those associated with primary human samples, where methods like Tn-seq are not feasible. One possible subset of genes that may be more predictive are those that are most highly differentially regulated. To test this, genes were ranked from high to low fold-change expression and correlated with fitness scores for ever-increasing subsets of genes along that ranking. No significant improvement in correlation was observed, indicating that the magnitude of differential in vivo expression is not more predictive of fitness (Figure S2). We found that the same is true for genes that contributed strongly to fitness, as ranking from low to high mutant fitness also does not enhance the correlation. As Tn-seq measures the fitness of single mutants, we hypothesized that genetic redundancy might mask a role of some genes in fitness, and that limiting our analysis to genes without predicted redundancy might improve expression-fitness correlation. To test this, Enzyme Commission (EC) numbers, which describe the enzymatic function of a gene product, were used to determine which genes lack functional paralogs elsewhere in the genome. However, limiting our analysis to those differentially expressed genes with a unique EC number did not substantially alter expression-fitness correlation (Table 1). It should be noted that this approach does not address more complex manifestations of redundancy, such as robustness in functional gene interaction networks [32], which may contribute to the lack of correlation between our Tn-seq and RNA-seq results.
We next examined whether the predictive power of gene expression for mutant fitness is better for certain functional classes of genes. Therefore, we examined expression-fitness correlation by COG category. We saw that differential expression and mutant fitness are more negatively correlated in both wound models for several COG categories (Figure 2B). One of these COG categories is amino acid metabolism and transport, suggesting that, relative to growth in MOPS-succinate, P. aeruginosa down-regulates amino acid biosynthetic genes in vivo to avoid the fitness cost associated with expressing them when they are not needed (Figure 2B). We also saw that differential expression and mutant abundance are more negatively correlated for genes in the energy production and conversion, lipid metabolism, and inorganic ion transport and metabolism COG categories. For genes in these categories, up-regulation is more predictive of a fitness defect of mutants lacking those genes (Figure 2B), suggesting that changes in metabolic gene expression are adaptive, conferring a fitness benefit on the organism. These results underscore the importance of scavenging available nutrients and limited-availability ions (such as amino acids and iron) while up-regulating key central metabolic pathways during infection.
Our analysis of the correlation between differential expression and conditional mutant fitness by COG category (Figure 2B) indicates that expression is a better predictor of fitness contribution for genes involved in primary metabolism. Therefore, to characterize the primary metabolism of P. aeruginosa during wound infection, we projected our transcriptome profiling results onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAYS database (Figure 3). As mentioned previously, our choice of a defined medium (MOPS-succinate) as a control condition provided a reference point from which to understand bacterial physiology and metabolism in the unknown nutritional environment of the infected wound. Our metabolic reconstruction suggests that genes encoding decarboxylating steps of the TCA cycle (isocitrate dehydrogenase and α-ketoglutarate dehydrogenase) are down-regulated, and that the gene encoding the entry point to the glyoxylate shunt (isocitrate lyase) is up-regulated. The glyoxylate shunt is a variation on the TCA cycle not present in mammals that allows bacteria, including P. aeruginosa, to grow on reduced carbon sources such as fatty acids by bypassing TCA cycle reactions that would result in the loss of carbon [33]. This serves to replenish TCA cycle intermediates utilized in biosynthesis, which is known generally as anaplerosis. We also observed up-regulation of ppc, which encodes a second non-mammalian anaplerotic enzyme, PEP carboxylase, in wound infections, though it is much more highly expressed in chronic wounds than in acute wounds (Table S2). Finally, expression of a number of genes associated with oxygen-limited environments is also up-regulated, including those encoding high-affinity terminal oxidases and the denitrification pathway [34], suggesting that at least some bacterial cells in these infections sense decreased oxygen tension. This is consistent with frequent observations of ischemia at wound sites in the clinic, and suggests that oxygen limitation affects the physiology of both the infecting organism and host tissue at wound sites [35]. The transcriptomic results suggest that P. aeruginosa differentially regulates a large portion of its genome in soft tissue infections, and that this reflects a response to differential availability of key metabolic factors such as carbon and energy sources, biosynthetic endproducts, and terminal electron acceptors. In the remainder of this manuscript, we will examine each of these in detail.
Infected host tissue is a complex nutritional environment for a bacterium, with many potential metabolites available for bacterial catabolism. Manipulation of key metabolites during infection has therapeutic potential in much the same way as arginine-auxotrophic cancers can be treated by depletion of available L-arginine [36]; however, the nutrients utilized by bacteria in wound infections are not known. Comparing the expression of primary metabolic genes in vivo to growth in defined minimal media led us to hypothesize that fatty acids are a primary carbon source available to P. aeruginosa in vivo (Figure 3). Examination of our Tn-seq data in detail (Table S4) revealed that the faoAB (or fadBA5) genes, which are required for robust growth on long-chain (C12 or greater) fatty acids [37], contribute to P. aeruginosa fitness in vivo (Figures 4A and S3A). This was confirmed by single mutant infections: both an faoA transposon mutant and an faoA deletion mutant are attenuated in both acute and chronic wounds, indicating that long-chain fatty acids are likely an important energy source in wounds (Figure 4BC). The faoAB genes have also been shown to contribute to resistance to tobramycin, so they could potentially contribute to resistance to an unspecified chemical stress in vivo as well [20]. However, our in vivo gene expression data suggests that growth in wound infections involves pathways active during growth on reduced carbon sources such as long-chain fatty acids (Figure 3). We did not observe an in vivo fitness defect for genes annotated as homologs of the Escherichia coli long-chain fatty acid outer membrane transporter gene fadL (fadL1, fadL2, or fadL3) in P. aeruginosa; however, these genes are not thought to be required for long-chain fatty acid transport in P. aeruginosa [38].
In addition to identifying primary carbon and energy sources during wound infections, our results also allow identification of biosynthetic end products that are available to P. aeruginosa in wound infections. We reasoned that biosynthetic pathways required in minimal media but dispensable in vivo would likely be responsible for the synthesis of metabolites available to P. aeruginosa in vivo. To identify biosynthetic genes, we used the manually curated PseudoCyc annotation [39]. Metabolites for which 33% or more of the biosynthetic genes contribute more to fitness in MOPS-succinate than in both acute and chronic wounds (P<0.05, negative binomial test, fold change ≥2) were deemed “available”, and include many amino acids, the electron carriers FAD and NAD, and the B vitamin thiamine (Figure 5A and Table S7). Metabolites for which 95% or more of the biosynthetic genes have a similar effect on fitness in MOPS-succinate and in both acute and chronic wounds were deemed “not available”, and include the amino acids glutamate, tyrosine, phenylalanine, aspartate, and asparagine, purines, many other vitamins and cofactors including the folate precursor p-aminobenzoate (PABA) and several other B group vitamins. The remaining metabolites that do not match either of the above sets of criteria were deemed “potentially available”. To confirm the validity of this approach, we constructed two in-frame, unmarked deletion mutants lacking the ability to biosynthesize metabolites predicted to be unavailable in wounds: one lacking pabC (which requires PABA for growth in a minimal medium) and one lacking purF (which requires purines for growth in a minimal medium) (Figure S3B). These two mutants were completely attenuated for virulence in the burn wound (Figure 5B). However, in-frame, unmarked deletion mutants unable to grow without histidine or without isoleucine, leucine, and valine, all of which are predicted to be available in wounds, were significantly more virulent than the pabC or purF mutants. Thus, by comparison with minimal media, we demonstrate that genome-wide bacterial mutant fitness can be used to comprehensively profile bioavailable metabolites in a complex, undefined environment. Bacterial-specific pathways responsible for biosynthesis of any of the unavailable metabolites identified may represent promising targets for therapeutic intervention in wounds.
While our focus on metabolism revealed numerous similarities in chronic and acute infections, the genomic techniques employed here also provided new insight into how these infection types differ (Table S8). As a motile bacterium, P. aeruginosa possesses the ability to detect and move toward nutrients (including long-chain fatty acids [38]), a process referred to as chemotaxis. Examination of our Tn-seq results revealed that several genes with putative roles in chemotaxis, including cheA, cheB, cheR1, and a homolog of cheW (PA3349) are required in burn, but not chronic wounds (Figure 6A). In addition nearly every annotated flagellar gene is required for fitness in burn wounds [29], but is dispensable in chronic wounds (Figure 6B). To further confirm the role of chemotaxis in acute wound infections, single-strain infections with a cheR1 transposon insertion mutant and an in-frame, unmarked cheR1 deletion mutant (Figure S3C) were performed. While both the cheR1 insertion and deletion mutants have virulence defects in burn wounds (Figure 6C), the cheR1 insertion mutant is as fit or more fit than wild-type P. aeruginosa in chronic wounds (Figure 6D). These results suggest that the ability to chemotax along a spatio-chemical gradient by utilizing flagellar motility is a key feature of acute but not chronic wound P. aeruginosa infections.
The opportunistic pathogen P. aeruginosa is remarkably versatile, able to thrive in a wide range of environments and cause infections in diverse tissue types. These infections can have wide-ranging timescales, from mere days in acute infections such as those in burn wounds or in the cornea to the decades-long pulmonary infections associated with cystic fibrosis [1]. Remarkably, P. aeruginosa is able to achieve this breadth of infectious capability with highly conserved genomic content [40]. This suggests that the capacity to be an acute or a chronic pathogen is innate to the organism, and is determined largely by the context in which the infection is found. Using two complementary genomic techniques, we have investigated the physiology and fitness of P. aeruginosa in two soft tissue infections, one acute and one chronic, and shown similarities and differences between them. Interestingly, with the exception of chemotactic motility, there do not seem to be many infection type-specific genetic pathways required for fitness in one infection versus the other, suggesting that components of host physiology, likely the immune system, dictate the fate of soft tissue infections.
While our data include numerous implications for the role of characterized virulence systems in wound infections, we have chosen to focus mainly on metabolic genes in this study. The reason for this is three-fold: (1) We found that, when compared to growth in a defined medium, metabolic gene expression could be easily interpreted and correlated well with mutant fitness. This approach allowed us to fully profile catabolism, anabolism, and respiration for an infecting bacterium solely from transcriptomic data, which may prove useful in the study of bacterial physiology in conditions in which mutant fitness experiments are not feasible (such as human infections). (2) Bacterial metabolism during infection is poorly understood. The primary carbon and energy sources utilized by bacteria during infection are only known for a few instances [41]. Our data suggest that by comparing Tn-seq data obtained after growth in vivo to that obtained in a defined minimal medium can make great inroads towards a greater understanding of metabolism during infection. (3) Modulation of the host metabolic environment has shown promise in treating other diseases characterized by fast-growing and invasive cells such as cancer [36], and has immediate therapeutic potential for treatment of infections. Our data suggest that interfering with long-chain fatty acid catabolism (Figure 4) or transport, or biosynthesis of several key metabolites (Figure 5) in P. aeruginosa wound infections may impair bacterial fitness in vivo.
As in other Gram-negative bacteria, the core chemotaxis system of P. aeruginosa transduces signals from methyl-accepting chemotaxis proteins (MCPs), each of which is thought to respond to a distinct signal, to the flagellar motor, ultimately resulting in chemotaxis along a gradient [42]. We found by Tn-seq that the major aerotaxis receptor gene aer is a fitness determinant in burn wounds, suggesting a role for aerotaxis in burn wounds (Tables S5 and S8). However, the P. aeruginosa genome encodes at least two aerotaxis MCPs [43], and mutants lacking either aer alone or aer and aer2 together exhibited full virulence in single-strain burn wound infections (data not shown), suggesting that multiple MCPs can contribute to chemotaxis in acute wounds. Thus, genetic redundancy may mask the role of other genes in fitness in studies of single mutant strains such as ours. This weakness may be exacerbated in bacteria with large genomes that contain more paralogs, like P. aeruginosa [44]. In addition to the function of paralogs, genetic redundancy may also result from robustness in genetic interaction networks, which is more difficult to predict a priori [32]. The relative lack of chronic wound-specific single mutant phenotypes (Figure 6B) may be partially attributable to this redundancy. Therefore, systematic approaches to examine the phenotypes of double and triple mutants are needed to uncover the basis for and importance of polygenic traits in bacteria.
A second weakness inherent to pooled selection approaches such as Tn-seq is cross-complementation, in which the lack of a particular gene product in one strain is complemented by the production of that product by a neighboring strain. This is often thought of in the context of “public goods”, which are often equated with secreted products [45]. However, we noted that several genes presumed to be involved in the production of secreted products, such as the siderophores pyochelin and pyoverdine, are required of an individual strain in co-infection (Table S6). This suggests that some secreted products may confer a benefit on the secreting cell without fully transferring those benefits to neighboring cells. The extent to which cross-complementation affects phenotypes of other mutants lacking certain products thought to be cytoplasmic is unclear as well. For example, are metabolic precursors shared between strains, and does that affect our ability to identify essential anabolic pathways by Tn-seq (Figure 5)? Further study on the exact nature and molecular basis of public goods is required, and can help inform our understanding of community interactions both within and between species during infection.
By investigating the correlation between gene regulation and knockout fitness, we showed that, generally, P. aeruginosa gene regulation in wound infections is nonadaptive. As an opportunistic pathogen whose evolutionary trajectory is not thought to be shaped by its fitness in mammalian infections, it is not surprising that regulation of factors that contribute to fitness in wounds is not necessarily tied to signals or cues present in the infection environment. In longer-lasting infections where P. aeruginosa can evolve to be more fit in its environment, such as in the cystic fibrosis lung, adaptive changes in global gene expression have been observed over time [46]. This suggests that P. aeruginosa gene regulation can be better “tuned” by evolution to express in vivo fitness determinants. It would be interesting to explore expression-fitness correlation in “professional” pathogens, as it may be improved in organisms that are more adapted for growth in the human host.
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Institutional Animal Care and Use Committee of Texas Tech University Health Sciences Center (Protocol Numbers 07044 and 96020).
P. aeruginosa PAO1 transposon insertion mutants, including the ∼100,000 transposon mutant library and individual transposon mutants in faoA (strain PW6048) and cheR1 (strain PW6640), were obtained from Colin Manoil (University of Washington) [20], [47]. Transposon insertions were confirmed by PCR. For single-strain infection experiments, the parental PAO1 strain was used. The PAO1 strain used in RNA-seq experiments was obtained from Dennis Ohman (Virginia Commonwealth University). Growth in vitro was performed either in LB Miller broth, in morpholine-propanesulfonic acid (MOPS)-buffered minimal medium [48] with 20 mM succinate (hereafter referred to as MOPS-succinate), or in MOPS-buffered minimal medium with 0.2% oleic acid and 1% Brij-58 [37] (hereafter referred to as MOPS-oleate) shaking at 250 rpm at 37°C. In vitro cultures for Tn-seq analysis were grown as follows: frozen aliquots of the PAO1 transposon insertion library were washed twice with 1 mL MOPS-buffered media base, inoculated into 10 mL media at 106 CFU/mL and grown for approximately 9 generations (to ∼109 CFU/mL). In vitro cultures for RNA-seq analysis were grown overnight in the test media, diluted to an OD600 of 0.01, and grown to mid-logarithmic phase (OD600 = ∼0.5) before harvesting as described below.
Deletion constructs contain two 600–800 basepair (bp) fragments flanking the gene of interest in which the coding sequence of the gene of interest was replaced by the sequence 5′-GCGGCCGCC-3′ (preserving the native start and stop codons). This insert was cloned into plasmid pEXG2 [49] on a SacI-KpnI fragment, and these deletion alleles were introduced to strain PAO1 by allelic exchange to generate strains PAO1 ΔpurF, PAO1 ΔpabC, PAO1 ΔilvD, PAO1 ΔhisE, PAO1 ΔfaoA, and PAO1 ΔcheR1 as described [50]. Complementation plasmids were constructed by amplifying the coding sequence of the gene of interest (including the native start and stop codons) by PCR with Phusion Hot Start II DNA Polymerase (Thermo Scientific, Waltham, MA). The forward primer in these reactions had a 5′ tail of 5′-GCTATGACCATGATTACGAATTCNNNNNNNNTACAT-3′, and the reverse primer in these reactions had a 5′ tail of 5′-CATGCCTGCAGGTCGACTCTAGA-3′. PCR products and the plasmid pUCP18 (linearized by triple digestion with SacI, BamHI, and KpnI) were gel purified, and the PCR product was introduced to the plasmid backbone by Gibson assembly as described [51]. This generated a library of >300 plasmids in E. coli strain DH5a for each plasmid, and these plasmid libraries were prepared from E. coli. Then, these plasmid libraries were used to transform the appropriate mutant PAO1 derivative to be complemented by electroporation, and complementing plasmids were isolated as follows: (1) For plasmids pPurF and pPabC, the pPurF and pPabC candidate plasmid libraries were transformed into PAO1 ΔpurF and PAO1 ΔpabC, respectively, and complemented electroporants were isolated by plating on solid MOPS-succinate agar and restreaking colonies on solid MOPS-succinate agar for isolation. (2) For plasmid pFaoA, the pFaoA candidate plasmid library was transformed into PAO1 ΔfaoA, and complemented electroporants were isolated by plating on solid MOPS-oleate agar and restreaking colonies on solid MOPS-oleate agar for isolation. (3) For plasmid pCheR1, the pCheR1 candidate plasmid library was transformed into PAO1 ΔcheR1, and complemented electroporants were isolated by spotting the transformation mix on semisolid LB media containing 0.3% agar supplemented with 150 µg/mL carbenicillin and restreaking a region that had swam out from the initial spot for isolation on solid LB agar supplemented with 150 µg/mL carbenicillin. At least three individual complemented strains were tested as shown in Figure S3 for each deletion, with a representative strain shown for each. For the burn wound infections shown in Figure 5B, mid-logarithmic phase cells of the indicated PAO1 mutants were starved for 2 hours in MOPS buffer before inoculation as described below.
Murine burn wound infections were performed with adult female Swiss Webster mice essentially as described [11], with the following modifications. For Tn-seq experiments, 106 CFU of the PAO1 transposon mutant library was used as an inoculum, and wound tissue was harvested 24 hours post infection and stored in RNAlater (Qiagen) at room temperature for 24–48 hours, and subsequently at −20°C. For RNA-seq experiments, 105 CFU of wild-type PAO1 was used as an inoculum, and wound tissue was harvested 40 hours post infection as described above. For single strain infections, 102–103 CFU of the indicated strain was used as an inoculum, and animals were monitored for mortality daily for up to 7 days. Each experiment was performed at least twice with at least 5 animals per experimental group, and the average time of death for all animals is reported here.
Murine chronic wound infections were performed with non-diabetic adult female Swiss Webster mice essentially as described [23], with the following modifications. For Tn-seq experiments, 105 CFU of the PAO1 transposon mutant library was used as an inoculum, and wound tissue was harvested 3 days post infection and stored in RNAlater as described above. For RNA-seq experiments, 105 CFU of wild-type PAO1 was used as an inoculum, and wound tissue was harvested 4 days post infection as described above. For single strain infections, 105 CFU of the indicated strain was used as an inoculum, wound tissue was harvested 4 days post infection, and CFU/g tissue was determined by plating. Each experiment was performed at least twice with at least 5 animals per experimental group.
To prepare DNA for Tn-seq analysis, ∼100 mg sections of wound tissue or cell pellets were resuspended in 1 mL 1× Buffer A [52] +0.1% SDS, homogenized in a Mini-Beadbeater (Biospec) in 2 mL vials preloaded with Lysing Matrix B (MP Biomedicals) 3–5 times for 1 minute each, resting on ice in between each pulse. Proteinase K was then added to 1 mg/mL, and samples were incubated overnight at 50°C. Samples were then homogenized once more as above, separate sections from the same wound were pooled, and samples were extracted with an equal volume of 25∶24∶1 phenol∶chloroform∶isoamyl alcohol pH 8.0. DNA was ethanol precipitated from the aqueous phase, and was resuspended in 200–500 µL water after extensive pellet washing with 75% ethanol. Tn-seq sequencing libraries were prepared by a modified version of INSeq [16]. DNA was sheared to approximately 500 bp either in a S220 Focused-ultrasonicator (Covaris), a Hydroshear Sonicator (Digilab), or a Q880R Sonicator (Qsonica). 500 ng (in vitro samples) to 1 µg (murine wound samples) of DNA was used as template in two linear PCR reactions using the 5′ biotinylated oligonucleotide primer Kbio-T8OE-Out2 (5′-ATAAGAATGCGGCCGCGGGATGGAAAACGGGAAAGGTTCCGTCCAGGACGCTACTTGTG-3′) and performed with KOD Hot Start DNA Polymerase (EMD Biosciences) with the following program: 95°C, 5′; 99×(95°C, 30″; 68°C, 1′); 68°C, 10′. Kbio-T8OE-Out2 is specific to the “OE” end of transposon T8 [47], with two key features: (1) A NotI site is contained towards the 5′ end of the primer for NotI cleavage-mediated elution (see below), and (2) the primer sequence ends 12 bp from the end of transposon T8, leaving that additional 12 bp sequence for additional sequence quality control. Biotinylated linear PCR products were bound to Streptavidin-coupled Dynabeads (Invitrogen) and a second strand was synthesized as described [52], except that the oligonucleotide used to prime second strand synthesis had the sequence 5′-NSNSNSNSNS-3′. Double-stranded DNA was eluted from the Dynabeads by digesting with NotI-HF (New England Biolabs), and this DNA was prepared for Illumina sequencing with the NEBNext DNA Library Prep Master Mix Set For Illumina (New England Biolabs) according to the manufacturer's protocol. Libraries were sequenced at the Genome Sequencing and Analysis Facility at the University of Texas at Austin on a HiSeq 2000 (Illumina) on a 2×100 paired end run. All sequences are deposited with the National Center for Biotechnology Information Sequence Read Archive under Accession Number SRP033652.
To prepare RNA for RNA-seq sequencing libraries, cell pellets or ∼100 mg sections of wound tissue were homogenized 2–4 times in a Mini-Beadbeater in 1 mL RNA Bee (Tel-Test) in 2 mL vials with Lysing Matrix B, and aqueous phases of extractions from different sections of the same wound were pooled before continuing with the extraction. RNA was then prepared according to the RNA Bee manufacturer's protocol. DNA contamination was then removed by DNAse digestion as described [31]. rRNA integrity was then verified by agarose gel electrophoresis. Starting with 5 µg of total RNA, bacterial rRNA was depleted from all samples with the Ribo-Zero rRNA Removal Kit (Bacteria) (Epicentre), and then mammalian rRNA was depleted from all samples with the Ribo-Zero Gold Kit (Human/Mouse/Rat) (Epicentre) according to the manufacturer's protocol. Remaining RNA was fragmented with the NEBNext Magnesium RNA Fragmentation Module (New England Biolabs) according to the manufacturer's protocol with a 5′ incubation time. Illumina sequencing libraries were then prepared with the NEBNext Multiplex Small RNA Library Prep Set for Illumina (New England Biolabs) according to the manufacturer's protocol. Finished sequencing libraries were size selected on a polyacrylamide gel for fragments ∼140–300 bp. Libraries were sequenced at the Genome Sequencing and Analysis Facility at the University of Texas at Austin on a HiSeq 2000 (Illumina) on either a 1×100 single end or a 2×100 paired end run. All sequences are deposited with the National Center for Biotechnology Information Sequence Read Archive under Accession Number SRP033652.
RNA-seq reads were analyzed and differential gene expression was determined with the R package DESeq [53] largely as described [31] with the following modifications: the P. aeruginosa PAO1 genome (GenBank accession no. AE004091.2) was used for read alignment, and COGs used for enrichment analyses were obtained from the Pseudomonas Genome Database [54]. P values given for differentially expressed genes are adjusted for multiple testing. Enrichment of differentially regulated genes in a given COG category was determined by comparing the prevalence of up- or down-regulated genes assigned to a specific COG category to the prevalence of genes in the entire genome assigned to that COG category using Fisher's exact test.
Tn-seq reads were parsed, mapped, and tallied, and differential mutant abundance was determined using a custom Unix, Perl, and R pipeline (available at http://github.com/khturner/Tn-seq). First, reads containing the 12-bp transposon T8 end sequence 5′-TATAAGAGTCAG-3′ were identified (allowing for 1 mismatch or indel) using fqgrep (http://github.com/indraniel/fqgrep), and sequence up to and including the transposon end sequence were removed with the modified Perl script called “trimmer”. The remaining sequence was then mapped to the P. aeruginosa PAO1 genome (GenBank accession no. AE004091.2) using bowtie version 2.10 [55], and individual insertion sites and the number of reads originating from them were tallied with the Unix script “TnSeq.sh”. The data analysis method, contained in the Unix script “TnSeqAnalysis.sh” and the R script “TnSeqDESeq.R”, was inspired largely by the ESSENTIALS software package [56], and is described in detail below. After removing the 50 most abundant insertion sites from analysis to correct for amplification bias, insertion location vs. number of reads data was smoothed using locally weighted LOESS smoothing using a smoothing parameter (α) of 1 to correct for genomic position-dependent effects on apparent mutant abundance. Then, this data was normalized using DESeq [53] with default parameters. For gene knockout abundance analysis, a modified annotation was generated with the 3′ 10% of every gene removed (to ignore insertions that may not abolish gene function). Then, the smoothed, normalized number of transposon-derived reads per gene and the number of insertions mapping to each gene was tallied using this modified annotation in R. The number of transposon-derived reads was incremented by one for each gene to avoid dividing by zero when comparing to a condition with no mutants detected. Finally, differential mutant abundance was calculated using a negative binomial test with DESeq, artificially setting normalization factors to 1 (because the data was normalized per insertion).
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10.1371/journal.pcbi.1002233 | How Landscape Heterogeneity Frames Optimal Diffusivity in Searching Processes | Theoretical and empirical investigations of search strategies typically have failed to distinguish the distinct roles played by density versus patchiness of resources. It is well known that motility and diffusivity of organisms often increase in environments with low density of resources, but thus far there has been little progress in understanding the specific role of landscape heterogeneity and disorder on random, non-oriented motility. Here we address the general question of how the landscape heterogeneity affects the efficiency of encounter interactions under global constant density of scarce resources. We unveil the key mechanism coupling the landscape structure with optimal search diffusivity. In particular, our main result leads to an empirically testable prediction: enhanced diffusivity (including superdiffusive searches), with shift in the diffusion exponent, favors the success of target encounters in heterogeneous landscapes.
| Understanding how animals search for food is crucial for animal ecology. Although much has been learned about the main aspects of the so-called foraging problem, some important questions still remain unanswered. In this work we address the issue of the relevance of heterogeneity in the resources distribution to efficient animal foraging behavior. Our results unveil the key mechanism coupling landscape heterogeneity dynamics with optimal search diffusivity. Indeed, although the effect of (global) resource density on animal foraging behavior is well documented, much less has been known about how spatiotemporal landscape heterogeneity affects the efficiency of encounter interactions by foraging organisms. In this sense, we propose a new empirically testable theoretical prediction on the dynamics (e.g. diffusion exponent) of foraging organisms in heterogeneous environments. We also show that the conditions in which Lévy strategies are optimal are much broader than previously considered.
| The random search problem has lately received a great deal of attention [1], [2]. This is partly due to its broad interdisciplinary range of applications, which include, e.g., enhanced diffusion of regulatory proteins while “searching” for specific DNA spots [3], [4] and the finding of binding sites on transmembrane proteins by neurotransmitters in the brain [5]. Recently, this problem has also found interesting connections with human mobility and related topics [6]–[9].
A classical context in which the random search problem has been applied in the last four decades is animal foraging [1], [2], [10]–[27], with the searcher (i.e. forager) typically represented by an animal species in quest of target sites (prey, food, other individuals, shelter, etc.) in a search landscape.
Among the most studied random walk models proposed as plausible search strategies, we cite correlated random walks [12], [28], [29], Lévy flights and walks [13]–[17], [19], [20], [24], [25], [27], [30]–[39], intermittent walks [40]–[46], and composite Brownian walks [47], [48]. In particular, Lévy random searchers, with probability distribution of step lengths , for , have successfully explained [34] the emergence of optimal searches in landscapes with randomly and scarcely distributed target sites. On the other hand, when resources are plentiful Lévy strategies are unnecessary [34], and efficient Brownian optimal searches may arise with, e.g., a Poisson-like exponential distribution [24], [25]. Lévy flights and walks have been also shown to be relevant in several other contexts [1], such as in proteins searching for specific DNA sites [49], in which the optimal Lévy mechanism emerges directly from the underlying physics of the problem (polymer scaling theory in three dimensions).
In the regime of low density of resources of the random search problem, two limiting situations have been extensively considered [34]: (i) non-destructive searches, in which the searcher always departs from a position at the vicinity of the last target found with unrestricted revisits; and (ii) destructive searches, in which, once found, the target becomes inaccessible to future visits, so that the starting point of the searcher is, on average, faraway from all targets. In the former case the maximum efficiency is achieved [34] for (a “compromise” superdiffusive solution), whereas in the latter (ballistic motion). It is important to observe, nevertheless, that by varying the searcher's starting point [44], [48] or the degree of target revisitability or temporal regeneration [50], [51], intermediate values of the optimal Lévy exponent arise, .
It is also interesting to comment on the effect of an energy cost function on the efficiency of search strategies. Indeed, as reported in [50], [51], the range of -values associated with search paths in which the net energy gain (the balance between the energy income due to the finding of targets and the energy cost of the search process itself) remains always positive is actually limited. In such a case, low values of giving rise to very large search jumps might not be acceptable, since they imply a high energy cost, with intermediate values of emerging as the best strategy. In addition, we also refer to the study reported in [52] in which exact results for the first passage time and leapover statistics of Lévy flights are presented. In this case, the targets might not be always detected, being thus overshoot by jumps whose length distribution displays infinite variance.
Despite the intense progress in the fields of random searches and animal foraging, a number of relevant issues still remain open. A particularly important one is to understand the coupling mechanism between landscape spatiotemporal dynamics and efficient search motility, when resources are scarce and environmental information is limited. In this sense, the pervasiveness of different animal search strategies is expected to strongly depend on a few but essential features of actual landscapes. For instance, targets distributions in realistic search processes usually present heterogeneous properties through time and space, such as diverse degrees of temporal regeneration and spatial aggregation [26], [53], [54]. Although the effect of (global) resource density on animal foraging behavior is well documented [25], [26], [37], [42], [55], much less is known about how spatiotemporal landscape heterogeneity dynamics affects the target revisitability and/or searcher-to-targets distances, both known to be key properties to optimize perception-limited searches [44], [48], [50], [51]. Thus, a mechanistic understanding of how and which landscape features are related to search efficiency should be a relevant step towards a comprehensive view of animal foraging behavior.
Here we address the question of how the landscape heterogeneity influences the encounter success and search efficiency under conditions of constant (global) density of scarce resources. We develop a random search model in which diverse degrees of inhomogeneities are considered by introducing fluctuations in the starting distances to target sites. We thus ask what happens to the optimal search strategy in an heterogeneous landscape, as the searcher's initial distances to the targets fluctuate along the search. We answer to this query qualitatively for the general case and quantitatively for Lévy random searches in particular, in the constant density regime of scarce resources. In patchy or aggregated landscapes, we find that enhanced diffusivity (including superdiffusive strategies) favors the encounter of targets and the success of foraging. Eventually, for strong enough fluctuations in the starting distances to nearby targets a crossover to ballistic strategies might emerge.
These predictions are empirically testable through feasible experiments which investigate the dynamics (e.g. diffusion exponent) of foraging organisms in specially designed low-density environments of controlled heterogeneity.
We consider a random search model in which diverse degrees of landscape heterogeneity are taken into account by introducing fluctuations in the starting distances to target sites in a one-dimensional (1D) search space, with absorbing boundaries separated by the distance . Every time an encounter occurs the search resets and restarts over again. Thus, the overall search trajectory can be viewed as the concatenated sum of partial paths between consecutive encounters. The targets' positions are fixed – targets are in fact the boundaries of the system. Fluctuations in the starting distances to the targets are introduced by sampling the searcher's departing position after each encounter from a probability density function (pdf) of initial positions . Importantly, also implies a distribution of starting (a)symmetry conditions regarding the relative distances between the searcher and the boundary targets.
This approach allows the typification of landscapes that, on average, depress or boost the presence of nearby targets in the search process. Diverse degrees of landscape heterogeneity can thus be achieved through suitable choices of .
For example, a pdf providing a distribution of nearly symmetric conditions can be assigned to a landscape with a high degree of homogeneity in the spatial arrangement of targets. In this sense, the mentioned destructive search represents the fully symmetric limiting situation, with the searcher's starting location always equidistant from all boundary targets. On the other hand, a distribution which generates a set of asymmetric conditions is related to a patchy or aggregated landscape. Indeed, in a patchy landscape it is likely that a search process starts with an asymmetric situation in which the distances to the nearest and farthest targets are very dissimilar. Analogously, the non-destructive search corresponds to the highest asymmetric case, in which at every starting search the distance to the closest (farthest) target is minimum (maximum). Finally, a pdf giving rise to an heterogeneous set of initial conditions (combining symmetric and asymmetric situations) can be associated with heterogeneous landscapes of structure in between the homogeneous and patchy cases.
More specifically, the limiting case corresponding to the mentioned destructive search can be described by the pdf with fully symmetric initial condition,(1)where denotes Dirac -function. This means that every destructive search starts exactly at half distance from the boundary targets. In this context, it is possible to introduce fluctuations in by considering, e.g., a Poisson-like pdf [56] exponentially decaying with the distance to the point at the center of the search space, :(2)where , with the “radius of vision” of the searcher (see below), the normalization constant, and due to the symmetry of the search space.
On the other hand, the highest asymmetric non-destructive limiting case is represented by(3)so that every search starts from the point of minimum distance in which the nearest target is undetectable, . Similarly, fluctuations in regarding this case can be introduced by considering a Poisson-like pdf decreasing with respect to the point :(4)where , is a normalization constant, and . In Eqs. (2) and (4), the parameter controls the range and magnitude of the fluctuations. Actually, the smaller the value of , the less disperse are the fluctuations around and in Eqs. (2) and (4), respectively.
When looking for boundary target sites in a 1D interval, the searcher's step lengths are taken from a general pdf . At each step the probabilities to move to the right or to the left are equal. We define the “radius of vision” as the distance below which a target becomes detectable by the searcher. Thus, if the targets are located at the boundary positions and , the search keeps on as long as the walker's position lies in the range . Here we are interested in searches in environments scarce in targets, i.e. for . In this case, leaving the present position to look randomly for targets should occur much more frequently than simply detecting a site in the close vicinity, a regime favored when targets are plentiful.
Suppose initially that, as a target is found, the search always restarts from the same position in the interval . As discussed, the highest asymmetric (non-destructive) and fully symmetric (destructive) cases correspond respectively to setting (or , due to symmetry) and . After the encounter of a statistically large number of targets, the efficiency of the search, , is evaluated [34] as the ratio of the number of sites found to the total distance traversed by the searcher. Since this distance is equal to the product of the number of encounters and the average distance traveled between consecutive findings, , then .
Consider now that, instead of always departing from the same location after an encounter, the searcher can restart from any initial position in the range , chosen from a pdf . The fluctuating values of imply a distribution of values. Since searches starting at are statistically indistinguishable from searches starting at (in both cases the closest and farthest targets are at distances and from the starting location), the symmetry of the search space regarding the position implies . The average efficiency thus becomes(5)where due to the above mentioned symmetry.
To study the effect of fluctuations in the starting distances of a searcher, we note that the exact average distance in Eq. (5) can be formally expressed [57], [58] as(6)where the integral operator acts as follows:(7)and and are, respectively, the unity operator and the average length of a single step starting at . Specifically, we can write for a general pdf (8)The second and third integrals above represent steps to the left and to the right which are not truncated by the encounter of a target site at the boundaries; the first and last ones concern steps truncated by the detection of the targets at and , respectively (what actually happens at and , due to the searcher's “radius of vision”).
Despite the formal aspect of Eq. (6), the numerical calculation of with a given can be performed by discretizing [57], [58] the search interval , i.e. , with integer and . In this procedure, integrals are approximated by summations, and so on.
In the next section, we use this model to study the role of landscape heterogeneity on the search efficiency and diffusivity. The presented analysis is qualitative for the general case and quantitative for Lévy random searches.
Consider, first, the limiting case with no fluctuation in the starting distances. The underlying mechanisms of efficient searches with asymmetric and symmetric initial conditions are fundamentally distinct. In the fully symmetric (destructive) case the closest sites are located at equal initial distances from the searcher in the low-density regime. Thus, for a general distribution of step lengths characterized by a set of parameters , the one that leads to the largest efficiency must present the fastest possible diffusivity in order to reach these faraway targets. For example, in the case of the single-parameter power-law pdf , is maximized with ballistic strategy [34]: .
In contrast, in the highest asymmetric (non-destructive) situation or the most efficient search must compromise between performing large steps to access the farthest site and sweeping in detail at the vicinity of the closest site. In the parameter space, this solution, related to a set , displays intermediate diffusivity between normal (Brownian) and the fastest possible one, assigned to the set . In the same example, this implies [34] , in contrast with Brownian diffusion resulting from (see Figs. 1 and 2).
When the starting positions are not fixed, heterogeneous landscapes with stronger fluctuations in the distances to nearby targets lead to optimal search strategies with faster dynamics (enhanced diffusivity). The arguments giving rise to this general conclusion are as follows.
On one hand, sampling starting positions around corresponds to introduce fluctuations in the initial distances to the faraway boundary targets in the low-density regime, as discussed. In this case, we expect that starting positions far away from are chosen with smaller probabilities. This implies a decreasing pdf from to , such as found in Eq. (2). Consequently, both and increase monotonically from to (Fig. 3). The most relevant contribution to the product in Eq. (5) thus comes from positions near . No qualitative difference is expected to occur between and , indicating that searches with fully symmetric (fixed) initial condition and those comprising fluctuations in the faraway targets present similar optimal dynamics, related to the set , namely ballistic, if supported by .
On the other hand, in the asymmetric case fluctuations in the starting distances to the nearby boundary target can be introduced by a decreasing pdf from to , such as in Eq. (4). Therefore, as increases and diminishes, the initial position associated with the most relevant contribution to in Eq. (5) crosses over to somewhere in between and . Indeed, the slower decays, the larger such position becomes. As a consequence, the asymmetric optimal set in the absence of fluctuations might give away the role of the most efficient search strategy to some other intermediate compromising solution , which is closer to the symmetric set in the parameter space and, therefore, presents enhanced dynamics (e.g., a larger diffusion exponent). Eventually, for some proper choice of encompassing strong fluctuations with large weight near , the justification for such compromising solution might even fade away, so that , with strategies of fastest possible diffusivity becoming optimal. In this uttermost case fluctuations lose their local character, and a crossover from superdiffusive to ballistic search behavior may take place.
We observe that the above rationale should also apply, at least qualitatively, to searches in higher-dimensional spaces. In this situation, as the search path can be approximated by a sequence of nearly rectilinear moves, the general qualitative features of 1D random searches usually hold true in higher dimensions [34], [39]. Nevertheless, the finding of targets in 2D and 3D occurs with considerably lower probability, since the extra spatial directions yield a larger exploration space, resulting in lower encounter rates and search efficiencies. The impact of target spatial fluctuations on high-dimensional search strategies should also reduce [39]. We can thus conclude that, beyond representing the realistic exploration space of some animal species [27], the 1D analysis presented here is also useful in establishing upper limits for the influence of landscape heterogeneities in random searches. Therefore, the understanding of animal foraging behavior in 2D and 3D, as well as other practical realizations of the random search problem, might also benefit from the present results.
We next apply the above arguments, valid for a general pdf , to the particular case of Lévy random searchers.
We now specifically consider a random searcher with step lengths chosen from the pdf(9)and otherwise, with representing a lower cutoff length. We assign a “negative step length” if the searcher moves to the left and take for simplicity. Equation (9) for corresponds to the long-range asymptotical limit of Lévy -stable distributions with index , characterized by the existence of rare, large steps alternating between sequences of many short-length jumps [13], [14], [16]. As its second moment diverges the central limit theorem does not hold, and anomalous (superdiffusive) dynamics governed by the generalized central limit theorem takes place. Indeed, Lévy random walks and flights are related to a Hurst exponent [13], [14] , with ballistic dynamics in the case , whereas diffusive behavior () emerges for . For pdf (9) is not normalizable and corresponds to the Cauchy distribution.
The search path eventually comprises truncated steps due to the encounter of targets, so that the power-law decay of Eq. (9) cannot extend all the way to infinity, thus implying an effective truncated Lévy distribution [59]. In spite of this, in the regime the search should retain the most relevant properties of a non-truncated Lévy walk to a considerable extent. Indeed, the ratio of the number of truncated steps to the non-truncated ones, essentially equal to the inverse of the average number of steps performed between consecutive targets, is given by and , for , in the highest asymmetric (non-destructive) and fully symmetric (destructive) cases, respectively [34], [57], [58]. Thus, except for ballistic walks, one has that if . Further, the justification for truncated distributions also arises naturally in the context of animal foraging since directional persistence due to scanning is likely to be broken at the finding of targets [19]. Indeed, infinitely long rectilinear paths are not allowed for searching organisms.
By inserting Eq. (9) into Eqs. (6) and (7), we numerically calculate through the discretization of the search space (see previous section). Results are displayed in solid lines in Fig. 3. Notice first the presence of the symmetry discussed above. In the absence of fluctuations in the initial distances, the existence of a maximum efficiency with an intermediate exponent (see Fig. 2) for searches starting at fixed (highest asymmetric condition) can be understood as follows: strategies with might access the farthest target at in a ballistic way after a small number of very large steps, implying a large and low efficiency; in contrast, searches with tend on average to find the closest site at after a great number of small steps, also giving rise to a large ; the efficient compromise between these two trends, leading to the lowest and maximum , is therefore represented by a strategy with an intermediate value, .
In the presence of fluctuations in the starting distances, the integral (5) must be evaluated. Although the explicit expression for , Eq. (6), is not known up to the present, a multiple regression can be successfully performed,(10)as indicated by the nice adjustment shown in Fig. 3, obtained with and . Thus, the integral (5) can be done using Eqs. (2), (4) and (10), with results displayed in Figs. 1 and 2 for several values of the parameter .
By considering fluctuations in the starting distances to faraway targets through Eq. (2), we notice in Fig. 1 that the efficiency is qualitatively similar to that of the fully symmetric condition, Eq. (1), in agreement with the general arguments of the previous section. Indeed, in both cases the maximum efficiency is achieved as . For the presence of fluctuations only slightly improve the efficiency. These results indicate that ballistic strategies remain robust to fluctuations in the distribution of faraway targets.
On the other hand, fluctuations in the starting distances to nearby targets, Eq. (4), are shown in Fig. 2 to decrease considerably the search efficiency, in comparison to the highest asymmetric case, Eq. (3). In this regime, since stronger fluctuations increase the weight of starting positions far from the target at , the compromising optimal Lévy strategy displays enhanced superdiffusion, observed in the location of the maximum efficiency in Fig. 2, which shifts from , for the delta pdf and Eq. (4) with small , towards , for larger (slower decaying ). Indeed, both the pdf of Eq. (4) with a vanishing and Eq. (3) are very acute at . It is also worth noticing that a lower is related to a larger Hurst exponent [1], [13], [14], and therefore to a larger diffusion exponent, as argued in the previous section.
As even larger values of are considered, fluctuations in the starting distances to the nearby target become non-local, and Eq. (4) approaches the limiting case of the uniform distribution, (see Fig. 2). In this situation, search paths departing from distinct are equally weighted in Eq. (5), so that the dominant contribution to the integral (and to the average efficiency as well) comes from search walks starting at positions near . Since for these walks the most efficient strategy is ballistic, a crossover from superdiffusive to ballistic optimal searches emerges, induced by such strong fluctuations. Consequently, the efficiency curves for very large (Fig. 2) are remarkably similar to that of the fully symmetric case (Fig. 1).
We can quantify this crossover shift in by defining a function that identifies the location in the -axis of the maximum in the efficiency , for each curve in Fig. 2 with fixed . As discussed, eventually a compromising solution with cannot be achieved, and an efficiency function monotonically decreasing with increasing arises for . In this sense, the value for which such crossover occurs marks the onset of a regime dominated by ballistic optimal search strategies.
The value of for each can be determined from the condition , so that, by considering Eqs. (4), (5) and (10),(11)with . Solutions are displayed in Fig. 4 and also in Fig. 2 as empty symbols, locating the maximum of each efficiency curve. In addition, the crossover value can be determined through . In the case of pdf (4), we obtain (Fig. 4) for and (regime ).
We also note that the scale-dependent interplay between the target density and the range of fluctuations implies a value of which is a function of . For instance, a larger (i.e., a lower target density) leads to a larger and a broader regime in which superdiffusive Lévy searchers are optimal. Nevertheless, the above qualitative picture should still hold as long as low target densities are considered.
Moreover, since ballistic strategies lose efficiency in higher dimensional spaces [44], it might be possible that in 2D and 3D the crossover to ballistic dynamics becomes considerably limited. In spite of this, enhanced superdiffusive searches, with , should still conceivably emerge due to fluctuations in higher-dimensional heterogeneous landscapes.
From these results we conclude that, in the presence of Poissonian-distributed fluctuating starting distances with , Lévy search strategies with faster (enhanced) superdiffusive properties, i.e. , represent optimal compromising solutions. In this sense, as local fluctuations in nearby targets give rise to landscape heterogeneity, Lévy searches with enhanced superdiffusive dynamics actually maximize the search efficiency in aggregate and patchy environments. On the other hand, for strong enough fluctuations with , a crossover to the ballistic strategy emerges in order to access efficiently the faraway region where targets are distributed. These findings are in full agreement with the general considerations discussed in the previous section.
At last, to further test the robustness of these results we have also considered the power-law distribution of starting positions, , with , , and as the normalization constant. Differently from distributions (2) and (4), the long tail in this pdf confers self-affine scale-invariant properties over a long spatial range in the low-density regime, . The evidence of scale-free distributions of targets has been reported in the context of animal foraging, e.g. in [24]. In the present analysis we have essentially verified all the general features previously discussed. In particular, all strategies with are ballistic, with compromising superdiffusive solutions arising for .
The effect of limited resources on animal motility is well documented in ecology. Scarcity coming from resource competition is known to induce higher dispersal rates [60], [61] and larger home ranges [62], [63]. Habitat fragmentation also reshapes dispersal kernels, often increasing dispersal distances [64]. In the context of foraging behavior, the role of (global) resource density has been considerably investigated, with strong evidence pointing to shifts from Brownian to superdiffusive search strategies as animals move from high to low productive areas. Examples range from microorganisms [37] to large marine predators [25], [26], [55]. In contrast, much less is known about the influence of heterogeneity in the resource distribution on the foraging success.
Most theoretical efforts relying on core random search theory have by far provided only a limited approach to the issue of optimal searches, since they mostly assume oversimplified landscapes [2], [40]. Nonetheless, a few simulation studies have addressed the effect of environmental heterogeneity, including target motion, on encounter success for different searcher types [19], [24], [39], [65], [66]. These works give support to the hypothesis that search processes are linked to target distributions and dynamics, thus agreeing with our results in that the optimal strategy can actually change, e.g. from superdiffusive to ballistic motion, depending on the landscape heterogeneity. In a more recent example, it was shown [65] that Lévy optimal foragers can be evolutionarily optimal in heterogeneous environments, for suitable details of the simulations and definition of efficiency. Our work advances on this topic by pinpointing a very general mechanism which seems essential to understand previous simulation results [19], [24], [39], [65].
By comprehensively describing the key mechanism coupling landscape dynamics and search diffusivity, we have shown that statistical fluctuations in the set of initial search conditions play a crucial role for determining which strategy is optimal. The presence of such fluctuations sets a clear basis for the non-universality of search patterns, and shows that enhanced diffusivity (including superdiffuse strategies) favors random encounter success in patchy and aggregated landscapes. As a consequence, the foraging conditions in which Lévy strategies appear as optimal are much broader than previously suggested [40], [44]–[46].
In dynamic and complex landscapes with scarcity of resources neither ballistic nor Lévy strategies should be considered as universal (see, e.g., [45], [46]), since realistic fluctuations in the targets distribution may induce switches between these two regimes. This observation has been confirmed by recent empirical results [25], [27], showing that foragers in the wild do not exhibit movement patterns that can be approximated, at all times, by Lévy, ballistic or exponential models. Nevertheless, the relevant finding is that in the low-density regime superdiffusive Lévy strategies remain as the optimal solution in a broad range of heterogeneous landscape conditions, with the optimal exponent dependent on specific environment properties. Crossovers between superdiffusive and ballistic strategies may also emerge depending on whether strong target spatial fluctuations are local or not, and if they depress or boost the presence of nearby targets. For instance, recent data on a species of jellyfish have reported [27] on Lévy flight foraging strategies with optimal index as low as . Moreover, studies on marine predators have also found [24] small values as . Such rather fast, enhanced superdiffusion (with respect to ) suggests the occurrence of foraging activity in a highly dynamic and heterogeneous landscape, as it is clearly the case for marine prey landscapes [25], [26], [67].
In the present work, the question of how the landscape heterogeneity affects the search efficiency in encounter interactions is addressed under conditions of constant global density of scarce resources. In such conditions we predict that efficient strategies with larger diffusion exponents (including superdiffusive ones) should arise, as heterogeneous environments with wider distributions of starting distances between the foraging organism and the nearby targets are considered. Similarly to what occurs in homogeneous landscapes [42], we do not expect density fluctuations in the scarcity regime to modify optimal Lévy solutions per se, but only to the extent that fluctuations in density modify the initial searcher-to-targets distances. In other words, provided that the asymmetry in the searcher-to-targets distances is maintained as density changes, optimal Lévy strategies should result insensitive to target density fluctuations. This means that for a Lévy searcher is less important to have advanced knowledge of the density than of the relative positions of the targets. Clearly, robustness to changes in environmental parameters (i.e. density) should be considered as an advantage in non-informed optimal search solutions [42].
If we acknowledge the presence of selective pressures responsible for the evolution and maintenance of non-oriented motility in organisms [68], our results lead to a neat empirically testable prediction: patchy and heterogeneous landscapes should promote the emergence of enhanced diffusivity and compromising optimal Lévy strategies. Even though the empirical inference of large scale movement patterns from heterogeneity properties of the landscape is a difficult task [26], specifically designed and controlled large scale experiments are feasible in the laboratory [68]–[71] and even in the field [54].
We hope the present study might shed light on unsettled issues related to the efficiency and associated dynamics of organisms performing random searches. Besides the well documented dependence of search efficiency on resource density [25], [26], [34], [37], [55], our results suggest another relevant aspect of non-universal random search behavior: landscape heterogeneity frames optimal diffusivity.
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10.1371/journal.pbio.1000488 | Stochastic E2F Activation and Reconciliation of Phenomenological Cell-Cycle Models | The transition of the mammalian cell from quiescence to proliferation is a highly variable process. Over the last four decades, two lines of apparently contradictory, phenomenological models have been proposed to account for such temporal variability. These include various forms of the transition probability (TP) model and the growth control (GC) model, which lack mechanistic details. The GC model was further proposed as an alternative explanation for the concept of the restriction point, which we recently demonstrated as being controlled by a bistable Rb-E2F switch. Here, through a combination of modeling and experiments, we show that these different lines of models in essence reflect different aspects of stochastic dynamics in cell cycle entry. In particular, we show that the variable activation of E2F can be described by stochastic activation of the bistable Rb-E2F switch, which in turn may account for the temporal variability in cell cycle entry. Moreover, we show that temporal dynamics of E2F activation can be recast into the frameworks of both the TP model and the GC model via parameter mapping. This mapping suggests that the two lines of phenomenological models can be reconciled through the stochastic dynamics of the Rb-E2F switch. It also suggests a potential utility of the TP or GC models in defining concise, quantitative phenotypes of cell physiology. This may have implications in classifying cell types or states.
| Mammalian cells enter the division cycle in response to appropriate growth signals. For each cell, the decision to do so is critically dependent on the interplay between environmental cues and the internal state of the cell and is influenced by random fluctuations in cellular processes. Indeed, experimental evidence indicates that cell cycle entry is highly variable from cell to cell, even within a clonal population. To account for such variability, a number of phenomenological models have been previously proposed. These models primarily fall into two types depending on their fundamental assumptions on the origin of the variability. “Transition probability” models presume that variability in cell cycle entry originates from the fact that entry in each individual cell is random but also governed by a fixed probability. In contrast, “growth-controlled” models assume that the growth rates across a population are variable and result in cells that are out of phase developmentally. While both kinds of models provide a good fit to experimental data, their lack of mechanistic details limits their predictive power and has led to unresolved debate between their practitioners. In this study, we developed a mechanistically based stochastic model of the temporal dynamics of activation of the E2F transcription factor, which is used here as a marker of the transition of cells from quiescence to active cell cycling. Using this model, we show that “transition probability” and “growth-controlled” models can be reconciled by incorporation of a small number of basic cellular parameters related to protein synthesis and turnover, protein modification, stochasticity, and the like. Essentially our work shows that each kind of phenomenological model holds true for describing a particular aspect of the cell cycle transition. We suggest that incorporation of basic cellular parameters in this manner into phenomenological models may constitute a broadly applicable approach to defining concise, quantitative phenotypes of cell physiology.
| Cell-to-cell variability in the timing of cell-fate commitment is widely observed in biological settings [1]–[4]. In particular, the variable timing of transition from the quiescent to the proliferative state is a well-documented phenomenon [5]–[8]. In a population of proliferating cells, such variability is reflected in the partitioning of the population into subpopulations at various phases of the cell cycle. This phenomenon is observed even in a population of isogenic cells that have been synchronized by serum starvation. Upon growth stimulation, cells reenter the cell cycle from quiescence and undergo the G1/S transition, but not all cells in the population proceed at the same rate. This rate also differs among different cell types [9],[10] and can be modulated by external conditions [11].
To account for the variable transition timing in cell cycle progression, two major types of models have been proposed: the transition probability (TP) models [11]–[15] and the growth-controlled (GC) models [16]–[18]. The TP models attributed temporal variability to random state transitions through different phases of the cell cycle. One of the earliest TP models was proposed to account for the inter-mitotic variability by assuming a single random transition from a non-proliferative A-state to a proliferative B-phase [15]. It was subsequently extended to account for the timing variability in cell cycle reentry starting from quiescent (G0) cells [11],[14]. In this case, the exponential drop in the fraction of G0 cells over time was suggested to indicate a probabilistic nature of the transition. The original model and its subsequent variants have provided excellent fits to various types of experimental data [11]–[15]. However, a major criticism of the TP models is that the transition probability from the A-state was assumed to be time-invariant, despite uneven cell division at mitosis and obvious cell growth or metabolism through the cell cycle [19]. As an alternative, the GC models proposed that the observed temporal variability arises from growth rate heterogeneity within a cell population, rather than random state transitions. Remarkably, this line of models has been able to provide equally good fits to various experimental data [16],[20]. Integrating these two lines of thinking, hybrid models proposed cell-size control and random transitions as regulatory elements for progression to cell division [21],[22]. However, understanding of the underlying mechanisms for cell-size control and random transitions was limited at the time. Consequently, although they provided excellent fits to experimental data, these models remain descriptive to date.
There has been an active debate between these two lines of thinking since their initial propositions. While never fully resolved, the debate gradually faded as the focus in the field of cell cycle studies moved to identifying the dynamical basis for various cell cycle regulations, including the restriction point (R-point) [23], which we have shown to be controlled by a bistable Rb-E2F switch [7]. We also showed that activation of this switch is correlated with the cell's reentry from quiescence into the cell cycle. Interestingly, cell cycle reentry was explored by both the TP and GC models, which were originally developed to describe actively growing cells. For example, the TP models ascribe quiescence and proliferation to low and high transition probabilities, respectively [11],[14]. In addition, the GC models have recently been proposed as an alternative explanation for the “R-point” [18].
The temporal variability described by the GC and TP models is based on the distribution of inter-mitotic times and may differ from temporal variability in E2F activation from quiescence. However, we suggest that the stochastic Rb-E2F model embodies the concepts assumed in the TP models and the GC models. Our model predictions and experiments suggest that stochastic activation of E2F can account for temporal variability in cell cycle entry, and the degree of such variability is determined by environmental cues and the regulatory network parameters. These results suggest that the TP and GC models are not mutually exclusive but rather reflect different aspects of the same temporal dynamics in cell cycle entry, as has been speculated [21],[24]. In addition, we show that stochastic activation of the Rb-E2F bistable switch under various environmental conditions can readily be mapped into both TP and GC models with a small number of parameters (Figure 1). We propose that these parameters can potentially serve as concise, quantitative phenotypes of cell states.
Our recent work has shown that traverse of the R-point is regulated by the Rb-E2F bistable switch [7]. This bistability results from interlocked positive feedback loops embedded in the Myc-Rb-E2F network (Figure S1, see Text S1 for further details). Given the bistable switching property of the Myc-Rb-E2F network, we hypothesized that this network, when subjected to noise, might demonstrate variable timing in E2F activation, which in turn might account for the temporal variability observed in cell cycle entry. This hypothesis is based on the strong correlation we previously observed between E2F activation and DNA synthesis [7]. To test this hypothesis, we developed a stochastic model for the Myc-Rb-E2F network using the chemical Langevin formulation [25],[26] as detailed in Materials and Methods. This formulation allows for implementation of intrinsic and extrinsic noise while retaining the deterministic framework. In this stochastic model, the intrinsic noise arises from the stochastic nature of the biochemical interactions among small numbers of signaling molecules. The extrinsic noise results from heterogeneity in cell size and shape, cell division, or cell cycle stage [27]–[32].
The fluctuations in the bistable switch result in significant discrepancies between stochastic and deterministic simulations [33]–[36]. Given a set of initial conditions and parameters in the Myc-Rb-E2F network, the simulated time-courses from a deterministic model are fixed (black line in Figure 2A), but those from a stochastic model show drastically variable trajectories (gray lines in Figure 2A). For example, the stochastic Rb-E2F model can generate two modes of E2F at Time = 50 h when stimulated with weak input as shown in Figure 2A. We define a switching threshold (horizontal red line in Figure 2A) to distinguish the low E2F mode, which corresponds to a non-activated subpopulation of cells, from the high E2F mode, which represents an activated population. This threshold can be used to calculate the percentage of activated cells over time. The minimum time required for E2F to reach the switching threshold is defined as the switching time (vertical red line in Figure 2A, for the deterministic simulation). Similarly, for strong input, the deterministic time-course simulations are fixed and stochastic time-course simulations again show variable trajectories (unpublished data). The distribution of E2F activity in stochastic simulations, however, exhibits a single mode (high E2F level) at strong inputs, rather than two modes as with weak inputs.
Based on our simulations and definitions in Figure 2A, we obtained G0 exit curves for weak and strong input conditions as shown in Figure 2B. These G0 exit curves are analogous to the α-curve in the TP model, which represents the frequency distribution of inter-mitotic times [15]. Both a G0 exit curve and an α-curve can be fitted by an exponential curve with two parameters (black dotted curve in Figure 2B, see Text S2 for further details): transition rate (KT) and time delay (TDP). This is because both exhibit an initial time delay followed by an exponential drop [11],[14],[15]. The transition rate of the G0 exit curve is inversely proportional to the temporal variability of the cell population. For example, a population of cells with more-synchronous E2F activation would have a higher transition rate than that of a population with less-synchronous E2F activation. If cells were completely synchronized, the G0 exit curve would have an infinite transition rate.
Our simulated E2F activation dynamics predict serum-dependence of both transition rate and time delay. For a weak input (KT = 0.029±0.0014 h−1 and TDP = 18.0±1.2 h, blue line in Figure 2B), most cells were expected to remain inactivated and the percentage of G0 cells would decrease slowly. This is because the impact of noise acting on the Rb-E2F bistable switch was only significant enough to activate E2F in some cells, but not in other cells. This would lead to a bimodal distribution of E2F activity (Figure S2A), which is consistent with previous experimental observations in mouse fibroblasts [13],[37],[38]. In contrast, the impact of noise was negligible in the case of strong input and all cells were predicted to be activated at a high transition rate (KT = 0.16±0.0076 h−1 and TDP = 7.7±0.27 h, red curve in Figure 2B).The response of the Rb-E2F bistable switch to noise would cause an increase in KT with increasing input strength (Figure 2C) as the population moves from a bimodal to a monomodal distribution at the high mode (Figure S2A). At sufficiently high input strength, further increase in input strength may have a negligible effect on KT (Figure 2C). In contrast, TDP may decrease as the population transitions from a bimodal to a monomodal distribution, and TDP may bottom out at sufficiently high input strength (Figure 2D). The dependence of KT and TDP on input strength can be recapitulated with a minimal bistable model (Figure S2B–D), suggesting that such dependence may be an intrinsic property of bistable systems.
To validate our model predictions, we measured E2F activity in the E2F-d2GFP cell line, which is derived from a rat embryonic fibroblast REF52 cell line and carries a destabilized green fluorescent protein reporter (d2GFP) under the E2F1 promoter. We have shown that this reporter system can be used to monitor E2F activity in response to serum stimulation previously [7]. Prior to serum stimulation, the E2F-d2GFP cells were synchronized at quiescence by serum-starvation (0.02% bovine growth serum, BGS) with basal E2F-GFP expression (Figure 3A). Upon weak serum stimulation (0.3% BGS), only a subpopulation of the cells switched to the high E2F mode over time. At earlier time points (0∼15th h), the difference in E2F level between the non-activated and activated cells was small. The difference between the two modes became increasingly clear, resulting in distinctive bimodality starting at 18th h. In contrast, upon strong serum stimulation (5% BGS), E2F activation was more synchronous. The whole cell population gradually switched to the high mode with greater temporal synchrony without demonstrating detectable bimodality at any tested time point (Figure 3A). It is possible that noise may partition the cell population into two subsets (active and inactive towards proliferation) temporarily even at high serum stimulation. However, simulations suggest that accumulation of E2F in the activated cells at earlier time points may not be significant enough to result in any detectable difference between the two subsets (Figure S2A).
Based on the distribution of E2F in Figure 3A, we calculated the percentage of non-activated cells and obtained a G0 exit curve for each serum condition (Figure 3B). Consistent with predictions in Figure 2B, we observed an increase in KT and decrease in TDP for increasing serum concentration (KT = 0.031±0.0036 h−1 and TDP = 5.1±1.1 h at 0.3% serum, and 0.16±0.011 h−1 and 1.1±0.27 h at 5% serum), reminiscent of modulation of the α-curve by serum [11],[15]. Consistent with model predictions in Figure 2C–D, we observed increase in KT and decrease TDP for increasing serum concentrations (Figure 3C and 3D). An independent experiment under the same conditions on a different day exhibited similar dependence of KT and TDP on serum (Figure S3).
The temporal dynamics of biological systems often depend strongly on network parameters [39].Consequently, the transition rate of cell cycle entry may be modulated by nodal perturbations. This is exemplified in a recent study on the yeast cell cycle [40], which demonstrated that a positive feedback by G1 cyclins is responsible for temporal coherence in gene expression and proper division timing of yeast cells. Loss of this feedback control in the cell cycle machinery was shown to promote incoherent gene expression and abnormal delays of yeast budding. Interestingly, a similar feedback module through a G1 cyclin (CycE) can be found in the Myc-Rb-E2F network also, suggesting its potential role in the control of temporal dynamics.
To investigate modulation of the transition rate by nodal perturbations in the Myc-Rb-E2F network, we introduced in silico perturbations of one particular node: the CycE/Cdk2 complex, which forms the CycE-mediated positive feedback loop. Our bifurcation analyses predict that weakening of the CycE-mediated positive feedback loop will desensitize the Rb-E2F bistable switch to serum stimulation, requiring a higher critical serum concentration (Figure 4A) for E2F activation. Similarly, we predict desensitization to serum when CycD is down-regulated or when Rb is up-regulated (unpublished data). Such desensitization is expected to modulate the temporal dynamics of E2F activation. When the positive-feedback strength by CycE is weakened, our simulations in Figure 4B (corresponding simulated distributions in Figure S4) predicted increase in the time delay and decrease in the transition rate. For strong feedback strength, KT was estimated to be 0.17±0.0090 h−1. This value was reduced to 0.15±0.006 h−1 and 0.12±0.0054 h−1 for intermediate and weak feedback strength, respectively. In contrast, TDP for strong feedback input ( = 7.4±0.25 h) was predicted to increase to 8.5±0.53 h for intermediate feedback strength, and to extend further to 10.8±0.15 h for weak feedback strength. Similar dependence of KT and TDP on the feedback strength was predicted for all serum concentrations (Figure 4C and 4D).
To test these predictions experimentally, we perturbed the Myc-Rb-E2F network by applying varying concentrations of a cyclin-dependent kinase inhibitor (CVT-313), which has a much higher affinity towards Cdk2 than to other Cdks (Figure S5) [41],[42]. In the context of the current study, which focuses on the cellular dynamics leading to E2F activation, the impact of the Cdk2 inhibitor is primarily the inhibition of the CycE/cdk2 complex. We note that the inhibitor would also affect other components of cell cycle regulation, (e.g., the CycA/cdk2 complex), which were not considered in the model due to their activity mainly downstream of the cell cycle entry point. When the CycE node was perturbed experimentally, we observed inhibitor dose-dependent changes in E2F activity, as measured by GFP fluorescence in the E2F-d2GFP cells. As shown in Figure 5A, increasing dose of the inhibitor drug reduced the fraction of cells in the high E2F mode at 24 h. For example, without the Cdk2 inhibitor, less than 1% serum was required for E2F activation in half of the cell population. With 2 µM Cdk2 inhibitor, 2% serum was required to achieve a similar fraction of E2F activation. Such desensitization to serum stimulation was seen for all inhibitor concentrations tested (Figure 5A).
Next, we tested modulation of temporal dynamics by the Cdk2 inhibitor. At 2% serum, we applied the Cdk2 inhibitor (CVT-313, 2 µM) to monitor its effect on E2F activation over time. Our results in Figure 5B show that the transition rate of the cell population decreased (from KT = 0.078±0.0073 to 0.058±0.0070 h−1) and time delay increased (from TDP = 9.1±0.70 to TDP = 12.0±0.86 h) with addition of the Cdk2 inhibitor. Such a decrease in KT with the inhibitor drug is consistent with our model predictions in Figure 4 and was observed for all serum concentrations tested, as shown in Figure 5C (distributions of E2F in Figure S6). As predicted, time delay generally decreased with increasing serum concentrations and it increased in the presence of the Cdk2 inhibitor (Figure 5D). It is noteworthy that the estimated time delay has a large error at low serum concentrations, leading to a non-monotonic dependence of TDP on serum concentrations. This is most likely due to the small number of E2F-activated cells at low serum at earlier time points. This makes estimation of parameters using least squares challenging, giving rise to large errors. We conducted another experiment on a different day under the same experimental conditions and observed similar trends in KT and TDP, as shown in Figure S7.
Throughout this study, we have analyzed the temporal dynamics of E2F activation by extracting a set of parameters defining the TP model (transition rate and time delay). This parameter extraction establishes a connection with the mechanistic Rb-E2F model. Similarly, the GC model parameters (mean growth rate and its variance , see Text S2 for further details) can be extracted from the stochastic dynamics of E2F activation, and a connection between the GC model and the mechanistic Rb-E2F model can also be established. The GC parameters were estimated from both simulation results in Figure 2 and experimental data in Figure 3, as shown in Table 1. These results show increasing mean growth rate and decreasing variance (normalized to the mean) with increasing input strength.
In addition, we predicted the dependence of the strength of the CycE-mediated positive feedback on the GC model parameters, as shown in Figure 6. Consistent with Table 1, our results predicted increasing growth rate (Figure 6A) and decreasing normalized variance (Figure 6B) for increasing input strength. However, decreasing the strength of the CycE-mediated positive feedback was predicted to reduce mean growth rate without affecting its normalized variance significantly. Such parameter extraction defining the phenomenological models provides a quantitative mapping between the phenomenological models and the mechanistic Rb-E2F model. It is noteworthy that both TP and GC models fit the data with comparable levels of uncertainty in the estimated parameters, suggesting that both models may provide similarly good fits to the stochastic dynamics of E2F activation.
Focusing on E2F activation, we have shown that the temporal variability in cell cycle entry from quiescence can be quantitatively modeled by stochastic activation of a bistable Rb-E2F switch [7]. In addition, we have shown that the degree of such variability can be modulated by varying the input strength or by perturbing the network parameters.
Our model predictions are overall consistent with experimental measurements. In particular, our analysis indicates that serum and a Cdk2 inhibitor drug exert opposite influences on the temporal dynamics of E2F activation: transition rate increases and time delay decreases with increasing serum, but transition rate decreases and time delay increases with increasing Cdk2 inhibitor concentrations. We suggest that such a well-calibrated stochastic model for the Rb-E2F switch may guide further experimental analyses to gain insights into the system-level dynamics underlying cell cycle entry. For example, our model predicts that reducing the CycD/Cdk4,6 activity may have similar effects on temporal dynamics of E2F activation as the Cdk2 inhibitor, while knocking down Rb may increase transition rate (unpublished data). In addition, we can predict stochastic dynamics of E2F activation under combinatorial perturbations including growth factors, inhibitor drugs targeting the Myc-Rb-E2F network, or mutations within this network.
Throughout this study, we have focused on a single transition during cell cycle progression (quiescence to proliferation) due to its experimental and computational tractability. To further simplify analysis, we have chosen not to model cell division or growth explicitly. Instead, the variability associated with these processes is lumped into the extrinsic noise terms in our SDE model. More explicit mechanisms to account for such variability may further improve the quantitative agreement between the modeling and the experiment. For example, our simulation results suggest that the major source of noise is extrinsic noise, while variability in the initial conditions can lead to minor yet discernable change in the temporal dynamics of E2F activation. This is evident when E2F activation dynamics are compared under two conditions: varying initial conditions and varying variance of the extrinsic noise (ω) in the stochastic model (see Materials and Methods). At a fixed variance of the extrinsic noise, increasing variability in the initial conditions (Gaussian-distributed with the mean being the base initial conditions and various variance values, Var) is predicted to decrease transition rate and time delay (Figure S8A–B). Similarly, increasing ω without any variability in the initial conditions (Var = 0) is predicted to decrease transition rate and time delay (Figure S8C–D), but these changes by extrinsic noise are predicted to be significantly greater than those by initial conditions. These decreases in KT and increases in TDP reflect the loss of synchrony in E2F activation due to increasing extrinsic noise or initial condition variability. This may explain reduced time delay in actively growing cells compared to that in quiescent cells [14].
Equally important, we further show that these predicted stochastic dynamics of the Rb-E2F model can be quantitatively mapped into two lines of phenomenological models reflecting seemingly conflicting views: the TP model and the GC model. For a given set of parameters defining the stochastic model, the simulated stochastic E2F activation at the population level can be uniquely described by a set of parameters defining the TP model or the GC model (compare Figure 4C–D and Figure 6A–B). Furthermore, different sets of parameters in the stochastic model would lead to different parameters in the TP or the GC models. We propose that this mapping provides a simple conceptual framework that reconciles the different views reflected in the TP and GC models, which have been a source of unresolved debate over the last several decades. In other words, the stochastic model can be considered as a common mechanistic basis for the two seemingly different models.
During the mapping from our stochastic model to the TP or GC models, details associated with individual signaling reactions are necessarily lost in the resulting TP or GC models, pointing to their limitations in offering mechanistic insights. However, a by-product of this mapping is a potential, unappreciated utility of the TP and GC models. On one hand, these phenomenological models are simple and are able to provide quantitative description of the population-level dynamics associated with variable cell cycle entry. On the other, specific changes in the underlying reaction networks can be manifested in changes in the parameters in these simple models. As such, together with a mechanistically based model, the TP and GC models can serve as a concise platform to define quantitative phenotypes that facilitate classification of cell types or cell states.
This utility may be particularly useful for cancer diagnosis, since most cancers have defects in the Myc-Rb-E2F signaling pathway [43],[44]. Recent approaches for cancer classification involve microarray-based gene expression profiling to develop cancer signatures [45], which have been used to reveal the activation status of oncogenic signaling pathways [46]. Here we suggest that oncogenic phenotypes resulting from deregulation in these pathways may also serve as cancer signatures. Using the mapping technique defined in this work, we can develop a library of predicted phenotypes (defined as TP or GC model parameters) based on the Myc-Rb-E2F network under various nodal mutations or stimulatory inputs. This library can be correlated with the oncogenic phenotypes (defined as TP or GC model parameters) of an unknown cancer cell type. In principle, this correlation can be used to infer the activation status of the Myc-Rb-E2F network of the cancer cell type. For a small number of test conditions, this may be challenging owing to the stochastic dynamics of cell cycle entry. However, increasing the number of test conditions may enhance the diagnostic potential of this approach.
The deterministic version of the Rb-E2F model, developed in our previous work [7], served as a basis for the stochastic Rb-E2F model. To capture stochastic aspects of the Rb-E2F signaling pathway, we adopt the chemical Langevin formulation [25],[26],[47] as shown in Eqn (1).where Xi(t) represents the number of molecules of a molecular species i (i = 1, …, N) at time t, and X(t) = (X1(t), …, XN(t)) is the state of the entire system at time t. X(t) evolves over time at the rate of aj[X(t)] (j = 1, …, M), and the corresponding change in the number of individual molecules is described in vji. and are temporally uncorrelated, statistically independent Gaussian noises. This formulation retains the deterministic framework (the first term), and reaction-dependent and reaction-independent noise. The concentration units in the deterministic model were converted to molecule numbers, so that the mean molecule number for E2F would be approximately 1,000. We assumed a mean of 0 and variance of 1 for Γj (t), and a mean of 0 and appropriately determined variance for ωj (t) (see Text S1 for more details). The resulting stochastic differential equations (SDEs) were implemented and solved in Matlab.
Actively growing E2F-d2GFP cells [7] were serum-starved in Dulbecco's modified Eagle's medium (DMEM) with 0.02% of bovine growth serum (BGS). After 24 h of serum starvation, they were stimulated with varying serum concentrations for cell cycle entry in the presence or absence of Cdk2 inhibitor CVT-313 (from Calbiochem: Cat #238803). Cell cycle progression was blocked at the DNA synthesis stage by hydroxyurea (HU block), which we have shown has insignificant impact on the GFP signal [7]. At various time points, these cells were collected and fixed in 1% formaldehyde for fluorescence assay.
E2F-d2GFP rat embryonic fibroblasts were assayed for a destabilized green fluorescent protein reporter (d2GFP) for E2F activity. The intensity of d2GFP was measured with a flow cytometry system (BD FACSCanto II).
E2F-d2GFP cells were serum-starved (BGS = 0.02%) for 24 h before they were treated with varying concentration of the Cdk2 inhibitor (CVT-313, EMD # 238803) and serum. After 24 h of serum/inhibitor drug treatment, cell lysates were collected and Western blotting was conducted with primary antibodies recognizing Rb phosphorylation at Cdk4-specific serine 780 (Santa Cruz, #sc-12901-R) and at Cdk2-specific threonine 821 (Santa Cruz, #sc-16669-R). These were conjugated with anti-rabbit secondary antibodies (GE Healthcare, #NA934) for detection. As a loading control, actin was measured with actin-recognizing primary antibodies (Santa Cruz, #sc-8432) conjugated with anti-mouse secondary antibodies (GE Healthcare, #NA9310).
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10.1371/journal.pgen.1005879 | 3’UTR Shortening Potentiates MicroRNA-Based Repression of Pro-differentiation Genes in Proliferating Human Cells | Most mammalian genes often feature alternative polyadenylation (APA) sites and hence diverse 3’UTR lengths. Proliferating cells were reported to favor APA sites that result in shorter 3’UTRs. One consequence of such shortening is escape of mRNAs from targeting by microRNAs (miRNAs) whose binding sites are eliminated. Such a mechanism might provide proliferation-related genes with an expression gain during normal or cancerous proliferation. Notably, miRNA sites tend to be more active when located near both ends of the 3’UTR compared to those located more centrally. Accordingly, miRNA sites located near the center of the full 3’UTR might become more active upon 3'UTR shortening. To address this conjecture we performed 3' sequencing to determine the 3' ends of all human UTRs in several cell lines. Remarkably, we found that conserved miRNA binding sites are preferentially enriched immediately upstream to APA sites, and this enrichment is more prominent in pro-differentiation/anti-proliferative genes. Binding sites of the miR17-92 cluster, upregulated in rapidly proliferating cells, are particularly enriched just upstream to APA sites, presumably conferring stronger inhibitory activity upon shortening. Thus 3’UTR shortening appears not only to enable escape from inhibition of growth promoting genes but also to potentiate repression of anti-proliferative genes.
| MicroRNAs (miRNAs) are regulators of gene expression. Typically they recognize a binding site in genes' sequences and exert a repressive effect. This scheme prescribes a regulatory network that determines which gene is regulated by which miRNA. Yet this is a static sequence-based scheme that might not support dynamic changes in network wiring. Can genes become subject to, or be released, from the regulation of a miRNA in a manner that depends on the physiological state of cells? Here we describe such a dynamic mechanism. It is established that miRNA regulation is often more effective when their binding sites reside near the end of the target mRNA or right after the coding sequence STOP codon. Thus, a site distant from the mRNA’s end might be latent as it will not bind efficiently its corresponding miRNA. Yet, in particular physiological states, e.g. cancer or rapidly proliferating cells, mRNA ends tend to become shortened. So far it was suggested that such shortening may serve to release proliferation-favoring genes from miRNA repression by eliminating their binding sites from the mRNA. We propose a mirror image, complementary mechanism that acts upon genes that need to be repressed during proliferation. Specifically, we propose that mRNA shortening can dynamically activate latent repressive miRNA binding sites by bringing the mRNA end close to them. We mapped the ends of all mRNAs in proliferating cells and found that cancer-enriched ends are strikingly positioned closely downstream to a high density of potentially latent binding sites, which are retained in the short mRNA but are now close to the new ends. This may enable such potentially latent miRNA sites to become dynamically activated upon proliferation. Remarkably, this mechanism targets preferentially pro-differentiation and antiproliferative genes, which are often repressed in cancer.
| RNA polyadenylation is a pivotal molecular process, which plays important roles in ensuring the stability, nuclear export and efficient translation of mRNA. Cleavage of the mRNA in its 3’ untranslated region (UTR), prior to addition of the poly(A) tail, is instructed by a polyadenylation signal (PAS) [1]. In fact, most genes contain multiple PASs with different affinities to the cleavage machinery, resulting often in alternative polyadenylation (APA) sites. Typically, the distal PAS, which creates the longest 3’UTR, contains the canonical signal (AAUAAA), while alternative sites more often contain variations on that signal; as such, the distal site is usually more conserved than the alternative PAS and is more frequently used [2].
While the usage pattern of APA sites is highly conserved, specific cell types and conditions appear to be more prone than others to increased use of APA [2]. One example is that of cell proliferation, where a shift towards increased APA site usage has been observed, leading to shortening of many 3’UTRs [1,3]. Likewise, the transition from differentiated cells to induced pluripotent stem cells is also accompanied by global 3’UTR shortening [4]. A tendency to use proximal APA sites is seen also in particular tissues, such as placenta, ovaries and blood [5]. In contrast, processes such as embryonic development and myogenic differentiation of cultured myoblasts are accompanied by progressive lengthening of 3’UTRs [1]. Notably, cancer cells have been reported to exhibit even more extensive 3’UTR shortening than non-cancerous proliferating cells [6].
microRNAs (miRNAs) are small non-coding RNAs that regulate the translation and stability of their target mRNAs. They recognize such mRNAs by one or several motif site sequences, often located within the 3'UTR of the mRNA, which are complementary to bases 2–8 (called "seed") in the 5' end of the mature miRNA [7].
An interesting aspect of 3’UTR shortening is the interplay with miRNAs. If the binding site of a particular miRNA resides within the part of the 3’UTR that is removed upon shortening, typically when an APA site is used, regulation of the target mRNA by this miRNA is abrogated (Fig 1A). Sandberg et al. [3] and Mayr and Bartel [6] reported that in proliferating or cancerous cells, some APA isoforms of proto-oncogenes tend to be more stable, generate more protein and promote higher neoplastic transformation rates. This was found to be due to their ability to escape miRNA regulation, as the corresponding binding sites were eliminated in the shorter 3’UTRs [3,6]. The notion of escaping miRNA regulation via APA is also supported by additional examples, e.g. ABCG2 that escapes regulation by miR-519c by 3’UTR shortening in drug resistant cells [8], and Hsp70, which is alternatively polyadenylated upon ischemia or heat shock and thereby escapes miR-378* regulation [9].
A typical 3’UTR comprises many potential miRNA sites; however, only a small fraction of those are functional in any given cell type and state. The position of the site along the 3'UTR can influence its functionality. In particular, conserved functional miRNA binding sites tend to be positioned near the beginning and end of the 3'UTR [10]. Nonetheless, potentially functional sites might still reside closer to the middle of the full length (FL) 3’UTR. When an APA site is used, the distal part of the FL 3’ UTR is eliminated, together with the functional miRNA binding sites residing within it. However, the region immediately upstream to the APA site, previously located away from either ends of the 3’UTR, now becomes positioned close to the new 3’ end. This can potentially render miRNA sites located in that region more functional for miRNA-mediated repression (Fig 1A). In this manner, APA may augment the functionality of potential miRNA binding sites residing 5' to the APA site. A recent study indeed supports this conjecture, in a cell type-specific manner. Nam et al. [11] compared transcripts with different 3’UTR lengths in HEK293 and HeLa cells, and found that in the cells where the miRNA binding site was closer to the 3’ end of the transcript (due to the different in 3’UTR lengths), repression by miRNAs was stronger [11]. We now report that this is a global phenomenon, where 3’ UTRs in diverse cell lines are strategically shortened precisely downstream to conserved miRNA binding sites; this occurs particularly in differentiation-related genes, and involves preferentially targets of miRNAs that are induced in proliferation and cancer. 3’ UTR shortening may also serve as a dynamic tool to functionalize otherwise latent, binding sites residing more proximally.
In order for an APA event to potentiate targeting by miRNAs, potentially functional sites should exist 5' to the APA site, at a distance from the APA that is comparable to that typically seen between conserved miRNA sites and the canonical 3’ UTR ends. We have previously shown this region to be ~250 nucleotides from either end of the 3'UTR [12].
To identify transcriptome-wide APA sites, we subjected WI-38 human embryonic lung fibroblasts and their immortalized derivatives obtained through sequential serial transfers towards increased proliferation and transformation [13,14] to 3' sequencing and analysis [15]. The analysis included primary fibroblasts (“Control”), slow growers (early passage after immortalization), fast growers (extensive passaging after immortalization) and fast growers transformed by constitutively activated mutant H-RasV12 (“Ras”). 5765 genes were found to have at least one APA site in at least one of the cell types in this experimental system. Somewhat unexpectedly, we did not detect significant differences in the overall extent of global shortening between the different cell types. Remarkably though, when we aligned all genes with at least one APA event according to their most proximal APA site, a significant enrichment of conserved binding sites was observed within the 300 bases immediately upstream to the APA site (Fig 1B for control cells, S1A Fig for all time points). A similar picture emerged from the analysis of previously published 3'seq data [15,16], obtained in different cell lines (Fig 1C–1E). Of note, in those earlier experiments, APA was compared in MCF10A and BJ cells under growth arrest vs. proliferation and transformation, revealing significant shortening in the proliferating cells. Our inability to detect comparable shortening on a genome-wide scale in the WI-38 analysis may therefore stem from the fact that all compared states involved proliferating cells; alternatively, this may reflect the embryonic origin of WI-38 cells. Notwithstanding, though we do not see a genome wide tendency for shortening in these samples, we do observe many genes that do feature a shortened version, and we analyze them below.
A trivial reason for the similar enrichment of conserved miRNA sites upstream to APA sites in the different datasets could be that in all datasets the same genes undergo the same APA events. However, this pattern was still retained also when we performed a similar analysis only on APA-positive genes that differ between pairs of cell lines (examples in S1B–S1E Fig). Moreover, analysis of 3'seq data from mouse muscle tissue [16] revealed a similar peak of conserved miRNA binding sites upstream to the APA sites (Fig 1F). In the mouse tissue fewer genes were found to undergo APA, an observation which might reflect the highly differentiated state of the cells. Hence, in multiple cell types and in two mammals, distinct APA sites reside preferentially closely downstream to conserved miRNA binding sites, effectively repositioning such centrally-located miRNA binding sites and placing them in proximity to the 3’ end of the shortened transcript. In support of the emerging notion, when we compared the distribution of conserved miRNA binding sites between genes possessing at least one APA site in WI-38 cells and those without APA sites, we found (S2 Fig) that genes with APA sites tend to harbor fewer conserved binding sites near the distal end of their full length 3'UTR, relative to those without an APA (p-value = 6.5e-279, Student T-test). Conversely, genes with APA sites are relatively enriched in conserved miRNA binding sites within the proximal half of their 3’UTR (S2 Fig). This observation is intriguing: if APA merely serves to eliminate miRNA binding sites residing near the 3' end of the full length mRNA, one would expect APA-positive genes to be more enriched for functional miRNA binding sites near that end, providing them with an efficient on/off switch controlled by APA. The fact that the opposite trend is actually observed strongly suggests that the interplay between APA and miRNAs may allow regulation that is richer than mere binding sites elimination. Specifically, this may serve as further indication that the shorter 3’ end, positioned immediately upstream to the APA site, can dynamically potentiate new miRNA binding sites as they become positioned closer to the 3’ end of the shorter transcript.
Analysis of the conservation state of each miRNA binding site by itself is an accepted indication for its functionality [10,17,18]. However, such conservation might be due to other attributes, for example another functional feature of the 3’UTR residing in this location. We therefore asked whether the high conservation of the miRNA sites located upstream to APA sites can be attributed to conservation of their neighborhood, or is preferentially targeting the miRNA sites? To address this question we looked at the conservation profile around APA sites, for genes with and without conserved miRNA binding sites in the 300 bases 5’ to the APA sites. As can be seen in S3 Fig, the profile of the genes without conserved sites 5’ to APA sites is lower specifically in this area, indicating that the high conservation present for the other genes might be due to the presence of miRNA targeting, and not merely APA sites. We also looked at the conservation profile of all conserved miRNA binding sites located in the 300 bases 5’ to APA sites. For that we used PhastCons and PyhloP [19,20] as provided by the UCSC browser. We observed a sharp peak of conservation exactly overlapping the 7 bases of the binding sites, whereas its surroundings are significantly less conserved (Fig 2A), strongly arguing in favor of a selective pressure to conserve specifically the conserved miRNA binding sites. Another measurement of site conservation is the PCT score, which controls for the 3’UTR surroundings, dinucleotide conservation and other parameters unrelated to miRNA functionality [21]. Importantly, this score allows to assess the extent to which conservation of a site is likely to be due to miRNA functionality. We compared the distribution of PCT scores for miRNA binding sites 300 bases 5’ and 3’ to APA sites, and found that the scores are significantly higher for the sites located just 5’ to the APA sits (Fig 2B, p-value = 2e-7, Student T-test). Moreover, we employed the Context++ scoring system of miRNA binding sites, which takes into account many additional parameters of each site and its surroundings beyond mere conservation, and provides a score for the probability that this site is indeed functional [22]. We compared the scores of the binding sites 300 bases 5’ and 3’ to APA sites, for conserved and non-conserved sites. For both groups, the scores for sites located 5’ to APA sites were significantly lower (hence indicating higher functionality) (Fig 2C and 2D, p-value = 2e-6, 7e-165, Student T-test). Together with the PCT score analysis, this argues that these sites are more likely to be indeed functional.
In sum, the above analyses strongly suggest that the miRNA binding sites located just 5’ to APA sites are functional beyond sequence conservation. Since the Context++ scores of non-conserved miRNA binding sites were also better for the ones that are positioned just 5’ to APA sites, we can conclude that even the non-conserved binding sites are probably more functional for miRNA targeting when located in that region.
In non-proliferating cells, most genes express mainly the full length version of their 3’UTR. However, in proliferating cells and particularly in cancer, increased usage of APA sites has been shown to enable miRNA binding sites elimination from the 3’UTRs of proliferation-associated genes [1,6]. By the same rationale, we predicted that if our above observations are physiologically relevant, then genes becoming more susceptible to miRNA regulation (and hence more effectively repressed) owing to APA would tend to be those that should be preferentially downregulated during proliferation and in cancer. To address this prediction, we initially compared two gene sets: those at the core of the cell cycle machinery, and those involved in patterning of the embryo during development. We chose these two gene sets as they represent two opposing classes of archetypical proliferation and differentiation genes [23]. Yet, these gene sets are relatively small and for only a portion of them we detected APA events. We therefore sought to expand these gene sets to include functionally related genes and thus gain further statistical power. To that end, we expanded each gene set to include additional genes either by similar codon usage (see M&M) or based on the GSEA tool [24]. We then analyzed the miRNA binding site landscape around the APA sites of the two gene sets. Remarkably, we observed a significant difference between the two groups of genes: while the proliferation-related genes (“Pro-Prolif.”) display only modest enrichment of conserved miRNA binding sites immediately upstream to the APA site, the differentiation-related genes (“Pro-Diff.”) show a markedly elevated abundance of conserved miRNA binding sites in the corresponding region (Fig 3A and S4A–S4C Fig). This strongly suggests that differentiation-related genes are more prone than proliferation-related genes to regulation by miRNA binding site potentiation via APA. This is in line with the documented increased APA usage during proliferation and cancer, when differentiation-related genes are expected to be downregulated.
These results suggest that, in addition to its documented ability to alleviate miRNA-mediated repression of proliferation genes, 3’ UTR shortening is also used to potentiate preferentially the repression of differentiation genes.
To obtain clues about the miRNAs that bind sites potentiated by APA, we performed comparative miRNA microarray analysis on WI-38 cells and their progressively transformed derivatives [13,14]. We found that most of the miRNAs upregulated particularly in the highly proliferative stages (fast growers and Ras-transformed) belong to the miR-17-92 cluster (Fig 3B and S5 Fig). Indeed miR-17-92 is a well-studied proliferation-associated cluster [25], which includes 6 miRNAs with 4 different binding site sequences. Target genes of these miRNAs are significantly enriched for regulation of differentiation and negative regulation of proliferation (S1 Table), consistent with the notion that these miRNAs promote cell proliferation. Notably, in comparison to all miRNAs on the array, conserved sites for members of the miR-17-92 cluster are significantly enriched immediately 5' to APA sites (Fig 3C). Furthermore, while for all miRNAs in the genome we see a marked increase in the abundance of conserved binding sites near the 3' end of the full length 3’UTR, conserved binding sites of the miR-17-92 cluster are relatively less enriched in that region (Fig 3D). Thus, as compared to the bulk of the cellular miRNAs, miR-17-92 cluster members preferentially bind sites that are located upstream to APA sites rather than near the distal end of the full length 3’UTR. APA is therefore expected to preferentially potentiate the repressive effects of those proliferation-associated miRNAs.
Overall, the above findings further support the conjecture that while APA enables proliferation-associated genes to escape miRNA regulation, it confers increased regulation upon pro-differentiation genes.
It has long been known that APA eliminates functional miRNA sites by truncating the 3’UTR [3,6]. Our present study provides evidence that alternative polyadenylation can also functionalize miRNA binding sites positioned upstream to the APA site. The position of the miRNA binding site along the 3’UTR greatly affects its functionality [10]; we now show that when the RNA is shortened by APA, a new subset of conserved potential miRNA binding site is placed close to the new 3’ end. Presumably, this enables those sites to become more functional (Fig 1A). Previously, Nam et al. concluded that binding sites that are brought closer to the end of the 3’UTR become more functional [11]. We now extend this conclusion and put it within a novel genome-wide dynamic regulatory program. Moreover, we show that this proposed mechanism is potentially of broad relevance, affecting a large number of genes across many tissues and cell types. In our present study, functionality of miRNA binding sites was inferred indirectly by several computational methods. Although the conclusions from the different methods are highly concordant and strongly support our conclusions, it will be of further value to perform directed experiments, monitoring the functionality of those sites and their preferential engagement within the context of the shorter transcript, through the use of methods such as CLIP analysis.
APA enables proto-oncogenes to escape miRNA regulation by eliminating binding sites located towards the distal end of the full length 3’UTR [3,6]. Complementing the picture, our study shows that anti-proliferative genes can also be modulated by APA, but in an opposite direction: they may gain functional binding sites and become more susceptible to repression. This is not to say that only anti-proliferative genes gain functional sites as a consequence of APA events; reality is likely more complicated, and it is entirely possible that other groups of genes would also gain functional miRNA binding sites by a similar mechanism. However we do see a significant enrichment for conserved binding sites prior to APA sites in genes with pro-differentiation characteristics, which might indicate that these genes will be more significantly sensitized to miRNAs when global shortening occurs. As in all other aspects of biology, context matters. Hence, such sensitization becomes more meaningful under conditions where the pertinent miRNAs, targeting the functionalized sites within the shortened 3’UTR, are also upregulated. Indeed, this is what we see in the case of the miR-17-92 cluster, which is upregulated in highly proliferative cells. Notably, significant enrichment of binding sites was seen specifically for miRNAs upregulated in the proliferating state, rather than for all expressed miRNAs. It is conceivable that, in other contexts, a similar APA-mediated mechanism may serve to selectively render other groups of genes more responsive to repression by the miRNAs that are specifically upregulated in those particular contexts.
What could be the logic behind this dynamic mode of regulation? Consider a miRNA that was induced at a particular physiological condition. If all targets of the miRNA are equally accessible to it that would all be equally affected. If, on the other hand, some of the targets harbor the binding site close to the 3’ UTR’s 3’ end while others contain the sites closer to the UTR’s center, only the first subset of targets would be affected. The second subset could at once become functional targets upon APA and UTR shortening (Fig 4). This mechanism may thus acquire an additional mode of control to the miRNA regulatory network.
Low passage WI-38 cells were obtained from ATCC (CCL-75). Slow growing hTERT-immortalized WI-38 cells and their fast growing derivatives, generated by extended passaging in culture of the slow growing cells [13], were kindly provided by Varda Rotter. Ras-Transformed derivatives of the fast growing WI-38 cells were obtained by infection with a recombinant retrovirus expressing H-RasV12 as described in [14].
WI-38 cells were grown in 37° in MEM supplemented with 10% non-heat-inactivated fetal bovine serum (Sigma), pen-strep, sodium pyruvate, L-glutamine solution (Beit HaEmek). RNA was extracted using Nucleospin miRNA kit (Macherey-Nagel), according to manufacturer’s instructions.
miRNA array analysis was done in duplicates, using the miRNA Complete Labeling and Hyb Kit (Agilent, 5190–0456) according to the manufacturer's instructions. Briefly, for each sample 100ng RNA was dephosphorylated, denatured, labeled with Cyanine 3-pCp and purified using Micro Bio-Spin 6 Columns. Hybridization was done for 20h with Agilent SurePrint G3 Unrestricted miRNA 8x60K (Release 19.0) arrays. Arrays were scanned using an Agilent DNA microarray scanner, and analyzed using the AgiMicroRna package in R [26] with the RMA algorithm. The heat map was generated with Matlab Clustergram function for the mean fold change of each sample vs. control (mean of the duplicate arrays).
The 3’Seq protocol is based on Jenal et al. [16] and incorporates additional modifications described in Martin et al., 2012[27]. Basically, 25 μg of total RNA were heat-fragmented for 12 minutes in 1x Fragmentation Buffer (Ambion) at 70°C to generate RNA fragments of ~100 nucleotides. Next, the 3’end poly(A) RNA fragments were selected using the Oligotex mRNA Kit (QIAGEN) and RNA was end-repair with T4 polynucleotide kinase for 45 minutes at 37°C following manufacturer’s instructions. Afterwards, RNA 3’ends were blocked for ligation by incubation with 1mM Cordycepin 5′-triphosphate (Sigma) and 10U of polyA polymerase (PAP, NEB), in 1x PAP buffer for 30 minutes at 37°C. Finally, a P7 RNA adapter (5’-CAAGCAGAAGACGGCAUACGAGAU-3’) was ligated to the 5’end using 2U of T4 RNA ligase I and 2.5uM of RNA adapter, for 4h at room temperature. Between each step, RNA was purified using Agencourt RNAClean XP magnetic beads (Beckman Coulter) following the manufacturer’s instructions. At this point, RNA fragments were converted to cDNA employing the Superscript III RT kit (Life Technologies) and an anchored oligo(d)T stem loop primer containing a barcoded Illumina adaptor as in Martin et al (See S3 Table). Next, cDNA was purified twice with Agentcourt AMPure XP magnetic beads (Beckman Coulter) using a ratio 1.5:1 beads:sample. To generate the final 3’Seq library, the cDNA with the correct adaptor sequences was enriched/amplified using Phusion DNA polymerase (Life Technologies) and primers P7 and Illumina_Truseq, for 12 cycles following manufacture’s recommendations. Finally, the 3’seq library was size selected with AMPure XP magnetic beads by two rounds of purification with a ratio 1:1 beads:sample, before being sequenced on an Illumina HiSeq2000 system.
In our protocol of profiling transcript 3' ends, sequenced reads start with a barcode for sample multiplexing which is followed by six Ts whose end marks the precise location where the poly(A) tail starts. These six Ts therefore allow the mapping of poly(A) cleavage sites (CSs) with a nucleotide resolution. After trimming the barcode and six Ts, reads were aligned to the human genome (hg19) using TopHat [28]. Up to two mismatches were allowed in the reads’ seed region (the first 28 nt). As CS location often fluctuates around a major site, we merged reads from all samples and identified "read runs” (that is, genomic intervals that are "tiled" by multiple reads where the distance between the start of consecutive ones is below 10 nt). We considered the local maxima of these runs as the CS locations. We required a spacing of at least 50 nt between consecutive CSs. (In case of lower spacing between CSs, the one supported by a higher number of reads was chosen). Only CSs supported by at least 10 reads (at the location of the CS run maximum) were considered in subsequent analyses. The median length of the runs was 11 nt. Overall, 41,972 CSs were detected in our dataset. Priming of the oligo-dT primer to genomic regions that are A-rich (“internal priming”) could lead to false call of CSs. To reduce the rate of such false calls we extracted genomic sequences of 50 nt centered at the location of the putative CSs, and filtered out CSs that contained in that region a stretch of 10 nt of which at least 8 were As and the rest were Gs. 4,307 suspected CSs were filtered out.
miRNA binding sites were defined as perfect 7-mers, which are reverse complement to the seed of the miRNAs, for all human and mouse miRNAs listed in miRBase release 17 [29]. Conserved binding sites were taken from TargetScan release 6.2 [18].
PCT scores of all miRNA binding sites of type “7mer-m8” (perfect 7mer) for the conserved miRNA families were taken from TargetScan release 6.2 [18]. Context++ scores of all miRNA binding sites of type “7mer-m8” were taken from TargetScan release 7 [22]. Only miRNA binding sites from genes with at least 500 bases around the APA sites were taken into the analysis.
All analyses were done only for genes whose accession number was included in TargetScan 3’UTRs list (as defined in their website). A cleavage site was assigned to a gene if it was included in the coordinates of its 3’UTR (and 20 bases further, after the 3’ end of the 3’UTR). A gene was considered to have an APA site only if it has at least two cleavage sites assigned to its 3’UTR. In all analyses, the APA site that we took into account was the 5’ most in the 3’UTR.
To compute the statistical significance of the main signal—enrichment of conserved miRNA binding sites at particular distances from the APA site, we created a randomized null model. In this null model each binding site’s location was recorded and for each gene a random position of an APA site was drawn from the full length 3’ UTR (omitting the first and last 1000 bases in order to allow inspection of that vicinity around the randomly chosen location). The distance from the binding site and the randomized APA location was computed and the procedure was repeated 10,000 times. This yielded a distribution of distances as shown in all plots, once for conserved binding sites, and once for non-conserved binding sites. * indicates p-value < 0.05 for the null hypothesis that for a specific distance from the APA site, the number of conserved binding sites is similar as in a random APA site, or higher.
PhastCons and PhyloP scores of each base in the genome (hg19) were taken from UCSC [19,20]. The profile around APA sites of genes was for genes with at least 1000 bases from each side of the APA sites. The profile around conserved miRNA binding sites was for all miRNA binding sites located in the 300 bases 5’ to the APA site. The sites were aligned and the mean conservation profile was calculated.
We defined the “Pro-Proliferation” and “Pro-Differentiation” gene sets as follows: We began with the Gene Ontology sets termed “M-phase of cell cycle” and “Pattern Specification”, two gene sets that were recently shown [23] to serve as archetypical proliferation and differentiation genes, with distinct codon usage. To augment the number of genes belonging to each of the two sets we searched the entire genome for additional genes whose codon usage was highly similar to either of the two groups, thus expending the two sets from 92 and 82 genes originally to 229 and 136 (For correlation threshold 0.7: 226 and 147, for correlation threshold 0.8: 172 and 125). We expanded the two original gene sets (“M-phase of cell cycle” and “Pattern Specification”) also by correlation to other GO groups. These groups were taken as the top 50 groups for each original group using the GSEA tool track C5 [24].
After computing the miRNA binding site distribution around APA sites for the genes in each of the sets we estimated a p-value on the difference between conserved miRNA binding sites density at each distance from APA sites as follows: we repeated 10,000 on randomly partitioning the genes with high correlation to the two groups into two groups, one with 229 genes and one with 136 genes for the codon usage expansion threshold 0.75 correlation, and 175 and 167 genes in the expansion by the GSEA tool. In each such random partition we recorded, at each location relative to the APA, the fraction of conserved miRNA binding sites. The p-value was estimated as the fraction out of the 10,000 repetitions in which the real partition into “Pro-Proliferation” and “Pro-Differentiation” resulted in a difference in binding sites count. * indicates one-sided p-value < 0.05 for the null hypothesis that for two groups of these sizes, the difference in number of conserved binding sites is as for the two original groups or higher.
The miR-17-92 binding sites analysis was similar to the codon usage. Here too randomization was done 10,000 taking a random group of genes in the same size of the genes with binding sites for miR-17-92 miRNAs (104), and computing for each distance from the APA site the difference in the number of conserved binding sites for the random group and for all genes. * indicates one-sided p-value < 0.05 for the null hypothesis that for a random group of genes in the same size as the original one, the difference between the number of conserved binding sites between this group and all genes is as good as for the original group or higher.
Analysis of target genes for miR-17-92 cluster miRNAs was done using Gorilla tool [30]. Genes with at least two conserved binding sites for miRNAs in the cluster were defined as the “target set”, and were analyzed in comparison to the background group which was the whole genome.
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10.1371/journal.pgen.1004991 | Interaction between the tRNA-Binding and C-Terminal Domains of Yeast Gcn2 Regulates Kinase Activity In Vivo | The stress-activated protein kinase Gcn2 regulates protein synthesis by phosphorylation of translation initiation factor eIF2α. Gcn2 is activated in amino acid-deprived cells by binding of uncharged tRNA to the regulatory domain related to histidyl-tRNA synthetase, but the molecular mechanism of activation is unclear. We used a genetic approach to identify a key regulatory surface in Gcn2 that is proximal to the predicted active site of the HisRS domain and likely remodeled by tRNA binding. Mutations leading to amino acid substitutions on this surface were identified that activate Gcn2 at low levels of tRNA binding (Gcd- phenotype), while other substitutions block kinase activation (Gcn- phenotype), in some cases without altering tRNA binding by Gcn2 in vitro. Remarkably, the Gcn- substitutions increase affinity of the HisRS domain for the C-terminal domain (CTD), previously implicated as a kinase autoinhibitory segment, in a manner dampened by HisRS domain Gcd- substitutions and by amino acid starvation in vivo. Moreover, tRNA specifically antagonizes HisRS/CTD association in vitro. These findings support a model wherein HisRS-CTD interaction facilitates the autoinhibitory function of the CTD in nonstarvation conditions, with tRNA binding eliciting kinase activation by weakening HisRS-CTD association with attendant disruption of the autoinhibitory KD-CTD interaction.
| The survival of all living organisms depends on their capacity to adapt their gene expression program to variations in the environment. When subjected to various stresses, eukaryotic cells modulate general and gene-specific protein synthesis by phosphorylating the α-subunit of eukaryotic translation initiation factor 2 (eIF2α). The yeast Saccharomyces cerevisiae has a single eIF2α kinase, Gcn2, activated by uncharged tRNAs that accumulate in amino acid starved cells, which bind to a regulatory domain homologous to histidyl-tRNA synthetase (HisRS). Gcn2 also contains a C-terminal domain implicated in autoinhibition of Gcn2. Our findings identify a direct interaction between the CTD and a novel regulatory surface in the HisRS domain that is required for inhibition of Gcn2 function in non-starved cells, which is down-regulated by uncharged tRNA. The results further suggest that tRNA binding to the pseudo-active site in the HisRS domain remodels its proximal CTD-binding surface to weaken HisRS/CTD interaction and thereby release the autoinhibitory function of the CTD to activate kinase function. This study provides new molecular insights into how tRNA binding can modulate regulatory interactions among the HisRS, CTD, and kinase domains of Gcn2 to elicit kinase activation.
| Eukaryotic cells harbor stress-activated protein kinases that allow cells to reduce bulk protein synthesis while simultaneously activating the transcription of genes encoding stress management proteins. The target of these kinases is Ser-51 of the α-subunit of translation initiation factor 2 (eIF2α). Phosphorylation of eIF2α reduces the function of eIF2 in recruiting methionyl initiator tRNA to the 40S ribosomal subunit by impairing the recycling of eIF2-GDP to eIF2-GTP by guanine exchange factor eIF2B and thereby reducing the cellular concentration of eIF2·GTP·Met-tRNAiMet ternary complexes. The inhibition of ternary complex assembly diminishes the rate of general translation but enables translation preinitiation complexes to bypass multiple “decoy” AUG start codons in the mRNA leader of GCN4 mRNA and translate the coding sequences for Gcn4, a transcriptional activator of amino acid and vitamin biosynthetic genes in budding yeast (reviewed in [1]). A similar mechanism up-regulates translation of mammalian ATF4 and ATF5 mRNAs when eIF2α is phosphorylated by Gcn2 or one of the other mammalian eIF2α kinases PKR, PERK and HRI [2] [3]. PKR is a key component of the innate immune response, PERK is crucial for responding to ER stress, and HRI couples globin synthesis to heme availability in reticulocytes [4]. Interestingly, rodent Gcn2 mediates the animal’s aversion to amino acid-deficient diets [5], dampens protein synthesis in muscle during leucine starvation [6], and functions in lipid homeostasis [7] and in learning and memory formation [8]. Mammalian Gcn2 has also been implicated in tumor cell survival, innate and T-cell mediated immune responses, and DNA repair (reviewed in [9]); and recently mutations in human Gcn2 were linked to pulmonary hypertension [10]. Hence, elucidating the molecular mechanism of Gcn2 regulation is of importance to multiple aspects of human development and physiology.
Because eIF2α kinases act by inhibiting translation, their functions must be tightly regulated to limit eIF2α phosphorylation to the appropriate stress conditions. The Gcn2 kinase domain (KD) is intrinsically inert and depends on interactions with four other domains within Gcn2 to achieve an active conformation (Fig. 1A) [11]. Latency of Gcn2 KD activity depends on a rigid hinge connecting the N- and C-lobes of the KD, promoting a partially closed active site cleft and occluded ATP-binding pocket, and also on a non-productive orientation of helix αC in the N-lobe that blocks the proper positioning of ATP phosphates for catalysis (Fig. 1B) [12,13]. Binding of uncharged tRNA to the region immediately C-terminal to the KD, related in sequence to histidyl-tRNA synthetase (HisRS) is required to activate Gcn2 in amino acid-starved cells (Fig. 1A) [14,15,16,17]. Authentic HisRS is the enzyme that aminoacylates tRNAHis for protein synthesis. Consistent with the fact that Gcn2 is activated by starvation for any amino acid [15], the Gcn2 HisRS-related domain (henceforth, just HisRS domain) is not specific for binding histidyl tRNA [17]. An N-terminal segment in the HisRS domain that interacts with a portion of the KD containing the hinge is required for kinase activation [18], suggesting that tRNA binding might alter the HisRS-KD interface to evoke an active conformation of the KD. A pseudokinase domain (YKD), incapable of binding ATP or Mg+2 in vitro [19], is located just N-terminal to the KD and is also required for kinase activation (Fig. 1A) [16,20]. Our recent work established that the YKD must interact directly with the KD for kinase activation and identified likely KD-YKD contact sites that can be altered to either impair or constitutively activate Gcn2 kinase function in vivo [21].
The extreme C-terminal domain (CTD) of Gcn2 plays multiple roles in kinase regulation, both positive and negative, including dimerization, ribosome binding, and autoinhibition of the KD (Fig. 1A) [1]. Activation of Gcn2 is dependent on KD dimerization [22] in a back-to-back, parallel orientation (Fig. 1B), as described for the active dimer of PKR [23]. While the KD, HisRS region, and CTD are all capable of self-interaction as isolated domains, only the CTD is essential for dimerization and attendant activation of full-length Gcn2 [24,25]. Gcn2 likely dimerizes constitutively through CTD self-interaction [24], and it is possible that the mode of KD dimerization switches from the antiparallel orientation seen in the crystal structure of the inactive conformation of the Gcn2 KD [12] to the parallel, PKR-like mode of dimerization required for kinase function (Fig. 1B) [22] [23]. Recent work elucidated the three-dimensional structure of the CTD dimer, which is evident with some differences in both yeast and mammalian Gcn2 [26].
In addition to dimerization, the CTD mediates ribosome association of Gcn2 (Fig. 1A) [27], which depends on conserved basic residues that mediate RNA binding by the CTD in vitro and are crucial for activation of Gcn2 in vivo [28]. Gcn2 activation also requires trans-acting factors Gcn1 and Gcn20, which form a complex that must interact with both the N-terminal “RWD” domain of Gcn2 and translating ribosomes for Gcn2 activation in starved yeast cells [29,30,31,32,33]. These and other findings [34], support the model that Gcn2 is activated by uncharged tRNA that binds first to the decoding center of a translating ribosome and is subsequently transferred to the HisRS domain in Gcn2, with Gcn1/Gcn20 enhancing one or both of these reactions involving uncharged tRNA [33]. However, stable association of mammalian Gcn2 with ribosomes was not observed [26], and it was proposed that the RNA binding activity of the CTD supports tRNA binding by mammalian Gcn2, in the manner described previously for yeast Gcn2 [17].
The yeast Gcn2 CTD also appears to interact with the KD in a manner that impedes kinase activation (Fig. 1A) [17,18], as a mutation that constitutively activates Gcn2 kinase function, GCN2c-E803V (substitutes Glu-803 for Val in the KD) weakens interaction between the isolated KD and CTD and also increases tRNA binding by Gcn2 in vitro [17,18]. Consistent with an autoinhibitory function, eliminating the CTD from mouse Gcn2 activates eIF2α phosphorylation and abrogates stimulation by uncharged tRNA in vitro [35]. The finding that tRNA competed for interaction between the isolated KD and a HisRS-CTD segment of yeast Gcn2 in vitro [17] led to the proposal that the HisRS and CTD domains both dissociate from the KD on tRNA binding. However, complete dissociation of the HisRS domain seems incompatible with the subsequent finding that an N-terminal segment of the HisRS region interacts with the KD and is crucial for Gcn2 activation at a step following tRNA binding, suggesting that this portion of the HisRS domain remains engaged with the KD in the activated state [18]. In addition to the autoinhibitory CTD-KD interaction, the CTD mediates an inhibitory interaction with translation elongation factor eEF1A that can be overcome by uncharged tRNA [36].
While it is clear that tRNA binding to the HisRS domain is required for activation, and a stimulatory interaction of the HisRS-N region with the KD seems likely, it was unclear how tRNA binding might antagonize the autoinhibitory KD-CTD interaction and simultaneously promote stimulatory association of the YKD with the KD. In an effort to answer this question, we identified substitutions in the HisRS region that restore kinase activation by the gcn2-m2 variant, which harbors substitutions in conserved residues of the predicted HisRS active site cleft that impair tRNA binding in vitro and kinase activation in vivo [15,16,17]. We reasoned that such m2 suppressors could alter a regulatory surface in the HisRS whose interactions with another domain are modulated by tRNA binding in a manner mimicking the tRNA-bound state of WT Gcn2. Interestingly, the locations of these suppressors led us to identify a regulatory patch predicted to be surface-exposed and proximal to the region in the HisRS domain corresponding to the active site of authentic HisRS, below dubbed the “pseudo-active site”, which can be altered to either activate or impede Gcn2 function. Our finding that certain of the (Gcn-) inactivating substitutions strengthen HisRS-CTD interaction without affecting tRNA binding in vitro implies that one stimulatory consequence of tRNA binding is to weaken HisRS-CTD association. This inference leads to an appealing model for how tRNA binding releases the autoinhibitory KD-CTD interaction, promotes YKD-KD association, and thereby activates Gcn2.
In an effort to identify residues in the Gcn2 HisRS domain involved in regulation of kinase function by uncharged tRNA, we randomly mutagenized the coding sequence for the HisRS domain in a plasmid-borne gcn2-m2 allele and selected for suppressors of the sensitivity to 3-aminotriazole (3-AT) conferred by this allele in yeast cells. The m2 mutation substitutes two residues in highly conserved motif 2 in the pseudo-active site of the HisRS domain, impairing tRNA binding by Gcn2 in vitro and abolishing activation of Gcn2 kinase function in vivo [15,17]. Defective activation of Gcn2 confers sensitivity to 3-AT, an inhibitor of histidine biosynthesis, by preventing Gcn2-dependent induction of GCN4 translation and attendant derepression of histidine biosynthetic enzymes under Gcn4 control. Thus, transformants of a gcn2∆ strain harboring WT GCN2 or GCN2c-M788V, whose product is constitutively activated [37], grow well on 3-AT medium, whereas gcn2-m2 transformants do not (Fig. 2A, rows 1–3).
Interestingly, we identified 3 mutations in the HisRS domain that suppress the 3-ATS phenotype of the m2 mutation, with the strongest growth on 3-AT displayed by the m2,T1328S mutant (Fig. 2A, 3-AT, rows 4–6 vs. 2). As expected, a mutant allele combining all three suppressors with m2 also confers a strong 3-ATR phenotype (Fig. 2, row 7). Comparison of the single and triple suppressor alleles at elevated temperature (37°), which exacerbates sensitivity to 3-AT, reveals that combining the suppressor mutations in one allele confers greater resistance to 3-AT than that given by any of the single suppressors (S1 Fig.).
The allele combining all three suppressors with m2 additionally conferred resistance to a combination of tryptophan analog 5-fluorotryptophan and histidine analog triazolealanine (Fig. 2A, 5-FT/TRA, row 7). The 5FTR/TRAR phenotype signifies constitutive, Gcn4-mediated derepression of tryptophan and histidine biosynthetic enzymes, known as the Gcd- phenotype [37]. Accordingly, the GCN2c-M788V allele confers growth on 5-FT/TRA medium, whereas GCN2+ cells, and Gcn- strains like gcn2-m2, are sensitive to these analogs (Fig. 2A, rows 1–3). GCN2c-M788V alters the ATP binding pocket of the KD to elevate kinase activity at low levels of uncharged tRNA [12,37]. Thus, it appears that combining all three m2 suppressors confers constitutive activation of Gcn2, even in the presence of the m2 mutation.
In accordance with their suppression of the 3-ATS phenotype of m2, all three suppressor mutations also restored Gcn2 kinase function under starvation conditions. Western blot analysis of whole cell extracts (WCEs) revealed that 3-AT evokes the expected increase in eIF2α phosphorylated on Ser-51 (eIF2α-P) relative to total eIF2α in GCN2 cells, whereas m2 cells have no detectable eIF2α-P; and M788V cells display high-level eIF2α with and without 3-AT treatment (Fig. 2B, lanes 1–6). Importantly, each of the suppressor alleles restored 3AT induction of eIF2α-P in m2 cells, without increasing Gcn2 abundance (Fig. 2B, lanes 7–14). In agreement with its 5FTR/TRAR phenotype, the m2 mutant harboring all three suppressors also displayed a greater than WT level of eIF2α-P in nonstarvation conditions (Fig. 2B, lane 13 vs. 1), indicating constitutive activation of Gcn2. Consistent with these findings, the m2 suppressors increase expression of a Gcn4-dependent HIS4-lacZ reporter [37]. HIS4-lacZ expression in 3-AT-starved cells is ~8-fold lower in m2 versus WT cells, and each of the mutants containing one or more suppressor mutations displays substantially higher reporter expression in 3-AT treated cells compared to that seen in the m2 single mutant, although only a slight increase was observed for the A1353V suppressor (Fig. 2C). The particularly large increases in HIS4-lacZ expression observed for the m2 strains harboring T1328S or the triple suppressor mutation are consistent with their marked 3-AT-resistant phenotypes (Fig. 2A).
It is noteworthy that all of the suppressor strains display an induction of eIF2α-P in response to 3-AT treatment (Fig. 2B, lanes 7–14, 3-AT + vs.–), implying that their Gcn2 variants can be activated by uncharged tRNA accumulating in histidine-deprived cells. As demonstrated below, the m2 mutation reduces, but does not abolish tRNA binding by Gcn2 in vitro. It was possible, therefore, that the suppressor mutations overcome the activation defect of m2 simply by restoring robust tRNA binding by the HisRS domain. Alternatively, they could increase the ability of low-levels of tRNA bound by the m2 variant of the HisRS domain to activate Gcn2. If the latter was true, we reasoned that the suppressor mutations should elevate eIF2α phosphorylation when separated from the m2 mutation in nonstarvation conditions by enabling Gcn2 activation by the low, basal level of uncharged tRNA present in amino acid-replete cells. Consistent with this last prediction, when separated from m2, the Y1092C and triple suppressor mutations each evoked strong resistance to 5-FT/TRA (Fig. 2A, rows 10,11,14). They also conferred marked increases in eIF2α-P (Fig. 2D, lanes 5,7,13) and HIS4-lacZ expression (Fig. 2E, lanes 3,4,7) under nonstarvation conditions, comparable in degree to that given by GCN2c-M788V. The A1353V single mutation conferred smaller increases in eIF2α-P accumulation and derepression of HIS4-lacZ (Fig. 2D, lane 1 vs. 11; Fig. 2E, column 1 vs. 6). Thus, it appears that the triple suppressor mutation, Y1092C, and to a lesser extent A1353V increase the ability of low-level uncharged tRNA present in nonstarved cells to stimulate Gcn2 kinase function. The triple mutant was chosen as the exemplar Gcd- variant for subsequent biochemical studies described below.
Surprisingly, despite being the most effective suppressor of m2, the T1328S single mutation produced only a slight increase in eIF2α-P (Fig. 2D) and no increase in resistance to 5-FT/TRA or HIS4-lacZ expression (Fig. 2A & E). Thus, although T1328S restores robust activation of the m2 variant in starved cells, it does not appreciably activate otherwise WT Gcn2 in nonstarvation conditions.
To evaluate the locations of the m2 suppressors in the predicted structure of the HisRS domain, we constructed a multiple sequence alignment of Gcn2 HisRS domain sequences from various fungal species (S2 Fig.), and also an alignment of a subset of these HisRS domain sequences with authentic HisRS enzymes from diverse eukaryotic species (S3 Fig.). The latter reveals regions of considerable sequence similarity between authentic HisRS and the Gcn2 HisRS domains spanning the region extending from motifs 1 and 2, conserved in all class II aminoacyl tRNA synthetases, portions of the insertion domain between motifs 2 and 3 and the HisA and HisB motifs unique to HisRS enzymes, and the N-terminal half of class II motif 3. Interestingly, the three m2 suppressors Y1092C, T1328S, and A1353V alter residues in the vicinity of motif 2, HisB, and within motif 3, respectively (Fig. 3A and S3 Fig.). Conserved residues of these motifs include active site residues that directly contact different moieties of the intermediate HAM formed in the first step of tRNA aminoacylation (Fig. 3A and S3 Fig., residues labeled with H (histidyl), P (phosphate), S (sugar), or A (adenine)). These critical residues are color-coded in the “ribbons” depiction of the crystal structure of the T. cruzi HisRS-HAM complex shown in Fig. 3C (salmon (H), cyan (P), orange (S), or dark gray (A)). Interestingly, six GCN2c mutations described previously [37] also alter residues located in or nearby the conserved HisRS motifs in the primary sequence, including F1134L and D1138N (motif 2), A1197G (insertion domain), N1295D and H1308Y (near HisA), and G1338D (motif 3) (Fig. 3A and S3 Fig.).
Because the structure of the Gcn2 HisRS domain is unknown, we used the sequence alignment between Gcn2 and authentic HisRSs (S3 Fig.) and the crystal structure of T. cruzi authentic HisRS to predict the locations of m2 suppressors and GCN2c substitutions in the three-dimensional structure of the Gcn2 HisRS domain (Fig. 3B). It is striking that all three m2 suppressors and 5 of the 6 previously identified GCN2c mutations alter residues within, or in proximity to, the pseudo-active site of the HisRS domain (green residues: m2 suppressors; blue residues previously known GCN2c mutations). In fact, several mutations alter residues corresponding to amino acids in HisRS that make direct contacts with the adenine (F1134L), histidyl (D1138N), or ribose (A1353V) moiety, while others are located only one or two residues away in the polypeptide chain from amino acids contacting the histidyl (Y1092C and T1328S) or phosphate (N1295) moiety of HAM (Fig. 3A, S3 Fig.; cf. Fig. 3B-C). In the cases of T1328S, A1197V and G1338D, these residues are predicted to be surface-exposed and (at least for T1328S and G1338D) in proximity to one another (Fig. 3D-E) at the “top” of the predicted pseudo-active site cleft of the Gcn2 HisRS domain (Fig. 3B).
The predicted locations of these last three substitutions led us to consider a model in which this surface of the HisRS domain interacts with another region in Gcn2 to regulate kinase function in a manner that is modulated by binding of uncharged tRNA to the pseudo-active site. In this view, the putative regulatory interaction involving this patch of the HisRS domain would be altered by the Gcd- m2 suppressors and GCN2c substitutions mapping in the HisRS domain in a way that mimics the effect of uncharged tRNA binding to the pseudo-active site of the WT Gcn2 HisRS domain.
We reasoned that if the foregoing hypothesis is correct, then it should be possible to isolate Gcn- substitutions affecting the same exposed surface of the HisRS domain altered by the m2 suppressor T1328S and Gcd- substitution G1338D, but with the opposite effect on its putative regulatory interactions with other Gcn2 domains. To test this idea, we first determined the degree of sequence conservation of residues on this face of the HisRS domain by projecting the sequence conservation scores obtained from the alignment of fungal Gcn2 HisRS domains (S2 Fig.) onto a surface representation of the crystal structure of T. cruzi HisRS (Fig. 3E). We then determined the phenotypes conferred by substituting two highly conserved residues, Arg-1325 and Asp-1327, which are surface exposed and located in proximity both to one another and the residues altered by T1328S and G1338D (Fig. 3D-E).
Strikingly, substitutions of Arg-1325 with Ala or Glu (R1325A, R1325E) and substitutions of Asp-1327 with Ala or Lys (D1327A, D1327K) completely abrogate Gcn2 function. Thus, all four substitutions confer strong sensitivity to 3-AT (Fig. 4A), eliminate detectable eIF2α-P in both nonstarvation and starvation conditions (Fig. 4B), and evoke low basal expression of the HIS4-lacZ reporter at levels comparable to, or even below, that given by the m2 mutation (Fig. 4C); and all of these Gcn- phenotypes occur without any reduction in the level of Gcn2 itself (Fig. 4B). These findings are consistent with the possibility that highly conserved residues Arg-1325 and Asp-1327 are critical constituents of a regulatory patch exposed on the surface of the HisRS domain near the pseudo-active site cleft (Fig. 3E).
Interestingly, a mutant combining the strong Gcn- mutation D1327K with the Gcd- triple m2 suppressor Y1092C/T1328S/A1353V described above exhibits a 3-AT-sensitive phenotype (Fig. 4A) and a defect in derepression of HIS4-lacZ expression (Fig. 4C) nearly indistinguishable from those seen for the D1327K single mutant, indicating that the strong activation defect conferred by D1327K is epistatic to the constitutively activating phenotype of the Gcd- suppressor substitutions.
We proposed above that the Gcd- substitutions identified as m2 suppressors restore Gcn2 kinase function to the m2 variant by altering a regulatory interaction of the HisRS domain in a way that mimics the effect of uncharged tRNA and allows for Gcn2 activation at low levels of bound tRNA. To bolster this view and eliminate the alternative possibility that they simply overcome the effect of m2 of impairing tRNA binding, we purified the gcn2-m2 product and the Gcn2 variant harboring the m2 substitutions in combination with all three suppressor substitutions, and compared them to WT Gcn2 for binding [32P]-labeled total tRNA using a gel mobility shift assay to detect Gcn2-tRNA complexes. In accordance with previous results [15,17], the m2 product displayed an obvious defect in tRNA binding compared to WT Gcn2; however, unlike the results of our previous studies, it retained appreciable tRNA binding activity (Fig. 5A). (This disparity in results might be attributable to the fact that, unlike the gcn2-m2 protein examined here, this variant was unstable and subject to degradation when purified from a different yeast strain used in previous studies [17].) The fact that m2 does not abolish tRNA binding in vitro but completely impairs activation of Gcn2 in vivo might indicate that the m2 substitutions in the pseudo-active site cleft impair a regulatory interaction of the HisRS domain with another region in Gcn2 in addition to reducing tRNA binding. Importantly, the presence of all three suppressors in a quadruple mutant harboring the m2 substitutions did not increase the tRNA binding activity compared to that measured for gcn2-m2 (Fig. 5A). These findings are consistent with our conclusion that the suppressor substitutions restore eIF2α-P formation by enhancing kinase function at the low tRNA occupancy permitted by the m2 substitutions, rather than restoring high-level tRNA binding to the HisRS domain.
We also examined whether Gcn- substitutions affecting the conserved, surface-exposed residues Arg-1325 and Asp-1327 proximal to the pseudo-active site affect tRNA binding. Importantly, we saw little or no effect on tRNA binding by the Gcn- substitutions D1327A and D1327K (Fig. 5B), implying that they impair activation of Gcn2 by disrupting the ability of bound tRNA to trigger activation of kinase function rather than reducing the amount of bound tRNA. By contrast, the Gcn- substitution of Arg-1325, R1325A, abolished tRNA binding by Gcn2 (Fig. 5C), making it likely that substitutions of this residue impair Gcn2 activation by reducing the level of bound tRNA, although they could also disrupt the proposed regulatory interactions involving the HisRS pseudo-active site. In accordance with previous findings, deletion of HisRS residues 1048–1071 evokes a greater than WT level of tRNA binding by Gcn2 (Fig. 5C), supporting our previous conclusion that removing this N-terminal segment of the HisRS domain impairs Gcn2 activation by disrupting a stimulatory interaction of the tRNA-bound HisRS domain with the KD rather than impairing tRNA binding by Gcn2 [18]. Based on its reduced electrophoretic mobility, the gcn2-∆1048–1071 variant might also exhibit a less compact conformation compared to WT Gcn2.
We wished to confirm that the key regulatory mutations of interest, the Gcn- substitutions D1327A and D1327K, and the Gcd- triple substitution Y1092C/T1328S/A1353V, alter Gcn2 kinase activity in vitro in the manner predicted by their phenotypes in vivo. To this end, we conducted in vitro kinase assays with the relevant purified Gcn2 proteins using [γ-32P]-ATP and a truncated form of recombinant eIF2α as substrates, and employed SDS-PAGE/autoradiography to detect the reaction products. It was shown previously that WT Gcn2 displays similar kinase activity whether purified from starved or nonstarved cells, but that the m2 mutation reduces kinase activity in vitro, indicating that WT Gcn2 becomes activated in vitro by deacylated tRNA in cell lysates prior to purification [16]. Thus, although yeast Gcn2 cannot be activated further by adding tRNA to kinase assays, the activity levels of Gcn2 variants with HisRS domain substitutions should reflect their abilities to be activated by tRNA during purification. Consistent with their Gcn- phenotypes, the D1327A and D1327K variants also exhibit substantially reduced autophosphorylation and eIF2α substrate phosphorylation activities in vitro (Fig. 5D). Moreover, the Gcd- variant Y1092C/T1328S/A1353V exhibits an obvious increase in kinase activity relative to WT Gcn2 (Fig. 5D).
Our finding that Gcn- variants D1327A/D1327K are completely defective for Gcn2 activation (Fig. 4A) but retain robust tRNA binding activity (Fig. 5B) made them good candidates for mutations that alter a regulatory interaction of the HisRS region that mediates allosteric activation of kinase function by uncharged tRNA. Previously, we demonstrated that distinct segments of the isolated HisRS domain interact with the isolated KD or CTD of Gcn2 [18]. As noted above, the N-terminal HisRS segment (HisRS-N) interacts with the KD and the Δ1048–1071 deletion in this region impairs Gcn2 activation without reducing tRNA binding, thus identifying a stimulatory HisRS-N/KD interaction [18]. Moreover, the C-terminal HisRS segment (HisRS-C) was shown to interact with the CTD [18], and as it encompasses Asp-1327, we hypothesized that the Gcn- D1327A/D1327K substitutions impair Gcn2 function by altering the HisRS-CTD interaction.
We obtained evidence supporting this hypothesis using the yeast two-hybrid assay. In agreement with previous results [24], a LexA-fusion to the WT Gcn2 HisRS domain shows little interaction with a fusion of the B42 activation domain to the CTD. Remarkably, introducing Gcn- substitutions D1327A or D1327K into the lexA-HisRS fusion greatly enhanced this two-hybrid interaction (Fig. 6A). By contrast, the Gcd- triple substitution Y1092C/T1328S/A1353V had no significant effect on the HisRS/CTD interaction when introduced into otherwise WT lexA-HisRS. Interestingly, however, these Gcd- substitutions diminished the enhanced two-hybrid interaction conferred by the D1327K Gcn- substitution (Fig. 6A).
In an effort to confirm the two-hybrid findings, we examined in vitro interaction of a LexA-CTD fusion expressed in yeast cells with immobilized GST fusions containing mutant or WT HisRS segments purified in yeast. As both the HisRS and CTD segments have RNA binding activity [17,28], the reactants were treated with micrococcal nuclease to eliminate indirect association between these segments bridged by RNA. Paralleling the two-hybrid results, the D1327K substitution greatly increased binding of LexA-CTD to GST-HisRS compared to the low-level binding observed for both WT GST-HisRS and the variant harboring the Gcd- triple substitution Y1092C/T1328S/A1353V; and introducing the Gcd- triple substitution reduced binding by the D1327K variant (Fig. 6B). It could be argued that the truncated species of the GST-HisRS-D1327K fusion that are not observed for WT GST-HisRS (lower blot, lane 3 vs. 2) mediate the relatively greater binding of LexA-CTD by GST-HisRS-D1327K; however, this interpretation is inconsistent with the fact that the GST-HisRS fusion harboring substitutions D1327K/Y1092C/T1328S/A1353V contains even greater levels of the truncated species but binds relatively smaller amounts of LexA-CTD compared to GST-HisRS-D1327K (Fig. 6B, lane 5 vs. 3). Our finding that Y1092C/T1328S/A1353V does not reduce the HisRS/CTD interaction when introduced into the otherwise WT HisRS segment might be explained by proposing that a physiologically relevant interaction between the WT HisRS and CTD domains cannot be captured outside of the context of full-length Gcn2 in two-hybrid or pull-down assays unless the HisRS segment contains the Gcn- substitutions D1327A/D1327K that stabilize HisRS/CTD association. Together, the findings in Fig. 6A-B suggest that the D1327A/D1327K substitutions impair activation of Gcn2 by strengthening the HisRS-CTD domain interaction, while the Gcd- substitution Y1092C/T1328S/A1353V activates Gcn2 by weakening HisRS/CTD association.
A corollary of this last conclusion is that the HisRS-CTD domain interaction in WT Gcn2 stabilizes the inactive conformation of Gcn2, which would persist constitutively in the Gcn- mutants D1327A/D1327K with attendant impairment of Gcn2 activation. If so, then binding of uncharged tRNA to the HisRS region might be expected to weaken the HisRS-CTD domain interaction as one means of activating Gcn2. Supporting this possibility, we found that the enhanced HisRS-CTD two-hybrid interactions conferred by D1327A or D1327K in vivo were abolished by starving the cells for isoleucine/valine by treatment with sulfometuron methyl (SM) (Fig. 6C), an inhibitor of the ILV2-encoded biosynthetic enzyme, which is known to increase the level of uncharged tRNAIle and tRNAVal and activate Gcn2 in vivo [15,38]. By contrast, the previously demonstrated two-hybrid interaction between LexA-KD and B42-CTD fusion proteins was unaffected by SM treatment (Fig. 6D), as was expression of the two-hybrid reporter conferred by the LexA-B42 activator. These findings are consistent with the idea that accumulation of uncharged tRNAIle and tRNAVal and their attendant increased binding to the LexA-HisRS-D1327A and LexA-HisRS-D1327K fusions weakens the ability of these LexA-HisRS proteins to form complexes with the B42-CTD fusion in vivo. They also support the idea that the gcn2-D1327A and gcn2-D1327K variants are defective for a regulatory interaction with the CTD that is normally disrupted by uncharged tRNA binding to the HisRS domain.
To provide additional evidence that tRNA binding to the HisRS domain reduces its ability to interact with the CTD, we examined the effect of tRNA on this interaction in vitro. Consistent with previous results [18,24], [35S]-methionine labeled HisRS fragment can be pulled down with immobilized GST fusions containing the Gcn2 KD, CTD or HisRS region itself, with the last interaction reflecting dimerization of the HisRS domain [18] (Fig. 6E). Addition of increasing amounts of purified yeast tRNAPhe reduced interaction of the [35S]-HisRS fragment with all three GST fusions; however, the magnitude of the reduction was larger for GST-CTD (~5.4-fold) compared to GST-KD (~1.8-fold) or GST-HisRS (~2.1-fold). Moreover, interaction of [35S]-HisRS with GST-CTD was unaffected by an equivalent concentration of an unstructured model mRNA [39] (Fig. 6F), suggesting specificity for tRNA in weakening the HisRS-CTD domain interaction. Together, these findings provide evidence that a tight interaction between the HisRS and CTD domains favors the inactive conformation of Gcn2 and that tRNA binding to the HisRS domain activates Gcn2 at least partly by weakening the HisRS-CTD interaction. As discussed below, based on previous findings indicating that direct interaction of the CTD with the KD contributes to the latency of Gcn2 kinase function [18], we propose that the HisRS-CTD interaction helps to stabilize this inhibitory CTD-KD interaction in a manner that is diminished by uncharged tRNA binding to the HisRS domain in amino acid-starved cells.
The model alluded to above envisions that the non-activated state of Gcn2 is characterized by domain interactions between the HisRS-C and CTD, and between the CTD and KD, which are destabilized by tRNA binding to the HisRS domain to evoke the activated state. We reasoned that the Y1092C/T1328S/A1353V Gcd- substitutions, which destabilize the HisRS/CTD interaction and confer constitutive activation of Gcn2, would evoke a conformational change in full-length Gcn2 that mimics the activated, tRNA-bound state of WT Gcn2. Supporting this possibility, we found that the Gcd- triple substitution increases the sensitivity of full-length purified Gcn2 to digestion by elastase, reducing the amount of full-length protein remaining after a fixed time of incubation compared to WT Gcn2 or the Gcn- variant D1327K (S4 Fig.). Trypsin digestion also reduced the amounts of the largest intermediates in addition to the full-length protein for the Y1092C/T1328S/A1353V variant compared to the WT and D1327 proteins (S4 Fig.). Judging by the amount of full-length Gcn2 remaining after partial digestion, the Gcn- variant D1327K appears to be somewhat less sensitive than WT Gcn2 to protease digestion, consistent with the tighter HisRS/CTD domain interaction conferred by D1327K (Fig. 6A-C); although this difference is less pronounced than that between WT and the Y1092C/T1328S/A1353V variant (S4 Fig.). These results support the idea that activation of Gcn2 by the Y1092C/T1328S/A1353V substitutions involves the elimination of inhibitory domain interactions, which favors a less compact conformation of Gcn2. It should be noted that in separate experiments we observed a decrease in protease sensitivity of WT Gcn2 on addition of excess tRNAPhe. While this result is ostensibly at odds with the notion that tRNA binding evokes a more extended, protease-sensitive conformation of Gcn2, it seems possible that contacts between tRNA and the HisRS or CTD domains would reduce protease access to these Gcn2 segments and compensate for loss of protein-domain interactions in the tRNA-free state of Gcn2.
In this study, we used a genetic approach to identify a novel regulatory surface in the HisRS domain of Gcn2, juxtaposed to the pseudo-active site cleft where tRNA binds, which participates in the activation of kinase function in amino acid starved cells through its association with the Gcn2 CTD. One of the residues belonging to this regulatory surface, Thr-1328, was identified by isolating suppressors of the m2 lesion in motif 2 of the HisRS domain, a mutation that reduces tRNA binding to Gcn2 and abolishes activation of kinase function in starved cells. Two other m2 suppressors alter residues Tyr-1092 and Ala-1353 located within the pseudo-active site cleft. The suppressor substitution Y1092C, as well as the combination of all three suppressor substitutions in the same protein, confer constitutive activation of Gcn2 function in the absence of the m2 substitutions—the Gcd- phenotype—and we showed that the triple substitution does not suppress the tRNA binding defect evoked by m2. Hence, rather than influencing the level of tRNA binding, we propose that these mutations evoke a conformational change in the HisRS domain that mimics the consequences of tRNA binding to the WT HisRS region, which then mediates activation of the adjacent KD. In this view, the m2 suppressors allow rearrangement of Gcn2 to the active conformation at a lower occupancy of tRNA in the HisRS pseudo-active site. This alteration would compensate for the reduced affinity for tRNA of the m2 variant, and in the cases of Y1092C and A1353V allow for activation of otherwise WT Gcn2 by the basal level of uncharged tRNA in non-starved cells to produce the Gcd- phenotype. Our conclusion above that T1328S corrects the activation defect conferred by m2 but does not appreciably activate otherwise WT Gcn2, ie. T1328S is not Gcd-, indicates that replacing Thr with Ser at this position promotes tRNA binding only in the context of the m2 alterations of the HisRS pseudo-active site. This restricted efficacy of T1328S is consistent with the fact that Thr-1328 is not evolutionarily conserved in Gcn2 HisRS domains, and is even substituted with Ser in some species (S2 Fig.). Given that the m2 lesion abolishes Gcn2 activation in vivo but only reduces tRNA binding in vitro, the m2 substitutions might also impair regulatory interactions of the HisRS domain that can be compensated by the m2 suppressors.
A second line of evidence supporting this model is that all 6 previously identified GCN2c mutations affecting the HisRS domain [37] involve substitutions mapping within, or proximal to, the pseudo-active site cleft. These mutations were identified by screening randomly mutagenized GCN2 alleles for the Gcd- phenotype, rather than selecting for m2 suppressors. The striking clustering of these 6 GCN2c substitutions in the predicted structure of the HisRS domain suggests that the pseudo-active site cleft is the key regulatory hub in this domain. The GCN2c mutations D1138N and F1134L alter residues in proximity to those substituted by the m2 suppressors Y1092C and A1353V within the pseudo-active site (Fig. 3B) and thus, according to our model, would evoke a rearrangement of the active site that mimics the effect of tRNA binding. The GCN2c mutations G1338D and A1197G introduce substitutions proximal to the active site, but located on a distinct surface, with Gly-1338 nearly adjacent on that surface to Thr-1328 (altered by the m2 suppressor T1328S). We envision that this surface patch in the WT HisRS domain communicates with the pseudo-active site and is remodeled by tRNA binding in a manner mimicked by the Gcd- substitutions G1338D, A1197G, and m2 suppressor T1328S.
A third line of genetic evidence supporting this model came from making targeted alanine substitutions of two HisRS domain residues that are invariant among Gcn2 homologs and exposed on the putative regulatory surface that circumscribes the Gcd- substitutions G1338D, A1197G, and m2 suppressor T1328S. Ala or Lys substitutions of the highly conserved residue D1327 completely abolish Gcn2 function in vivo while retaining robust tRNA-binding activity in vitro. It is remarkable that Gcn- and Gcd- substitutions of nearby residues belonging to this patch of the HisRS surface have opposite effects on Gcn2 activation. We envision that the Gcn- substitutions D1327K/D1327A either impede the proposed conformational remodeling of this surface patch induced by tRNA binding or alter the affinity of the remodeled surface for its binding partner within Gcn2.
The latter possibility is supported by our finding that Gcn- substitutions D1327K/D1327A enhance interaction between the HisRS and CTD domains, whereas the Gcd- triple substitution Y1092C/T1328S/A1353V partially reverses this effect in the quadruple mutant also containing D1327K. These findings imply that tight binding between the HisRS regulatory patch identified here and the CTD stabilizes the inactive conformation of Gcn2. Consistent with this, the increased yeast two hybrid interactions between the HisRS and CTD domains evoked by Gcn- substitutions D1327K/D1327A are eliminated under conditions of isoleucine/valine starvation, in which the uncharged cognate tRNAs accumulate and Gcn2 is activated. Furthermore, interaction between the WT HisRS and CTD domains was antagonized in vitro by tRNA, but not by an equal concentration of unstructured mRNA. These findings support the idea that one aspect of Gcn2 activation by uncharged tRNA involves the ability of tRNA bound to the HisRS domain to weaken HisRS/CTD interaction.
As noted above, we previously identified an autoinhibitory CTD/KD interaction that appears to be disrupted by tRNA binding to the HisRS domain [17,18]. More recently, we obtained strong evidence that the YKD domain stimulates Gcn2 activity by directly interacting with the KD, and proposed that the inhibitory CTD/KD interaction would compete with this stimulatory YKD/KD interaction, and that tRNA binding to the HisRS domain would shift the balance towards the stimulatory YKD/KD interaction [21]. Integrating our current findings with these previous results suggests the attractive possibility that tRNA binding to the HisRS domain antagonizes the HisRS/CTD interaction to promote a more open conformation of Gcn2 in which the CTD is less tightly bound to the KD. This would allow the YKD to compete more effectively with the CTD for binding to the KD, thereby eliminating autoinhibition by the CTD and correcting structural impediments to kinase activity inherent in the Gcn2 KD (Fig. 7A). Thus, the ability of tRNA binding to weaken the HisRS/CTD interaction would provide a mechanism that serves to replace the inhibitory KD/CTD interaction with the stimulatory YKD/KD interaction. Consistent with the idea that activation of Gcn2 involves rearrangement to a more open conformation lacking domain interactions between the CTD and both the HisRS-C and KD, we found that the activating Gcd- substitution Y1092C/T1328S/A1353V increases the sensitivity of purified WT Gcn2 to digestion by elastase and trypsin. However, high-resolution structural analyses of full-length Gcn2 in the presence and absence of tRNA are clearly required for a rigorous test our model in Fig. 7A.
A distinctive feature of authentic HisRS enzymes is that substrate binding involves an induced-fit mechanism in which histidine binding evokes movement of the “insertion domain” and HisA loop in a way that properly orients a key catalytic arginine residue (Arg-259/Arg-314 of E. coli/T. cruzi HisRS) for the formation of histidyl adenylate (HAM). Binding of ATP evokes additional motion of the m2 loop, which moves yet again on ejection of pyrophosphate following HAM formation [40] [41]. The presence of a bound HAM analogue increased the affinity of E. coli HisRS for tRNAHis [42], which might indicate that conformational changes induced by HAM binding also evoke a rearrangement of the active site that optimizes contacts with the acceptor stem of tRNAHis. Interestingly, it appears that the propensity of HisRS for histidine-induced rearrangement of the active site has been exploited to enable another HisRS-related protein, HisZ, to regulate the catalytic subunit of the octameric subfamily of ATP-phosphoribosyltransferase, HisG, an enzyme of histidine biosynthesis. HisZ contains the allosteric binding site for feedback-inhibition of HisG by histidine, and it is thought that conformational changes in the HisZ pseudo-active site evoked by histidine-binding evoke a remodeling of the HisG dimer interface to stabilize the inactive conformation [43]. Thus, in contrast to Gcn2, the binding of histidine rather than tRNA to the HisRS subunit (HisZ) allosterically regulates the catalytic activity of the binding partner (HisG), and the allosteric molecule (histidine) inhibits rather than stimulates the associated enzyme activity. Nevertheless, it seems plausible to propose that the pseudo-active site in the Gcn2 HisRS domain has evolved to evoke a conformational rearrangement of proximal, surface-exposed residues in response to binding of tRNA (rather than histidine) in the manner envisioned by our model. It is intriguing that the HisA loop, highly conserved among Gcn2 homologs (S2C Fig.), is predicted to be juxtaposed between the 3’ end of tRNA and the HisRS regulatory surface identified here (S5 Fig.), and thus could provide a path for transducing the aminoacylation status of the 3’ end of bound tRNA to the HisRS-CTD regulatory interface.
As noted above, we previously identified a positive regulatory interaction between the HisRS-N segment and the KD and mapped the KD-interacting region between residues 1028–1120 [18], which encompasses the N-terminal dimerization determinant we identified in the Gcn2 HisRS domain [18] (Fig. 7B, see orange and brown surfaces on the two protomers of the T. cruzi HisRS dimer). Interestingly, this region is contiguous with that corresponding to the portion of the HisRS-C segment that interacts with the CTD [18] (Fig. 7B, light and dark cyan surfaces that harbors the surface-exposed residues altered by the regulatory substitutions D1327A/D1327K and T1328S identified here (red residues in Fig. 7B). It is tempting to propose that the contiguity of the HisRS-N and HisR-C segments will juxtapose their respective interaction partners, the KD and CTD, and enable cooperativity in KD/CTD interaction (Fig. 7B-C). This model also seems compatible with the antiparallel mode of KD dimerization observed in the crystal structure of the inactive state of the Gcn2 KD [12] (Fig. 7C, red arrow connecting KDs in the two protomers). Eliminating the HisRS-C/CTD interaction on tRNA binding, as we proposed above, would eliminate the proposed cooperativity and destabilize CTD binding to the KD, allowing the YKD access to the KD instead (Fig. 7A). Release of the inhibitory HisRS-C/CTD interaction could also facilitate isomerization of the KDs to the parallel mode of dimerization required for their activation, and this alternative mode of dimerization could be further stabilized by the stimulatory YKD-KD interaction (Fig. 7A).
Multiple sequence alignments were generated using MUSCLE at http://www.ebi.ac.uk/Tools/msa/muscle/. ConSurf [44] and PyMOL [45] were used to obtain sequence conservation scores and project the surface representation of sequence conservation on the crystal structure of the Trypanosoma cruzi authentic HisRS (PDB:3HRK, Fig. 3E). To obtain a hypothetical model of the Gcn2 HisRS-uncharged tRNA complex, the co-crystal structure of S. cerevisiae AspRS-tRNAAsp complex (PDB: 1ASZ[Ref: PubMed: 8313877]) was aligned with T. cruzi HisRS (PDB: 3HRK) by superimposing the highly conserved catalytic core domain (327 residues) using Dali pairwise comparison with default parameters [Ref: PubMed: 19481444]. This alignment produced a robust Z score of 12.9, a RMSD of 3.0 Å, and minimal clashes between tRNAAsp and HisRS. A similar alignment procedure was previously used to model HisRS interaction with tRNAHis. [Ref: PubMed 7556055; PubMed 11329259] The locations of Gcn2 residues involved in this study were then projected onto the T. cruzi HisRS crystal structure based on the sequence alignment between Gcn2 and authentic HisRSs (S3 Fig.).
Plasmids employed are listed in Table 1. For Gcd- mutations identified by random mutagenesis, p2201 was subjected to error-prone PCR mutagenesis using the GeneMorph II kit (Stratagene) by using primer pairs PS-3 (5’-TCTATTTGATAACTCAGTTCCAAC-3’) and PS-4 (5’- TCAGGAATATGTATAAGAAAGGTGAC-3’). The KpnI-NheI 1.8-kb GCN2 fragment encoding the HisRS-CTD was isolated from plasmid DNA prepared from a pool of E. coli transformants harboring mutagenized plasmids and subcloned into p2201. Plasmid DNA prepared from a pool of the resulting E. coli transformants was introduced into yeast strain H1149 and transformants were selected on SC-Ura medium containing 15 mM 3-AT. Resident plasmids were isolated from colony-purified transformants and subjected to DNA sequence analysis to identify the relevant mutations. As multiple mutations generally occurred, QuikChange® site-directed mutagenesis (Stratagene) was used to produce plasmids pSL501, pSL502 and pSL503, containing only single mutations in GCN2. Site-directed mutagenesis was also used to generate the novel derivatives (listed in parenthesis) of the following previously constructed plasmids: p2201 (pSL501-pSL507), p722 (pSL508-pSL525), pHQ430 (pSL535-pSL538), pHQ601 (pSL539-pSL541). Plasmids pSL526, pSL527, pSL529, pSL530 and pSL542 were generated by replacing the 3.0-kb BspEI-NheI fragment in pSL101 or pSL102 with the corresponding fragment from p722 derivatives harboring the appropriate GCN2 mutations.
Transformants of H2684 bearing plasmids pSL101, pSL102, pSL526, pSL527, pSL529, pSL530 and pSL542 were grown to saturation in SC-Ura medium, diluted to A600 = 0.2 in SC-Ura containing 10% galactose as carbon source and grown to A600 of ∼2.5. Cells were harvested (∼25 g), washed with cold distilled water containing EDTA-free protease inhibitor cocktail (PIC) (Boehringer Mannheim) and 0.5 mM PMSF, resuspended in ice-cold binding buffer (BB) (100 mM sodium phosphate [pH 7.4], 500 mM NaCl, 0.1% Triton X-100, EDTA-free PIC, 1 μg/ml leupeptin, and 1 mM PMSF) and disrupted using SPEX freezer mill (model 6870). Lysates were clarified by centrifugation at 39,000 × g for 2 h at 4°C and mixed with 1 ml of M2-FLAG affinity resin (Sigma) overnight at 4°C. The resin was washed three times with 10 vol of BB and Gcn2 was eluted with 100 units of AcTEV protease in 500 μl of 1X TEV buffer (50mM Tris-HCl [pH 8.0], 0.5 mM EDTA, 1mM DTT). The eluates were concentrated with an Amicon Centricon filter (exclusion limit of Mr 10,000) and dialyzed against 10 mM Tris-HCl [pH 7.4], 50 mM NaCl, 20% glycerol and stored at −800 C. The eIF2α−ΔC protein was purified from E. coli as previously described [16]. Preparation of GST and GST fusion proteins of GCN2 were carried out as described previously [24].
β-galactosidase assays of HIS4-lacZ expression were conducted on WCEs prepared from cultures grown in SD medium containing only the required supplements. For non-starvation conditions, saturated cultures were diluted 1:50 and harvested in mid-logarithmic phase after 6 h of growth. For starvation conditions, cultures were grown for 2 h under repressing conditions and then for 6 h after the addition of 3-AT to 10 mM or sulfometuron methyl (SM) to 0.5 μg/ml. β-galactosidase activity was assayed as described previously [46] and expressed as nanomoles of o-nitrophenyl-β-D-galactopyranoside hydrolyzed per min per mg of protein.
For Western analysis, WCEs were prepared by trichloroacetic acid extraction, as described previously [47], and immunoblot analysis was conducted as described [24] using phosphospecific antibodies against eIF2α-P (Biosource International) and polyclonal antibodies against eIF2α [48] or Gcn2 [49].
Assays of Gcn2 autophosphorylation were conducted as described previously [11]. Binding of tRNA by Gcn2 was measured using a gel mobility shift assay as described previously [21].
Pull-downs of LexA-CTD in yeast WCEs with GST-HisRS fusion proteins were conducted as follows. Immobilization of GST fusion proteins on glutathione-Sepharose 4B beads was carried out by incubating the purified fusion proteins at 0.5 μg/μL of beads (bed volume) in buffer A (20mM Tris/HCl pH7.5, 100mM NaCl, 0.2mM EDTA, 1mM DTT) containing 0.1% Triton X-100 at room temperature for 30 min with rocking. The beads were washed and resuspended in the same buffer. Five hundred μg of WCE prepared from pHQ311 transformants of HQY132 was treated with 12,000 units of micrococcal nuclease in the presence of 2mM CaCl2 for 10 min at 37°C. Nuclease-treated WCE was then added to beads (10-μL bed volume) containing 5 μg of bound GST fusion proteins and the volume was increased to 200 μL with buffer A. The mixtures were incubated at 4°C for 2 h with rocking. The beads were collected by brief centrifugation in a microcentrifuge, washed three times with 500 μL of buffer A, resuspended in 40 μL of Tris-Glycine SDS Sample Buffer (Novex), and fractionated by SDS-PAGE, transferred to nitrocellulose membranes, and probed with antibodies against GST or LexA. The immune complexes were visualized by enhanced chemiluminescence (ECL; GE Healthcare Life Science) according to the vendor’s instructions.
Pull-downs of [35S]-HisRS domain fragments were conducted as follows. In vitro transcription/translation with [35S]-methionine was conducted using the TNT T7 Coupled Reticulocyte Lysate System (Promega) according to the vendor’s instructions. The resulting [35S]-HisRS domain fragments were partially purified by ammonium sulfate precipitation as described previously [50] and resuspended in 50 μL of buffer A (described above) containing 12.5% glycerol. Immobilization of GST fusion proteins on glutathione-Sepharose 4B beads was carried out by incubating the purified fusion proteins at 0.5 μg/μL of beads (bed volume) in buffer A containing 0.1% Triton X-100 at room temperature for 30 min with rocking. The beads were washed and resuspended in the same buffer. Five microliters of [35S]-HisRS domain fragments were added to beads (10-μL bed volume) containing 5 μg of bound GST fusion proteins along with the indicated amount of tRNAPhe (Sigma-Aldrich, # R4018), or synthetic mRNA (GGAAUCUCUCUCUCUCUCUCUGCUCUCUCUCUCUCUCUCUCUC) synthesized by T7 polymerase as described in [39], and the volume was increased to 200 μL with buffer A. The mixtures were incubated at 4°C for 2 h with rocking. The beads were collected by brief centrifugation in a microcentrifuge, washed three times with 500 μL of buffer A, resuspended in 40 μL of SDS sample buffer, and fractionated by SDS-PAGE. For detecting the [35S]-HisRS domain fragments, the gels were fixed with a solution of isopropanol:water:acetic acid (25:65:10), treated with Amplify (GE Healthcare Life Science), dried, and subjected to fluorography at −80°C.
Plasmids encoding the appropriate LexA- and B42-Gcn2 fusions were cotransformed into yeast strain HQY132. The transformants were selected on synthetic complete medium lacking uracil, histidine, and tryptophan (SC−Ura−His−Trp). Two-hybrid interactions were indicated by β-galactosidase activities in cell extracts of three or more independent transformants. For these assays, cells were grown for 38 h to saturation in SC−Ura−His−Trp and were diluted 1:50 into the same medium containing galactose (2%) and raffinose (1%) as carbon sources (SC/Gal/Raf−Ura−His−Trp). When indicated, sulfometuron was added to the medium at a final concentration of 0.5μg/mL. Cells were harvested in the mid-logarithmic phase after 6 h of growth. β-Galactosidase assays were carried out as described above.
Aliquots of 8 μg of purified Gcn2 were incubated with 0.001 units of elastase (Sigma-Aldrich) or 2 pg of trypsin (Sigma-Aldrich) in 10 mM Tris-HCl [pH 7.4], 50 mM NaCl, 20% glycerol for 5 min at room temperature and reactions were quenched by adding SDS sample buffer to a final concentration of 1X followed by heat inactivation at 95°C for 5 min. Digested samples were separated by SDS/PAGE and stained with Coomassie brilliant blue.
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10.1371/journal.pntd.0002142 | The Incidence of Human Cysticercosis in a Rural Community of Eastern Zambia | A community-based longitudinal study was performed in the Eastern Province of Zambia, in which repeated serological samplings were done to determine the incidence of human cysticercosis. Three sampling rounds were carried out at six months intervals. A total of 867 participants presented for all three samplings. All samples were tested for the presence of cysticercus antigens using a monoclonal antibody-based enzyme-linked immunosorbent assay (sero-Ag-ELISA), while a randomly selected sub-sample of 161 samples from each sampling round was tested for specific antibodies using a commercial enzyme-linked immunoelectrotransfer blot (EITB) assay. Stool samples (n = 226) were also collected during the final round of sampling for taeniosis diagnosis by coprology and coproantigen ELISA. Cysticercosis seroprevalence varied from 12.2% to 14.5% (sero-Ag) and from 33.5% to 38.5% (sero-Ab) during the study period. A taeniosis prevalence of 11.9% was determined. Incidence rates of 6300 (sero-Ag, per 100000 persons-year) and 23600 (sero-Ab, per 100000 persons-year) were determined. Seroreversion rates of 44% for sero-Ag and 38.7% for sero-Ab were recorded over the whole period. In conclusion, this study has shown the dynamic nature of T. solium infections; many of the people at risk become (re)infected due to the high environmental contamination, with a high number turning seronegative within a year after infection. An important number of infections probably never fully establish, leading to transient antibody responses and short-term antigen presence.
| Human neurocysticercosis is an infection of the central nervous system caused by the larval stage of the pork tapeworm (Taenia solium). The infection occurs mainly in developing countries and is associated with poverty, poor sanitation and free-range pig management. It is estimated to be responsible for 30% of cases of acquired epilepsy in endemic areas. The limited number of human studies on this infection in Sub-Saharan Africa determined a high occurrence of cysticercosis. This study aimed to learn more about the transmission dynamics of this parasite in a rural endemic area in Eastern Zambia. A longitudinal study was carried out in which 867 participants were blood sampled three times, with a 6-month interval. Samples were analysed for the presence of cysticercal circulating antigens and specific antibodies. Results indicate that about 1 on 3 people get exposed to infection while only 1 on 10 people actually acquire infection. The study shows the dynamic nature of T. solium infections; many of the people at risk become (re)infected due to the high environmental contamination, with a high number turning seronegative within a year after infection. An important number of infections probably never fully establish, leading to short-term antibody and antigen presence.
| Human (neuro) cysticercosis, an infection caused by the metacestode larval stage of the pork tapeworm Taenia solium, is a serious but neglected zoonotic disease and a major public health problem in many developing countries of Latin America, Asia and Africa [1], [2]. Humans are the definitive hosts harbouring the adult tapeworm (leading to taeniosis). Carriers of the tapeworm shed eggs into the environment that are infective not only to the pig intermediate host (leading to porcine cysticercosis) but also to humans who then act as an accidental intermediate host [3] leading to human cysticercosis. When the larval stages invade the nervous system they cause neurocysticercosis (NCC), which is the most important parasitic disease affecting the nervous system and accounts for about 30% of all acquired epilepsy cases in endemic areas [4]. In terms of Disability Adjusted Life Years (DALYs), the global burden of epilepsy is estimated at 7.8 million DALYs with 6.5 million of these occurring in T. solium endemic regions of the world [5].
The few community based human prevalence studies carried out in Africa have indicated sero-prevalences of human cysticercosis ranging from 7–22% [e.g. 6], [7], . In a recent study in Zambia, a sero-prevalence of 5.8% has been recorded in a rural community in the eastern part of Zambia [9].
Studies that report incidence of human cysticercosis are even more scarce and absent for Sub-Saharan Africa. Two longitudinal studies in villages in Peru indicated human cysticercosis incidence rates of 25% and 8% by specific antibody analysis [10]. In a simulation model based on data obtained in a rural community in Ecuador an annual incidence rate of 14% was described [11].
Obviously, more information is needed on the transmission dynamics of this parasite. The present study aimed at determining the incidence of human cysticercosis in an endemic area.
The University of Zambia Biomedical Research Ethics Committee granted ethical clearance (IRB0001131) for the study. Further approval was sought from the Ministry of Health of Zambia, from the local district health authorities and the area chief. Meetings were held with the people in the villages through their leaders (headmen) to explain the purpose of the study, request their permission to conduct the study and also to invite them to participate. Participation was requested of individuals of all ages after written informed consent. For individuals below the age of 16, permission was sought from their parents or guardians by way of written informed consent. All participants found positive for taeniosis and other helminths were provided with treatment, namely niclosamide and mebendazole respectively. Those positive for cysticercosis were referred to the District hospital for follow-up and the recommended standard of care provided to them if required.
The study was carried out in the Vulamkoko community in Katete district of the Eastern province of Zambia (figure 1). The Vulamkoko Rural Health Center (RHC) provides health care in this community with a catchment population of 23,613 (clinic headcount records). The climate is tropical with two main seasons, the rainy season (November to April) and the dry season (May to October/November). The mean rainfall varies from 500 to 1200 mm/year with temperatures above 20°C most of the year. The most common ethnic group in Katete is the Chewa people. They practice subsistence agriculture raising animals and growing crops. People's homes in this area are of adobe and have no sanitary facilities. Pigs have access to the nearby bushes that are used as latrines by the villagers.
The community was selected for the study on the basis of known endemicity for porcine cysticercosis [12], presence of free roaming pigs, backyard slaughter of pigs without meat inspection, continued observation of cysticerci in the meat, absence of any cysticercosis related control programs and the community's willingness to participate. All willing villages within a radius of 7 km from the RHC were selected. The willingness of the RHC to collaborate, and the availability of staff and adequate working space was also taken into account.
A community-based longitudinal study was carried out between October 2009 and October 2010, with three main sampling rounds (R1, R2, R3) with six months intervals (figure 2). Participants who were not sampled in the first round of sampling and willing to participate were entered in the study only during the second round of sampling.
Meetings were held in the selected villages and individuals of all ages of all households invited to participate in the study. The sampling unit was an individual in a household. Each willing participant, after written informed consent, was registered and had a blood sample taken by qualified health personnel every six months during a 12-month period (a total of 3 samples). During the last sampling round, a stool sample was also requested from the participants.
A questionnaire was administered to each participating household to obtain information on general household characteristics, pig management and sanitation (Mwape et al., submitted).
About 5 ml of blood were collected, serum extracted, aliquoted and stored at -20°C until use. Submitted stool samples were divided into two aliquots, one placed in 10% formalin and the other in 70% ethanol and stored until use. All the samples were transported to Lusaka for analysis [9].
The serum samples were tested for circulating cysticercal antigens using the monoclonal antibody-based B158/B60 antigen enzyme linked immunosorbent assay (sero-Ag-ELISA) as described by Dorny et al. (2004) [13]. To determine the test result, the optical density of each serum sample was compared with a series of 8 reference negative human serum samples at a probability level of P<0.001 [13].
Due to budgetary restrictions, not all samples could be analysed for presence of specific antibodies. Therefore, from the individuals that gave samples at all the sampling rounds, a Stata® (Stata Corp., College Station, TX) generated random subset sample, taking into account the age and sex distribution, was tested for presence of specific antibodies against cysticercosis using a commercial kit, Immunetics® (Immunetics Inc.). The assay was performed according to the manufacturer's instructions.
The stool samples, only collected during the last sampling round, were microscopically examined for Taenia ova using the formalin-ether concentration technique as described by Ritchie (1948) [14]. Additionally, the samples were analysed for the presence copro-antigens using a polyclonal antibody based antigen ELISA (copro-Ag-ELISA) as described by Allan et al. (1990) [15] with slight modifications [9].
All collected data were entered into an excel (Microsoft Office Excel 2007®) spreadsheet and analyses were conducted in Stata 10 (Stata Corp., College Station, TX). The sampled population was distributed in 10 age categories of 10 years intervals and in function of sex.
A total of 3167 serum samples (from 1206 individuals from 32 villages) and 226 stool samples were examined for cysticercosis and taeniosis, respectively.
Entrees and exits of participants are explained in figure 2. A total of 1129 individuals were sampled at baseline (R1), 1069 at R2 and 969 at R3. A total of 867 (76.8%) gave samples during all sampling rounds. Reasons for lack of follow up at R2/R3 consisted of refusal to continue participating (2.7%/5.3%), away at time of sampling (3.8%/6.6%), reported sick and could not be sampled (1.2%/1.1%), died of other causes, as assessed by the RHC (0.3%/0.7%), relocated to other areas (1.2%/2.2%) and those that could not be traced (2.8%/3.1%).
From the 867 individuals sampled at R1, R2 and R3, 358 (41.3%) were men and 509 (58.7%) women; the age ranged from 2 to 87 years with a median age of 18 years. The number of people living in a HH ranged from 1 to 15 with a median of 6. From the 867 individuals that gave samples for all the sampling rounds, a random sample of 161 individuals were tested for specific antibodies against cysticercosis in each round (the same 161 participants were tested in each round).
Household characteristics (recorded from 516 HH) included; 69% of the HH kept pigs with 98% of these rearing on free-range, 46.6% of the HH did not have latrines. About 72.2% slaughter pigs in their backyards, 96.2% had at least one individual who consumed pork (boiled, fried or roasted). Only 0.6% had the meat inspected. The data obtained in the questionnaire are described in more detail in another report (Mwape et al., submitted article).
Table 1 shows the overall and by sex cysticercosis sero-Ag and sero-Ab prevalences per sampling.
Sero-Ab prevalence figures (33.5–38.5%) were significantly higher than sero-Ag prevalence figures (12.2–14.5%). No significant differences were observed in sero-Ab and sero-Ag prevalences between males and females. The sero-Ab prevalence does not change between sampling rounds, the sero-Ag prevalence was significantly higher in sampling round 2 than in round 1. The probability of being sero-Ag positive increased with age for men for all three sampling rounds.
Taeniosis prevalence was determined to be 11.9% by copro-Ag-ELISA. Eleven and a half percent of the participants that tested copro-Ag positive, were also sero-Ag positive. Thirteen percent of the participants that tested copro-Ag negative, tested sero-Ag positive. Taenia eggs were not detected by coprological examination in any of the stool samples. Other helminth ova detected included hookworms in 20 of the samples (8.8%), Schistosoma spp. in 7 (3.1%) and Trichuris trichiuria in 2 (0.9%).
The present study is the first to estimate the incidence of human cysticercosis based on specific antibody as well as antigen detections; adding to the very short list of publications reporting the incidence of human cysticercosis [16]. The high taeniosis prevalence (11.9%) in this study is strongly indicative for a high environmental contamination with T. solium eggs, and subsequent high exposure risk. The high sero-antibody results (33.8–38.5% sero-Ab prevalence) as well as the fact that less than half of the sampled population (44.7%) remained negative (sero-Ab) throughout the study period corroborate this finding, as presence of specific antibodies is indicative for exposure to infection [17]. About 32% (34/106) of the participants negative at the start of the study turned Ab positive at one point; an additional 6 tested participants positive at R1, but negative at R2, turned positive again at R3 (table 3), indicating that more than one on three people have been (re) exposed and reacted to infection during the study period.
The sero-Ag results present a different picture. A much higher percentage (78%) of people remained negative throughout the study; and only 11.5% of the participants negative at the start of the study turned positive at one point (table 3). As presence of antigen indicates establishment of infection rather than exposure, these results strongly indicate that about one on three people are exposed to infection, whereas the infection only establishes in about one on ten people. Findings from studies in Peru in pigs and human and in Ecuador in human [11] also suggest exposure without infection or mild infections that are aborted by the natural immunity of the individual, expressed by the presence of transient antibodies [18]. The higher levels of sero-Ab prevalence and seroconversion in comparison with sero-Ag prevalence/conversion, as well as the high seroreversion levels, identified in this study, support this finding.
Another interesting outcome from this study is the rather short-term presence of antigen in 31 participants (negative at R1, positive at R2, and again negative at R3, table 3). Whether this is due to an only partial establishment of infection (immature cysticerci), or establishment and quick degeneration (self cure?) of cysticerci is not clear. It was noted that individuals who became seronegative were those with samples that had low antigen titers (Data not shown). In humans, it is described that cysticerci in the brain usually stay viable during years, while probably cysticerci in the muscles tend to degenerate more quickly [19]. However later, Garcia et al. (2010) [20] challenged this theory in the case of single cysticercal granuloma's, for which they hypothesize that instead of being caused by a late degenerative process, the granulomas are rather due to an early parasite death. In experimental infections in pigs often infections do not establish, or (partially) establish (with the corresponding increase in antigen levels) and abort shortly afterwards. Deckers et al., (2008) [21] indeed demonstrated circulating cysticercus antigens as early as three weeks after experimental infection, which is before full maturation of the cysticerci. Many factors, among which the size of the (re) infection, the immune status of the host, age and sex play a determining role in the (non) establishment of infection [22]. Results from this study suggest that presence of antigen doesn't necessarily always signify presence of a viable, well established infection, however could be indicative for short term partial establishment, and perhaps a ‘transient’ antigen presence should be considered. As such, serological results from field studies, should be looked at critically. Individuals with positive test results shouldn't be automatically considered as ‘infected with T. solium’, as is often done in reports from field studies.
Significantly higher sero-Ag reversion than seroconversion was determined up to the age of 60 years. Previous studies have indicated higher levels of active infection in elderly people, which was suggested to be due to a lowered host immune response [11]. The higher seroreversion rates than seroconversion rates observed in younger people, but not in older people in this study, could indeed be indicative of an improved clearing of the infection in younger people. The simulation models described in Praet et al. (2010) [11] suggest a continuous exposure of the population with seroreversion (antibody) rates depending on the number of exposures, which relates to age as well as the immunological status of the individual. Antibody seroreversion rates of 60% after first exposure and 20% after second and subsequent exposures were obtained.
This is the first study to report cysticercosis incidence based on sero-Ag analysis (6300 per 100000 persons-year). The sero-Ab incidence rate (23600 per 100000 persons-year) is comparable to that reported in Peru by Garcia et al. (2001) [10] and in Ecuador [11]. A higher average porcine cysticercosis sero-Ab incidence rate of 53% has been reported in Peru [18]. Since pigs are highly coprophagic, it is expected that they would be exposed more frequently and to higher levels of infection as compared to humans and hence record a higher incidence rate especially for sero-Abs.
In conclusion, this study has shown the dynamic nature of T. solium infections, many of the people at risk become (re)infected due to the high environmental contamination, with a high number turning seronegative within a year after infection. An important number of infections probably never fully establish, leading to transient antibody responses and possibly even ‘transient’ antigen presence.
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10.1371/journal.pntd.0003167 | Genetic Diversity of Brazilian Aedes aegypti: Patterns following an Eradication Program | Aedes aegypti is the most important vector of dengue fever in Brazil, where severe epidemics have recently taken place. Ae. aegypti in Brazil was the subject of an intense eradication program in the 1940s and 50s to control yellow fever. Brazil was the largest country declared free of this mosquito by the Pan-American Health Organization in 1958. Soon after relaxation of this program, Ae. aegypti reappeared in this country, and by the early 1980s dengue fever had been reported. The aim of this study is to analyze the present-day genetic patterns of Ae. aegypti populations in Brazil.
We studied the genetic variation in samples of 11 widely spread populations of Ae. aegypti in Brazil based on 12 well-established microsatellite loci. Our principal finding is that present-day Brazilian Ae. aegypti populations form two distinct groups, one in the northwest and one in the southeast of the country. These two groups have genetic affinities to northern South American countries and the Caribbean, respectively. This is consistent with what has been reported for other genetic markers such as mitochondrial DNA and allele frequencies at the insecticide resistance gene, kdr.
We conclude that the genetic patterns in present day populations of Ae. aegypti in Brazil are more consistent with a complete eradication of the species in the recent past followed by re-colonization, rather than the alternative possibility of expansion from residual pockets of refugia. At least two colonizations are likely to have taken place, one from northern South American countries (e.g., Venezuela) that founded the northwestern group, and one from the Caribbean that founded the southeastern group. The proposed source areas were never declared free of Ae. aegypti.
| The mosquito, Aedes aegypti, was historically very important as the major vector of yellow fever, whereas today it is most notorious for being the major transmitter of dengue fever. In the 1940s and 50s, the Pan-American Health Organization organized a campaign to eradicate Ae. aegypti from the New World. They were partly successful, with Brazil being the largest country to be declared free of Ae. aegypti. Within ten years of relaxation of control efforts, Ae. aegypti reappeared in Brazil and today is the vector of the most intense dengue epidemics in the New World. Here, we present population genetic data that are most consistent with the species having truly been eradicated from Brazil rather than simply pushed into small refugia as a consequence of the eradication campaign. The re-infestation most likely resulted from two sources: 1) from northern S. American countries like Venezuela into northwest Brazil and 2) from the Caribbean into the southeast of the country.
| Dengue fever is a viral disease transmitted by Aedes mosquitoes that occur in tropical and subtropical areas around the world. Due to a widespread distribution, this disease could be more important than malaria in terms of economic impact and morbidity [1]–[4]. It is estimated that more than two billion people (over 40% of the world's population) are at risk of infection by one or more dengue serotypes [5], [6].
Brazil is especially vulnerable to dengue epidemics, with ten times more cases than other Latin American countries during recent outbreaks [6], [7]. The main vector of dengue in Brazil is the mosquito Aedes aegypti, which is also a vector for yellow fever and chikungunya viruses [8]. Ae. aegypti is a particularly adaptable invasive species that has successfully colonized most tropical and subtropical regions of the world. This is due to the vector's highly anthropophilic behavior and ability to lay its desiccation-resistant eggs in man-made water containers, widely available in most developing countries where water distribution and sanitary conditions are rudimentary. Modern transportation and commerce have greatly contributed to the passive geographical spreading of this vector and, consequently, to disease dissemination. Due to the lack of an effective vaccine, currently, dengue control programs rely almost exclusively on vector control efforts [3], [9].
Historically, neurotoxic insecticides have been the method of choice to control Ae. aegypti populations [10]–[12]. However, the large-scale unregulated use of insecticides, has exerted intense selective pressures on mosquito populations leading to the development of resistant strains not only in Brazil but also worldwide [13]–[18]. This undesired outcome increases the need for the creation of new vector control methods. Several emerging technologies are based on various genetic strategies (RIDL, RNAi, HEG, Wolbachia) and are either under development or are already being field-tested [19]. Regardless of the methods employed, knowledge of the genetic variability and population subdivision of mosquito populations is pivotal for the development of rational dengue control programs.
In this context, Brazil is a particularly interesting country regarding dengue epidemiology because it has gone through a well-documented vector eradication program [10], [20]. In the first half of the 20th century, when dengue was not yet a public health issue, Ae. aegypti populations were widespread and responsible for several yellow fever epidemics, especially in the northeast of Brazil. Motivated by the success achieved by the Anopheles gambiae control program, the Brazilian government launched, in 1947, an initiative to eradicate Ae. aegypti populations based on the use of DDT. In 1958, during the XV Conferencia Sanitária Panamericana in Puerto Rico, Brazil was declared free of Ae. aegypti. The species was again recorded in the late 1970's, probably as a consequence of a reduction in the efficacy of the vector control measures employed [10], [20]. The first well-documented outbreak of dengue fever in the country occurred in 1982, in Roraima state, north Brazil [21]. Today, the entire country is endemic for dengue and the last outbreak in 2013 accounted for more than 1.5 million cases (BRAZIL/Health Ministry, 2014). Therefore, dengue fever has become a major public health issue, especially because all four DENV serotypes co-circulate in the country [7].
The first studies to assess the genetic structure of Brazilian Ae. aegypti populations were based upon RAPD markers and revealed high levels of interpopulation genetic differentiation [22], [23]. Allozyme-based studies also indicated a high degree of genetic structure and limited gene flow between regions connected by highways and railroads, suggesting that passive mosquito dispersal is not extensive [24], [25]. Within cities, such as the densely populated Rio de Janeiro, local genetic differentiation has also been found indicating that this species has extremely limited dispersal capability [26], [27].
The analyses of mtDNA sequence data (ND4 and COI) of several Brazilian populations revealed the co-occurrence of two distinct lineages in the country [28], [29]. A study of frequencies of the kdr (knock-down resistance) mutations, which confer pyrethroid resistance, found at least three distinct genetic groups in 30 Brazilian populations. [18].
Microsatellites are assumed neutral, highly variable codominant markers commonly used in population genetics. However, they have never been used in a nationwide study of Ae. aegypti in Brazil. Here, we present the results of the analysis of 12 microsatellite loci in Brazilian Ae. aegypti populations in an effort to better understand the genetic structure of this vector in the country, which may lend insights into the presumed recolonization following eradication events.
Ae. aegypti samples were field-collected from 11 sites in Brazil (Table 1). Eggs were collected in multiple ovitraps per locality (to avoid sampling of siblings) and reared to adults for proper taxonomic identification. Samples from generation F0 up to F2 were preserved in 70–100% ethanol or dry at −80°C for further analysis. Eight previously studied populations from different countries across South, Central and North America [30] were included in the analyses (Table 1).
Total genomic DNA was extracted with the DNeasy Kit (Qiagen) following the manufacturer's protocol. Individual genotypes were scored for 12 previously published microsatellite loci [30], [31].
Microsatellite alleles were scored using Gene Mapper software (Applied Biosystems). The experiments were performed in the Yale Laboratory using the same ABI machine as used by Brown et al. [30] and alleles scored in accordance with that publication, so the data presented here are directly comparable to data in Brown et al. [30].
To infer the statistical reliability of our markers, each locus was tested for deviations from Hardy-Weinberg expectations on the web version of Genepop v1.2 [32], [33]. The same program was used to test all loci pairs for linkage disequilibrium (LD). Markov chain parameters were set at 10,000 dememorizations, 1,000 batches and 10,000 iterations per batch for both HWE and LD. Critical significance levels were corrected for multiple tests using the Bonferroni correction. The probability of null allele occurrence in each locus within each population was calculated using MicroChecker v2.2.3 [34]. When null alleles were found, FreeNA [35] was used to infer the extent of bias imputed by their presence on FST values. Genetic diversity per locus and in each population was estimated by unbiased expected heterozygosity using GenALEx v6.5 [36]. The same program was used to compute allele frequencies for all loci across populations and for the Analysis of Molecular Variance (AMOVA). Sample size corrected allelic richness and percentage of private alleles were calculated using HP-Rare v1.0 [37], [38]. The software Arlequin v3.5.1.2 [39] was used to compute FST values and their significance between all pairs of populations with 1,000 permutations. Cavalli-Sforza and Edwards distances were computed using the software package Phylip 3.6 [40]. The Cavalli-Sforza distance was chosen since it has been shown to be more robust when null alleles are present [35], [41]. Programs of the Phylip package (SEQBOOT, GENEDIST, NEIGHBOR, CONSENSE) were used to construct a neighbor-joining tree with 1,000 bootstrap replicates. A factorial correspondence analysis (FCA) was performed with the software Genetix v4.0.5 [42] to better analyze the Brazilian samples. Isolation by distance was tested on the IBD web server v3.23 [43] and also through a Mantel test of correlation between geographical (LnKm) and genetic distance matrices (FST/(1-FST)). For both analyses significance was inferred with 1,000 permutations.
The Bayesian approach used in the software STRUCTURE v2.3.2 [44] was used to infer the number of genetic clusters (K) in the whole data set, without prior information of sampling locations. An admixture model was used where alpha was allowed to vary and independent allele frequencies were assumed with lambda set to one. We performed ten independent runs for each value of K (K = 1 to the maximum supposed number of populations) with a burn-in phase of 200,000 iterations followed by 600,000 replications. The program Structure Harvester v0.6.93 [45] was used to summarize these results and determine the most likely number of clusters by calculating ΔK [46]. Results from STRUCTURE were summarized with the program CLUMPP v1.1.2 [47] and visualized using the program Distruct v1.1 [48]. The program GeneClass2 v2.0 [49] was used for self-assignment tests to infer the degree to which an individual mosquito could be assigned to a specific population. Self-assignment tests were performed with reference populations based on geography and clusters identified by the program STRUCTURE.
Although 15 of the 1,244 (1.2%) locus-by-locus tests for LD remained significant after Bonferroni correction, no two loci were consistently correlated across populations. Eleven of the 231 (4.76%) FIS values deviated significantly from Hardy-Weinberg expectations at the 5% significance level after sequential Bonferroni correction (Table S1). Of the 20 population-specific tests for each marker, zero (AC1, AC2, AC4, CT2, AG5, B2 and B3), one (AG1, AG2 and A1), three (AC5) and five (A9) tests were significant. For A9, all significant tests resulted from an excess of homozygotes, probably due to null alleles as reported in Brown et al. [30]. Micro-checker results suggest that locus A9 has a high probability of having null alleles in 11 populations and AC5 in five. Null allele frequency varied from 0 to 0.32 among populations for the A9 locus and 0 to 0.21 for the AC5 locus (Table S2). Other loci had null allele frequencies predicted as well (in four populations for AG2, three populations for AC1, two populations for B3 and one population for AC4, AG1 and AC2), although none with frequencies >0.14 (Table S2). Null alleles at microsatellite loci are commonly found in insects [50]–[52] and have been demonstrated to be especially common in species with large population sizes [35], which is likely the case for Ae. aegypti populations.
The decrease in diversity caused by null alleles can lead to an overestimation of statistics such as FST and identity values [53], especially when there is low gene flow among populations [35], [54]. Nevertheless, simulation studies have shown the bias to be small for lower FST values and almost none when assignment methods are used [54]. A comparison between FreeNA corrected and non-corrected pairwise FST values shows very small deviations in our dataset (Table 2).
Gene frequencies, heterozygosities (Ho and He), and allelic richness for all loci studied are given in Table S3. All populations have similar diversity measures. AMOVA results show that within population differences account for 83% of the genetic variation found. Private allelic richness was low (Np<0.08) with only Pau dos Ferros, São Gonçalo, Dominica and Miami with estimates greater than 0.16 (Table S3). Overall FST value (FST = 0.175; 95% confidence interval 0.146–0.204) indicates a moderate level of population differentiation (Table 2). Coatzacoalcos and Houston were the only populations to have higher FST values (ranging from 0.24 to 0.38 in Coatzacoalcos and 0.11 to 0.31 in Houston). Miami and some Brazilian populations had FST values lower than 0.10 (Table 2). The genetic distance based NJ tree is reasonably consistent with geographic distances among populations (Figure S1) and was corroborated by Mantel tests of isolation by distance that found significant correlation between the geographical and genetic distance matrices (P<0.001, R2 = 0.53; Figure 1A). When only Brazilian samples were analyzed, weaker isolation by distance was detected by the Mantel tests (P = 0.01, R2 = 0.31; Figure 1B).
A model-based clustering algorithm was used to identify subgroups with distinctive allele frequencies without prior information on population structure. In all analyses, most individuals from the same geographical origin shared similar membership coefficients in inferred clusters. The Evanno et al. [46] method identified K = 2 as the most likely number of clusters, but small peaks on the ΔK graph are also apparent at K = 5 and K = 13 (Figure S2). The two-cluster analysis groups include all Brazilian populations with Dominica, with the exception of Tucuruí and Marabá (Figure 2). Tucuruí and Marabá are more similar to populations from Venezuela, Mexico, Puerto Rico, and North America. Indeed, the FCA of the Brazilian samples and the NJ tree show that Tucuruí and Marabá (98% bootstrap support; Figures S1 and 3) are very different from all other Brazilian populations. In addition to these two, Mossoró, Aracajú and to some extent Pau dos Ferros also form a slightly differentiated genetic cluster on the FCA analysis (Figure 3).
While the pattern described by the above two genetic clusters is the best supported by the ΔK method [46], subtle substructure can be discerned by a more detailed analysis. The five-cluster STRUCTURE plot (Figure 2) shows that most populations have mixed ancestry and only Coatzacoalcos (Mexico) shows a pure genetic composition. In this analysis, the Brazilian samples from Tucuruí and Marabá now group together with Mossoró, Aracajú and, to some extent, Pau dos Ferros, consistent with the FCA analysis that indicates that these last three populations are indeed genetically differentiated as well. The thirteen-cluster plot (Figure 2) further describes the extent of Ae. aegypti complex genetic composition in each population. The analysis reflects admixture between groups probably due to recent gene flow among populations, although common ancestry cannot be excluded. The isolation by distance detected among samples also indicates that gene flow occurs between adjacent populations (Figure 1). The thirteen-cluster analysis further separates the Brazilian populations in five distinct clusters (Figure 2), with some mixed ancestry observed, especially in the population proximate to Rio de Janeiro (São Gonçalo), a well-known tourist destination.
Results from GeneClass2 show that when geographical locations were used as the reference populations, 83% of individuals were correctly assigned back to their population of origin. When the number of clusters inferred by STRUCTURE were used, this number increased drastically for K = 2 (94.6%) but not so much for K = 5 (92%) and even less for K = 13 (86.5%), corroborating the higher peak found for K = 2 in the Evanno plot (Figure S2).
Since STRUCTURE seems to identify the higher hierarchy in population differences [46], to better understand the relationships within the two groups identified (Blue and Red in Figure 2, K = 2 plot) we performed additional analyses. When the blue group, that encompasses Tucuruí and Marabá with EUA, Mexico, Venezuela and Puerto Rico, is analyzed; the optimal number of clusters determined by the ΔK method are K = 2 and K = 8 (Figure S3A and B). At K = 2, the two Mexican populations are differentiated from the rest and display some mixed ancestry with other populations (Figure S4). North America seems to be the most influenced by the Mexican genetic background as was already determined by Brown et al. [30]. Brazilian and Venezuelan populations have less background from Mexico than North America and are, therefore, similar. With K = 8, all populations except the two Brazilian ones seem to be genetically differentiated (Figure S4).
When the red group is analyzed K = 2, K = 3, and K = 5 provide some insights (Figure S5). The two-cluster analysis separates the Brazilian populations from Dominica but a high degree of mixed ancestry can be observed in Jacobina, from the Northeast of Brazil. The three-cluster analysis further differentiates the Brazilian populations showing that Mossoró, Aracajú, Pau dos Ferros and, to some extent, Natal and Maceió group together, although high levels of mixed ancestry can be observed in most populations (Figure S5). The differentiation of Mossoró, Aracajú and Pau dos Ferros from other Brazilian populations can also be seen on the FCA analysis (Figure 3). The five-cluster analysis further separates Mossoró, Aracajú and Pau dos Ferros in one cluster and shows the geographically close Maceió population to have genetic similarities with Southeastern populations (São Gonçalo and Cachoeiro). Some degree of mixed ancestry can be observed in all populations and this is most apparent in São Gonçalo, Jacobina, Maceió, Pau dos Ferros, Natal, and Cachoeiro (Figure S5). Pau dos Ferros is a small city in the state of Rio Grande do Norte that probably has both the influence of the geographically closer Mossoró and of its state capital, Natal. Interestingly, the two samples from Cachoeiro (2008 and 2012), sampled four years apart, show some degree of differentiation. In a recent study carried out in São Paulo state, Brazil, no differentiation between five sampling years was found [55].
Despite these subtle genetic patterns, we have strong evidence to conclude that Brazilian populations of Ae. aegypti separate into two major genetic groups with distinct affinities to populations outside Brazil as indicated in Figure 4.
Brazil was officially declared free of Ae. aegypti in 1958 [20], but reappearance of the species occurred shortly after relaxation of control measures. In its assessment of the efficacy of its eradication program, the Pan American Health Organization (PAHO) admitted that eradication had not been successful in Venezuela, Suriname, Guyana, South USA and a few Caribbean Islands [10]. It is believed that re-colonization of Brazil happened in the 1970's probably from mosquitoes from neighboring countries [7], [20].
Our results indicate that two major genetic groups are present in Brazil, one descending from Venezuela and probably other northern American countries and another one from the Caribbean (Figure 4). Bracco et al. [28] using the mitochondrial ND4 gene have also observed two major lineages in Brazil. The first genetic group identified suggests that mosquitoes from Venezuela and possibly the USA have contributed to the northern Brazilian population. Venezuela seems to be an important source of mosquitoes as well as dengue virus serotypes into Brazil [56]–[58]. Indeed, Silva et al. [59], also using the mitochondrial ND4 gene, have found that populations from the Northern states in Brazil seemed to be similar to those from Venezuela and Peru. In that study, no Caribbean Island was sampled. Venezuelan Ae. aegypti are highly susceptible to DENV2 virus [60] and this could be the reason Lourenço-de-Oliveira et al. [56] have observed that northern Brazilian populations are more susceptible to DENV2 virus than are southern ones. The second genetic group comprises Brazilian southeast and central-west populations and is genetically similar to Dominica in the Caribbean (Figure 2).
Brazil went through a nationwide vector control program based on pyrethroid insecticides from 2001 to 2009. Nevertheless, Linss et al. [18] detected three kdr genetic groups in the country (North, Northeast and Southeast-Central). Since differential selection pressures acting in the area studied could not account for their findings, the authors argued that the pattern observed could have resulted from genetic differences in the Ae. aegypti strains that founded those populations (Linss et al. [18]).
In our results, although the most important genetic break occurs between Northern populations and all others (Figures 2 and 4), the FCA also shows that Mossoró, Aracajú and, to some extent, Pau dos Ferros can be differentiated (Figure 3). When a higher cluster number is analyzed on the Bayesian clustering analysis, we see that the same three populations cluster together with the two Northern ones (Figure 2). Other studies of Brazilian Ae. aegypti have identified a genetic break between northern and southern populations [22]–[24], [29], [59], [61], although the exact location of the break is not always consistent. It is conceivable that the dynamics and mode of inheritance of different genetic markers can account for somewhat different patterns, e.g., cytoplasmic mtDNA versus nuclear genes and neutral genes versus selected alleles such as at insecticide resistance genes (kdr). The isolation by distance found within Brazilian samples suggests some connectivity among populations, so it is not surprising that the two lineages that may have initially re-invaded Brazil are now exchanging genes and perhaps merging.
The origin of these two genetic units seem reasonably clear from our data, although with only a single Caribbean sample (discounting Puerto Rico, considered part of the US) to compare, the origin of the southern lineage is less well established. Bracco et al. [28] suggested that Asia may have been the origin of the southern group, however, they did not sample any Caribbean Islands. Brazil has a long history of international trade within the Americas and Caribbean and only recently has this been shifted to Asian countries. Another indication that indeed Caribbean and not Asian populations might be the source of a Brazilian Ae. aegypti is the fact that Linss et al. [18] have found, in Brazilian populations, the same Caribbean kdr mutation allele, Val1016Ile and not Val1016Gly, that is commonly observed in Asian populations. Furthermore, Brown et al. [62] studying a diverse set on SNPs and nuclear gene sequence data have found that Ae. aegypti probably came from West Africa into the New World, where it dispersed to Asia and Australia. In their study, a Brazilian population from the Southeast (Cachoeiro) is in the same clade as Venezuelan and Caribbean populations, consistent with our findings.
While our data are consistent with the re-colonization hypothesis, we cannot exclude alternatives. The two major genetic groups observed today may have existed prior to 1958; following relaxation of vector control, the expansion from refugia within Brazil could have re-established the pattern present today. However, one expects small refugia to drift to heterogeneous gene frequencies such that subsequent expansion would lead to a mosaic of genetic units not geographically structured. Our data do not support such a scenario. Furthermore, a low genetic diversity would be expected due to a bottleneck period, which was not observed either. Measures of diversity (0.39<Ho<0.67) and allelic richness (2.46<Na<4.44) are similar in Brazilian samples and other populations from the Americas, even when compared to countries where eradication did not occur () [30]. Studies with mitochondrial DNA markers (COI and ND4) have also found high genetic variability in Brazilian samples [28], [29]. Thus, while we cannot rule out incomplete eradication, for the reasons stated, recolonization from regions outside Brazil that were never declared free of Ae. aegypti is a simpler explanation consistent with the patterns observed in present day Brazil populations of this vector.
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10.1371/journal.pntd.0004965 | DenHunt - A Comprehensive Database of the Intricate Network of Dengue-Human Interactions | Dengue virus (DENV) is a human pathogen and its etiology has been widely established. There are many interactions between DENV and human proteins that have been reported in literature. However, no publicly accessible resource for efficiently retrieving the information is yet available. In this study, we mined all publicly available dengue–human interactions that have been reported in the literature into a database called DenHunt. We retrieved 682 direct interactions of human proteins with dengue viral components, 382 indirect interactions and 4120 differentially expressed human genes in dengue infected cell lines and patients. We have illustrated the importance of DenHunt by mapping the dengue–human interactions on to the host interactome and observed that the virus targets multiple host functional complexes of important cellular processes such as metabolism, immune system and signaling pathways suggesting a potential role of these interactions in viral pathogenesis. We also observed that 7 percent of the dengue virus interacting human proteins are also associated with other infectious and non-infectious diseases. Finally, the understanding that comes from such analyses could be used to design better strategies to counteract the diseases caused by dengue virus. The whole dataset has been catalogued in a searchable database, called DenHunt (http://proline.biochem.iisc.ernet.in/DenHunt/).
| The ‘Dengue Human Interaction Database’, called DenHunt, available at http://proline.biochem.iisc.ernet.in/DenHunt/ was created to catalog all interactions between dengue viral and human components published in peer-reviewed literature. There are three types of dengue-human molecular interactions in the database: direct physical interactions between dengue virus and human components, indirect interactions of human proteins affecting viral replication with no current evidence of them directly interacting with the viral components, and differentially expressed genes in dengue infected cell lines or patients. DenHunt could be used to draw network maps of human-dengue relationships which would aid in understanding dengue viral pathogenesis and such knowledge in turn can reveal new strategies for inhibiting viral replication. We also demonstrate that DenHunt could be used to compare common and diverse mechanisms of pathogenesis caused by infectious and non-infectious diseases thereby help in understanding disease mechanisms in general.
| Dengue, an emerging infectious disease, is presently the most common arboviral disease globally. Approximately 2.5 billion people live in dengue infested regions worldwide and 390 million dengue infections are reported per year [1]. Dengue infection leads to complications ranging from mild dengue fever to more severe dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). It is not fully understood why most patients clear dengue infections quickly without any complications, whereas others develop a potentially fatal vascular leakage syndrome or severe hemorrhages. The large size of the population prone to infection by dengue vouches for the importance of the development of vaccines for prevention and antiviral therapies to manage/treat dengue viral infections. Despite intense research efforts, no approved vaccine or antiviral therapy is yet available. Early clinical diagnosis and careful clinical management by experienced physicians and nurses to increase survival of patients are still the most commonly used strategies to treat dengue infections.
The major hindrance in understanding the host response to dengue infection and development of vaccines and antiviral therapies is the lack of an adequate animal model that can display the full spectrum of dengue immunity and disease response. Regardless of the difficulties in in vivo dengue viral research, significant efforts have been directed towards understanding the pathogenesis of dengue infection using in vitro platforms, such as cell lines as well as clinical samples such as patient’s blood, peripheral blood mononuclear cells (PBMCs) and serum. The knowledge obtained using conventional studies as well as from high-throughput technologies, such as functional genomics, transcriptomics, proteomics and yeast-two-hybrid techniques have given us important insights into the role of dengue virus interactions with its host in viral replication and pathogenesis. This valuable information remains disseminated along with other published literature in databases essentially in PubMed, making it difficult and time-consuming for dengue viral researchers to access and utilize the information for detailed computational analysis. Hence, there exists an immediate need for generating a database that provides readily usable simplified data pertaining to dengue-human interactions by collating all the existing information in literature.
Many databases have been generated for different pathogens that provide curated interactions between pathogen—host components. Specific virus databases, such as HCVpro [2] and HIV-1 Human Protein Interactions Database (HHPID) [3, 4], have been developed to host all known HCV or HIV1 –human PPIs respectively. Other databases catalogue: a) known host-viral interactions for many viruses, eg. VirHostNet 2.0 [5] and VirusMint [6] replaced by VirusMentha [7], b). host-pathogen interactions for many pathogens of viral, bacterial, fungal origin in PIG (the pathogen interaction gateway) incorporated into PATRIC [8, 9] and PHI-base [10] and c) host-pathogen interactions for many pathogens along with other intra organismal PPIs such as MINT [11], IntAct [12] and BioGRID [13]. However, dengue-human interactions are poorly represented in these databases with interactions extracted only from a few publications.
To fill this lacuna, we have developed a Dengue Human protein Interaction database that we call “DenHunt” which serves as a freely accessible, periodically updated comprehensive resource for the dengue research community. The objective of this study is to describe the development of the database, summarize its contents, demonstrate the complexity of the dengue-human protein interaction network and compare it with networks of proteins interacting with other pathogens or involved in other diseases. Our database is unique, compared to others as we have curated data from many publications and incorporated all genes associated with dengue viral infection into different categories such as direct interactions, indirect or functional interactions and differentially expressed genes. We show that the information from such databases can help in creating network maps of how the virus disrupts cellular signaling and immune system pathways. We also list known FDA approved drugs against the dengue virus interacting human proteins that are being used to treat various other diseases.
For each interaction in the entire interaction dataset, the National Library of Medicine (NLM) PubMed identification numbers (PMIDs) of the publication describing the interaction, Entrez Gene IDs and gene symbols of the proteins involved in the interaction, the type of patient sample or cell line used in the study, the experiments used to detect the interactions, and the viral serotype used in the study is available in the database. The database will be periodically updated and with increasing information of dengue-host interactions published in scientific literature, we expect DenHunt to attain higher coverage and curation depth and become a valuable resource for comprehensive analysis of dengue viral mechanisms and interactions, thereby enhancing our fundamental understanding of the disease.
The abstracts and publications containing original research describing association of human host proteins with dengue virus were queried using the term “dengue” or the specific dengue protein along with the keywords listed in S1 Table. These keywords were extracted from a publication that describes the construction of the HIV-1 human protein interaction database [3]. The interactions retrieved from these publications were segregated manually into direct interactions, indirect interactions and differentially expressed interactions. For a majority of abstracts, the full-text of the article was reviewed. However, in case of abstracts for which full length articles were not available, the interactions were catalogued based on the abstract alone, only if they contained complete descriptions of the interactions. Since the articles were obtained from peer-reviewed publications, all identified interactions were incorporated into the database without placing further judgment on the scientific validity of the report. Most papers describe the protein in the study as a gene synonym or alias. In this regard, the official human protein symbol and Entrez GeneID were extracted from online tools such as bioDBnet [14], Synergizer [15], GeneCards [16] and NCBI Entrez Gene database [17].
Information retrieved from relevant publications was collected and the interactions were manually catalogued into a table containing some or all of the following fields:
The DenHunt database was created by integrating curated dengue and human molecular interactions and the pathways involved. The LAMP (Linux-Apache-MySQL-PHP) platform was used to develop DenHunt. The web interface was developed using BootStrap (http://twitter.github.com/bootstrap) which provides cascading style sheets framework and JavaScript functionality. The database can be primarily queried based on the dengue viral or human protein involved in the interaction. The direct interactions related to query term can be visualized as a network constructed using Cytoscape.js (js.cytoscape.org), a JavaScript based library for analysis and visualization of the network. The dataset of interactions is efficiently stored in a relational database schema. The results are stored as tables which are sortable and searchable, and allow easy access to the data of interest. The entire dataset can be downloaded as a flat file from the download section.
We compiled a comprehensive list of human proteins which: i) interact directly with viral proteins, ii) indirectly affect dengue infection and iii) are reported to be DEGs (consistently up or down regulated in dengue infection) in at least 4 different publications in dengue infected cell lines or patients (S1 Dataset). This consolidated list was subjected to pathway analysis using the online tool WebGestalt [18, 19] and KEGG Mapper—Search Pathway tool [20]. The KEGG (Kyoto Encyclopedia of Genes and genomes) pathways enrichment was carried out by using the hypergeometric test and the P-value was adjusted by the Benjamini & Hochberg (BH) method. Only pathways that had a minimum number of 3 genes per pathway and adjP-value ≤ 0.01 were selected. We selected all the pathways identified by the KEGG Mapper—Search Pathway tool that had 20 and more dengue interacting human proteins. Enriched pathways belonging to normal biological processes were grouped into broad categories as described in the KEGG database. The broad categories and the number of genes that belong to each category are plotted as a pie chart. Pathways containing 20 or more dengue viral interacting human proteins which belong to the top 4 broad categories are plotted as a bar graph. The gene list was also queried in the KEGG Search&Color pathway to obtain graphical representations of the dengue interacting proteins in different cellular processes that the virus may target to aid its replication.
The KEGG human disease pathways that were enriched by WebGestalt and KEGG Mapper were split into two groups: infectious and non-infectious disease group. The diseases caused by pathogens of bacterial, parasitic and viral origin were assigned to the infectious diseases group. All remaining diseases were assigned to the non-infectious disease group. An edge is placed between the gene and its associated disease and visualized as a network in Cytoscape.
A subset of proteins was extracted from the entire list of dengue virus interacting human proteins where knockout, gene silencing or inhibition studies were carried out. The list of known drugs against this subset of dengue viral interacting human proteins was extracted from bioDBnet [14]. Each drug obtained was checked whether it exhibited pharmacological action against the dengue virus interacting human protein from the drug and drug target database DrugBank [21].
Consolidation of the current knowledge on dengue published till date could help in the construction of the network of molecular events occurring during the viral life cycle. There are around 14,559 publications describing dengue viral research in PubMed till 31 October 2015. Literature describing dengue-human interactions was extracted from PubMed by queries using the keywords listed in S1 Table. The retrieved interactions were classified into three types: (i) Direct interactions, where the human proteins physically interact with the viral proteins or RNA, (ii) Indirect or functional interactions, where the human proteins affect viral replication but there exists no current evidence of them directly interacting with the viral components and (iii) Differentially expressed interactions, genes or proteins whose expression patterns are altered during dengue viral infection.
Out of 6576 publications retrieved after keyword search, we identified 682 direct, 382 indirect and 4120 differentially expressed interactions from 103, 151 and 41 references in PubMed respectively. Table 1 summarizes the Dengue-Human interactions catalogued from these papers. 21% of the total interactions were described in more than one paper. Of the total 4613 dengue interacting human proteins, be it direct, indirect or differentially expressed genes, 41 proteins were detected in all the three categories, and 339 were detected in two of the three categories. Data for all these three different types of interactions were compiled into three different sections in the DenHunt database, and the detailed list of all the interactions can be downloaded from the database website and is also available as S2 Dataset.
It is widely accepted that successful invasion of the host by the pathogen involves targeting multiple components of host cellular machinery. To better understand the molecular mechanisms underlying dengue pathogenesis, we determined statistically significant over-represented or enriched KEGG pathways in the dengue virus interacting human proteins. A comprehensive list of human proteins which are involved in direct interactions, indirect interactions and are reported to be DEGs in at least 4 different publications in dengue infected cell lines or patients (S1 Dataset) was subjected to gene set enrichment analyses using the online tool WebGestalt. The reason we selected genes or proteins that were DEGs in at least 4 publications was to pick a high confidence gene list for all our downstream analysis as the techniques used to determine DEGs, such as microarray and proteomics, are greatly prone to errors. KEGG pathways that contained at least 3 dengue interacting proteins and had an adjusted p-value ≤ 0.01 were selected. We obtained 158 KEGG pathways of which 87 belonged to normal biological processes and 71 to disease pathways. To ease representation of these enriched pathways, the pathways belonging to normal biological processes were grouped into broad categories as described at the KEGG database (S4 Dataset) and plotted as a pie chart (Fig 2a). The top 4 broad categories, which have the maximum number of representative genes, are Signal transduction (193 genes), Immune system (193 genes), Transport and catabolism (107 genes) and Metabolism (98 genes). Pathways that have 20 and more dengue interacting human proteins that belong to these top 4 broad categories are represented in Fig 2b, 2c, 2d and 2e.
Most of the host proteins identified as dengue virus receptors play important roles in cellular signaling, pathogen recognition and innate immune response [34]. Since dengue virus uses multiple receptors to enter cells, it is likely that viral infection will activate several cellular signaling and immune pathways. As expected, many proteins involved in cellular signaling and immune system pathways interact directly or indirectly with dengue virus as can be seen in Fig 2a, 2b and 2c. Apart from these pathways, dengue virus replication is closely associated with processes that are involved in transport and catabolism such as endocytosis and phagocytosis. The vesicular trafficking processes, such as receptor mediated endocytosis and the classical secretory exocytosis, are intimately associated with processes of viral entry, maturation and exit [35, 36]. Therefore, it is necessary for the proteins involved in the vesicular trafficking system to interact with viral proteins extensively. Corroborating these studies, we identified many proteins belonging to endocytosis and phagocytosis pathways interacting with DENV (Fig 2d). We also observe quite a few dengue virus interacting human proteins belonging to the amino acid and carbohydrate metabolism processes (Fig 2e). This is expected, as viral replication relies heavily on the host metabolic resources (amino acids and nucleotides) to produce large numbers of progeny which constitute the viral RNA and proteins.
Pathway enrichment analysis showed that majority of the dengue virus interacting human proteins belongs to the signal transduction and immune system pathways. Further, we mapped the dengue virus interacting human proteins to the KEGG pathway maps using the “Search&Colour Pathway” tool from the KEGG database. All the graphical images for the pathways, which have 20 and more dengue interacting human proteins, belonging to signal transduction and immune systems categories are given in the S1 Fig. Here, we describe the role of two pathways, NF-κB and retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathway, in viral infection (Fig 3). These pathways play an important role in viral infection in general and in particular in dengue infection as evidenced by at least 85 papers that analyze proteins belonging to these pathways in connection to dengue viral infection.
NF-κB is termed the central mediator of the immune response and is an attractive target for many pathogens including dengue virus [37]. Dengue viral protease 2B-3 interacts with and cleaves IκBα/β, a NF-κB inhibitor, and activates NF-κB [38]. Many of the activators of NF- κB that help in degradation of IκBα/β, also interact with dengue viral proteins (Fig 3a). The other immune system pathway discussed here is the RIG-I-like receptor signaling pathway which responds to viral infection by recognizing viral replication intermediates and double stranded RNA (dsRNA) and activate interferon regulatory factors (IRFs). They, then, turn on transcription of interferons alpha and beta, as well as other interferon-induced genes [39, 40]. As can be seen from Fig 3b, different viral proteins interact with many proteins of the RIG signaling pathway to inhibit IRF3 and thereby hamper IFNα/β production [23].
The manifestation and severity of an infectious disease depends on the ability of a pathogen to interfere with host cell functions and defense. Indeed, during pathogen-host co-evolution, hosts have developed an armory of complex defense mechanisms to eliminate the pathogens. Conversely, pathogens have evolved strategies, in part driven by molecular interactions, to evade host cellular defense and to sustain their control over the cellular machinery. We studied the involvement of the dengue viral interacting human proteins in other infectious diseases that belong to the bacterial, viral and parasitic category as given in the KEGG database using WebGestalt (S5 Dataset). The dengue virus interacting human proteins and their association with other infectious diseases was visualized in Cytoscape (Fig 4a). 273 dengue interacting human proteins are associated with other infectious diseases and 168 of them are associated with more than one infectious disease. A similar result was observed in a study where they analyzed the landscape of the human proteins that interact with pathogens and showed that proteins of many viruses share the same interacting human protein, and thus share common infection and immune evasion strategies [41].
There have been studies which suggest that the molecular perturbations triggered by virus-host PPIs could also be involved in many non-infectious diseases of genetic or environmental predisposition [42]. We explored the participation of dengue viral interacting human proteins in the pathologies of various complex non-infectious diseases caused by genetic or environmental factors as given in KEGG database using the tool WebGestalt (S6 Dataset). The dengue viral interacting genes and their association with other complex non-infectious diseases was visualized in Cytoscape (Fig 4b). 249 dengue interacting human proteins are associated with non-infectious diseases and 135 of them are associated with more than one non-infectious disease. The analysis shows that dengue virus interacting proteins are also involved in a wide range of pathologies, with most of them related to cancers followed by auto immune disorders.
We also observed 140 dengue interacting human proteins associated with both infectious and non-infectious diseases (S7 Dataset). This shows that many human proteins of dengue-human interactome are also involved in the response to pathogenic infections and other complex non-infectious diseases. Pathway analysis of the common genes, using KEGG Mapper, showed an enrichment of pathways belonging to immune system and signal transduction pathways (Fig 4c). This implies an unexpected overlapping between the pathways associated with infectious and non-infectious diseases and a perturbation in these pathways might result in the manifestation of diseases.
Drug discovery for dengue viral diseases poses unique challenges on many fronts. One major drawback in the development of drugs or vaccines against dengue is that the disease is caused by four DENV serotypes, with all of them co-circulating in many parts of the world. An effective vaccine or drug must act on all 4 serotypes. The other problem is the unavailability of reliable animal models for credible preclinical evaluation of in vivo efficacy of investigational drugs and vaccines. Analysis of the literature and the patent databases shows that several strategies are being used to develop anti-virals against dengue diseases. Drugs in the pipeline are targeted against (a) viral factors which include viral protein inhibitors (NS3pro inhibitors, NS3 helicase inhibitors, RdRp inhibitors, a-glucosidase inhibitors) [43–50], (b) host cell entry inhibitors [51–56], (c) host factors that are involved in the host immune response [57, 58] and (d) unknown targets which include small molecule inhibitors [59, 60] and herbal inhibitors [61–64].
Majority of the drugs, mentioned above, are targeted towards viral proteins which could force the virus to evolve into resistant strains for survival. Therefore, therapeutics which target host proteins required by pathogens to replicate and persist within the host organism could be an attractive alternative. As seen in the previous section, many human proteins of the dengue-human interactome are involved in the pathogenesis of other infectious and non-infectious diseases and thus represent a powerful resource to identify broad-spectrum drugs. Further, the possibility to target dengue virus-human protein interactions considerably broadens the landscape of drugs that could be developed against dengue infections.
Drug repurposing or repositioning is the process of finding new indications for existing drugs [65]. The advantages of this approach are lower costs of drug development [65, 66]. A number of success stories, such as sildenafil (Viagra) repositioned from a common hypertension drug to a therapy for erectile dysfunction [66] and thalidomide repositioned to treat multiple myeloma and leprosy complications [67], support these approaches. We mined potential FDA drugs against dengue virus interacting human proteins, used to treat other diseases.
In order to extract potential drug targets from the list of dengue virus interacting human proteins, we pulled out only those proteins which were important for viral replication that we term as potential DVHFs. 263 dengue virus interacting proteins were considered to be potential DVHFs because they were described to be essential for viral replication in publications, as inhibiting these proteins led to a reduction in viral replication (S8 Dataset). We, then, extracted the FDA approved drugs that had known pharmacological action against the DVHFs from DrugBank (Table 3, S9 Dataset). 20 of the 263 DVHFs had known FDA approved drugs targeted against them and 9 of these DVHFs were associated with more than one infectious or non-infectious disease. Two of the proteins CCR5 and HMGCR are already established drug targets against another virus, HIV [68, 69]. Maraviroc and lovastatin, inhibitors of CCR5 and HMGCR respectively, have already been shown to inhibit dengue viral replication in in vitro studies [70, 71].These drugs could be tested for their anti-dengue viral effect and it is possible that some of these drugs, either singly or in synergistic combinations may prove to be effective antiviral agents. The use of such drug repositioning strategies which makes the use of known targets, drugs and disease pathways would lead to faster computer to bench studies and reduce the risk and cost of drug discovery approaches to Neglected Tropical Diseases such as diseases caused by dengue virus.
The underlying aim of all dengue viral research is to understand the pathogenesis associated with dengue infection and the development of effective clinical interventions to inhibit viral infection and replication, thereby preventing progression towards to the severe forms of the disease such as DHF and DSS. Dengue–host interaction data, a representation of existing knowledge about dengue infection on a molecular level, will thus be invaluable to future research in this area. DenHunt was developed as a user friendly public repository to capture and organize manually curated information from the available scientific literature on the interactions between dengue virus and host proteins
This information will be of immense importance in improving our understanding of how the dengue virus replicates in the context of the whole cell and how the host-viral interactions control dengue replication and mediate viral pathogenesis. Such insights can also be extrapolated to understand mechanisms of infectious diseases in general. DenHunt could be used to make detailed maps tracking cellular interactions that drive dengue viral replication, and provides a discovery space to the research community for researching and better understanding the dengue viral pathogenesis. An example of how such databases can aid in understanding viral replication is provided by Brass et al. [72] who used the information in the HHPID database to systematically analyze and categorize human proteins required for HIV-1 replication.
Our pathway analysis section shows how dengue virus targets multiple components of the same pathway to mediate effects such as apoptosis or inhibition of IFNα/β production. Although the approach adopted here is purely qualitative, we have amply demonstrated how an integrated repository such as DenHunt could be used to harness already existing data to elucidate dengue viral pathogenesis mechanisms. The key to gain new understanding from DenHunt in viral pathogenesis would lie in its integration with other sources of multidimensional data such as time-course dengue infected gene expression data in the context of an integrated dengue–host PPIs to identify “activated modules” or “highest activity paths” as has been done for other diseases such as HIV [73] and Mycobacterium tuberculosis [74]. Therefore, these networks could serve as an initiating point for various systems level modeling and computational studies.
The high mutation rate and development of resistance strains of RNA viruses can quickly restrain the effectiveness of drugs targeting viral proteins. This observation has led to research on developing drugs that disrupt virus-host interactions rather than viral proteins itself. Few such successful examples with regard to HIV are the viral entry inhibitor maraviroc [68], and a fusion inhibitor enfuvirtide [75]. The availability of a large set of dengue interacting human proteins raises the possibility of identifying putative novel drug targets by in silico methods. We have illustrated that at least seven percent of the dengue interacting human proteins are associated with other infectious and noninfectious diseases and a subset of these proteins are already reported to be targeted by FDA-approved drugs to treat other diseases.
We have developed a comprehensive dengue virus-human interaction network database called DenHunt, which contains a compilation of a curated set of experimentally verified dengue-human interactions. Our database would enable construction and visualization of the essential map of the dynamic networks of interactions that occur during viral life cycle in humans. Detailed characterization of the relationships between these interactions that include multidimensional data given in the database such as direct physical interactions, indirect interactions, gene expression patterns, gene silencing studies, virus serotype, cell type, disease stage, etc., will lead to improved understanding of the conflict between dengue and its human host.
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10.1371/journal.pgen.1000357 | Short Telomeres Initiate Telomere Recombination in Primary and Tumor Cells | Human tumors that lack telomerase maintain telomeres by alternative lengthening mechanisms. Tumors can also form in telomerase-deficient mice; however, the genetic mechanism responsible for tumor growth without telomerase is unknown. In yeast, several different recombination pathways maintain telomeres in the absence of telomerase—some result in telomere maintenance with minimal effects on telomere length. To examine non-telomerase mechanisms for telomere maintenance in mammalian cells, we used primary cells and lymphomas from telomerase-deficient mice (mTR−/− and Eμmyc+mTR−/−) and CAST/EiJ mouse embryonic fibroblast cells. These cells were analyzed using pq-ratio analysis, telomere length distribution outliers, CO-FISH, Q-FISH, and multicolor FISH to detect subtelomeric recombination. Telomere length was maintained during long-term growth in vivo and in vitro. Long telomeres, characteristic of human ALT cells, were not observed in either late passage or mTR−/− tumor cells; instead, we observed only minimal changes in telomere length. Telomere length variation and subtelomeric recombination were frequent in cells with short telomeres, indicating that length maintenance is due to telomeric recombination. We also detected telomere length changes in primary mTR−/− cells that had short telomeres. Using mouse mTR+/− and human hTERT+/− primary cells with short telomeres, we found frequent length changes indicative of recombination. We conclude that telomere maintenance by non-telomerase mechanisms, including recombination, occurs in primary cells and is initiated by short telomeres, even in the presence of telomerase. Most intriguing, our data indicate that some non-telomerase telomere maintenance mechanisms occur without a significant increase in telomere length.
| Telomeres shorten with each cell division and are normally maintained by telomerase. Tumor cells typically up-regulate telomerase to maintain telomeres during rapid growth; however, some tumors lack telomerase and utilize alternative mechanisms to maintain the telomeres. Studies in yeast indicate that recombination contributes to telomere maintenance in the absence of telomerase. To examine whether recombination contributes to non-telomerase mechanisms for telomere maintenance in mouse and human cells, we utilized mouse primary and tumor cells, which were genetically deleted for telomerase. In addition, we assayed human primary cells that had short telomeres due to reduced telomerase activity. Our data indicate that cells with short telomeres have an increased level of telomere recombination, which can occur in telomeric or subtelomeric regions. We also found that, in addition to tumor cells, primary cells can utilize non-telomerase mechanisms for telomere maintenance. Lastly, we found that, unlike human telomerase negative tumors, some mechanisms of telomere maintenance do not result in dramatic telomere lengthening. Thus, mammalian cells likely utilize several different non-telomerase mechanisms for telomere maintenance. These findings will be useful for understanding the process of telomere lengthening in various types of human tumors.
| Telomere length is maintained by the ribonucleoprotein complex telomerase [1]. However, telomerase expression in humans occurs primarily in early development, germ cells, and in stem cells and is not detected in primary cells [2],[3]. Most human tumor cells have detectable telomerase activity, however some proliferating tumors lack telomerase and thus maintain telomeres by alternative mechanisms that are collectively termed ALT for alternative lengthening of telomeres [4],[5]. While mTR−/− mice have a reduced frequency and rate of tumor formation, some tumors form and can grow rapidly in these mice [6],[7]. However, the mechanism by which tumors grow in the absence of telomerase is not known.
Telomerase deficient mice were initially generated by deleting the gene encoding the telomerase RNA (mTR) component [6]. Although mTR−/− mice lack telomerase activity, no phenotype is observed in the first generation, due to the long telomeres observed in laboratory mouse strains [6],[8]. When mTR−/− mice are bred, progressive telomere shortening occurs in successive generations. Early generation, mTR−/−G1, mice are obtained by crossing mTR+/− mice. Crossing the knockouts through successive generations results in mTR−/− G2–G6 generations. Late generation mTR−/− G4–G6 mice have short telomeres and show loss of fertility due to germ cell apoptosis. Wild-derived mouse strains such as CAST/EiJ have significantly shorter telomere length distributions, similar to humans [9]. CAST/EiJ mTR+/− mice bred for increasing generations show progressive telomere shortening and loss of tissue renewal capacity [10]. The phenotypes in the mTR+/− CAST/EiJ mice mimic the human genetic disease, dyskeratosis congenita, due to haploinsuffiency for telomerase [10]. Wildtype mice derived from an intercross between late generation heterozygous parents (termed WT*) have shorter telomeres and also display tissue renewal defects [10]. Thus, telomere shortening and consequent loss of tissue renewal capacity occurs in CAST/EiJ mice even in the presence of telomerase, and provides the opportunity to examine the effects of short telomeres in the presence of telomerase.
Several lines of evidence indicate that ALT occurs by DNA recombination in human tumors and immortalized cells [11]. First, the initial description of ALT demonstrated that the telomeres are exceptionally long and heterogeneous in human tumors and immortalized cell lines which lack telomerase [4]. Second, telomere lengths in ALT cells fluctuate during proliferation, and this fluctuation can be detected by examining the change in the telomere lengths at the p- and q-arm of the Y-chromosome in a rapidly growing culture [11]–[13]. Third, a unique plasmid sequence integrated as single copy at the telomere was found duplicated at other chromosomes following serial transfer of human ALT cells [14]. ALT associated nuclear promyelocytic leukemia bodies (APBs) are found in a subset of human ALT cell lines and contain various recombination proteins [15]. However it is uncertain what functional role APBs contribute to ALT mechanisms and they are often considered as only a marker for some ALT cell lines [16]. Finally, human ALT tumors show frequent telomere sister chromatid exchanges (T-SCEs), that are detectable using chromosome orientation FISH (CO-FISH) [17],[18].
Evidence that recombination contributes to telomere length maintenance was initially discovered in Saccharomyces cerevisiae. Yeast lacking an essential component of telomerase showed progressive telomere shortening and loss of viability, however survivors appeared after successive streaking of the colonies [19]. Studies of these yeast survivors showed that telomere recombination contributes to length maintenance, and requires the RAD52 pathway [19]. Survivors can be classified as Type I or Type II based on their telomere patterns and growth rate [20],[21]. Type I survivors require Rad51, Rad54, Rad55 and Rad57 [22]. The telomeres of Type I survivors are short and the cells have amplified Y′ sequence. They are likely generated by Rad51-dependent break-induced replication (BIR). Type II survivors grow much more rapidly than Type I survivors. They have elongated telomere sequence tracts, require Rad59, Rad50 and other components of the MRX complex, and are predominately generated by Rad51-independent BIR [22],[23]. Therefore, in yeast, telomere elongation in the absence of telomerase occurs mostly though BIR [22],[23]. Studies in Kluyveromyces lactis have also provided insight on telomere recombination mechanisms. In particular, K.lactis deleted for telomerase (ter1) showed that telomere recombination is initiated by short telomeres [24]. In mouse and human cells the T-SCE assay, which is frequently used to measure telomere recombination, will not detect recombination by BIR mechanisms. Further, T-SCEs are exchanges and thus will not result in net telomere elongation as occurs in BIR. Thus we sought to use other assays to examine telomere recombination in telomerase null mouse cells.
To examine the role of short telomeres during telomere recombination in mammalian cells we assayed cells using pq-ratios, outliers, CO-FISH, and Q-FISH from two different strains of telomerase deficient (mTR−/−) mice. We found that late passage CAST/EiJ mouse embryonic fibroblasts (MEFs) and Eμmyc+mTR−/− lymphomas with short telomeres, exhibit telomere maintenance with minimal changes to the overall length distribution. Consistent with telomere recombination, we observed an increase in pq-ratio changes and outliers in mouse cells with increasing numbers of short telomeres. We directly showed that subtelomeric recombination is increased in cells with elevated pq-ratio changes. These pq-ratio changes were seen associated with short telomeres even in telomerase positive cells, suggesting that telomerase itself does not protect against recombination. Our data suggest that, several distinct recombination-based mechanisms can contribute to telomere maintenance in mammalian cells.
C57BL/6J mTR−/− and CAST/EiJ mTR−/− were generated as described [6],[10]. Mice used for the intergenerational cross were generated as previously described [25],[26]. EμMyc+mTR−/− mice were bred and B-cell lymphomas were collected as previously described [7]. Mice were genotyped by Transnetyx (Cordova, TN). For serial transfers of tumors, B-cell lymphomas were isolated from mice and resuspended in PBS at 1×107 cells/ml. A total of 1×106 cells (0.1 ml) were subcutaneously injected into three SCID mice, two sites each (Taconic). All animals were housed and bred in a pathogen-free environment and procedures approved by the Institutional Animal Care and Use Committee at The Johns Hopkins University.
Splenocytes and bone marrow, from the tibias and femurs, were harvested from 8–10 week old animals. Bone marrow was collected by flushing the bones with 1× PBS, pH 7.4 (GIBCO) with a 23-gauge needle. Cells were resuspended in MarrowMax media (GIBCO), and immediately incubated with 0.1 µg/ml of KaryoMax Colcemid solution (GIBCO) for 20 minutes, and harvested for metaphase spread analysis. Cells were swelled in 75 mM KCl hypotonic solution at 37°C for 15 minutes, and fixed with (3∶1) methanol: acetic acid with three repeated exchanges prior to dropping onto slides. Splenocyte suspensions were generated using 70 µm Nylon cell strainers (BD Falcon), and were activated in RPMI 1640 with L-glutamine (GIBCO) supplemented with 1× penicillin-streptomycin-glutamine, 10% heat inactivated fetal bovine serum, 10 mM HEPES buffer, 1 mM sodium pyruvate (GIBCO), 1× non-essential amino acids (GIBCO), 50 µM ß-mercaptoethanol (Sigma), 10 µg/ml LPS (Sigma), 1 U/ml IL-2 (Roche), and 5 µg/ml ConA (Sigma). Splenocytes were cultured for 48 hours prior to the addition of 0.1 µg/ml of KaryoMax colcemid solution. Cells were incubated in colcemid for 20 minutes and metaphases prepared as described above. Lymphomas were collected from mice and single cell suspensions were generated using a 70 µm nylon cell strainer (BD Falcon). Cells were grown in a (1∶1) mixture of DMEM and Iscove's modified Eagle's media supplemented with 4 mM L-glutamine, 100 Units/ml of penicillin-streptomycin, 100 µM ß-mercaptoethanol, and 10% heat inactivated fetal bovine serum. Metaphases from human lymphocytes were acquired as previously described [27]. Mouse embryonic fibroblasts were harvested in 1× PBS containing 1× penicillin, streptomycin, and fungizone (PSF, Invitrogen) at embryonic day 13.5. Cells were incubated in 0.25% trypsin-EDTA for 20 minutes at 4°C, and further digested at 37°C for 5 minutes. Cells were transferred to DMEM containing 10% FCS and PSF. 24 hours post plating attached cells were washed in 1× PBS and new DMEM was added and split ∼24–48 hours later when cells were ∼80% confluent.
Metaphase spreads were processed for Q-FISH as previously described [26]. For CO-FISH, the cells were incubated for 24 hours in 30 µM of 5′-bromo-2′deoxyuridine (BrdU, Sigma) and 10 µM 5′bromo-2′deoxycytidine (BrdC, Berry & Associates). Bone marrow was incubated for 20 minutes and splenocytes for 2 hours in 0.2 µg/ml of colcemid and harvested for metaphases as described above. Metaphase spreads were rehydrated in 1× PBS pH 7.4 for 15 minutes and fixed in 4% formaldehyde in 1× PBS pH 7.4 for 2 minutes. All washes were done between treatments with 1× PBS and dehydrated in an ethanol series of 70%, 90% and absolute ethanol. Slides were treated with 1 mg/ml of pepsin at 37°C for 10 minutes, fixed in 4% formaldehyde in 1× PBS pH 7.4, dehydrated and treated with 500 µg/ml of RNase A in 2× SSC for 10 minutes at 37°C, and stained with 0.5 µg/ml of Hoechst 33258 (Sigma) for 15 minutes at room temperature, air-dried, and 100 µl of 2× SSC was added with a cover slip prior to a 30 minute UV exposure at 365 nm in an 1800 Stratalinker (Stratagene). Slides were digested with 3 U/µl of Exo III in 1× buffer (Promega) for 10 minutes at 37°C. Slides were hybridized with a telomere probe as described above for Q-FISH. Antibody staining to BrdU was used in order to avoid metaphases that underwent two rounds of replication. For BrdU staining, slides were washed in 2× SSC and incubated for 30 minutes at 37°C in a 1∶100 dilution of FITC anti-BrdU (Molecular Probes, Invitrogen) in PN buffer (0.1 M NaH2PO4/0.1 M Na2HPO4 pH 8.0, 1% Triton X-100) as described [28]. The slides were rinsed two times for 5 minutes each in PN buffer, prior to mounting with Vectashield. For multicolor FISH the subtelomeric clone (JHU1193) was acquired from Invitrogen (RPCI-23 391E5) and purified using HiPure Plasmid Filter Purification kit (Invitrogen). A mouse chromosome 2 specific paint probe was acquired from Applied Spectral Imaging (ASI). BAC DNA (1.5 ug) was labeled by Biotin-Nick Translation (Roche), ethanol precipitated with 15 µg mouse Cot-1 DNA (Invitrogen), and 1.5 µg of fish sperm DNA (Roche), and resuspended in 10 µl of formamide using a thermomixer at 37°C. 20 µl of probe hybridization buffer (20% Dextran sulfate, 2×SSC) was added, and the probe was denatured at 80°C for 5 minutes and pre-annealed at 37°C for one hour. Slides were pretreated as described for Q-FISH, denatured at 80°C in 70% formamide/2× SSC, and dehydrated in an ethanol series, prior to hybridization for 48 hour at 37°C. Slides are washed at 45°C in 50% formamide, 2× SSC for 3× 5 minutes and at 60°C in 0.1× SSC for 3× 5 minutes. Slides were dipped in 4× SSC/0.1% Tween 20 and 50 µl of denatured and pre-annealed chromosome 2 paint probe was hybridized for 48 hours at 37°C. Slides were washed in 0.4× SSC at 72°C for 3× 5 minutes and washed 2× 2 minutes each in 4× SSC/0.1% Tween 20. For detection of the BAC hybridization, slides were blocked in (3% BSA/4× SSC/0.1% Tween 20) for 30 minutes at 37°C for 30 minutes. Slides were washed for 2 minutes in 4× SSC/0.1% Tween 20 and streptavidin Alexa Fluor 488 (Molecular Probes, Invitrogen) conjugated antibody was diluted 1∶100 and incubated at 37°C for 45 minutes. Slides were washed in 4× SSC/0.1% Tween 20 at 45°C, 3× 5 minutes and dehydrated in an ethanol series. Slides were stained and visualized as described for Q-FISH.
Telomere ratios were determined by initially measuring by Q-FISH the telomere lengths with TFL-Telo (Version 2.0) [29]. For each chromosome, the telomere signals were scored for location (p or q), and the final value of q/p was determined for each chromosome of multiple metaphases. 5-fold ratio values (q/p≥5 or q/p≤0.2) were plotted and normalized for the total number of chromosomes examined for each genotype. T-tests were used to determine statistical significance. Chromosomes with a single signal-free end were considered to have a q/p greater than 5-fold. The number of outliers was calculated by generating box plots using Stata 8.0. The box plots were generated using the Q-FISH values from the same metaphases examined for telomere ratio analysis. The test for statistical significance of outliers was done using the Wilcoxon rank sum test as described [30].
Bone marrow and splenocytes were resuspended in 1% PBSa agarose at a final concentration of 1×107 cells/ml, incubated in LDS (1% lithium dodecyl sulfate/100 mM EDTA pH 8.0, 10 mM Tris pH 8.) at 37°C O/N with constant agitation, and washed twice in 20% NDS for 2 hours at 37°C with constant agitation. Prior to digestion, plugs were washed twice in TE for 30 minutes and then washed twice in 400 µl of 1× Buffer #2 (NEB) for 30 minutes prior to MseI restriction digestion. Plugs were digested overnight and loaded on a 0.7% TAE agarose gel. Samples were run at 100V for 6–8 hours. Following denaturation (0.5 M NaOH/1.5 M NaCl) and neutralization (1.5 M NaCl/0.5 M Tris-HCL pH 7.4), the DNA was transferred in 20× SSC to a Nylon Membrane (Amersham Hybond N+) by weighting method overnight and cross-linked with UV Stratalinker (Stratagene). Pre-hybridization was done at 65°C in Church's buffer for 2 hours. A radioactive telomere probe was made by random-prime labeling using Prime-It II (Stratagene) with a slight modification. Briefly, 25 ng of a 500 bp telomeric 5′-TTAGGG/CCCTAA containing probe, acquired from EcoR1 digestion of JHU821 or 1 KB Plus DNA ladder (Invitrogen) was labeled using 33 µM of dATP, dTTP, 50 µCi of α-32P dCTP (3000 Ci/mmol) and 50 µCi α-32P dGTP (3000 Ci/mmol). Unincorporated nucleotides were removed using a G50 column (GE Healthcare). Labeled probe was counted and 106 counts/ml (telomere probe) or 105 counts/ml (ladder) was denatured at 100°C for 5 minutes and added to the pre-hybridization solution and hybridized overnight at 65°C. Membranes were washed 3× 15 minutes each in 6× SSC and 1%SDS at 65°C, and 3× 15 minutes each in 1× SSC and 1% SDS at 65°C and exposed to a phosphorimager screen and detected on a Fuji phosphorimager.
To examine the contribution of non-telomerase mechanisms to telomere maintenance, we serially passaged CAST/EiJ mTR−/− mouse embryonic fibroblasts (MEFs), which have short, homogeneous telomere lengths [9],[31]. After extensive passage, MEF cultures were immortalized as seen for mTR−/− MEFs on the C57BL/6J background (Figure 1A) [6]. Despite extensive passage of these MEFs, Q-FISH and Southern analysis indicated minimal changes to the telomere lengths in both mTR+/− and mTR−/− cell lines when compared to early passage cultures (Figure 1 D–F, compare p3 to p29 and p45, data not shown). These findings indicate that despite immortalization, MEFs lacking telomerase do not undergo extensive telomere lengthening. We reasoned that non-telomerase mechanisms for telomere maintenance may be occurring, similar to Type I survivors in yeast, which result in telomere maintenance without extensive lengthening.
We next asked whether such maintenance also occurs in transformed tumor cells lacking telomerase. We utilized Eμmyc+ transgenic mice that harbor the c-myc oncogene expressed by the B-cell specific Eμ promoter [32]. These mice invariably develop B-cell lymphoma and die from the tumor by six months of age. We generated Eμmyc+ mice with short telomeres. The tumor progression was dramatically reduced in the Eμmyc+ mTR−/− G6 mice with short telomeres [7]. However some tumors did form in these mice, and allowed for analysis using Q-FISH (Figure S1A). We first examined the telomere lengths in primary bone marrow and splenocytes from Eμmyc+mTR+/+, Eμmyc+mTR−/− G1, and Eμmyc+mTR−/− G4 and G6 tumor-free mice (Figure S1B and C). In primary cells, the telomere lengths were shorter in the late Eμmyc+mTR−/− generations compared to the early generations and when compared to Eμmyc+mTR+/+ cells. We next examined telomere lengths of the tumors that formed in Eμmyc+mTR+/+, Eμmyc+mTR−/− G1, and Eμmyc+mTR−/− G4 and G6 mice. We found that the telomerase negative tumors did not exhibit exceptionally longer telomeres when compared to primary bone marrow or splenocytes (Figure S1A–C). To examine if the telomeres could be maintained in the absence of telomerase, tumors with short telomeres (Eμmyc+mTR−/−G5) were serially passaged by transplantation into new recipient mice (Figure 1 B and C). Telomere lengths were examined, and again we observed minimal changes in telomere length in primary versus secondary lymphomas. Thus we conclude that both immortalized primary mouse cells and tumor cells can maintain telomere lengths in the absence of telomerase using non-telomerase mechanisms for telomere maintenance.
Since we observed that telomerase negative tumors and CAST/EiJ MEFs appear to be utilizing non-telomerase mechanisms to maintain telomeres, we sought to determine the basis of this telomere maintenance mechanism that occurred without significant changes to telomere lengths. We examined whether short telomeres may initiate the non-telomerase maintenance mechanisms operating in these cells. We utilized a previously described pq-ratio assay, which can account for different types of telomere recombination mechanisms [12]. This assay is based on the Q-FISH assay, which quantitates the telomere length on metaphase spreads [29]. As cells divide in culture, both telomeres of a given chromosome should shorten by similar amounts. This results in a near constant telomere ratio at the p and q arms for each individual chromosome in a population of growing cells. However, if recombination occurs, it will alter the length of at least one telomere by a random amount. Thus the ratio of the telomere signal on the two ends of a chromosome that underwent recombination will differ from the other copies of that chromosome in the population. For pq-ratio analysis, we initially calculated the pq-ratio for mouse chromosome 1 in a population of growing cells (Figure S2A). Cells expressing telomerase had few changes in the telomere ratio (Figure S2B). Similar to human ALT cell lines, we observed that mouse tumor cells lacking telomerase were increased for changes in the pq-ratios [12] (Figure S2C). Given that this analysis of a single chromosome could be expanded to examine the changes within the entire set of chromosomes of a given metaphase, we proceeded by examining the pq-ratio of every chromosome in a minimum of ten different metaphases (Figure 2 and Figure S3). We first examined primary bone marrow from mTR+/+ mice and early and late generation mTR−/− mice (mTR−/−G1 and mTR−/−G4). Metaphase spreads were examined for telomere length using Q-FISH (Figure S1 D and E). Late generation mTR−/−G4 mice had significantly shorter telomeres than mTR+/+ or early generation mTR−/−G1 mice. We next examined the pq-ratios from metaphase spreads from a population of growing cells and plotted the ratio for each genotype (Figure 2A–F). To quantitate the changes in the pq-ratios, we focused on the percent of chromosomes that had a pq-ratio change of greater than 5-fold (Figure 2G). In mTR+/+ bone marrow the pq-ratios seldom changed from a value near 1 (Figure 2A, B, and G). Compared to mTR+/+, mTR−/−G1 primary bone marrow cells were significantly increased for changes in pq-ratios (Figure 2C, D and G). mTR−/− G4 cells with even shorter telomeres also showed a significant increase in the number of altered pq-ratios compared to mTR+/+ (Figure 2E, F and G). These changes in pq-ratios occurred in all metaphases examined, and were not specific to individual metaphases. Similar findings were also observed with splenocytes from the same genotypes (Figure 2G). The greater amount of changes in the pq-ratios in cells with shorter telomeres likely reflects non-gradual additions or deletions of telomere sequence that occurs during recombination.
We next asked whether Eμmyc+mTR−/− primary cells with short telomeres were also increased for changes in the pq-ratios. Comparisons between Eμmyc+mTR−/−G1 and Eμmyc+mTR+/+ primary bone marrow showed a 11-fold increase in variable pq-ratios (Figure 2H). We also observed a 40-fold increase in pq-ratio changes between Eμmyc+mTR−/− G4 and G6 compared to Eμmyc+mTR+/+ bone marrow (Figure 2H). Similar trends were observed in splenocytes (Figure 2H). Thus similar to mTR−/− primary cells with short telomeres, Eμmyc+ mTR−/− primary cells with short telomeres were increased for changes in the pq-ratios.
To determine whether similar changes in pq-ratios occurred in telomerase negative tumors, we examined Eμmyc+ tumors (Figure 2H and Figure S3). Eμmyc+mTR−/−G1 tumors showed a 37-fold increase in pq-ratios that changed compared to Eμmyc+mTR+/+ tumors. Eμmyc+mTR−/−G6 tumors showed a 52-fold increase compared to Eμmyc+mTR+/+ tumors. These findings suggest that telomerase deficient tumor cells with short telomeres are likely increased for telomere recombination. We then compared the number of pq-ratio changes in tumors and primary cells and found that tumors from both early Eμmyc+mTR−/− G1 and late Eμmyc+mTR−/− G4/G6 generation mice were increased in the amount of pq-ratio changes compared to the primary bone marrow from the same generation (Figure 2H). Eμmyc+ mTR+/+ tumors were also slightly increased for changes in the pq-ratios compared to Eμmyc+mTR+/+ primary cells, perhaps due to selection for increased recombination during growth of the tumor. Similar observations were made between the primary splenocytes and tumors. Our data indicate that telomerase negative tumor cells have an elevated amount of telomere recombination compared to primary cells. Furthermore, it illustrates that some non-telomerase telomere maintenance mechanisms have a minimal effect on the average telomere length.
While the pq-ratio assay is very sensitive, the data may become biased as the telomeres shorten. Small changes on a short telomere may be over-represented and telomeres with no signal will not be represented at all. Thus we used a second statistical test to assay for length changes. We determined the distribution of the telomere lengths and quantitated the number of outliers (Figure 3A), which are telomere lengths more than two standard deviations from the median. These outliers numerically represent exceptionally long and short telomeres in the distribution.
The telomere length distribution acquired from Q-FISH for each genotype was determined and examined to identify outliers (shown as dots, Figure 3A). We found mTR−/−G4 bone marrow and splenocytes cells had a significantly greater number of outliers per metaphase compared to mTR+/+ cells (Figure 3B). This result is consistent with the findings observed with the pq-ratio analysis, and suggests that short telomeres may initiate telomere recombination.
Our data suggest that abrupt changes in telomere length occur on short telomeres. To directly test whether recombination is occurring in these cells, we developed a subtelomeric recombination assay. We examined the mouse genome sequence for unique loci contained in subtelomeric regions, and identified a terminal BAC clone located at H4 on mouse chromosome 2, which also contained subtelomeric sequences directly adjacent to telomere repeats in the genome. We initially hybridized this sequence to mTR+/+ cells to confirm the copy number of this subtelomeric BAC clone and found the clone hybridized to only two chromosomal termini in wildtype bone marrow metaphase spreads (Figure 4A). We reasoned that if recombination in the telomeric region occurs in the subtelomeric regions, as it does in yeast, amplification and transfer of this sequence to the telomeres of other chromosomes would occur. To determine the frequency of this sequence amplification and transfer, we hybridized metaphase spreads with both the BAC clone and a chromosome 2 specific paint probe (Figure 4B and C). We first examined the Eμmyc+mTR−/− G5 lymphomas. Consistent with subtelomeric recombination, we observed 51% of the metaphases had amplified this subtelomeric region in the Eμmyc+mTR−/− G5 lymphomas with short telomeres (Figure 4D). We observed no metaphases with amplified subtelomeric sequence in primary mTR+/+ bone marrow. In the Eμmyc+mTR+/+ lymphomas there was a low level of amplification of this sequence likely due to increased recombination in the tumors (10%). In addition we observe the amount of subtelomeric recombination in mTR−/− MEFs, when restored for mTR have a reduced number of changes in both pq-ratios and the frequency of subtelomeric recombination (unpublished data). Given that early passage primary cells lack fusions, its unlikely that bridge fusion breakage cycles contribute to the amplification of the sequence. This subtelomeric amplification and transfer of a unique locus to additional chromosomes correlated directly with increased changes in the pq-ratios (Figure 4E), suggesting that BIR could account for both processes.
To more specifically examine whether short telomeres are substrates for recombination, we used mice from an intergenerational cross [25],[26]. All progeny from this type of cross inherit chromosomes with 50% short and 50% long telomeres. Previous studies showed that when late generation mTR−/− mice were crossed with mTR+/− mice, the shortest telomeres were specifically elongated in mTR+/− mice [25].
For our analysis, we crossed late generation telomerase mTR−/− G5 mice with mTR+/− mice (Figure 5A). Bone marrow was harvested from mTR−/− iG6 and mTR+/− iG6 mice and analyzed by Q-FISH. We observed that the shortest telomeres from mTR+/− iG6 mice were extended, while critically short telomeres persisted in mTR−/− iG6 mice (Figure 5B, arrow). When we examined the pq-ratios, we found a significant increase in the pq-ratios that changed in mTR−/− iG6 mice compared to mTR+/− iG6 mice (Figure 5C). Outlier analysis of telomere lengths from this intergenerational cross also indicated a significantly greater number of outliers per metaphase in the mTR−/− iG6 mice compared to the mTR+/− iG6 mice (Figure 5D). These data strongly suggest that short telomeres are substrates for recombination. We also noted that the mTR+/− iG6 mice displayed a small amount of pq-ratio changes and had some outliers. This small, yet detectable amount of pq-ratio changes and outliers in the mTR+/− intergenerational mice suggested that perhaps telomere recombination occurs in the presence of telomerase.
To test more directly whether some short telomeres may recombine in the presence of telomerase, we utilized mTR+/− CAST/EiJ mice. As described previously, telomere shortening occurs in late generation mTR+/+ and mTR+/− (termed WT#* and HG#) CAST/EiJ mice [10]. We assayed littermates from both early and late generation heterozygous intercrosses, including a cross from the first generation of heterozygous mice (HG1) which yielded WT2*, HG2, and knockout KO(G2) mice and a cross of HG5 parents which yielded WT6*, HG6, and KO(G6) mice (Figure 6A and B). Since telomeres are shorter and more homogenous in the CAST/EiJ strain compared to C57BL/6J, we examined telomere length by both Southern blotting and Q-FISH (Figure 6C and Figure S4 A–C) [9]. We observed that the WT* mice from the late generation HG5 intercross (WT6*), had shorter telomeres than true wildtype (WT) and early generation WT2* mice. We also observed that the telomeres from the late generation HG6 mice were shorter than HG2 mice. We next examined the pq-ratio changes and the number of telomere outliers. WT6*, HG6, and both KO(G2) and KO(G6) mice, had a significant increase in changed pq-ratios compared to WT mice (Figure 6D and Figure S4D). These findings illustrate pq-ratios changes increase in cells with short telomeres, even in the presence of telomerase. Consistent with this finding, we also observed an increase in the amount of outliers in bone marrow and splenocytes cells with short telomeres (Figure 6E and Figure S4E). Together the pq-ratio and outlier analysis suggest that short telomeres may initiate recombination even when telomerase is present at wildtype levels as in the WT6*.
Since T-SCE has been documented in ALT cells, we wanted to determine whether this type of recombination could account for the pq-ratio changes occurring in primary cells. Using the CO-FISH assay we examined littermates from late generation (HG5×HG5) CAST/EiJ mice and WT CAST/EiJ mice for T-SCE. Primary bone marrow from WT, WT6*, HG6, and KO(G6) mice was isolated and examined by CO-FISH (Figure 7). In WT6* and HG6 mice with short telomeres, we observed only a small amount of T-SCEs (0.05 T-SCEs/chromosome, Figure 7E). Similar frequencies were observed in bone marrow from wildtype mice. In the KO(G6) bone marrow cells the amount of T-SCEs was significantly higher (0.25 T-SCEs/chromosome) when compared to WT6* and HG6. However, in splenocytes we observed a similar frequency of T-SCEs for all genotypes (0.04–0.07 T-SCEs/chromosome). The dissimilar amount of T-SCEs between cell types could be due differences in the type of recombination mechanism contributing to the telomere maintenance in splenocytes versus bone marrow. The difference in the number of T-SCEs in the WT6* and KO(G6) bone marrow is unlikely due to differences in replication rate of these cells, since both genotypes have similar proliferation rates (Morrish, Armanios and Alder, unpublished data). Instead the increase in T-SCEs in KO(G6) bone marrow cells compared to WT6* implies that T-SCEs may be one type of recombination mechanism that occurs with short telomeres.
While human ALT tumor cells show greatly elongated telomeres, we wondered if primary human cells with short telomeres might utilize telomere recombination, without telomere lengthening. We thus examined both pq-ratios and outliers in lymphocyte cells from dyskeratosis congenita patients with short telomeres due to a mutation in hTERT (K902N) (Figure 8A–C) [27]. Individuals with short telomeres had a significantly greater amount of changes in pq-ratios in comparison to non-carriers (hTERT+/+) in the family (Figure 8D). Analysis of the frequency of outliers per metaphase also demonstrated that carriers with short telomeres had a significantly greater number of outliers compared to non-carriers with longer telomeres (Figure 8E). Thus, short telomeres in human primary cells show evidence of increased telomere recombination, even in the presence of limiting telomerase. This data suggests that some telomere maintenance mechanisms may occur in human cells without a substantial increase in the telomere length distribution.
Telomere lengthening is predominantly carried out by telomerase, however other mechanisms including recombination can contribute to telomere length changes. In yeast, short telomeres can stimulate telomere recombination, perhaps due to the loss of telomere capping [24]. Consistent with recombination occurring at short telomeres in mammalian cells, we found an increase in pq-ratio changes and outliers in both telomerase negative and positive cells. Late generation mTR−/− cells with the shortest telomeres showed the greatest amount of pq-ratio changes, and outliers in primary bone marrow and splenocytes. Furthermore, late generation mTR−/− tumor cells were elevated for these changes compared to primary cells. This increase in telomere length fluctuation likely occurs by a recombination-based mechanism, as we found an increased rate of subtelomeric sequence amplification in cells with short telomeres. Thus like in yeast cells, telomeric and subtelomeric recombination is elevated at short, possibly dysfunctional telomeres.
The increase in telomeric recombination occurred even in cells having functional telomerase alleles. Primary cells with short telomeres from CAST/EiJ WT*, mTR+/−, and in human samples with mutations in hTERT showed increased telomere length changes, supporting the idea that telomere recombination mechanisms can occur in the presence of telomerase. Additionally the increase in subtelomeric recombination in late passage mTR+/+ MEFs further demonstrates that both telomerase and recombination can maintain short telomeres. This increased telomere recombination at short telomeres in the presence of telomerase suggest that the telomerase enzyme does not directly contribute to end protection. Thus, the initiation of telomere recombination is more likely due to the disruption of the capping structure at short telomeres, and not the loss of telomerase.
Studies of survivors in yeast indicate that telomere recombination can occur by multiple mechanisms. Specifically, two different survivor pathways have been described for telomere recombination. Type I survivors have short telomere tracts and BIR occurs in subtelomeric sequences called Y′ elements. Type II survivors have long telomere tracts and BIR occurs within the telomere repeats themselves [20],[22]. Our findings suggest that mammalian cells also can use various types of recombination mechanisms for telomere maintenance, and that ALT does not occur by a single mechanism. BIR is considered the predominant mechanism in yeast for telomere elongation in survivors [23]. We identified an increased number of metaphases in telomerase deficient lymphomas with subtelomeric recombination, indicative of additional recombination based mechanisms. For instance, degradation of short telomeres into subtelomeric regions likely exposes various types of repetitive sequences. When sequence homology with another chromosome is encountered, strand invasion and copying of the terminal region occurs. The transfer of this unique subtelomeric locus from mouse chromosome 2 to different chromosomes is consistent the possibility of a BIR-like mechanism.
While T-SCEs are typically used as the main measure of telomere recombination in human ALT cells, it is important to note that BIR pathways would not be detected by the CO-FISH method. Although we did detect T-SCE in the bone marrow cells, the number of T-SCEs did not explain the frequency of telomere length variations, indicating others mechanisms must also play a role [33],[34]. In addition, CO-FISH is limited when telomeres are very short, since T-SCEs at short telomeres are very difficult to detect due to the resolution of the telomere probe [35]. In addition, some of the accumulating changes in pq-ratios may also arise as a consequence of stalled replication forks that might be accompanied with dysfunctional telomeres. In Schizosaccharomyces pombe, deletion of Taz1, the ortholog of the telomere binding proteins TRF1 and TRF2, can result in replication fork stalling [36],[37]. These studies suggest that dysfunctional telomeres due to the immediate loss of telomere end binding proteins in mammalian cells may result in replication fork stalling, however such intermediates would be predicted to invoke recombination. Thus using pq-ratio and outlier analysis allows detection of various types of recombination.
The use of ALT for telomere maintenance in human cells has characteristically been associated with a dramatic lengthening of telomeres [38]. In contrast, we find in many instances telomere maintenance can occur without extensive telomere elongation. In Eμmyc+mTR−/− G6 transgenic mice, short telomeres dramatically limit tumor growth [7]. However, in a few mice, tumors somehow overcome the short telomeres and continue to grow. When these Eμmyc+mTR−/− tumors were transferred serially through several mice the telomere length was not significantly changed indicating that these tumor cells must utilize non-telomerase mechanisms for telomere length maintenance. However, in sharp contrast to many ALT cell lines, telomeres from these mTR−/− cells did not exhibit a dramatic telomere lengthening. This result of telomere maintenance without significant telomere elongation is very similar to what is seen in Type I survivors in yeast. In Type I survivors the telomeres are very short and yet they are maintained following many doublings and exemplify that not all telomere recombination mechanisms result in dramatic telomere lengthening [21]. In contrast the telomere repeat tracts in Type II survivors are dramatically longer than in wildtype cells. Both Type I and Type II survivors can be generated in a population of cells. Yet due to the growth advantage of Type II survivors in liquid culture, only Type II survivors are seen following extensive growth. The difference between human ALT tumor cell lines and the mouse tumor cells shown here suggests that there may be multiple pathways for recombination in mammals as there are in yeast. One type may predominate in human ALT tumors and immortalized cells and another type may be more favored in primary human cells and mouse cells. Future studies will provide insight into the mechanism of the different pathways and their requirement for the growth of tumors in the absence of telomerase.
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10.1371/journal.pgen.1007943 | Marine environmental DNA biomonitoring reveals seasonal patterns in biodiversity and identifies ecosystem responses to anomalous climatic events | Marine ecosystems are changing rapidly as the oceans warm and become more acidic. The physical factors and the changes to ocean chemistry that they drive can all be measured with great precision. Changes in the biological composition of communities in different ocean regions are far more challenging to measure because most biological monitoring methods focus on a limited taxonomic or size range. Environmental DNA (eDNA) analysis has the potential to solve this problem in biological oceanography, as it is capable of identifying a huge phylogenetic range of organisms to species level. Here we develop and apply a novel multi-gene molecular toolkit to eDNA isolated from bulk plankton samples collected over a five-year period from a single site. This temporal scale and level of detail is unprecedented in eDNA studies. We identified consistent seasonal assemblages of zooplankton species, which demonstrates the ability of our toolkit to audit community composition. We were also able to detect clear departures from the regular seasonal patterns that occurred during an extreme marine heatwave. The integration of eDNA analyses with existing biotic and abiotic surveys delivers a powerful new long-term approach to monitoring the health of our world’s oceans in the context of a rapidly changing climate.
| All environments contain genetic remnants of the life they contain and support. For example, samples collected from the ocean contain biological material such as microscopic organisms, shed cells, excrement and saliva—the DNA from which reveals the surrounding marine biodiversity. Environmental DNA (eDNA) approaches have the ability to identify marine species that are notoriously difficult to identify using morphology alone. Here we develop, and apply, a DNA ‘toolkit’ to five years of samples collected from a single site in the Indian ocean. It is rare to find a temporal series of samples of this duration that are also suitable for DNA analysis. We show that eDNA techniques have the capacity to monitor ocean biology in fine detail. We demonstrate how the biological communities of plankton and fish respond to normal seasonal changes and, more importantly, to that of an uncharacteristic heatwave. The methods embodied in this paper are applicable to marine studies across the globe and, as such, pave the way for the design of long-term monitoring programs using eDNA.
| Changes in ocean temperatures, chemistry and currents are occurring faster now than at any time in human history [1, 2]. These changes will certainly impact the productivity in marine environments that is critical for social and economic wellbeing [3]. These impacts have driven the expansion of global efforts to monitor marine biota and track ecosystem health [1, 4, 5]. Abiotic environmental data are already collected by various methods across all oceans [4, 6], but thorough sampling of marine biota is far more restricted and challenging [5]. Robust biomonitoring programs that link biological changes to the physio-chemical state of the oceans will help to identify ecological trends and predicting future trajectories.
Since 1931, the biomass and morphological species in zooplankton communities have been used extensively for oceanic biomonitoring [7]. Zooplankton are the trophic link between phytoplankton and larger predators [8]. These highly diverse communities have been described as ‘beacons of change’ [9], as their community composition is known to respond to fluctuations in both abiotic and biotic factors [5, 9, 10]. Most zooplankton are ectothermic, so they are sensitive to temperature changes that affect their physical activity and physiology [9]. Many species are also fast growing and short-lived and so communities typically respond rapidly to changes in environmental conditions [5, 9–11].
The importance of extended temporal sampling to describe changes within planktonic communities has long been recognised [1, 4, 5, 12–14]. A long-term analysis has the ability to define baselines and understand what is ‘normal’ for a community [4] and provides a mechanism to gauge ecosystem health [11]. There are several extended studies targeting zooplankton [1, 4, 5, 12, 14–17], yet these typically focus on a narrow range of taxa [1, 11, 13, 18–20].
Morphological identification of zooplankton is time consuming and expensive [4, 21]. Samples must be in good physical condition, particularly for taxonomic identifications reliant on the presence of fragile appendages. This problem is worst for easily damaged, soft-bodied phyla such as Cnidaria and Ctenophora [22]. Many marine animals, including fish and larger crustaceans, have a larval planktonic phase, and identification of larvae to species is difficult or impossible, even for skilled taxonomists [21, 23, 24]. Morphological studies tend to overestimate the relative abundance of those taxa that are readily identified, but overlook a significant fraction of marine animal groups. Consequently there is growing recognition that morphology by itself will struggle to meet with the increasing need for holistic marine biomonitoring in conservation and management decisions [4, 25].
Environmental DNA (eDNA) is transforming our ability to study marine biodiversity. Recent metabarcoding studies on eDNA extracted from water [26–28], sediment [29], scat [25, 30–33] and plankton [21, 34] demonstrate its capacity to profile a vast range of biota. While these studies focus strongly on spatial and community differences, the ability for eDNA to act as a long-term temporal biomonitoring tool is unexplored.
Environmental DNA is defined in Taberlet et al [35] as a “complex mixture of genomic DNA from many different organisms found in an environmental sample… [including] material resulting from filtering air or water, from sifting sediments, or from bulk samples”. Here, due to historic sampling, we analyse eDNA purified from bulk zooplankton samples systematically collected monthly over five-years from a single ecologically significant site at Rottnest Island, Western Australia [36] (Fig 1A). This temporal window of sampling includes a “marine heatwave” anomaly that had significant impacts on the south Western Australian marine ecosystem [37–39]. We test the capacity for eDNA metabarcoding to track biotic shifts, examine how eDNA signatures relate to abiotic variables, and lastly outline the value and practical implementation of multi-year eDNA programs
Shotgun DNA sequencing has been used for eDNA community analysis [40] but it is cost prohibitive and dominated by prokaryotic taxa [28]. Single marker metabarcoding approaches have proven useful for biological monitoring, but their taxonomic focus has to be narrow because each assay is by definition limited in scope. Even supposedly “universal” DNA metabarcoding assays have proven inadequate to identify a comprehensive range of target taxa in our global oceans [28]. To address the challenge of pinpointing a range of metazoan taxa, we developed a novel multi-gene (COI, 16S & 18S) metabarcoding ‘toolkit’ capable of working with both degraded and intact eDNA, and able to identify a wide variety of taxa found within zooplankton communities. We used three existing metabarcoding assays and designed five more (S1 Table) to target a range of crustaceans, molluscs, fish and cnidarians known to be present at the reference site [41]—a site that has been monitored using a variety of methods since 1951 [6].
Overall, while the majority of the eDNA extracted during this study originates from the plankton sampled (including larva and eggs), a small amount (impossible to quantify) would derive from sloughed cells or faecal material from larger organisms. From this total DNA more than four hundred distinct eukaryotic taxa were identified in this five-year study. These taxa were identified from more than nine million metabarcode sequences clustered into four thousand unique high abundance groups. Across all time points and assays, a total of 20 eukaryotic phyla were detected containing 245 families (Fig 1; S2–S7 Tables). Fig 1B also depicts the surface temperature and chronology of collection at the monitoring site. Most detections (70%) were within Arthropoda (including 62 families) and, of these, 87% were from Hexanauplia (including 24 families), the class that contains all copepods. The metabarcoding method employed here identified some of the gelatinous and larval zooplankton such as over 15 genera of hydrozoa and 50 genera of actinopterygii, many to species level. In practice, all assays, with the exception of the Fish assay, detected an extremely broad range of taxa. The Copepod 3 assay alone was responsible for over 1100 assignments across ten Animalia phyla; almost a quarter of all detections. It is, however, the integration of all assays that has revealed some of the breadth of biodiversity within this ecosystem over the five-year period. Had the study been limited to the 18S Universal assay, fewer than 70 assignments would have been made.
While Fig 1C showcases the taxa that our assays detected, more than 40% of the DNA sequences could not be reasonably assigned within a taxonomic framework. As a consequence of this problem, we applied a taxonomy-independent approach so that the analyses were not biased by the limitations of reference databases or the accuracy of the underpinning taxonomy. Operational Taxonomic Units (OTUs) enabled a more comprehensive exploration of the correlations between biotic and abiotic change over time.
Biological monitoring at a single point in time is typically inadequate to describe total biodiversity or to explore changes in diversity over time. Collecting multiple time-stamped samples reveals greater total (gamma) biodiversity and allows measurement of beta diversity as a temporal change. For each assay, OTU biodiversity analysis involved both counting of the number of discrete OTUs—a measure hereafter referred to as “Richness”—and the presence/absence composition of the OTUs—referred to as “Assemblage”. OTUs from each assay were examined independently so that comparisons were all made within the same experimental frameworks.
There are varying approaches for presenting eDNA metabarcoding data in terms of Assemblage and Richness. Some authors rarefy their data to normalise results for differing sequencing depth among libraries. We made the decision not to do this because sequence number and OTU accumulation curves had plateaued for each sample indicating that we had sampled the majority of the OTUs in each case (For example; S1 Fig), Pearson’s correlation tests showed there was no evidence to suggest a significant correlation between the number of sequences (i.e. sequencing depth) and the number of OTUs obtained for the 18S and 16S assays (S2 Fig). However, sequencing depth and number of OTUs (Richness) were moderately correlated (R2<0.522) for the COI assays (S2 Fig). Nevertheless, as sequencing depth variation is spread evenly across the samples (S2 Fig), we consider it unlikely that Richness or Assemblage estimates are compromised by this data treatment.
Our initial analyses of eDNA (Table 1) demonstrated strong seasonality in the Assemblage from those assays that predominately detect meroplankton, including fish, molluscs and cnidarians. This seasonality was not reflected in Richness, with the exception of the Fish assay. A pairwise analysis between seasons (S8 Table) indicated that the most consistent differences in Assemblage were detected between summer:winter, followed by spring:winter and spring:autumn. The least significant Assemblage changes were identified by the assays that predominantly detect holoplankton e.g. the Copepod assays. These detected no significant changes (after post-hoc correction) between winter:autumn, and summer:spring. These results provide a detailed example for multi-year marine biodiversity surveys based on eDNA.
The Fish assay revealed strong seasonality in both Richness and Assemblage (Fig 2). A pairwise analysis showed significant changes between all seasons for the Assemblage as well as Richness (S8 Table), the two exceptions were for Richness between the adjacent seasons summer:spring and winter:autumn. Most fish are only present in the zooplankton community after broadcast spawning their eggs or during their pelagic larval phase, so these seasonal changes make biological sense [24]. Seasonal fluctuations have been previously observed in fish using eDNA extracted from water [43, 44]. However, these studies were limited to durations of six and twelve months respectively. The current study provides additional and enduring evidence for the ability of eDNA to detect of seasonality over an extended period (5 years) and further incorporates a much broader range of biodiversity.
OTUs that characterise particular time periods were identified by indval analysis [45]. The strong seasonality in the Fish OTUs suggests that they might be driving significant differences identified in the seasonal indval analyses across all assays (S9 Table), but this was not the case. Spring was characterised by a significant indicator matched to Labridae (a speciose fish family), but Calcinus dapsiles (a hermit crab) and Evadne spinifera (a water flea) were the summer’s four top indicators. Calcinus dapsiles are only planktonic as larvae and only present seasonally, but E. spinifera is part of the plankton for its entire life.
Flaccisagitta enflata (a chaetognath or predatory arrow worm) and the copepods Farranula gibbula and Centropages orsinii were the most significant indicators for autumn. The copepods, Canthocalanus pauper and Centropages furcatus were found in winter. The genetic assignment of C. orsinii and C. furcatus are of interest as they are typically tropical species found in the Indian Ocean [46] indicating that they are likely to have been swept south by the warm water Leeuwin current (Fig 1A) in each year [47]. These indicator species analyses generate lists of target taxa that provide a more refined picture of seasonal changes in biodiversity—S9 Table lists all significant seasonally variable OTUs.
The years 2010 to 2014 showed changes in the Assemblage identified by several of the assays (Table 1); the pairwise analysis (S10 Table) identified when these changes occurred. The OTUs that most strongly characterise each year are presented in S11 Table. Six assays showed significant changes in Assemblage between 2010 and 2011 and each of the three subsequent years (S10 Table). In particular, the Assemblage from Copepod 1, Mollusca and Cnidaria assays responded strongly. This pattern suggests a biotic regime shift in response to an environmental anomaly. S11 Table lists all significant yearly variable OTUs.
The Rottnest Island area has global significance as it is situated within a site of high biodiversity that is largely endemic [36]. This sample set was particularly significant because it encompasses two uncharacteristic summer temperature extremes in 2011 and 2012. The WA marine heatwave was originally defined as occurring between November 2010 and April 2011 [38]. However, similarly high sea surface temperatures (SST) were recorded during the following year [48–50] (Fig 2B & S3 Fig). In this study, periods for the heatwaves were: “Heatwave 1”, a five-month heatwave, as described in Pearce and Feng (2013); and “Heatwave 2”, which encompasses Heatwave 1 and extends across a 17-month period from November 2010 –May 2012 (Fig 1B). The Assemblage from most assays (except Crustacea, Fish) responded significantly to the designated heatwave periods (Table 1).
The most significant changes in the Assemblage were between the periods pre- and post-Heatwave 1 (S12 Table). For Heatwave 2, significant differences were seen before, after, as well as during the thermal event (S12 Table). Analyses of both heatwave periods suggest that there were significant, and potentially persistent, changes that occurred within the zooplankton communities as a result of these collective temperature anomalies. Only ongoing research will determine whether these changes are permanent, however, climate-mediated change has already been reported in the same study area where Wernberg (et al.) [39] reported that a kelp dominated nearshore ecosystem shifted to a more tropicalised system containing seaweed turf.
The value of employing assays with different taxonomic specificities is shown by the lack of significant heatwave-induced Assemblage changes observed for some assays. No change was detected using the Crustacea and Fish assays. The taxa detected by these assays are generally long-lived with pelagic larval phases, so any significant change in these groups is likely to occur gradually and would only be detected with an even longer-term study. The Heatwaves had less significant effects on Richness, however the Copepod 1 and 3 assays demonstrated changes in Richness, particularly between before and after the thermal anomaly periods (S12 Table).
The Copepod 1 assay illustrates the effects of Heatwaves 1 and 2 on the Assemblage and Richness (Fig 3). The Copepod 1 assay was designed in silico to focus on the genus Triconia, but, as is common in metabarcoding approaches, in vitro, the assay detects a much wider range of copepods as well as other arthropods.
OTUs characterising the periods defined by the heatwaves were identified by indval analysis. The OTUs corresponding to Paracalanus indicus (a copepod) and Pythiales (an order of water mould) are strong indicators for the ‘before’ periods (S13 & S14 Tables). The Copepod 1 OTUs characterising the heatwave ‘during’ periods were significantly different; only ten OTUs (11%) overlap. The best indicator for Heatwave 1 was Hexanauplia (the class which contains all copepods); this OTU is also an indicator for Heatwave 2 (S13 & S14 Tables). For the ‘after’ periods, nine OTUs are shared between them (15%). Nine anonymous copepod OTUs (15%) were strongly associated with the ‘after’ of both heatwave periods. This demonstrates the advantage of the OTU approach and provides an opportunity for taxonomists to link these sequences to the species that they provisionally represent.
These time-stamped metabarcoding data show, for the first time, that eDNA metabarcoding is able to track biotic shifts in response to seasonal and annual changes, as well as identify a known temperature anomaly that threatened global biodiversity hotspots on the west coast of Australia. This result has obvious implications for biomonitoring of oceans in the face of anthropogenic pressures including climate change, acidification, pollution, fishing and aquaculture impacts. The Assemblage and Richness data provided by eDNA metabarcoding can be integrated with other abiotic factors to develop a more holistic picture of how biomes respond to a variety of environmental factors.
Biological samples analysed in this study were collected alongside complementary measurements of physical and chemical characteristics of the sampling site. Sea surface temperature (SST) and the concentrations of salinity and silicate (an important nutrient in oceans), were all important explanatory abiotic variables for both Richness and Assemblage across the majority of metabarcoding assays (Table 2). These variables feature in either the ‘best’ or the most parsimonious alternative models for all of the assays used (Tables 2 and S15).
SST and salinity explained a large portion of the biological variation we observed. The assay most sensitive to the abiotic factors was Copepod 3; where SST, and concentrations of salinity, and silicate explained 22.7% of the variation in Assemblage, and SST and salinity concentration explained 39.2% of the variation in Richness (Table 2).
Richness increased significantly with warmer SST for most assays, with the exception of Copepod 1 and Copepod 2, which showed an insignificant negative relationship to SST (Table 2). Richness conversely decreased with increasing salinity for the Copepod, Crustacean, and Universal assays, but reacted positively in the Cnidaria, Fish, and Mollusca results. Silicate correlations had the opposite pattern, being positively correlated with Richness in the Copepod, Crustacean, and Universal results, but negatively correlated to Richness when measured against the Cnidaria, Fish, and Mollusca assays (Table 2). These results are likely due to an indirect link between the environmental variables to the zooplankton composition via direct links upon the phytoplankton [51]. These results illustrate the different niches that zooplankton can exploit within an ecosystem. As one group of zooplankton find conditions uninhabitable and diminishes locally, another group will thrive within the niche.
A recent editorial on marine monitoring [53] argued for a pressing need to make the shift from site-specific approaches to a functional, whole-sea system of monitoring. Here we show that eDNA metabarcoding is capable of responding to this challenge. Multi-year sample sets appropriate for eDNA analysis have not been previously available. Had this study been limited to a single point in time or even over the course of a year, where the longer-term patterns of change would be missed. Our study included two ‘marine heatwave’ periods and these data demonstrated that, using an effective eDNA metabarcoding toolkit, ecologically significant trends can be identified in response to a known environmental perturbation.
The biodiversity detected by our multi-assay eDNA metabarcoding ‘tool kit’ was vast, and while many barcodes could be assigned within the existing taxonomic framework, almost as many could not. While it could be argued that indicator species/OTUs should perhaps be the primary focus for taxonomic scrutiny employing both morphology and genetics, it is clear that as databases and assays improve, so too will the power of eDNA to identify the taxa present in complex ecosystems like this one. The results highlighted both the importance of collecting time-stamped samples (i.e. environmental biobanks [54]) and the significance of multi-gene metabarcoding for the long-term monitoring of marine ecosystems. For example, had only the universal 18S marker been used, much of the genetic depth of information would have been lost. While the 18S markers are typically longer and produce results across a broad range of taxa, it is more conserved than other barcodes and often results must be confined to a family level of identification. The study illustrates the need to balance the cost of the multi-marker approach with the amount of data that can be generated. The future implications of this data are that eDNA will generate much-needed baseline biotic data, and identify disturbance gradients, recovery profiles and potential ‘biotic tipping points’.
All sampling took place at the Rottnest Island National Reference Station (NRS), an Integrated Marine Observing System (IMOS [6]) site, Western Australia (Fig 1A). The site is situated at the midpoint of the sub-tropical zone of the Leeuwin current, approximately 20 km off the southwest coast of Western Australia. Abiotic sampling has occurred regularly at this site since 1951 and biological sampling by the IMOS program since 2008 [6]. The plankton sampling regime was instigated at this time and historically three separate monthly samples were taken; one for morphological analysis; one for biomass measurements and a third tow for later DNA analysis. We were provided access to these final samples.
Vertical plankton tows were taken on 55 occasions from October 2009 to January 2015, from the same site, in an almost regular monthly regime (Fig 1B). A 0.6 m wide, 3 m long drop net [55] with a100 μm mesh, which free falls at 1 ms-1, was dropped for 45 s. The seabed depth at the Rottnest Island sampling site is 50 m, so this sampling covered 90% of the water column. Plankton was collected on the downward fall; the motion of retrieval closes the net for the upward haul. The nets are washed in fresh water (with detergent if clogged), hung out to dry and stored dry between monthly sampling.
Samples were washed down and concentrated at the codend of the drop net and transferred into a sample jar using seawater. Samples were packed on ice until placed in long-term storage at -80°C immediately after return to the laboratory. Samples were later subsampled for this study and the sub-samples preserved at -20°C prior to DNA extraction.
Each plankton sample was homogenised, using a hand-held blender (OMNI Tip Homogenizer) and a hard tissue probe. About 20 μL of the resulting slurry was digested and extracted using DNAeasy Blood and Tissue kit (Qiagen) following the tissue protocol and a 2 x 100 μL elution in AE buffer. An extraction control was created during this phase. Extracts were stored at -20°C.
Over 20 group-specific PCR amplicon metabarcode assays were tested for use in this study. Sequences used for in silico assay design were downloaded from the National Center for Biotechnology Information (NCBI) GenBank database [56]. Database coverage was limited across all genes, so in most instances the cytochrome oxidase I (COI) gene provided the best option for metabarcoding.
Sequences were aligned in Geneious Version R8 and consensus sequences were derived from these alignments [57]. Sequences were examined for relatively conserved regions flanking 100-200bp hyper-variable targets (S4 Fig). This examination resulted in the creation of several new metabarcoding assays. These assays, along with some that were previously described, were then tested against 20 pilot plankton samples to determine which assays, when combined, produced the broadest coverage of taxa found within zooplankton (S16 Table). From these, eight assays, including five targeting COI (predominately, three for different copepods and one each for molluscs and cnidarians), one targeting 18S rRNA (“universal”) and two targeting16S rRNA (one each for actinopterygii and malacostraca), were selected for use in this study (S1 Table).
The 55 DNA extracts were assessed using qPCR for their response to each of the eight assays, which were applied to each sample’s neat extract and two dilutions (1/10 and 1/100). Extraction, non-template and positive controls (where available) were included for each assay. Each reaction comprised: 1 x Taq Gold buffer (Applied Biosystems [ABI], USA), 2 nM MgCl2 (ABI, USA), 0.4 mg/mL BSA (Fisher Biotec, Australia), 0.25 mM dNTPs (Astral Scientific, Australia), 0.4 μM each of forward and reverse primers (Integrated DNA Technologies, Australia), 0.6 μL of 1/10,000 SYBR Green dye (Life Technologies, USA), 1 U of Taq polymerase Gold (ABI, USA), 2 μL of DNA, and made up to 25 μL with PCR grade water. PCR conditions for all reactions included 95°C for 10 min followed by 50 cycles of 95°C for 30 sec, Ta (S1 Table) for 30 sec and 72°C for 45 sec, with a final extension of 72°C for 10 min. All reactions were set up in an ultra-clean laboratory used for trace and environmental DNA.
Fusion tagged primers incorporating specific unique combinations of six to eight base pair MID (Multiplex IDentifier) tags, assay specific primers and Illumina adaptor sequences were assigned, in duplicate, to each DNA extract (and any negative control that produced a positive result during qPCR) in a single PCR step (giving a total of over 400 unique MID tagged combinations). Many samples are multiplexed within a single library and the MID tags allow for later separation and assignment of the individual sequences to their specific assays and samples. To prevent cross contamination within the NGS workflow, the MID tag primer combinations had not been used previously for marine samples and were not reused. Conditions for the fusion tagged PCR reactions were identical to the qPCR (above) and were carried out in duplicate, using the appropriate dilution determined by the qPCR. Reactions were monitored for efficient amplification by scrutinising qPCR dynamics. Tagged amplicons were combined in roughly equimolar concentrations to produce multiplexed sequencing libraries. On each library the fusion tags were not ‘saturated’, meaning that, while there are ten reverse tags to every forward tag, each run allowed for several unused forward and reverse combinations. If unused tag combinations are subsequently detected after sequencing, the tagging process is repeated to ensure there is no tag cross over. The libraries were then size-selected using a Pippin Prep (Sage Sciences, USA) instrument and quantified using a Lab Chip (PerkinElmer, USA). All sequencing was performed using Illumina’s MiSeq following the manufacturer’s protocol with the exception of the use of custom sequencing primers and with 20 pM PhiX, on either a Standard or Nano flow cell and 300–500 cycle kits.
Sequences were assigned to the appropriate samples by their MID tags using Geneious R8 [57]. Initial filtering steps included ensuring the MID tags, gene specific primers and sequencing adapters, were all present in each sequence without error. Those sequences not matched were discarded from future analyses. The primers, adaptors and MID tags were removed from each of the sequences that passed these criteria, which were then filtered using a fastq filter (E_max > 0.5—USEARCH v8 [58]).
To increase the robustness of the data set, sequences were then separated into groups of unique sequences using USEARCH v8 [58]. Of these sequences, any group which contained < 1% of the total number of unique sequences was discarded—the filtered data are available for download on Data Dryad: doi:10.5061/dryad.sc673ds. This process, which may eliminate low abundance taxa, is conservative in that it ensures the removal of possible erroneous amplicons. Amplicons that passed the second filtering processes were queried against the National Center for Biotechnology Information (NCBI) GenBank nucleotide database [59] using BLASTn (Basic Local Alignment Search Tool [60]) with the default parameters and a reward of value of 1.
The search output files were imported into MEGAN v5 (METaGenome ANalyzer [61]) and visualised using the LCA (lowest common ancestor) parameters: min bitscore 100.0, and reports restricted to the best 5% of matches. Taxonomic assignment was considered only when the entire length of the query sequence matched the reference database. Taxonomic hierarchy was determined using the World Register of Marine Species [62]. Negative controls were all found to be clear with the exception of the 18S Universal assay, which showed some fungal contamination.
Clustering of similar sequences to produce OTUs was performed with USEARCH v8 [58]. The OTUs were formed from all filtered sequences from each assay using a 97% similarity threshold across all samples. The procedure also removed any potential chimeric sequences and any groups of unique sequences with an abundance of < 0.1% of the total number of unique sequences across all samples. Sequences discarded during this process were then mapped back on to existing OTUs to ensure the inclusion of all relevant data and those amplicons, which could not be mapped, were discarded. The OTUs were then assigned to the samples that they originated from and were converted to a presence/absence matrix. This approach also minimises any data misrepresentations as a result of potential unequal sequence amplification from marker choice or tag bias. The OTUs were statistically analysed in response to both temporal and abiotic factors.
Statistical analyses, were performed using PERMANOVA+ [42] add on for Primer 7 [63] and R [64] with labdsv [45], and vegan [65]. The analyses were performed on the presence/absence OTU data matrix for the sequences obtained for each assay, thus allowing for all available genetic information to be taken into consideration. A total of 55 samples were used for analysis. The initial Pearson’s correlation test of the number of sequences produced by each assay, at each time point, and the number of OTUs was performed in R [64].
To prevent the inclusion of ‘outliers’ that might skew the results, the sequences for each assay were filtered to remove any OTUs that occurred only once in the study and also any samples that contained only one OTU. The richness and assemblage (genetic diversity) data for each sample were then examined using multivariate methods (PERMANOVA [66]) to test time-based relationships such as heatwave, seasonality and inter-annual effects). Annual and seasonal effects were tested using a nested design with three factors: Year (fixed, 5 levels), Season (nested in Year, random), and Month (Nested in Season, random). Tests for heatwave effects were conducted using a single factor (fixed, either 5 month or 17 month heatwave window) with three levels (before, during, after). To illustrate these patterns, two-dimensional nonmetric multidimensional scaling (nMDS) plots were formed in R (package vegan).
The indicator species that were characteristic of years, seasons, and heatwave events were identified using indval analyses in R (package labdsv). The indval indicator value is calculated using a combination of the fidelity of an OTU to a time period and the frequency at which it occurs during that same time period. All pairwise comparisons were performed using PERMANOVA.
The role of abiotic variables in explaining variation in both the multivariate OTU assemblage, and the univariate OTU richness was analysed with linear models for each assay. Multivariate analysis was done using distance based linear models (DistLM) in PERMANOVA+. Bray-Curtis similarity matrices were constructed from the presence/absence OTU data. The abiotic variables sea surface temperature (SST) and concentrations of salinity, silicate, nitrate, phosphate, and ammonium were available for selection by the model. The ‘best’ selection procedure and the AIC selection criteria were used to select the model that best explained the variation in the OTU assemblage that was recorded for each assay. The best alternative models for each number of variables that were within 2 AIC of the selected model were also reported (S15 Table).
Univariate OTU richness was analysed for each assay with generalised linear models (GLMs) fitted in R using the functions glm [64] and glm.nb [52]. The abiotic explanatory variables available were the same as those above. During analysis the distribution of the residuals of each model were plotted and examined to select the appropriate distribution. In all cases the negative binomial distribution with a log link was used [67]. The model with the lowest AIC was selected using the best of both forward and backward selection procedures. Models within 2AIC of the selected model were also reported. To aid in the interpretation of the relationship between each abiotic variable and the OTU assemblage composition and richness were also calculated and reported for each abiotic variable.
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10.1371/journal.pgen.1002203 | B Chromosomes Have a Functional Effect on Female Sex Determination in Lake Victoria Cichlid Fishes | The endemic cichlid fishes in Lake Victoria are a model system for speciation through adaptive radiation. Although the evolution of the sex-determination system may also play a role in speciation, little is known about the sex-determination system of Lake Victoria cichlids. To understand the evolution of the sex-determination system in these fish, we performed cytogenetic analysis in 11 cichlid species from Lake Victoria. B chromosomes, which are present in addition to standard chromosomes, were found at a high prevalence rate (85%) in these cichlids. In one species, B chromosomes were female-specific. Cross-breeding using females with and without the B chromosomes demonstrated that the presence of the B chromosomes leads to a female-biased sex ratio in this species. Although B chromosomes were believed to be selfish genetic elements with little effect on phenotype and to lack protein-coding genes, the present study provides evidence that B chromosomes have a functional effect on female sex determination. FISH analysis using a BAC clone containing B chromosome DNA suggested that the B chromosomes are derived from sex chromosomes. Determination of the nucleotide sequences of this clone (104.5 kb) revealed the presence of several protein-coding genes in the B chromosome, suggesting that B chromosomes have the potential to contain functional genes. Because some sex chromosomes in amphibians and arthropods are thought to be derived from B chromosomes, the B chromosomes in Lake Victoria cichlids may represent an evolutionary transition toward the generation of sex chromosomes.
| The diversity of sex chromosomes among animal species is well known, but how these sex chromosomes emerged during evolutionary history remains to be solved. One hypothesis for the origin of sex chromosomes is that a portion of the sex chromosome was derived from B chromosomes. In about 10% of eukaryotes, B chromosomes are found in addition to standard chromosomes (sex chromosomes and autosomes). B chromosomes have been thought to be selfish genetic elements with no functional effect on the phenotype of individuals and have been thought to lack protein-coding genes. Although B chromosomes share unique features with sex chromosomes, concrete evidence describing which B chromosomes have evolved to gain a function in sex determination has not been reported. In this study, we found that B chromosomes in one cichlid species from Lake Victoria have a functional effect on sex determination. Moreover, we found that they contained multiple protein-coding genes including morphogenetic related genes. These findings support the hypothesis that a portion of the sex chromosomes has been derived from B chromosomes and shed light on the study of the evolution of sex chromosomes.
| The species flock of endemic cichlid fishes in Lake Victoria is the largest known example of recent adaptive radiation and has been highlighted as a model system for the genetic study of speciation [1]–[3]. The evolution of the sex-determination system is suggested to drive the speciation of the cichlids because novel sex-determination genes tend to be associated with novel body colors which can drive reproductive isolation [1], [4]. Recent genetic studies suggest that the sex chromosomes of cichlids have turned over rapidly and that the sex-determination locus is different among species and populations [5]–[7]. Among closely related species of Lake Malawi cichlids, which are sister species of Lake Victoria cichlids, two unlinked sex-determination loci were reported [5]–[6]. In a few populations of Lake Malawi cichlids, multiple interacting loci control sex determination [6], suggesting an ongoing transition in the sex chromosomes. In one species of Lake Victoria cichlids, a sex-determination locus was inferred based on the analysis of a sex-linked phenotype [7]. The sex-determination system has been studied, however, in only a few species of Lake Victoria cichlids. Although cytogenetic analysis is important for studying the sex-determination system, it has not been performed in wild populations in Lake Victoria cichlids. However, a cytogenetic analysis of one species of Lake Victoria cichlid obtained from a commercial source revealed the presence of B chromosomes [8].
B chromosomes are chromosomes found in addition to the standard chromosomes (A chromosomes). They occur in many groups of fungi, plants and animals (10 species, >1300 species, and >500 species, respectively) and vary in number among individuals within a population [9]. They are dispensable for the normal life cycle of host individuals [9]. In most cases, the presence of B chromosomes has no effect on the host phenotype or is deleterious when the number per cell increases [10]. Because of their extensive distribution among many organisms, the possibility that B chromosomes have a beneficial effect on their hosts has been argued [10], but there has been no concrete evidence for this effect. Instead, the existence of B chromosomes has been explained by their selfish behavior such as their non-Mendelian inheritance and accumulation in the germ line [11]. B chromosomes are thought to be composed of repetitive sequences and to lack protein-coding genes [9], and they often form a heterochromatic block [10].
Here we analyzed the karyotypes of wild populations of 11 cichlid species in Lake Victoria. Most individuals possessed B chromosomes of varying sizes. In one species, B chromosomes were specific to females. We performed further analysis of the B chromosomes in this species to reveal their function in sex determination.
We collected live individuals from six localities (Figure 1) in Lake Victoria during several expeditions in 2005–2007 and 2009 to prepare the chromosome specimens and to produce breeding lines in the laboratory. We analyzed the karyotypes of wild-caught individuals of 11 cichlid species in Lake Victoria (a total of 51 individuals). The chromosome number varied from 44 to 47 and was not specific to the species or populations analyzed (Table 1). To reveal the inheritance pattern of this chromosome number difference, we analyzed the karyotypes of F1 and F2 generation of Lithochromis rubripinnis Seehausen, Lippitsch and Bouton, 1998 [12] from the Matumbi Island population (Figure 2A–2D; Table S1). The results indicated that chromosome number varied within clutches by the presence of two small chromosomes, designated as B1 (the larger) and B2 (the smaller). These data suggested that the chromosome number in the wild-caught individuals varies based on the number of B chromosomes. Their variations in size make it difficult to identify the B chromosomes precisely by morphology.
B1 and B2 chromosomes of L. rubripinnis were isolated by microdissection and used as probes for FISH analysis. Both of the probes (B1 and B2 probes) painted both B1 and B2 chromosomes but not the other chromosomes (Figure 2E and 2F). Although B1 chromosomes are larger than B2 chromosomes, B2 probe painted whole region of B1 chromosomes. These results indicated that the B1 and B2 chromosomes shared repetitive sequences that are specific to B chromosomes. We performed chromosome painting for the wild-caught Lake Victoria cichlids using the B1 probes. The number of chromosomes painted varied from zero to three, whereas the number of unpainted chromosomes (44) was the same among all individuals (Figure 3; Figure S1; Table 1; Table S2). These results demonstrate that these cichlids possess 44 A chromosomes and 0–3 B chromosomes. B chromosomes were different in size among populations (Figure S2). The B1 probe painted the whole region of all B chromosomes regardless of their size again (Figure S2A), suggesting that B chromosomes are composed of the same B-specific repetitive sequences. B chromosomes were observed in all examined populations at a high prevalence rate (86%; Table 1). The mean number of B chromosomes per individual, however, was low (1.45), suggesting that the accumulation of B chromosomes is restricted. A low prevalence rate of B chromosomes (40%) was reported in breeding individuals of Haplochromis obliquidens obtained from the commercial source [8]. This different prevalence rate raises the possibility that B chromosomes may possess some functions that are not essential for the survival of host individuals but are advantageous in wild populations.
Although B chromosomes were found in both males and females of most other species (Table S2), in wild individuals of L. rubripinnis from the Matumbi island population, all females (N = 4) possessed B chromosomes, whereas males possessed no B chromosomes (N = 2; Figure 4A). We confirmed this female-biased possession of B chromosomes by comparison of karyotypes of males and those of females using F1 and F2 generations in this population. The results showed that almost all females (in a total of N = 24) possessed B chromosomes, whereas no males (in a total of N = 10) possessed B chromosomes (Figure 4A; F1 and F2), indicating that B chromosomes are closely associated with females in this population. We confirmed the absence of B chromosomes in germ cells of F2 males by meiotic analysis (N = 3; Figure S3), indicating that the B chromosomes were specific to females in this population. To reveal the functional effect of B chromosomes on sex determination, we performed cross-breeding experiments between males without B chromosomes and females with different numbers of B chromosomes (0, 1, or 2) and scored the sex ratio of the offspring. The dams without B chromosomes (B−) generated nearly 1∶1 offspring sex ratios (proportion of females: 38% in cross #1 and 50% in #2; Figure 4B). These results indicate that one of the sex determination loci is located on an as yet unknown A chromosome. In contrast, the dams with a B chromosome(s) (B+) generated female-biased sex ratios. The proportion of females in the offspring was 74%, 91%, 79%, and 100% in cross #3 (B = 1 dam), #4 (B = 1 dam), #5 (B = 1 dam), and #6 (B = 2 dam), respectively (Figure 4B). The correlation of the number of B chromosomes in the dam with the proportion of females in the offspring suggests that the presence of female-specific B chromosomes in this species leads to a female-biased sex ratio. Karyotype analysis of the offspring showed ubiquitous distribution of B chromosomes in offspring from a cross with a skewed sex ratio (cross #6; Figure 4A) and an absence of B chromosomes in offspring from a cross with a nearly 1∶1 sex ratio (cross #1; Figure 4A), confirming the effect of the B chromosomes on sex determination. Although we cannot exclude the possibility that the B chromosomes have a male-specific lethal effect, this is unlikely, because we did not observe a higher death rate in the offspring of the B+ dam than in those of the B− dam (Figure 4B, see legend). According to these results, we concluded that B chromosomes have a functional effect on female sex determination. In the offspring of the dam with two B chromosomes (cross #6), four females (29%) possessed two B chromosomes (Figure 4A), indicating non-Mendelian inheritance of the B chromosomes. These observations (i.e., dispensability for host survival and non-Mendelian inheritance) confirmed that these female-specific chromosomes have features that are unique to B chromosomes.
Because all the B chromosomes of several species analyzed here were painted specifically by the B1 probe (Figure 3A–3D; Figure S1), they share sequences derived from the same ancestor. Because males in the other populations and species possessed B chromosomes (Table S2), B chromosomes in males have a different effect on sex determination than do the female-specific B chromosomes in spite of their having a shared ancestry. We cannot determine whether the B chromosomes in females in the other populations and species that we examined here are female specific or not. How extensively such female-specific B chromosomes are distributed in cichlids is currently unknown.
The New Zealand frog [13] possesses W chromosomes, which are functionally very similar to the female-specific B chromosomes described above. This species shows a 0W female/00 male sex-determination system. It additionally has B chromosomes that are not female specific and that share partial DNA components with the W chromosome [14]. The W chromosomes are not B chromosomes because they are indispensable for this frog. We speculate that the univalent W chromosome in the frog species might have differentiated from a B chromosome. In most cases, B chromosomes have similar features to sex chromosomes in terms of their meiotic behavior, morphology, and heterochromatic state [15], suggesting the possibility of their evolutionary relatedness. Y chromosomes may also be derived from B chromosomes in a few species of arthropods [16]–[17]. The female-specific B chromosome in cichlids studied here seems to be in an evolutionary transition from B chromosome to sex chromosome. A similar contribution of B chromosomes to the sex ratio in the characid fish Astyanax scabripinnis was reported [18], but it is still unclear whether there is a direct connection between the presence of a B chromosome and the sex ratio in those fish. The present case differs from the B chromosome contribution to sex determination found in some arthropods with haplodiploid sex determination because the B chromosome is involved in the exclusion of haploid genomes in those species [19].
The functional effect of B chromosomes on sex determination in L. rubripinnis suggested the possibility that these chromosomes might have some functional genes. To isolate partial DNA sequences of the B chromosomes, we performed differential screening. We screened B+ genomic DNA library and isolated DNA fragments hybridized with a B+ genomic probe but not a B− genomic probe. This identified a B chromosome–specific repetitive DNA sequence (named Bseq1; Figure S4). We isolated a BAC clone containing Bseq1 DNA (∼128 kb in total; named B-BAC) from the BAC library constructed from Haplochromis chilotes [20]. We analyzed karyotypes of this H. chilotes strain (N = 6) and confirmed that all individuals possessed two B chromosomes, indicating strong possibility for inclusion of B chromosome DNA in this BAC library (data not shown). We also confirmed that the BAC clone DNA is derived from B chromosomes by sequence analysis (see below). We determined 80% of the B-BAC sequence (104.5 kb; 18 contigs). Repetitive sequences occupied 59% of the sequence (Figure 5A; Table S3). Remarkably, we discovered five different protein-coding genes in this BAC clone, each of which is almost identical to its parental gene present in the A chromosomes (Table 2; Table S4). The gene density in this B-BAC was higher (4.5%; Figure 5A) than the gene density reported in the cichlid genome (<4%) [21]. No nonsense mutations were found in these genes (1581 a.a. in total). Although protein-coding genes have been reported in B chromosomes in three other cases, i.e. the fungus Nectria haematococca [22], several Canidae species [23], [24], and the locust Locusta migratoria [25], they have not been thought to have functional significance. The absence of nonsense mutations in the five protein-coding genes in the B chromosomes of cichlids described here might be an indication that the sequence of the B chromosomes has not degenerated from their ancestral sequence, and thus it appears that functional genes have maintained. However, it is possible that their expression might be suppressed by heterochromatin.
One example of the genes identified in the B chromosome is a morphogenesis-related gene, indian hedgehog b (ihhb). We estimated the copy number of ihhb in the genomes of B+ and B− individuals of L. rubripinnis by quantitative PCR (Figure 5B). The copy numbers of ihhb in the diploid genomes with 2 (B1 and B2), 1 (B1 or B2), and 0 B chromosomes, were estimated at 202, 193, 47, and 1.6, respectively. These results showed that there are >40 copies of the ihhb paralogs on B chromosomes whereas there is a single copy of the ihhb ortholog on the A chromosomes. Sequence analysis provided a tool for distinguishing the paralogs on the B chromosome from the orthologs on the A chromosome. Direct sequencing of ihhb exon 2 and the flanking region (Figure S5; total, 2387 bp) using B+ and B− genomes showed that ihhb orthologs on A chromosomes had a C and T at sites −940 and −88, respectively, in the ihhb region, whereas almost all ihhb paralogs on B chromosomes had a T and C at the same sites (20 individuals each; Figure S5). These results suggested that the ihhb gene was duplicated from an A chromosome to a B chromosome and formed multiple paralogs in B chromosomes. The B-BAC sequence contained the ihhb paralog sequence, indicating that the DNA fragment in the BAC clone was indeed derived from a B chromosome. Phylogenetic analysis of the ihhb regions (Figure S6) confirmed that these paralogs in the B chromosome emerged from their orthologs in the young Lake Victoria cichlid lineage.
FISH analysis using the B-BAC as a probe for L. rubripinnis showed intense signals on the short arm of the largest chromosome (chromosome 1) as well as on the B chromosomes (Figure 5C), suggesting that chromosome 1 is a strong candidate for the origin of the B chromosomes. Linkage group 3 (LG3) is a sex chromosome in Tilapia, which is a species that is related to Lake Victoria cichlids [26]. Markers for LG3 (GM385 locus and dmrt4 (dmo) gene) [26] were mapped to chromosome 1 in Lake Victoria cichlids (Figure S7). These findings suggest that the sex chromosome in Tilapia corresponds to chromosome 1 in Lake Victoria cichlids and that the sex-determination-related gene might be located on chromosome 1 of Lake Victoria cichlids. It is likely that the B chromosome in Lake Victoria cichlids has evolved from a part of chromosome 1 that contains the sex-determination-related gene and ultimately gained a function for sex determination in some lineages (the model is presented in Figure S8). However, we could not find genes related to sex determination in the B-BAC sequence. Further study of the sequence of the B chromosome is required to identify the genes that influence sex determination.
In this paper, we showed the recent evolution of a sex-determination system driven by female-specific B chromosomes in Lake Victoria cichlid fishes. The evolution of a sex-determination system can resolve sexual conflict [5], [27], [28]. Sexual conflict can arise when sexually antagonistic genes, which are beneficial to one sex and detrimental to the other, are found on autosomes. However, sexual conflict can be resolved if a gene experiencing sexual antagonism evolves linkage with a sex-determination gene. In this way, the evolution of a new sex-determination locus might resolve sexual conflict [27]. In fact, the sexual conflict produced by the orange-blotched body color pattern, which is beneficial to females but detrimental to males, has been resolved by the emergence of a new sex-determination locus in Lake Malawi cichlids, and the appearance of this color pattern is female-specific [5]. The evolution of this female-specific body color pattern possibly causes sexual isolation by male mate choice of this pattern in cichlids [7]. A direct association between the evolution of a new sex chromosome and sexual isolation was reported in sticklebacks [28]. By linking with the newly emerged female-specific sex determination locus, the female-beneficial sexual antagonistic traits such as female preference to male coloration that have generally observed in Lake Victoria cichlids [3] might have evolved rapidly and have driven speciation. It is, therefore, important for the study of speciation via sexual isolation to analyze the recent evolution of the sex-determination system caused by the female-specific B chromosomes that we have described here in Lake Victoria cichlids. Further studies of the molecular components of B chromosomes as well as the function of B chromosomes in wild populations of cichlids will shed light on the molecular mechanism of how and why a novel sex-determination system emerged during the evolution of these fish.
We collected live individuals of 11 species from 6 localities in Lake Victoria (Figure 1) during expeditions in 2005–2007 and 2009. The live fish were shipped to the Tokyo Institute of Technology in Japan for chromosome preparation, extraction of genomic DNA, and cross-breeding. The Malawi cichlid species (Cyrtocara moorii, Fossorochromis rostratus, Tyrannochromis macrostoma and Petrotilapia tridentiger) and Tanganyika cichlid species (Simochromis pleurospilus and Perissodus microlepis) were obtained from traders.
We crossed females of Lithochromis rubripinnis with conspecific males. F1 offspring were allowed to sib-mate to produce the F2 generation. Five F3 families (#1, #3, #4, #5, and #6) were produced by controlled crosses of one F2 male without B chromosomes and five F2 females with zero, one, or two B chromosomes. One F4 family (#2) was produced by a controlled cross of F3 parents without B chromosomes. The sex ratios of these F3 families and the F4 family were scored. Sex ratios were defined as the proportion of males in each clutch and were scored by counting the number of males with breeding coloration as described [29]. All fry exhibited cryptic coloration after hatching, but males begin to display breeding coloration 140 days after their birth [29]. Between 140 to 300 days, all males exhibited breeding color, and sex ratios were scored for each clutch within that time period. We confirmed that sex scored by this method was consistent with the gonadal sex by sacrificing 10 B− males, 10 B+ females and 10B− females and observing their gonads.
Chromosome preparation was performed as described [30], [31], with modifications. Chromosomes were prepared from cells of the caudal fin. Caudal fin tissue was cut into small pieces and placed on a collagen-coated dish (IWAKI, Tokyo, Japan). The cells were cultured in Leibovitz's L-15 medium (Invitrogen-GIBCO, Carlsbad, CA) supplemented with 20% fetal bovine serum, 1× antibiotic-antimycotic (PSA; Invitrogen-GIBCO), and 0.1 mg/ml kanamycin sulfate (Meiji Seika, Tokyo, Japan) at 28°C. Non-adherent cells appeared and increased from the caudal fin tissue for 30 days after the initiation of the culture. The cells were harvested after colcemid treatment (0.5 µg/ml) for 2 h, suspended in 0.075 M KCl, fixed three times in 3∶1 methanol/acetic acid, and then dropped onto glass slides and air-dried. More than 30 metaphase spreads for each individual were used for karyotyping. The nomenclature of chromosome morphology as suggested by Levan et al. was used [32], providing for two categories with different arm ratios (r): metacentric-submetacentric (MSM, 1<r≤3) and subtelocentric-telocentric (STT, r>3).
B chromosome microdissection and degenerate oligonucleotide–primed PCR (DOP-PCR) were performed as described [33], [34], with modifications. A single microdissected chromosome fragment, which was sufficient to produce the painting probes, was scraped into a tube. DNA from the scraped chromosome was amplified by first-generation DOP-PCR in a final volume of 15 µl containing 1.5 µl Thermo Sequenase DNA polymerase (GE Healthcare, Chalfont St Giles, UK), 1.5 µl Thermo Sequenase reaction buffer, 0.2 mM dNTPs, and 4 µM primer 6MW (5′-CCGACTCGAGNN NNNNATGTGG-3′). The first-generation DOP-PCR was conducted as follows: 10 min at 95°C; 12 cycles at 94°C for 1 min, 2 min at 30°C, a 6-min transition at 30°C–65°C, and a 3-min extension at 65°C; 30 cycles at 94°C for 1 min, 1 min at 56°C, and 3 min at 72°C; a final extension of 8 min at 72°C. The first-generation DOP-PCR product (3 µl) was used for second-generation DOP-PCR in a final volume of 10 µl. The second-generation DOP-PCR conditions were as follows: 5 min at 95°C; 25 cycles of 94°C for 1 min, 1 min at 56°C, and a 3-min extension at 72°C; a final extension of 8 min. The PCR product of the second-generation DOP-PCR (3 µl) was labeled by the third-generation DOP-PCR in a final volume of 10 µl containing 0.12 nmol/µl digoxigenin-11-dUTP (Roche Diagnostics, Basel, Switzerland). The third-generation DOP-PCR conditions were the same as the second-generation DOP-PCR conditions. The product of the third-generation DOP-PCR was used as the B chromosome probe for painting FISH analysis.
The B chromosome repetitive sequence (Bseq1) was amplified using the primers indicated in Table S5 and subcloned into the pGEM-TA plasmid vector (Promega, Madison, WI) to produce the Bseq1 probe. This clone and the BAC clone were labeled by nick translation with biotin-16-dUTP (Roche Diagnostics).
FISH analysis was performed as described [30], [31], with modifications. Hybridization was carried out at 37°C overnight. The slides that had been hybridized with the biotin- or digoxigenin-labeled probe were stained with fluorescein-conjugated avidin (Vector Laboratories, Burlingame, CA) or fluorescein-conjugated anti-digoxigenin (Roche Diagnostics), respectively, and stained with 0.25 µg/ml DAPI. FISH images were observed under a fluorescence microscope (Carl Zeiss, Oberkochen, Germany) using the 1 and 17 filter sets.
The actual size of all chromosomes was measured in five metaphase plates in a single individual using Axio Vision (Carl Zeiss). The mean size of the A chromosomes was calculated for each metaphase plate. The ratio of the size of a B chromosome to the mean size of the A chromosomes in the same cell was defined as the relative size of the B chromosome. We averaged the B chromosome sizes across the five metaphase plates. When there was more than one B chromosome in a single cell, we distinguished them by size.
We analyzed meiotic chromosomes of three F2 males of L. rubripinnis separately. Testes of a single male were nicked and suspended for 90 min in 1% sodium citrate and were fixed for 5 min in 1∶1 ethanol/acetic acid. The testes were placed into 3∶3∶4 ethanol/acetic acid/distilled water to extract the testicular cells. The cells were refixed three times in 1∶1 ethanol/acetic acid and then dropped onto glass slides and air-dried.
We performed differential screening to isolate the DNA fragments from the B chromosome. We first constructed a B+ genomic library. Using the DNeasy kit (QIAGEN, Venlo, Netherlands), B+ genomic DNA was extracted from Lithochromis rufus (2n = 46), which possesses two large B chromosomes. B+ genomic DNA was partially digested for 15 s with Sau3AI and subsequently subcloned into the pUC19 plasmid vector. We extracted the plasmid DNA from more than 400 clones and chose the plasmids with DNA inserts >500 bp (64 clones).
We separated the chosen plasmid DNAs by 1.5% agarose gel electrophoresis. DNA fragments were transferred from the gels to GeneScreen Plus membranes (Perkin-Elmer, Norwalk, CT) in 0.4 M NaOH and 0.6 M NaCl. Membranes were neutralized in 0.5 M Tris-HC1 (pH 7.0) and 1 M NaCl and then dried. We performed electrophoresis and transfer of the DNA fragments twice using the same amount of DNAs for each plasmid to make two copies of the membranes.
Next, we produced the probe of B+ genomic DNA and B− genomic DNA. B− genomic DNA was extracted from L. rubripinnis (2n = 44, without B chromosome). B+ genomic DNA for the probe was extracted from the same individual as that for the genomic library. One microgram of both B+ and B− genomic DNA was labeled for probes with [α-32P]dCTP using the BcaBEST™ labeling kit (Takara, Tokyo, Japan).
Hybridization was performed at 42°C overnight in a solution of 50% (v/v) formamide, 1 M NaCl, 1% SDS, 2× Denhardt's solution, and 100 pg/ml of labeled probe DNA. We used B+ and B− labeled probes for each membrane. After the hybridization, we washed the membranes and detected the signals. We compared the signal intensity of the membranes hybridized with the B+ probe and with the B− probe. Two clones, including the Bseq1 clone, showed stronger signals in the hybridization with B+ than with B−. We determined the sequences of these clones. The primers were designed according to the sequence of the Bseq1 clone (Bseq1F, Bseq1R; Table S5). This region was amplified by PCR using B+ genomic DNA.
A BAC clone (B-BAC) containing Bseq1 was screened and isolated from the Haplochromis chilotes BAC library [20]. B-BAC DNA was digested with BglII, BamHI, HindIII, PstI, XbaI, and SphI. Each of the resultant DNA fragments was subcloned into pUC19. We determined the sequences of the DNA fragments inserted into the plasmid. The flanking sequences were determined by direct sequencing using BAC clone DNA. Repeat masking was performed with RepeatMasker ver. open-3.2.9, with a Teleostei repeat library of database ver. RM-20090604 and the –s (slow and most sensitive) option. Subsequent repeat masking was performed under the same conditions using a tilapia repeat sequence library [35], which contains insufficiently characterized repeat sequences. The sequences in which the repetitive sequences were masked were used for a subsequent NCBI BLAST search (http://blast.ncbi.nlm.nih.gov/Blast.cgi) of the whole-genome shotgun data of five Lake Malawi cichlids (Maylandia zebra, Mchenga conophoros, Melanochromis auratus, Labeotropheus fuelleborni, and Rhamphochromis esox; 21) to identify unknown repetitive elements, because a number of uncharacterized repeats in the B-BAC sequences from H. chilotes were not masked with the RepeatMasker. The partial B-BAC regions that hit at least four different loci in one Lake Malawi cichlid with an E-value of <10−4 over 35 nt were chosen as unknown repetitive sequences. The sequences in which both known and unknown repetitive sequences were masked were used for a subsequent NCBI BLASTX search in the non-redundant protein sequence database of bony fishes with an E-value cutoff of 10−10. To analyze the coding regions of the five protein-coding sequences precisely, we performed a homology search with them in translated B-BAC sequences using Genetyx ver. 6.1.0 (Genetyx, Tokyo, Japan).
The primers for the ihhb region are indicated in Table S5 and Figure S5. Two fragments of the ihhb region were defined by the position of the primer pair Bseq1F and ihhbR5 and the pair ihhbF3 and ihhbR8. Each fragment was amplified from the genomic DNA by PCR. We purified the PCR products and determined the sequences using the primers Bseq1F, Bseq1R, ihhbF1, ihhbF2, ihhbF4, ihhbF5, ihhbF6, ihhbR1, ihhbR2, ihhbR3, ihhbR4, ihhbR5, ihhbR7, and ihhbR8. When the sequences included heterogeneous sites, we subcloned the PCR products into the pGEM-TA plasmid vector and determined the sequences of several clones to obtain the sequence information and eliminate PCR errors. Phylogenetic analysis was performed using MEGA4.0 [36]. Phylogenetic trees were obtained by neighbor-joining (NJ), minimum-evolution (ME), and maximum-parsimony (MP) methods with bootstrap tests.
The partial fragment of the ihhb region was amplified by PCR using the primers ihhbF3 and ihhbR7 and was cloned into the pGEM-TA plasmid vector for the standard control for calibration of qPCR. qPCR was performed in a 12.5-µl reaction using the Quantitative SYBR Green RT-PCR kit (Applied Biosystems, Foster City, CA). PCR amplification and product detection were conducted using Thermal Cycler Dice (TaKaRa) and the primers ihhbF4 and ihhbR6, which were derived from intron 1 and exon 2 of ihhb, respectively (Figure S5). The sequences of the primers completely matched the primer-annealing sites in all analyzed genomes. The Ct values were calculated by the second-derivative-maximum method. Relative quantification of the samples was calculated by fitting the Ct value to the standard curve of the vector. The copy number for the genomic DNA was calculated using the concentration and length (3508 bp) of the standard plasmid, together with the genomic size of 1.17×109 bp calculated by the reported C-value (1.2 pg) of the Lake Victoria species Haplochromis parvidens [37].
The GenBank (http://www.ncbi.nlm.nih.gov/Genbank) accession numbers for DNA sequences discussed in this paper are: AB601473–AB601502.
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10.1371/journal.pbio.1001645 | The CK2 Kinase Stabilizes CLOCK and Represses Its Activity in the Drosophila Circadian Oscillator | Phosphorylation is a pivotal regulatory mechanism for protein stability and activity in circadian clocks regardless of their evolutionary origin. It determines the speed and strength of molecular oscillations by acting on transcriptional activators and their repressors, which form negative feedback loops. In Drosophila, the CK2 kinase phosphorylates and destabilizes the PERIOD (PER) and TIMELESS (TIM) proteins, which inhibit CLOCK (CLK) transcriptional activity. Here we show that CK2 also targets the CLK activator directly. Downregulating the activity of the catalytic α subunit of CK2 induces CLK degradation, even in the absence of PER and TIM. Unexpectedly, the regulatory β subunit of the CK2 holoenzyme is not required for the regulation of CLK stability. In addition, downregulation of CK2α activity decreases CLK phosphorylation and increases per and tim transcription. These results indicate that CK2 inhibits CLK degradation while reducing its activity. Since the CK1 kinase promotes CLK degradation, we suggest that CLK stability and transcriptional activity result from counteracting effects of CK1 and CK2.
| The CK2 kinase is an ancient enzyme known to be at the heart of self-sustaining circadian clocks in animals, plants, and fungi. Circadian clocks are responsible for daily circadian rhythms in molecular, physiological, and behavioral processes. Their mechanism relies on transcriptional activators and repressors that constitute a feedback loop. The CLOCK (CLK) activator is required to initiate the transcription of the period (per) and timeless (tim) genes in the late day. The PER and TIM repressors accumulate in a delayed manner and translocate to the nucleus to repress their own gene. Degradation of these repressors allows the activator to start a new cycle. In the fruit fly Drosophila melanogaster, CK2-mediated phosphorylation of the PER and TIM repressors targets them for degradation. Here we find that the CLK activator is also regulated by CK2. In contrast to PER and TIM, CLK is stabilized by CK2α phosphorylation; we also show that PER and TIM are dispensable for this stabilization of CLK. The identification of CK2–CLK protein complexes and the in vitro phosphorylation of CLK by CK2 hint at a direct action of the kinase on the activator. Although transcription factor stabilization is generally expected to be associated with increased activity, genetic analysis indicates that CK2 represses CLK. Hence CK2 phosphorylation seems to be a key signal for the transcriptional activator complex to adopt a proper activation state at a given point of the circadian cycle.
| Circadian oscillations of gene expression, physiology, and behavior are found in a wide range of organisms. They are governed by temporally regulated feedback loops in which transcription factors activate the expression of their own inhibitors. In the Drosophila circadian oscillator, the CLOCK (CLK) and CYCLE (CYC) bHLH-PAS domain transcription factors activate expression of the period (per) and timeless (tim) genes at the end of the day. The delayed accumulation of PER and TIM and their transfer to the nucleus leads to transcriptional repression of CLK/CYC during the late night. The repression phase is also shaped by other repressors/activators such as CLOCKWORK ORANGE (CWO) and KAYAK-α [1]. Subsequent degradation of PER and TIM repressors in the morning allows transcription to resume towards the evening [2],[3]. Controlled phosphorylation, ubiquitylation, and proteasome-dependent degradation of PER and TIM set the timing of their delayed accumulation and clearance. The PER protein is phosphorylated by the DOUBLETIME (DBT, CK1δ/ε), CK2, and NEMO kinases and polyubiquitylated by the SCFSlimb ubiquitin ligase complex [4]–[12]. TIM associates with PER, preventing its degradation, but TIM itself is subjected to phosphorylation and subsequent breakdown. TIM phosphorylation involves the CK2 and SHAGGY (SGG, GSK-3) kinases and TIM degradation also depends on SCFSlimb and a CULLIN-3-based ubiquitin ligase complex [7],[8],[13]–[15]. Phosphatase activity counterbalances the effects of the aforementioned and probably also of other kinases: PP2A regulates PER abundance, while PP1 targets both PER and TIM [16],[17].
CLK phosphorylation cycles with a peak in the morning and a minimum in the early night [18]–[21]. Similarly, CLK immunoreactivity in head extracts or brain tissue seems to oscillate in phase with its phosphorylation [21]–[23], although harsh extraction liberates chromatin-bound CLK, which results in relatively constant CLK levels [20],[24],[25]. Whether oscillations of CLK immunoreactivity in neurons reflect rhythmic changes of total CLK protein amount is still unclear [23],[26]. Due to the cyclic regulation of CLK as opposed to constitutive expression of CYC, the CLK protein appears to represent the key rhythmic component of the circadian activator in Drosophila [27]. CLK DNA-binding and transcriptional activity show a robust oscillation with an evening peak that is associated with the rapid increase of per and tim mRNA levels [20],[22]. The release of CLK from DNA goes hand in hand with its hyperphosphorylation, which depends on both PER and DBT [19],[20],[22]. Since kinase activity of DBT does not seem to be required for hyperphosphorylation, it was proposed that DBT acts as an interface for the recruitment of other kinases into a complex with CLK [28]. The PER kinase NEMO destabilizes CLK in vivo and might thus be a CLK kinase [29]. CLK transcriptional activity in cultured cells is affected by calcium/calmodulin-dependent kinase II and mitogen-activated protein kinase [30]. Ubiquitylation is also involved in the regulation of CLK and BMAL1, the CYC ortholog in mammals [31],[32]. In Drosophila, USP8 was recently reported to decrease CLK activity by deubiquitylation [25].
The CK2 kinase has a key function in the clockwork of various organisms [33]. In Neurospora, CK1 and CK2 phosphorylate both the White Collar Complex (WCC) transcriptional activator as well as its inhibitor FREQUENCY (FRQ) to control their activity, subcellular localization, and stability [34]–[36]. In mammals, CK2 and CK1 destabilize PER2, although phosphorylation at specific CK2 target sites stabilizes the protein [37],[38]. The CK2 holoenzyme is formed by a tetrameric complex consisting of two catalytic (α) and two interacting regulatory (β) subunits [39]. The β subunits stabilize the α subunits that possess constitutive kinase activity. Phosphorylation of most substrates is enhanced by CK2β, while some substrates are more efficiently phosphorylated by free CK2α in the absence of CK2β [40]. In Drosophila, CK2α and CK2β affect PER and TIM abundance and subcellular localization, which correlates with a direct phosphorylation of both proteins by the CK2 holoenzyme in vitro [8],[9],[41]–[43]. The dominant-negative CK2αTik mutation strongly increases TIM stability even in the absence of PER, supporting TIM as the main target of CK2 [15]. The CK2αTik protein overexpression induces hyperphosphorylation of TIM that could be explained by enhanced phosphorylation or reduced dephosphorylation of TIM by other kinases and phosphatases [15].
Since the identity of the kinases involved in the control of CLK phosphorylation remains unclear, we asked whether CK2 plays a role in the phosphorylation and regulation of CLK. Our results indicate that inhibition of CK2α activity strongly increases CLK degradation, whereas CK2β does not affect CLK stability. The CK2 holoenzyme is recruited onto PER, TIM, and CLK mainly during late night, inducing CLK hyperphosphorylation in vivo and CK2 phosphorylates CLK in vitro. Specific CLK activity is increased in dominant-negative CK2αTik-expressing flies indicating repression of CLK by CK2α. Our findings define, to our knowledge, the first bona fide kinase of Drosophila CLK that plays a role in its degradation and hyperphosphorylation. The unstable but strongly active CLK acquired by CK2α inhibition joins the club of other circadian transcription factors with similar properties such as the WCC complex in Neurospora.
A putative role of CK2 in CLK regulation was first addressed by analyzing head extracts of flies expressing a dominant-negative version of the CK2α catalytic subunit. As previously reported [15],[42], w;tim-gal4;UAS-CkIIαTik flies (hereafter tim>Tik flies) were behaviorally arrhythmic (Table 1) and displayed weak and strongly delayed PER and TIM oscillations, with high levels of mildly phosphorylated PER and highly phosphorylated TIM (Figure 1A). As CLK efficiently binds to DNA in the evening, the estimation of CLK levels through the circadian cycle is affected by extraction conditions. In sonicated head extracts, CLK protein has been shown to stay at constant levels, in contrast to a robust cycle of its phosphorylation [19],[20]. However, the existence of oscillations in CLK levels remains discussed [22]–[25]. In our hands, sonicated extracts of control flies showed weak cycling of CLK levels, although peak time was rather variable between experiments (Figures 1A and S1A). Nonsonicated extracts always showed CLK levels cycling with a trough in the evening (Figure S1B). Importantly, both sonicated and nonsonicated extracts of tim>Tik flies showed very low CLK levels with reduced phosphorylation on the first day of constant darkness (DD) (Figures 1A and S1A and S1B). In order to better estimate CLK levels in tim>Tik flies, sonicated extracts were treated with λ protein phosphatase (Figure S1C). Again, a strong decrease of unphosphorylated CLK abundance was observed in tim>Tik animals. Moreover, Clk mRNA levels were about 1.5-fold higher in tim>Tik flies than in controls (Figure 1B), indicating that low CLK protein levels are not a consequence of reduced Clk expression. Consequently, the protein/mRNA ratio for CLK decreased to approximately 10% in tim>Tik (Figure S1D). Immunolabeling of whole-mount brains of tim>Tik flies also supported a strong reduction of CLK levels in the small ventral lateral neurons (s-LNvs) (Figure 1C), with no change in its nuclear-only localization (not shown).
To independently analyze the effect of decreasing CK2α activity on CLK, a UAS-CkIIα RNA interference (RNAi) construct was expressed under tim-gal4 control. Adult flies were kept at 29°C to increase Gal4-dependent expression. CLK in sonicated head extracts of w;tim-gal4/+;UAS-CkIIα-RNAi (tim>CkIIα-RNAi) flies showed a similar phenotype to that of tim>Tik flies, with reduced phosphorylation and levels throughout the cycle (Figure 1D). Dampened and delayed PER and TIM oscillations were observed with increased protein levels during the day. In support of its specificity, the induction of RNAi reduced CK2α protein levels (Figure S1E). Although high mortality of tim>CkIIα-RNAi flies after long incubation at high temperature prevented the assessment of their locomotor activity rhythms at 29°C, they displayed long period rhythms at 25°C (Table 1). Similar long period rhythms are observed in heterozygous w;tim-gal4/+;UAS-CkIIαTik/+ flies (Table 1), as previously reported [42].
TIM was described as the likely primary target of CK2α in the circadian clock, effects elicited on PER being only secondary [15]. We thus asked whether TIM was required for CK2α effects on CLK, by comparing the profile of CLK protein in sonicated head extracts of tim01 and tim01 tim>Tik flies. In the absence of TIM, the CK2αTik protein induced a prominent reduction in CLK phosphorylation and a significant decrease of protein levels (Figure 2A). Furthermore, Clk mRNA levels were about four times higher in tim0 tim>Tik compared to tim0, supporting a strong degradation of the CLK protein in tim0 tim>Tik flies (Figure 2A). Since a PER/DBT complex controls CLK phosphorylation [20],[28], we asked whether PER was required for CLK modifications by CK2α, even in the absence of TIM. Effects of the CK2αTik protein were thus analyzed in per0 tim0 double mutants, where CLK appeared minimally phosphorylated in a CkIIα+ background (Figures 2B and S2A). CLK levels and phosphorylation were further diminished in the presence of the CK2αTik protein (Figures 2B and S2A). Since the CK2αTik protein overproduction increased Clk mRNA levels by about twofold in per0 tim0 double mutants, the CLK protein/mRNA ratio was reduced just as in tim0 mutants (Figure 2B). A similar decrease of CLK protein levels was observed in per0 tim>Tik flies (Figure S2B) despite increased Clk mRNA levels, suggesting that CLK protein was again strongly destabilized in the absence of PER. These observations reveal that CK2α stabilizes CLK in the absence of PER and/or TIM. Since CLK phosphorylation is further decreased by CK2αTik expression, CK2α is important for the PER/TIM-independent minimal phosphorylation program of CLK.
The up-regulation of Clk mRNA levels in tim>Tik flies suggested that CK2α could repress Clk transcription. To test whether Clk transcription was affected in the tim>Tik genotype, Clk pre-mRNA levels were estimated. They were not increased in tim>Tik flies compared to controls, although a reduced antiphasic cycling was observed at DD1 (Figure S2C). The antiphasic oscillation was reminiscent of PER and TIM oscillations persisting in these flies (see Figure 1A). The increase of mature Clk mRNA levels in tim>Tik flies thus seems not to be the consequence of higher Clk gene transcription and rather supports a posttranscriptional control of Clk mRNA by CK2α. In agreement with a posttranscriptional control, the VRI and PDP1 regulators of Clk transcription were not affected in per0 tim>Tik flies (Figure S2D). Finally, since the transcriptional regulation of the cryptochrome (cry) gene is similar to the one of the Clk gene [44], we tested cry mRNA levels in per0 tim>Tik flies. No increase of cry mRNA levels was observed in the presence of the CK2αTik protein (Figure S2E), supporting a specific control of Clk mRNA levels by CK2α.
The data from tim>Tik flies strongly suggested that CK2α controls CLK stability independently from PER and TIM. To obtain direct evidence for this, CLK degradation kinetics were analyzed in a cycloheximide (CHX) chase-based assay in Drosophila Schneider 2 (S2) cells. Since transfected V5-tagged CLK induced both per and tim expression in S2 cells in our hands, we used RNAi against per and tim to eliminate any effect of PER and TIM proteins. After blocking protein synthesis with CHX, CLK showed robust degradation during the following 9 h (Figure 2C). When FLAG-HA-tagged CK2α was co-expressed, CLK degradation proceeded very slowly. The increase of CK2α levels by exogenous expression was rather limited in these conditions, indicating that a small increase in total CK2α protein can have substantial effects on CLK degradation. These results confirm the in vivo observations and strongly support a role for CK2α in the inhibition of CLK breakdown.
Since inhibition of CK2α affected CLK stability and phosphorylation, we asked whether CK2β knockdown would have similar effects. Pdf-gal4 UAS-CkIIβ-RNAi/+ flies have been reported to display long period rhythms [45]. Driving two CkIIβ-RNAi transgenes under the control of tim-gal4 (hereafter tim>CkIIβ-RNAi flies, see Materials and Methods) induced behavioral arrhythmicity (Table 1). The specificity of the CkIIβ RNAi was first behaviorally assessed by rescue experiments involving CkIIβ RNAi under the control of the strong PDF+ cell driver gal1118 [46] and the co-expression of different CK2β isoforms. The strongly altered behavior of w;; gal1118/UAS-CkIIβ-RNAi could be rescued by overexpression of the VIIa, VIIb, and VIIc CK2β isoforms (see [47]) (Table 1). Western blots against CK2β revealed a reduction in two isoforms in tim>CkIIβ-RNAi animals, while a third isoform remained unaffected (Figure S3A). TIM and PER cycling was profoundly altered in head extracts of tim>CkIIβ-RNAi flies at DD1 (Figures 3A and S3B). In contrast, CLK oscillations were only slightly affected. In particular, tim>CkIIβ-RNAi flies did not show the pronounced decrease in CLK levels that was observed in tim>Tik flies. Furthermore, CK2β depletion in a per0 background did not result in a marked reduction of CLK phosphorylation or quantity (Figure 3B). Since equivalent levels of Clk mRNA were observed in per0 flies with or without CkIIβ RNAi expression, their protein/mRNA ratios were identical (Figure 3C and D), in contrast to tim>Tik flies. Similarly, CLK was not affected when CkIIβ RNAi was expressed in a tim0 background (not shown). In conclusion, although CK2α and CK2β proteins similarly affect TIM and PER accumulation and phosphorylation, the CK2β subunit does not seem to be required for CK2α to control CLK degradation and phosphorylation.
The strong effects of CK2α inhibition on CLK suggested that the two proteins might physically interact. Flies expressing a FLAG-tagged CK2α protein under tim-gal4 control displayed strong behavioral rhythms with a 1 h period lengthening (Table 1). Anti-FLAG immunoprecipitation experiments were performed from FLAG-CK2α-expressing fly head extracts at different circadian times and showed co-immunoprecipitation of TIM, PER, and CLK mostly at the end of the subjective night and in the subjective morning when these proteins are mainly hyperphosphorylated (Figure 4A). Although relatively abundant medium-phosphorylated clock proteins were observed in the extracts at CT16, they were poorly co-immunoprecipitated with CK2α. The CK2α subunit thus appears to preferentially make complexes with highly phosphorylated forms of TIM, PER, and CLK. Flies expressing FLAG-tagged CK2β were also behaviorally rhythmic with a slightly lengthened period (Table 1), and FLAG-CK2β expression could rescue the severe period lengthening induced by CkIIβ RNAi (Table 1), indicating that the tagged protein was functional. Similarly to CK2α, CK2β was found to be associated with hyperphosphorylated TIM, PER, and CLK in the late subjective night and in the subjective morning, whereas little amounts of proteins were co-immunoprecipitated at other circadian times (Figure 4B). The results thus suggest that CK2 holoenzyme is involved in the hyperphosphorylation of CLK, PER, and TIM in the late night/morning part of the cycle.
Since CK2α strongly influences CLK stability in the absence of PER, anti-FLAG immunoprecipitations were also done in per0 tim>FLAG-CkIIα flies (Figure 4C). Minute amounts of hypophosphorylated CLK were co-immunoprecipitated in per0 extracts, nevertheless indicating that CLK-CK2α complexes may exist in the absence of PER. Conversely, CK2β did not co-immunoprecipitate with CLK in a per0 background (Figure 4C). The poor detection of CK2α–CLK complexes in the absence of PER suggested a very labile interaction between the two proteins or indirect PER-independent effects of CK2α on CLK.
CK2 subunits preferentially associate with clock proteins at times when those are present in the nucleus. CK2α was, however, described to localize to the cytoplasm of LNv-s [8]. We therefore set out to investigate whether CK2α could be present in the nucleus of LNv-s as well. Whole-mount adult brains were stained with an anti-CK2α antibody together with anti-PDF and anti-CLK. PDF is known to be exclusively cytoplasmic [48], while CLK is almost completely nuclear in our hands (see also [26]). Although CK2α predominantly localized to the cytoplasm of s-LNv-s, a fine cloud of CK2α staining co-localized with CLK to the nucleus (Figure 4D).
To further decipher the function of CK2α in CLK phosphorylation, CLK protein was analyzed in flies overexpressing wild-type CK2α. As previously reported [41], CK2α overexpression induced a modest lengthening of the behavioral period (Table 1). w;tim-gal4/UAS-CkIIα (tim>CkIIα) flies showed subtle changes of PER and TIM oscillations with a slightly delayed degradation of the TIM (CT 4–8) and PER (CT 8) proteins during daytime at DD1 (Figures 5A and S4A–B). CLK levels in CK2α overexpressing head extracts were higher at CT0 and lower at CT12 compared to controls, but overall protein levels were not significantly affected (Figure 5A). In contrast, CLK phosphorylation was strongly altered, with forms always more phosphorylated than the wild-type minimal phosphorylation that is observed at CT12 (Figure 5A). CLK phosphorylation was not increased by CK2α overexpression in a per0 background (Figure 5B), indicating that CLK hyperphosphorylation by CK2α required PER. The results thus support a PER-dependent hyperphosphorylation of CLK by CK2α, whereas CLK hypophosphorylation and stability appears to be mostly controlled by a PER-independent CK2α function.
The CLK phosphorylation defects in flies with altered CK2α functions and the presence of CLK-CK2α/β complexes suggested that CK2 might directly phosphorylate CLK. We thus asked whether the CK2 holoenzyme could phosphorylate CLK in vitro. Indeed, CLK was phosphorylated by CK2, and the presence of PER increased CLK phosphorylation by about twofold (Figure S4C). Addition of TIM protein did not affect the CK2-dependent phosphorylation of CLK. When only the CK2α catalytic subunit was used for the in vitro assay, CLK was phosphorylated with a similar efficiency and showed the same PER-mediated facilitation of its phosphorylation (Figure 5C). This confirms the in vivo results indicating that at least some of the CK2α effects on CLK phosphorylation do not require CK2β, and supports a direct phosphorylation of CLK by CK2α.
The strong influence of CK2α on CLK phosphorylation and stability suggests that CLK-dependent transcription could be affected in flies with altered CK2α activity. As previously reported [42], intermediate levels of per and tim mRNAs were observed in tim>Tik flies (Figure 6A).
per and tim pre-mRNA levels were measured to more directly estimate CLK transcriptional activity. Average nonoscillating levels of pre–per and pre–tim were observed in tim>Tik flies (Figure 6B,C), despite the very reduced amounts of CLK protein (see Figure 1A). It thus suggested that CLK transcriptional activity was strongly increased when CK2α activity was diminished. Flies expressing the CK2αTik protein in a per0, tim0, or per0 tim0 double mutant background revealed no statistically significant changes in per and tim mRNA levels compared to wild-type CK2α controls (Figures 6D and S5A). However, CLK protein levels were reduced to 25–50% in CK2αTik expressing flies (see Figures 2 and S2), suggesting that specific CLK activity was still increased. Effects of CK2α on the transcriptional activity of CLK thus appears to be at least partly independent of PER and TIM. per and tim mRNA levels were also measured in tim>CkIIβ-RNAi flies and showed intermediate levels compared to controls (Figure S5B). Since CLK quantity is unaffected by tim>CkIIβ-RNAi (Figure 3A), CK2ß does not strongly modify CLK transcriptional activity.
Finally, CLK-dependent transcription was tested by CLK-induced reporter gene expression in S2 cells. On a CLK-binding synthetic minimal enhancer composed of three per-derived E-boxes, CLK-dependent transcription was decreased in a dose-dependent manner by CK2α co-expression (Figure 6E). Since CLK quantity was increased in the presence of CK2α overexpression (see Figure 2C), the transcriptional decrease could hardly be a consequence of lower CLK levels.
Temporally controlled phosphorylation of clock proteins is a key feature of the transcriptional-translational negative feedback loop underlying the Drosophila circadian clock. Although the CLK activator shows robust oscillations of its phosphorylation levels, the phosphorylation mechanisms and how they affect CLK function remain largely unknown. Our study aimed at determining whether CK2 was involved in the control of CLK phosphorylation and how it would affect CLK circadian function. Overexpression of the CK2αTik dominant-negative enzyme or RNA interference against CkIIα substantially reduced CLK phosphorylation as well as protein levels. In accordance with the in vivo observations, co-transfection of CK2α with CLK in S2 cells increased CLK stability. This supports a function for CK2α in CLK stabilization. High CLK target gene transcription was induced by the CK2αTik protein despite the low-level accumulation of CLK, indicating that CK2α decreased the expression of CLK targets. This was further corroborated in the luciferase activity assay in S2 cells where overexpression of CK2α inhibited CLK activity. Furthermore, CK2α associated with hyperphosphorylated forms of CLK in the morning and it was able to directly phosphorylate the CLK protein in vitro. Effects of CK2α on CLK stability and activity did not require PER or TIM, but CLK phosphorylation by CK2α involves both PER-independent and PER-dependent functions. The results suggest that direct phosphorylation by CK2α stabilizes CLK and diminishes its transcriptional activity.
CLK protein levels but not Clk mRNA levels are low in tim>Tik and tim> CkIIα-RNAi flies, indicating that CK2 specifically affects CLK protein levels. Although a role of CK2α in CLK protein synthesis cannot be completely excluded, three sets of experimental data support a posttranslational action of CK2α on CLK. First, CK2α associates with CLK, PER, and TIM in protein complexes. Second, CK2α affects CLK phosphorylation state in vivo and is able to phosphorylate CLK directly in vitro. Third, CK2α stabilizes CLK even after protein synthesis blockage with CHX. Importantly, tim>Tik flies show reduced CLK phosphorylation, as predicted from kinase inhibition. This is in contrast with the effects of CkIIαTik on PER and TIM, for which highly phosphorylated forms of the proteins accumulate, although PER phosphorylation remains lower than the highest state in the wild-type [8],[15],[41],[42]. Nevertheless, CK2 is able to phosphorylate PER and TIM in vitro [8],[41],[49].
The CkIIαTik mutation affects TIM phosphorylation in the absence of PER, whereas TIM is required to observe effects of CkIIαTik on PER [15]. TIM was thus proposed to be a direct target of CK2 that drives CK2-dependent modification of PER [15]. One might expect that TIM or PER relays the effects of CK2 on CLK. This is not supported by the strong effect of CkIIαTik on the CLK protein in per01, tim01, or per01 tim01 double mutants. However, PER strongly influences CLK phosphorylation by CK2. First, overexpression of wild-type CK2α induces CLK hyperphosphorylation in per+ but not in per0 flies. Second, PER enhances in vitro phosphorylation of CLK by CK2α. Finally, the abundance of CLK/CK2 complexes observed in head extracts is much lower in per0 mutants. In comparison to per0, wild-type flies accumulate much more CLK/CK2α complexes, particularly in the morning when PER and CLK are abundant and hyperphosphorylated. The PER–CLK interaction is the strongest in the morning and the weakest in the early evening when PER is highly degraded. It seems unlikely that this temporal pattern of CLK interactions with PER is strongly altered in FLAG-CK2α overexpressing animals used for the immunoprecipitation since they show behavioral and molecular rhythms similar to wild-type flies. CLK/CK2α complexes strongly decrease after CT4 when high levels of CLK but not PER remain, suggesting that CLK/CK2α interactions follow phosphorylated PER abundance. PER hence could drive a large fraction of CLK/CK2α interactions with PER-free CLK being a weaker CK2 substrate. Since PER interacts in the late night with CLK species that no longer bind chromatin [22], it suggests that CLK/PER/CK2α complexes are mostly unbound to DNA.
PER/DBT-dependent phosphorylation marks CLK for degradation [19],[20]. Although the NEMO kinase destabilizes CLK [29], whether it acts as a PER/DBT-dependent CLK kinase is not known. Our results indicate that inhibiting CK2α activity increases CLK breakdown, whereas overexpressing CK2α induces accumulation of highly phosphorylated CLK. CK2α thus appears to have opposite effects on CLK stability, compared to DBT and NEMO. Since both CK1 (DBT) and CK2 show a preferential association with CLK in the morning, they might counteract each other to control CLK degradation and recycling for a next transcription cycle. Interestingly, a kinase complex that includes CK1 promotes the SUPERNUMERARY LIMBS (SLMB)-dependent proteolysis of the CUBITUS INTERRUPTUS (CI) transcription factor, whereas CK2 stabilizes CI by preventing its ubiquitylation [50],[51].
As opposed to tim>Tik flies, flies expressing CkIIβ RNAi did not show significantly decreased CLK levels. In addition, CK2α and the CK2 holoenzyme are both able to phosphorylate Drosophila CLK in vitro. Our data suggest that CLK, in contrast to PER and TIM, might be a substrate of CK2α alone rather than a substrate of the CK2 holoenzyme in vivo. Several studies suggest that CK2α and β do not act synergistically on a handful of substrates [52] or even play antagonist roles with CK2β inhibiting CK2α-dependent phosphorylation of some target proteins (see [40]). In mammals, CK2α is more efficient than the CK2 holoenzyme to phosphorylate BMAL1, and can also phosphorylate CLK [53].
As previously reported [15],[42], tim>Tik flies showed intermediate levels of per and tim transcripts. We corroborated the involvement of transcription in this phenomenon by determining per and tim pre-mRNA profiles. It has been proposed that the high levels of hyperphosphorylated TIM in tim>Tik would prevent normal PER-dependent transcriptional repression [15]. However, the fact that flies expressing the CK2αTik protein in a per0, tim0, or per0 tim0 double mutant background have only half dose (or less) of CLK but as high levels of per and tim transcripts as the CkIIα+ controls supports an additional PER/TIM-independent transcriptional function of CK2. Importantly, the small amount of remaining CLK protein in the late night in per+ tim>Tik flies drives similarly high pre–per expression as much more CLK in the wild-type (see Figure 6B). That also undermines CK2's involvement only in PER/TIM repressor function during CLK-mediated transcription. A likely explanation is that the low-level hypophosphorylated CLK is extremely active in flies with reduced CK2α activity. In line with the in vivo results, the luciferase activity assay in cultured S2 cells uncovered a dose-dependent repression of CLK activity by the CK2α subunit on a minimal enhancer-promoter element.
CK2α thus appears to control CLK-dependent transcription by increasing PER/TIM repressing capacity and jointly decreasing CLK activity by some other mechanism. CK2β supports TIM-dependent repression (Figure S5B), but may not contribute to the PER/TIM-independent control of CLK activity by CK2α. Since deubiquitylation of CLK by USP8 decreases its activity [25], it will be interesting to investigate whether CK2α phosphorylation affects CLK ubiquitylation.
In the Neurospora circadian transcriptional feedback loop, the FRQ repressor recruits CK1 and CK2 to promote phosphorylation of the WCC activator complex resulting in the inhibition of its transcriptional activity [35],[54]–[57]. Reactivation of WCC occurs through its dephosphorylation by phosphatases such as PP2A [54],[56]. WCC is destabilized when turning active and gets stabilized as soon as it resumes a transcriptionally inactive state [56],[57]. This is reminiscent of our finding about the role of CK2 in CLK activity regulation. Recently, BMAL1 and CLK were also shown to be “Kamikaze” activators in mammals in that their activity was dependent on proteasome function—highly unstable CLK and BMAL1 were the most active, while proteasome inhibition resulted in long-lived but less potent activators [31],[32],[58]. Our findings indicate that CK2 might be a key player in such a mechanism, by promoting CLK stability and decreasing its activity. It remains to be seen how CK2 and DBT-dependent kinase activities interact on CLK to set CLK transcriptional activity to a proper phase in the circadian cycle.
Drosophila melanogaster stocks were maintained on a 12 h∶12 h LD cycle on standard corn meal-yeast-agar medium at 25°C. ClkJrk is a dominant allele of Clk, which results in a truncated and highly unstable CLK protein [59]. per01 [60], tim01 [61], w;tim-gal4-62 [62], w;;gal1118 [46], per01w;;13.2(per(Δ)-HA10His) F21 [63], yw;;P{UAS-CkIIα.Tik} T1 [15], yw;P{UAS-CkIIα.L} 35 [41], w;UAS-FLAG-CkIIα [51], and lines carrying UAS transgenes encoding each of the five CK2β isoforms [64] have been previously described. The gal1118 driver line in the adult brain is expressed in the small and large LNv-s in addition to some few nonclock cells [46]. UAS-RNAi flies against CkIIβ (stocks 32377 and 106845) and CkIIα (stock 17520 R-2) are described in http://stockcenter.vdrc.at/control/main and http://www.shigen.nig.ac.jp/fly/nigfly/index.jsp, respectively. Both CkIIβ RNAi lines (32377 and 106845) were induced in all the experiments using CkIIβ RNAi except specifically indicated. The UAS-FLAG-CkIIβ construct was made by inserting a FLAG-CK2ß coding segment (kindly provided by A. Bidwai, West Virginia University) into the pUAST vector, and w;UAS-FLAG-CkIIβ transgenic flies were generated by standard procedures. For in vitro phosphorylation assays, Clk constructs with a 6-histidine fusion tag as well as per and tim were expressed from a SP6 promoter incorporated in a pAc-5.1 vector, as described previously [30]. The FMO02931 expression plasmid was obtained from the Drosophila Genomics Resource Center (DGRC). It contains the full CkIIα ORF tagged C-terminally with FLAG and HA and driven by the metallothionein promoter. We verified the CkIIα ORF and the promoter region by sequencing.
Behavioral assays for locomotor activity rhythms were carried out with 1- to 5-d-old males at 25°C in Drosophila activity monitors (TriKinetics). Illumination was provided by standard white fluorescent low-energy bulbs. Light intensity at fly level was in the range of 300–1000 µW/cm2. Flies were first entrained to 12 h∶12 h light-dark (LD) cycles for 4 d and then transferred to constant darkness (DD). Activity data were analyzed from the second to the ninth day in DD. Data analysis was done with the FaasX 1.16 software that is derived from the Brandeis Rhythm Package (see [65]) and is freely available upon request (Apple Mac OSX only). Rhythmic flies were defined by χ2 periodogram analysis of an 8-d dataset with the following criteria (filter ON): power ≥20, width ≥1.5 h, with no selection on period value. Power and width represent the height and width of the periodogram peak, respectively, and give the significance of the calculated period. Genotypes with a reduced number of rhythmic flies (<50%), low power (<50), and high s.e.m. of the period (>1) are considered arrhythmic. Experiments were reproduced two or three times with very similar results.
We entrained 1 to 5-d-old flies to 12 h∶12 h LD cycles for 4 d and transferred to DD (CT0 is 12 h after the last lights-OFF). Flies were collected on dry ice during the first day of DD (CT0–24). We homogenized 30–60 heads on ice in a modified RBS buffer [20]: 10 mM HEPES pH 7.5, 5 mM Tris-HCl pH 7.5, 50 mM KCl, 10% glycerol, 2 mM EDTA, 1% Triton X-100, 0.4% NP-40, 1 mM DTT, Complete Mini Protease Inhibitor Cocktail Tablet (Roche), Phosphatase Inhibitor Cocktail 2 and 3 (Sigma-Aldrich), and 20 mM β-glycerophosphate (3–4 µl buffer/head). A Brinkmann Heidolph Mechanical Overhead Stirrer RZR1 was used for the homogenization. After 1 min of extraction, tubes were incubated in ice for 30 min, then homogenized again for another minute. If sonication was included after this step, samples were sonicated on ice with a Vibracell ultrasonic processor (Bioblock Scientific) at 4W output for 5×10 s with 1 s breaks. Following Bradford protein concentration measurement (BioRad), supernatants were used for polyacrylamide gel electrophoresis. When supernatants were treated with λ protein phosphatase, 1,600 units of λ protein phosphatase (New England Biolabs) and 1 mM MnCl2 were added to sonicated extracts prepared in phosphatase inhibitor-free buffer [10 mM HEPES pH 7.5, 100 mM KCl, 0.1 mM EDTA, 5% glycerol, 0.1% Triton X-100, 5 mM DTT, and EDTA-free Complete Mini Protease Inhibitor Cocktail Tablet (Roche)] and subsequently incubated for 30 min at 30°C. Reaction was stopped by adding 1× NuPAGE LDS sample buffer (Life Technologies), 500 mM DTT, and incubation for 10 min at 70°C. We loaded 50 µg total protein on Novex 4% Tris-Glycine precast gels (Life Technologies) for PER, TIM, and CLK immunoblotting, except specifically indicated. When indicated, NuPAGE Novex 3–8% Tris-Acetate gels were used for TIM and CLK immunoblotting for a better resolution of hyperphosphorylated forms. Samples (50 µg) for FLAG, CK2α, and CK2β immunoblots were run on NuPAGE Novex 4–12% Bis-Tris precast gels (Life Technologies). Electrophoresis and blotting were done according to the manufacturer's instructions except for a 3 h running time for Tris-Acetate and a 2 h running time for Tris-Glycine gels. Equal loading was verified by Ponceau S staining on blotting membranes, which were blocked in 5% nonfat dry milk in TBST (Tris-Buffered Saline with 0.1% Tween-20) for 1 h at 25°C and then incubated with the primary antibody overnight at 4°C. The following primary antibodies were used diluted in 5% milk in TBST: rabbit anti-V5 (Sigma-Aldrich V8137, Lot 019K4827) at 1∶4,000, rabbit anti-myosin heavy chain (MHC, kind gift of Roger E. Karess, Institut Jacques Monod, Paris) at 1∶400,000, rabbit anti-CK2α (Abcam ab81435) at 1∶1,000, mouse anti-CK2β (Calbiochem 6D5 218712) at 1∶1,000, rat anti-TIM [7] at 1∶2,000, goat anti-CLK (Santa Cruz Biotechnology sc27070) at 1∶1000, rabbit anti-PER [66] at 1∶10,000, guinea pig anti-VRILLE [44] at 1∶5,000, and guinea pig anti-PDP1ε [67] at 1∶5,000. For immunoblotting of anti-FLAG immunoprecipitations, guinea pig GP90 anti-CLK [18] at 1∶1,000 was used since an aspecific IgG-derived band revealed with the SC27070 anti-CLK on the immunoprecipitates. Membranes were washed three times for 10 min, then the HRP-conjugated secondary antibodies (Santa Cruz Biotechnology) were added diluted in 5% milk in TBST: goat anti-rabbit (1∶10,000), goat anti-rat (1∶20,000), goat anti-mouse (1∶20,000), donkey anti-goat (1∶10,000), and goat anti-guinea pig (1∶10,000). In the case of anti-CK2β, TrueBlot ULTRA Anti-Mouse IgG-HRP (eBioscience, 1∶2,000) was used as a secondary antibody to circumvent problems resulting from primary antibody light chain detection after immunoprecipitation.
Blots were revealed with the Amersham ECL Plus reagent (GE Healthcare). SimplyBlue SafeStain (Life Technologies) was used to stain membranes after blotting. Images were quantified with the NIH ImageJ (1.43 k) software after background subtraction. Calculations were done and histograms were generated with Microsoft Excel.
Fly head extracts were prepared as described above, except that HE buffer [20 mM HEPES pH 7.5, 150 mM KCl, 0.1 mM EDTA, 0.1% NP-40, 5% glycerol, Complete Mini Protease Inhibitor Cocktail Tablet (Roche), Phosphatase Inhibitor Cocktail 2 and 3 (Sigma-Aldrich) and 20 mM β-glycerophosphate] was used for the homogenization. We incubated 1 or 2 mg total protein overnight at 4°C with either 25 µl EZview Red anti-FLAG M2 Affinity Gel (Sigma-Aldrich) or 25 µl Protein G Sepharose (Pierce) mixed with 10 µl of anti-CLK antibody (Santa Cruz Biotechnology SC27069). Following three 10 min washes, bound complexes were eluted in 1× NuPAGE LDS sample buffer (Life Technologies) without DTT for 10 min at 70°C. Supernatants were complemented with DTT (500 mM) and reduced for 10 min at 70°C.
Drosophila Schneider 2 (S2) cells [kind gift of Anne Plessis (Institut Jaques Monod, Paris)] were maintained in SFX-Insect Medium (HyClone) supplemented with 10% fetal bovine serum (Sigma-Aldrich) and 1% penicillin-streptomycin solution (Sigma-Aldrich) as previously described [68]. Complementary single-stranded RNA-s were in vitro transcribed from purified PCR templates containing the T7 RNA polymerase promoter site on both ends, using the MEGAscript T7 Kit (Life Technologies). Reactions were purified with the MEGAclear Kit (Life Technologies) and precipitated in ethanol/sodium acetate for concentration followed by resuspension in 40 µl H2O and annealing of the two strands (30 min at 65°C and slow cooling to room temperature). RNA quality and quantity was assessed by spectrophotometry and agarose gel electrophoresis. Primers for per target sequence amplification by PCR were: TTAATACGACTCACTATAGGGAGAAAGGAGGACAGCTTCTGCTGC and TTAATACGACTCACTATAGGGAGAGATATGATCCCGGTGGCCGTG and for tim were: TTAATA; CGACTCACTATAGGGAGACTGGTTACTAGCAACTCCGCA and TTAATACGACTCA; and CTATAGGGAGAGCAGGATATTTCTCAGCAGCA.
pAc-Clk-V5/His6 [10], pAc-Renilla luciferase (kind gift of M. Rosbash), and p3x69-luc (containing three copies of per E-box as enhancer element [10],[69]) were already described. Transient transfection was performed with Effectene (Qiagen) using plasmids purified with the Plasmid Midi Kit (Qiagen). DNA quantities were equalized for transfection by addition of empty pAc vector. The induction of CkIIα under the control of metallothionein promoter was achieved by adding 500 µM CuSO4 to the cells 1 d after transfection.
For luciferase activity assays, 106 cells were seeded in six-well plates, left to proliferate in serum-free medium for 48 h, were transfected in serum-free medium, supplemented with serum and antibiotics 4 h later, and harvested 48 h posttransfection. Cells were washed in PBS and lysed on plate with Passive Lysis Buffer according to the Dual-Luciferase Reporter Assay System manual (Promega). Lysates were cleared by centrifugation at 4°C and 10 µl of supernatant was measured for firefly and Renilla luciferase activities with the Dual-Luciferase Reporter Assay System (Promega) on a Mithras LB 940 luminometer (Berthold Technologies). Firefly luciferase activities were normalized to corresponding Renilla luciferase activities to control for transfection efficiency and protein concentration. Experiments were made in duplicates or quadruplicates and repeated at least twice.
For degradation assays, cells were seeded in 60 mm dishes (2.5×106 cells/dish) and treated with per and tim dsRNA (37.5 µg) in serum-free medium for 48 h followed by transfections in medium containing serum and antibiotics. One day posttransfection, cells were split in four equal volumes and seeded in 12-well plates followed by induction with CuSO4. One day after induction, cycloheximide (CHX, Sigma-Aldrich) was added to each well at a final concentration of 0.58 mM, and cells were harvested 0, 3, 6, and 9 h after the beginning of CHX treatment. After harvest, cells were centrifuged for 5 min at 2,000 g at 20°C, washed once with PBS, and pellets were frozen at −80°C until extraction. Protein extraction was achieved by lysing cells in 40 µl of HE buffer (described above) supplemented with 0.5% Triton X-100 by means of pipetting and vortexing. After centrifugation for 10 min at 14,000 rpm at 4°C, supernatants were subjected to Bradford assay. We used 20 µg protein for polyacrylamide gel electrophoresis. Blots were revealed with anti-V5 for CLK and with anti-MHC as a loading control. Both blots were quantified by ImageJ. V5 reactivity was normalized to MHC reactivity for each sample, which was used for the calculations that are plotted in Figure 2E.
In vitro transcription/translation and phosphorylation reactions were carried out as described previously [30], with the following differences: CLK protein with a N-terminal 6-histidin fusion tag as well as PER and TIM were expressed in TNT SP6-Quick Coupled High Yield Wheat Germ expression system (Promega) for 2 h at 25°C with the addition of 0.2 mM staurosporine to block phosphorylation. Subsequently CLK protein was precipitated with 20 µl nickel-nitrilotriacetate (Ni-NTA) agarose for 90 min at 4°C either with or without prior addition of PER or TIM expressing lysates. Affinity purified CLK protein with or without co-precipitated PER or TIM was subjected to on bead phosphorylation reactions by human casein kinase II holoenzyme (New England Biolabs) or recombinant human CK2α subunit (KinaseDetect, DK-5792 Aarslev, Denmark) in 50 µl phosphorylation buffer (20 mM Tris-HCl, pH 7.5, 50 mM KCl, 10 mM MgCl2) with 0.5 µCi/µl γ–32P-ATP at 30°C for the holoenzyme and at 37°C for CK2α. The amount of CLK-incorporated 32P-phosphate was quantified by autoradiography and densitometry after SDS-page electrophoresis and blotting to nitrocellulose membrane. The intensity of the 32P-signal was normalized by total CLK protein level, as quantified by Western blot analysis.
Total RNA was prepared from adult heads (about 35) using the Promega SV Total RNA Isolation System. It was quantified using the Nanodrop ND-1000 spectrophotometer, and the integrity of the RNA was verified using the Agilent 2100 bioanalyser with the eukaryote total RNA Nano assay. RNA was treated with rDNase (NucleoSpin RNA Kit, Macherey-Nagel) in solution after RNA isolation to ensure optimal conditions for pre-mRNA detection. One µg of total RNA was reverse-transcribed in a 50 µl final reaction in presence of 0.4 µM oligodT(15) or random hexamer primers (for detection of pre-mRNA-s), 8 mM dNTP, 40 units of RNasine, and 400 units of M-MLV RTase H-minus (Promega), during 3 h at 37°C. Quantitative PCR was performed with a Roche LightCycler (mRNA-s) or an Applied Biosystems 7900HT Fast Real-Time PCR System (pre-mRNA-s) using the SYBR green detection protocol of the manufacturer. We mixed 3 µl of a 25× diluted cDNA (or 1 ng/µl) with FastStart DNA MasterPLUS SYBR green I mix with 500 nM of each primer, and the reaction mix was loaded on the capillaries and submitted to 40 cycles of PCR (95°C/15 s; 60°C/10 s; 72°C/20 s for the Lightcycler and 50°C 2 min; 95°C/20 s; [95°C/1 s–60°C/25 s]×40 for the ABI instrument), followed by a fusion cycle in order to analyze the melting curve of the PCR products. Negative control without the reverse transcriptase was introduced to verify the absence of genomic DNA contaminants. Primers (see Table S2) were defined within exons (for mRNA-s) or in one intron and one exon (for pre-mRNA-s) using the PrimerSelect program of the Lasergene software (DNAStar). BLAST searches were performed to confirm gene specificity and the absence of multilocus matching at the primer site. The amplification efficiencies of primers were generated using the slopes of the standard curves obtained by a 10-fold dilution series of 4, with all experimental points falling within this range. The efficiency of the q-PCR amplifications for all pairs of primers is indicated in the table. Amplification specificity for each q-PCR reaction was confirmed by dissociation curve analysis. Determined Ct values (see Table S2) were then used for quantification, with the tubulin gene as reference. Each sample measurement was made at least in duplicate (technical replicate).
Experiments were done on whole-mounted adult brains as previously described [46]. Primary antibodies were rabbit anti-PER [66] at 1∶15,000, guinea pig GP47 anti-CLK [26] at 1∶15,000, mouse anti-PDF (Developmental Studies Hybridoma Bank) at 1∶50,000, and rabbit anti-CK2α. (Abcam, ab81435) at 1∶100. Secondary goat antibodies (Life Technologies) were Alexa 647- or Alexa 594-conjugated anti-rabbit at 1∶5,000, Alexa 488- or Alexa 647-conjugated anti-guinea pig at 1∶2,000, and Alexa 594- or Alexa 488-conjugated anti-mouse at 1∶2,000. Fluorescence signals were analyzed with a Zeiss AxioImager Z1 microscope with an ApoTome structured illumination module and an AxioCam MRm digital camera. Images for subcellular localization of CK2α were acquired with a Zeiss LSM-700 confocal microscope. Fluorescence intensity of individual cells was quantified from digital images of single focal planes with the NIH ImageJ software. We calculated a fluorescence index: I = 1 00(S-B)/B, which gives the fluorescence percentage above background (S (Signal) is fluorescence intensity and B (Background) is average intensity of the region adjacent to the positive cell). Index values were then averaged for the four PDF-positive s-LNv cells of 12–20 brain hemispheres for each time point.
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10.1371/journal.pgen.1000958 | The Polyproline Site in Hinge 2 Influences the Functional Capacity of Truncated Dystrophins | Mutations in dystrophin can lead to Duchenne muscular dystrophy or the more mild form of the disease, Becker muscular dystrophy. The hinge 3 region in the rod domain of dystrophin is particularly prone to deletion mutations. In-frame deletions of hinge 3 are predicted to lead to BMD, however the severity of disease can vary considerably. Here we performed extensive structure-function analyses of truncated dystrophins with modified hinges and spectrin-like repeats in mdx mice. We found that the polyproline site in hinge 2 profoundly influences the functional capacity of a microdystrophinΔR4-R23/ΔCT with a large deletion in the hinge 3 region. Inclusion of polyproline in microdystrophinΔR4-R23/ΔCT led to small myofibers (12% smaller than wild-type), Achilles myotendinous disruption, ringed fibers, and aberrant neuromuscular junctions in the mdx gastrocnemius muscles. Replacing hinge 2 of microdystrophinΔR4-R23/ΔCT with hinge 3 significantly improved the functional capacity to prevent muscle degeneration, increase muscle fiber area, and maintain the junctions. We conclude that the rigid α-helical structure of the polyproline site significantly impairs the functional capacity of truncated dystrophins to maintain appropriate connections between the cytoskeleton and extracellular matrix.
| Dystrophin functions like a large molecular spring between the muscle cytoskeleton and the extracellular matrix in order to protect the membrane from contraction-induced injury. Mutations in dystrophin can lead to a severe muscle wasting disease called Duchenne muscular dystrophy (DMD) in young boys. DMD patients are typically wheelchair bound by 9–13 years of age and die at approximately 30 years. There are also mutations within the dystrophin gene that lead to internal truncations of non-essential regions, such as the internal rod domain that leads to a mild form of the disease called Becker Muscular Dystrophy. However, these internal truncations frequently occur at a “hot spot” within the rod domain where the resulting disease severity is difficult to predict. Here we found that consecutive proline residues, that function much like a molecular ruler, can dramatically influence the function of these internally truncated dystrophins within skeletal muscles. Using this information, we designed a dystrophin mini-gene that can accommodate the limited packaging size of recombinant adeno-associated virus. This virus can deliver the dystrophin mini-gene to most muscles throughout a dystrophic mouse to prevent muscle degeneration and partially restore muscle function.
| Duchenne muscular dystrophy (DMD) is a lethal X-linked recessive disease caused by mutations in the 2.2 MB dystrophin gene [1]–[3]. In skeletal muscle, dystrophin provides a flexible connection between the cytoskeleton and the dystrophin-glycoprotein complex at the sarcolemma, myotendinous junction (MTJ) and neuromuscular junction (NMJ) [4]–[6]. Mutations that affect the mechanical integrity of this molecular scaffold render muscles more susceptible to contraction-induced injury leading to cycles of necrosis and regeneration [3].
As a general rule, most frame-shift mutations in dystrophin lead to DMD whereas internal truncations (in-frame deletions) lead to a milder form of the disease called Becker muscular dystrophy (BMD) [7]–[14]. The severity of BMD can also vary depending on whether a critical region of dystrophin is deleted and the amount of dystrophin being expressed [7]–[14]. Dystrophin consists of a N-terminal actin-binding domain, a large central rod domain, a cysteine rich region and a C-terminal domain (Figure 1A) [15], [16]. The central rod domain contains 24 spectrin-like repeats, 4 hinges and a second actin-binding domain [15]–[20]. The locus encoding the N-terminal actin-binding domain and the region near hinge 3 of dystrophin are more susceptible to deletion mutations [7]–[13]. In-frame deletions of the central rod domain typically lead to a mild BMD [8]–[13]. However, in-frame deletions at the “hot spot” near hinge 3 can lead to more variable phenotypes [8]–[13], [21].
The role of dystrophin in vivo has been largely defined by the structure-function relationship of truncated dystrophins in humans and mice [8]–[13], [22]–[24]. Rational design of dystrophin mini-genes has been highly effective in preventing and reversing functional abnormalities of dystrophic muscles [22]–[29]. In particular, we previously developed a microdystrophin (ΔR4-R23/ΔCT; defined as those with 4 or fewer spectrin-like repeats [24]) that accommodates the limited cloning capacity of recombinant adeno-associated viral vectors (rAAV) [24]. Intravenous injection of rAAV vectors pseudotyped with serotype 6 capsid (rAAV6) expressing microdystrophinΔR4-R23/ΔCT can prevent and reverse most aspects of dystrophic pathology in mdx muscles [24], [28], [30]–[35]. MicrodystrophinΔR4-R23/ΔCT also significantly protects muscles from contraction-induced injury [24], [28], [30]–[35].
While the microdystrophinΔR4-R23 transgene provides a clear benefit to dystrophic muscles [24], more detailed analyses have revealed a potentially serious abnormality in some muscle groups. The microdystrophinΔR4-R23/mdx transgenic mice have chronic Achilles myotendinous strain injury, which leads to the formation of ringed fibers and fragmentation of the neuromuscular junctions [33], [36]. In the present study we examined whether the domain composition or the small size of microdystrophinΔR4-R23/ΔCT led to this myopathy in mdx mice. We found that the hinge regions of microdystrophin, rather than its small size can profoundly influence skeletal muscle maintenance, maturation and structure.
We initially screened several truncated dystrophins and found that inclusion of hinge 2, but not hinge 3 could lead to the structural abnormalities we observed in some muscles of the microdystrophinΔR4-R23 transgenic mice (Text S1; Figures S1, S2, S3). We subsequently compared the efficacy of two microdystrophins that differ only in their inclusion of hinge 2 (microdystrophinΔR4-R23/ΔCT) or hinge 3 (microdystrophinΔH2-R23+H3/ΔCT) (Figure 1A) to examine whether the hinge composition of microdystrophin could influence various aspects of muscle disease.
We administered a sub-optimal dose of 2×1012 vector genomes of a rAAV6 pseudotyped vector expressing either microdystrophinΔR4-R23/ΔCT or microdystrophinΔH2-R23+H3/ΔCT intravenously into 2 week-old mdx4cv mice. We used a sub-optimal dose of rAAV6-microdystrophins so that we could examine whether changing the hinge domain increased or decreased the functional capacity of microdystrophin. Six months after treatment, both microdystrophins were expressed in a similar percentage of gastrocnemius and tibialis anterior (TA) muscle fibers (ranging from approximately 61% to 71%; P = 0.238 when comparing between the microdystrophins; Figure 1B and 1D). Western blots confirmed similar expression levels of truncated dystrophins in treated gastrocnemius muscles (Figure 1C). Both microdystrophins restored dystrophin-associated proteins to the sarcolemma except for nNOS (Text S1; Figure S4). MicrodystrophinΔR4-R23/ΔCT containing hinge 2 significantly prevented muscle degeneration (∼11% central nuclei for treated muscles verse ∼78% for untreated mdx muscles; P<0.001), and limited the fiber area of skeletal muscles (12% smaller than wild-type; P<0.05; Figure 1E), consistent with previous studies [24], [32], [33]. MicrodystrophinΔH2-R23+H3/ΔCT containing hinge 3 was significantly better able to prevent muscle degeneration (1–2% central nuclei; P<0.05 compared to microdystrophinΔR4-R23/ΔCT), and surprisingly increased average muscle fiber cross sectional area (34% larger than wild-type; P<0.001; Figure 1E). Thus, replacing hinge 2 of microdystrophinΔR4-R23/ΔCT with hinge 3 significantly improved its capacity to prevent muscle degeneration and promote skeletal muscle maturation.
The tendon extends deep folds into wild-type skeletal muscles to minimize membrane stress under shear (Figure 2)[37]. Most of the folds in the mdx junctions did not extend as far into the gastrocnemius muscles (Figure 2). rAAV6-microdystrophinΔR4-R23/ΔCT severely disrupted the Achilles myotendinous junctions in mdx mice. Many of the junctional folds were missing and myofibril degeneration was evident (Figure 2). Approximately 17% of the adjoining mdx gastrocnemius muscles had ringed fibers. In contrast, rAAV6- microdystrophinΔH2-R23+H3/ΔCT with hinge 3 retained the normal architecture of the Achilles myotendinous junction and we found no ringed fibers in the adjoining gastrocnemius muscles (Figure 2). Thus, the hinge domains influenced whether microdystrophin was capable of maintaining the myotendinous junction and myofibril structure in mdx gastrocnemius muscles.
We also examined neuromuscular synapses in mdx mice treated with rAAV6-microdystrophins. Most neuromuscular synapses in wild-type mice (∼97%) form a continuous tertiary structure as shown by staining whole muscle fibers with α-bungarotoxin (Figure 3A). Neuromuscular synapses in mdx mice begin to fragment temporally coincident with muscle degeneration [38]. Approximately 89% of neuromuscular synapses were fragmented in the gastrocnemius muscles of mdx mice (Figure 3B). We had previously shown that the neuromuscular synapses in transgenic microdystrophinΔR4-R23/mdx gastrocnemius muscles fragmented temporally coincident with the formation of ringed fibers [36]. In the present study we found that rAAV6- microdystrophinΔR4-R23/ΔCT containing hinge 2 maintained continuous synapses in only 46% of the mdx gastrocnemius muscles (Figure 3A and 3B). In contrast, approximately 84% of synapses were continuous in mdx gastrocnemius muscles treated with rAAV6-microdystrophinΔH2-R23+H3/ΔCT containing hinge 3 (Figure 3A and 3B).
Neuromuscular synapses also contain folds in the postsynaptic membrane that align directly adjacent to vesicle release sites (active zones) in the pre-synaptic nerve terminal (arrows; Figure 3C). The number of synaptic folds in mdx mice was significantly reduced compared to wild-type (P<0.01; Figure 3C and 3D) as previously described [4], [39]. The number of folds was restored in microdystrophinΔR4-R23/ΔCT and microdystrophinΔH2-R23+H3/ΔCT treated muscles (Figure 3C and 3D). The synaptic folds extended significantly further into microdystrophinΔR4-R23/ΔCT treated mdx muscles compared to wild-type muscles (P<0.001; Figure 3C and 3E), as previously described in transgenic microdystrophinΔR4-R23/mdx mice [36]. In contrast, the number and length of synaptic folds in microdystrophinΔH2-R23+H3/ΔCT treated mdx muscles was similar to wild-type (Figure 3C–3E). Thus, microdystrophinΔH2-R23+H3/ΔCT containing hinge 3 can maintain the structure of neuromuscular junctions in mdx muscles.
Contraction-induced injury can initiate muscle degeneration in mdx mice [40]. Skeletal muscles from mdx mice have a lower force producing capacity than wild-type muscles and are more susceptible to contraction-induced injury (Figure 4). We found that sub-optimal doses of both rAAV6-microdystrophinΔR4-R23/ΔCT and rAAV6- microdystrophinΔH2-R23+H3/ΔCT maintained the peak force producing capacity of mdx gastrocnemius and tibialis anterior muscles (Figure 4A). Both microdystrophins also significantly improved the specific force (force per cross sectional area of muscle) production in mdx muscles (P<0.05; Figure 4B). The specific force was not restored to wild-type partly because the sub-optimal dose of rAAV6-microdystrophin did not prevent the pseudo hypertrophy normally found in mdx muscles (P = 0.454 when comparing the muscle mass between mdx and treated mdx muscles; one-way ANOVA). Each microdystrophin significantly protected the treated limb muscles from contraction-induced injury (P<0.001; Figure 4C and 4D). However, we found no significant difference between the peak force, specific force or protection from contraction-induced injury when comparing between the two microdystrophins with either hinge 2 or hinge 3.
Together, our results suggested that the structural abnormalities observed in some treated mdx muscles could be traced to the presence of hinge 2 within the microdystrophin. We next examined the molecular composition of the hinges to define what was unique about hinge 2. The hinges in dystrophin are defined as such because of the higher concentration of proline residues, which function to limit the continuation of the α-helical coiled-coils of the spectrin-like repeats through the entire length of the dystrophin rod domain [19]. Both hinge 2 and hinge 3 have six proline residues and the lengths of these hinges are similar [19]. We hypothesized that the placement of the prolines most likely results in their different functions [5], [19]. Hinge 2 has 5 consecutive proline residues (polyproline; Figure 5A) whereas the proline residues in hinge 3 are more evenly distributed throughout the hinge [19]. Polyproline residues are thought to have their own defined rigid helical structure [41], [42], and this could affect the functional capacity of microdystrophinΔR4-R23/ΔCT.
To test this hypothesis we compared muscles expressing the original microdystrophinΔR4-R23/ΔCT with a newly developed microdystrophinΔpolyP/ΔR4-R23/ΔCT that lacks the polyproline site in hinge 2 (Figure 5A). We delivered 6×1010 vg of each microdystrophin into mdx gastrocnemius muscles at 2 days of age and examined the mice 7 weeks after treatment. Both microdystrophins were expressed in a similar percentage of muscle fibers (Figure 5B; 59–68%), and were expressed at similar levels (Figure 5C). Each microdystrophin significantly reduced muscle fiber degeneration (Figure 5D). As expected, the original microdystrophinΔR4-R23/ΔCT limited muscle fiber cross-sectional area (Figure 5E), was associated with disrupted myotendinous junctions (Figure 5F), led to the formation of ringed fibers (Figure 5F), and perturbed neuromuscular junctions (Figure 5G–5I). In contrast, the mdx muscles treated with microdystrophinΔpolyP/ΔR4-R23/ΔCT did not show any abnormalities in muscle fiber maturation or structure (Figure 5). Thus, the presence of this polyproline site in hinge 2 of microdystrophinΔR4-R23/ΔCT prevented the appropriate integration of muscles into the nerve-tendon environment.
Most gene therapy strategies for DMD require the generation of highly functional truncated dystrophins. rAAV is an efficient and safe vector for systemically delivering truncated dystrophins to striated muscles to prevent muscle degeneration in animal models of DMD ([28]; reviewed in [43]). We had previously generated a microdystrophinΔR4-R23 that was highly capable of mitigating muscle degeneration and improving the mechanical function of mdx skeletal muscles [24], [28]. However, the microdystrophinΔR4-R23 transgene leads to chronic strain injury at the Achilles myotendinous junction [33]. This led to the formation of ringed fibers that function to protect skeletal muscles from contraction-induced injury, even better than wild-type mice [33]. The formation of the rings led to fragmentation of the neuromuscular junctions [36]. Other effects of the transgene included smaller muscle fibers [24], and increased length of synaptic folds [36]. Here we found that each of these phenotypic changes was recapitulated in mdx gastrocnemius muscles treated with rAAV6-microdystrophinΔR4-R23/ΔCT. A screen of several newly developed dystrophin mini-genes revealed that the hinge 2 region influenced the functional capacity of microdystrophinΔR4-R23/ΔCT. Replacing hinge 2 with hinge 3 led to several advantages such as better protection of skeletal muscles (only 1–2% central nuclei 6 months post treatment), larger muscle fibers and normal junctions. Deleting the polyproline site from hinge 2 of microdystrophinΔR4-R23/ΔCT also prevented these structural abnormalities.
MicrodystrophinΔH2-R23+H3/ΔCT with hinge 3 significantly increased peak force, specific force and protected muscles from contraction-induced injury. However, the morphological improvements of microdystrophinΔH2-R23+H3/ΔCT treated muscles did not translate into a functional improvement compared to microdystrophinΔR4-R23/ΔCT treated muscles. This could result from the molecular and cellular responses to myotendinous strain injury that help protect the rAAV6-microdystrophinΔR4-R23/ΔCT treated muscles from contraction-induced injury [33]. Another possibility is that the presence of some dystrophin negative fibers masked any functional difference between the two proteins. The inclusion of hinge 2 in microdystrophin limited muscle fiber area whereas the inclusion of hinge 3 increased muscle fiber area (Figure 1). Larger muscle fibers in microdystrophinΔH2-R23+H3/ΔCT treated mice could have two distinct advantages: They could replace some of the muscle mass lost in advanced stages of disease and they could be better protected from contraction-induced injury [44]. However, the sub-optimal dose of either rAAV6-microdystrophin did not prevent the pseudo hypertrophy in mdx mice and no mechanical advantages could be discerned when comparing treatments. Saturating levels of rAAV6-microdystrophins or transgenic mice will most likely be required to detect minor differences in the mechanical properties of muscles expressing various truncated dystrophins.
Our most effective truncated dystrophins developed for gene therapy have been designed to maximize functional interactions between specific spectrin-like repeats and hinge domains. This design has been influenced by genetic studies in mice and man as well as biophysical studies in vitro on the structure, folding and physical properties of both individual and tandemly expressed spectrin-like repeats and hinge domains [24], [45]–[52]. Individual spectrin-like repeats are not all interchangeable, and ones adjacent to hinges have distinct properties from those flanked by other spectrin-like repeats [21], [24], [47], [51], [52]. Also, spectrin-like repeats rarely function as isolated units [15], [24], [50]–[53]. Instead, they appear to fold into nested domains interrupted by various insertions (hinges) that disrupt the uniformity and rigidity of the spectrin-like repeat rod domain [24], [45]–[48], [53]–[55]. These interruptions appear important for the elastic and flexible structure that dystrophin requires in its role as a force transducer and shock absorber in muscle [56]–[59]. Our studies suggest that the most functional truncations of dystrophin retain a central hinge domain that is flanked by spectrin-like repeats found adjacent to a hinge in the wild-type dystrophin [24]. Disruption of this linkage could influence protein folding, stability and function leading to the variable phenotypes in patients associated with deletions at or near hinge 3, which is encoded on exons 50–51 [21].
Individual spectrin-like repeats are composed of 3 helical domains connected by non-helical linkers, which fold into a triple helical coiled coil structure (Figure 5A; [45], [47]). The linker regions between discreet repeats are also typically short and relatively unstructured to allow a smooth connection between the third helix of a preceding repeat and the first helix of the next repeat (Figure 6A). However, hinge domains interrupt the nested nature of adjacent spectrin-like repeats and allow more flexibility in the rod domain (Figure 6B). This degree of flexibility appears to be significantly different when hinge 2 or hinge 3 is present. While both hinges contain 6 prolines, which act to disrupt alpha helical structures, in hinge 3 they are dispersed whereas 5 of the 6 prolines in hinge 2 are clustered together (Figure 5A, Figure 6C and 6D; [10]). Polyproline residues form a rigid α-helix [41], [42], much like a molecular ruler [60]. We suggest that the location of this polyproline sequence within a highly truncated rod domain induces a severe structural disruption that can affect the ability of dystrophin to form a mechanically flexible connection between F-actin and β-dystroglycan. Spectrin-like repeats 1-3 have been shown to associate with the sarcolemmal membrane, while the WW domain in hinge 4 forms a critical portion of the β-dystroglycan binding domain [45], [61]. A rigid rod domain induced by polyproline in hinge 2 may directly impair the ability of microdystrophin to form a flexible interaction with either or both of these structures (Figure 6C). In contrast, when hinge 2 is present in full-length dystrophin, a significantly greater number of spectrin-like repeats are present between the hinge and the β-dystroglycan binding domain, allowing greater flexibility in the overall structure.
It is difficult to predict the function of the polyproline site from patients with in frame deletions of exon 17 (hinge 2) of dystrophin. The described deletions (Leiden Muscular Dystrophy Pages) usually encompass larger regions of dystrophin than the polyproline site and it is not clear how these deletions affect protein stability. Our finding that hinge 3 microdystrophin can prevent muscle degeneration suggests that the polyproline site is not a necessary component of dystrophin, similar to previous reports on longer forms of truncated dystrophins [24], [62].
Flanigan et. al., 2009 has proposed that approximately 62% of all DMD patients could be treated with oligonucleotides that skip exons 45–55 (from spectrin-like repeat 18–22)[14]. This would create a truncated dystrophin that contains hinge 2 but not hinge 3, similar to, but much larger than our microdystrophinΔR4-R23 transgene. It will therefore be of interest to determine whether the polyproline site in hinge 2 can influence the functional capacity of larger, truncated dystrophins. It will also be of interest to examine whether the polyproline site affects the functional capacity of truncated utrophin constructs that are designed for gene therapy of DMD [63], [64].
We utilized C57Bl/10 wild-type mice and mdx4cv mice. All experiments are in accordance with the institution of animal care and use committee (IACUC) of the University Of Washington.
The expression vector CMV-ΔR4-R23/ΔCT which uses the cytomegalovirus immediate early promoter and enhancer to drive expression of a microdystrophin cDNA was generated as previously described [32]. We generated the ΔH2-R24/ΔCT, ΔR2-R23+R18-H3/ΔCT, ΔH2-R23+H3/ΔCT and ΔPolyP/ΔR4-R23/ΔCT constructs using recombination PCR with CMV-ΔR4-R23/ΔCT as the template [65]. The primers used to generate ΔH2-R24/ΔCT, ΔR2-R23+R18-H3/ΔCT, ΔH2-R23+H3/ΔCT and ΔPolyP/ΔR4-R23/ΔCT are found in Table S1. The resulting expression vectors were sequenced and co-transfected with the pDGM6 packaging plasmid into HEK293 cells to generate recombinant AAV vectors comprising serotype 6 capsids that were harvested, purified, and quantitated as described previously [29]. The resulting titer was determined by comparison to previously known concentrations of rAAV6-CMV-lacZ and ΔR4-R23/ΔCT by Southern analyses with a probe to the CMV promoter. The rAAV6-microdystrophins were delivered intravenously by tail vein injection at two weeks of age or directly into the mdx gastrocnemius muscles at 2 days of age while the mice were anaesthetized.
Gross muscle morphology was analyzed as previously described [24], [32]. Primary antibodies included the N-terminus of dystrophin (1∶800; [23]), utrophin A (1∶300; gift from Stanley Froehner, University of Washington), mouse monoclonal anti-α-dystrobrevin (Transduction laboratories; 1∶200), rabbit polyclonal anti-Syn17 (α-syntrophin; 1∶200; [66]), rabbit polyclonal anti-nNOS (Alexis; 1∶200). Secondary antibodies included Alexa 488, Alexa 594 rabbit polyclonal or Alexa 488 mouse monoclonal secondary antibodies (Molecular Probes; 1∶800). The sections were mounted in anti-fade mounting media containing DAPI (Vector Labs). Fluorescent sections were imaged using a Nikon eclipse E1000 fluorescent microscope (Nikon; NY) and captured using a DeltaVision fluorescence microscope. Muscle fiber areas were quantified using Image J (NIH).
For immunoblots, n = 4 gastrocnemius muscles from mdx mice and mdx mice treated with rAAV6-microdystrophinΔR4-R23/ΔCT or rAAV6-microdystrophinΔH2-R23+H3/ΔCT were thawed from OCT blocks and placed into extract buffer (50 mM Tris-HCl, 150 mM NaCl, 0.2% sodium dodecyl sulfate, 10% glycerol, 24 mM Na Deoxycholate, 1% NP40, 47.6 mM Na Fluoride, 200 mM Na orthovanadate, Roche). Protein concentrations were determined by Coomassie Plus Bradford Assay (Peirce). Equal amounts of protein (15 mg) were resolved on a 4–12% SDS polyacrylamide gel. The blots were incubated in rabbit polyclonal antibodies to dystrophin (1∶500; kind gift from James Ervasti, University of Minnesota) and mouse monoclonal antibodies to α-sarcomeric actin (1∶500; SIGMA).
We also performed immunoblots on frozen tissue sections from n = 4 gastrocnemius muscles treated with rAAV6-microdystrophinΔR4-R23/ΔCT and microdystrophinΔPolyP/ΔR4-R23/ΔCT as previously described [67], with minor modifications. Briefly, we cut twenty-five 20 µm sections and diluted the sections into 200 µl lysis buffer (4% SDS, 25 mM Tris pH 8.8, 40% glycerol, 0.5 M phenylmethylsulfonyl fluoride, 100 mM dithiothreitol and bromophenol blue). Samples were briefly sonicated (10 sec at 4°C), heated to 95°C for 5 minutes, centrifuged for 5 minutes at 13,200×g and electrophoresed on a 4–12% SDS-polyacrylamide gel. The blots were incubated in primary rabbit polyclonal antibody against the N-terminus of dystrophin (1∶500; kind gift from James Ervasti, University of Minnesota). All blots were developed with ECL Plus (Pierce) and scanned with the Storm 860 imaging system (Amersham Biosciences).
Electron microscopy was performed as previously described [33]. The junctional fold number and lengths were measured from n = 4 mice at 6 months of age using Image J (NIH) and compared using Students t-test (Prism). The counts represent the fold numbers and lengths from all fibers (dystrophin positive and negative).
We quantitated the number of ringed myofibers in EM images and thick (1 µm) toluidine blue sections from at least 4 animals per group. At least 300 muscle fibers from n = 4 gastrocnemius muscles were examined from wild-type, mdx4cv and mdx4cv mice expressing the various microdystrophins.
Neuromuscular synapses were analyzed in whole mount immunofluorescence stained muscles and quantitated as previously described [36]. The acetylcholine receptor clusters were stained with TRITC conjugated α-bungarotoxin (αBTX; 1∶800; Molecular Probes). Synapses were classified as continuous if they presented with 3 or less continuous regions of AChR clustering and discontinuous if they presented with more than 3 regions of AChR clustering. More than 50 synapses were analyzed from treated and untreated gastrocnemius skeletal muscle fibers from n = 4 mice. The counts in treated muscles include both dystrophin positive and negative fibers. We compared the proportion of continuous synapses using a Students t-test.
Muscle physiology was performed as previously described for tibialis anterior [29] and gastrocnemius [33] muscles. We examined six-month-old wild-type, mdx, and mdx mice treated with rAAV6-microdystrophinΔR4-R23/ΔCT or rAAV6-microdystrophinΔH2-R23+H3/ΔCT (n = 5).
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10.1371/journal.pcbi.1002185 | Transmission Selects for HIV-1 Strains of Intermediate Virulence: A Modelling Approach | Recent data shows that HIV-1 is characterised by variation in viral virulence factors that is heritable between infections, which suggests that viral virulence can be naturally selected at the population level. A trade-off between transmissibility and duration of infection appears to favour viruses of intermediate virulence. We developed a mathematical model to simulate the dynamics of putative viral genotypes that differ in their virulence. As a proxy for virulence, we use set-point viral load (SPVL), which is the steady density of viral particles in blood during asymptomatic infection. Mutation, the dependency of survival and transmissibility on SPVL, and host effects were incorporated into the model. The model was fitted to data to estimate unknown parameters, and was found to fit existing data well. The maximum likelihood estimates of the parameters produced a model in which SPVL converged from any initial conditions to observed values within 100–150 years of first emergence of HIV-1. We estimated the 1) host effect and 2) the extent to which the viral virulence genotype mutates from one infection to the next, and found a trade-off between these two parameters in explaining the variation in SPVL. The model confirms that evolution of virulence towards intermediate levels is sufficiently rapid for it to have happened in the early stages of the HIV epidemic, and confirms that existing viral loads are nearly optimal given the assumed constraints on evolution. The model provides a useful framework under which to examine the future evolution of HIV-1 virulence.
| Recent studies have suggested that virulence in HIV-1 is partly a characteristic of the virus which is carried from one infection to the next. An infection with intermediate virulence will produce more transmissions during the infectious lifetime because it optimises the trade-off between rate of transmission and duration of infection. Natural selection acts on the heritable variation to increase the relative prevalence of strains with intermediate virulence. In this study we model the evolution of virulence in the viral population as these more successful strains are preferentially transmitted. We fit this model to data from transmitting couples, and find that the model fits the data well. We use this fit to estimate the contribution of the host and the virus to virulence, which complements recent estimates of the heritability of virulence. We also estimate the rate at which the viral determinants of virulence evolve between infections, and this provides predictions for how rapidly the virulence of HIV-1 evolves in a population. We suggest that natural selection on transmissibility results in substantial evolution of virulence in the population. This is sufficiently rapid for virulence to have reached current levels over the available timescale of the human epidemic.
| The median time between HIV-1 seroconversion and progression to symptomatic Acquired Immune Deficiency Syndrome (AIDS) is approximately 10 years [1]. However, there is considerable variation in this rate of progression, with substantial proportions of infected individuals progressing to AIDS in less than 5 years, or remaining AIDS-free after 20 years. Explaining this variability is an important goal of HIV pathogenesis research. Many cofactors which influence time to AIDS have been identified e.g. host genetics [2], host age [1], and recently viral factors have been implicated [3]–[10].
In this paper we explore the extent to which viral factors which influence virulence, changing from one infected individual to the next, may have evolved under natural selection in the early phase of HIV-1's history. Between-host selection, leading to changes in the virulence of HIV-1, has potential major implications for the number of human life years affected.
Virulence is often defined as the excess mortality of the host which occurs as a result of infection with a pathogen. In the case of HIV the excess mortality is nearly 100%, so virulence can be better defined by the reciprocal of the time from infection to death, or time to AIDS. However, since this can only be defined at the host's death, we use set-point viral load (SPVL) as a proxy for virulence. This refers to the relatively stable density of virions in the blood which characterises asymptomatic infection. There is considerable population level variation in SPVL, in spite of its relative stability within the individual [11]. SPVL is widely used as a prognostic indicator for AIDS, as individuals with a higher SPVL have a higher rate of CD4+ cell decline, and they tend to progress more rapidly to AIDS [12], [13] and die sooner as a consequence [14]. As a result of its relative constancy during asymptomatic infection, SPVL can be measured at a wide range of time points in an individual's infection [15].
A simple conceptual model of how SPVL may evolve by between-host natural selection (i.e. selection for the more transmissible genotypes) requires consideration of the transmission potential of individuals of different SPVL. The transmission potential, defined as the product of duration of infection and infection rate, increases with either component of this product. A positive correlation between SPVL and transmission rate has been convincingly demonstrated within heterosexual couples with initially discordant serostatus [16]–[18]. Since there is also a negative correlation between SPVL and duration of asymptomatic infection [12], there is therefore a trade-off between duration of and transmission rate during asymptomatic infection. Previous work has quantified this trade-off to suggest that SPVL most commonly observed in infections maximise the transmission potential, suggesting that the distribution of SPVL was shaped by natural selection [19].
Natural selection requires that a trait has heritability from one generation to the next, in addition to variation and differential reproductive success. A number of recent studies have identified and quantified this heritable component of SPVL variation which is maintained from one infection to the next [3], [5], [6], [9], [10].
Recent studies from the Netherlands [20] and Italy [21] have found that the mean log10 SPVL has increased over the recorded history of an HIV-infected cohort, and the rate of CD4+ cell decline has increased. However different transmission groups have demonstrated different patterns of evolution of SPVL. In the initial stages of the epidemic (mid 1980s) injecting drug users showed slower CD4+ declines than heterosexuals or men having sex with men, but this difference decreased over the subsequent decade [21]. A study with similar methodology in Switzerland found stable virulence over the same time period [22]. This suggests that such trends may be area- and risk-group specific. In two studies showing an increase, the levels of SPVL in the earlier time points are lower [20], [21] than those which are optimal for transmission [19]. Various studies of the rate of CD4+ decline also suggest an increasing virulence [23], [24]. A study of the in vitro replicative fitness of viruses sampled at different time points reported a decrease in replicative fitness over the course of the epidemic in Amsterdam [25] although a subsequent study of the same city which controlled for time of seroconversion found an increase [26]. Overall, observational results on changing virulence are inconclusive, though they suggest either an equilibrium or a slow increase in that direction.
The lack of evidence for consistent population level trends in SPVL evolution [21], [22] suggests a) the global distribution of SPVL has stabilised at an equilibrium level; b) the rate of evolution is very slow or c) the distribution of SPVL is determined by factors which do not evolve. However, we think c) unlikely, first due to the observations on the heritability of SPVL described above, and second because there is evidence for evolution of SPVL occurring in particular areas or risk groups [20], [21].
To address the expected dynamics of SPVL evolution, we developed and analysed a deterministic mathematical model of between-host transmission and evolution incorporating known parameters linking SPVL to the duration of infection and the rate of transmission. The broad aim was to investigate the hypothesis that viral genotypes of intermediate virulence are naturally selected by transmission [19].
The primary of aim of this study was to use the observed distribution of SPVL to estimate the quantities of unknown host and viral factors which affect the process of between-host evolution. Comparing the model to data allowed us to calculate the likelihood of the unknown parameters.
The secondary aim was to assess whether the model, under these parameter estimates, allows convergence of the SPVL distribution towards an intermediate level, or at least to slowly changing levels consistent with observational studies, regardless of the virulence of the founding strain, and whether this can occur within a plausible timescale. The estimated time of origin of HIV-1 is before the most recent common ancestor, which has been dated to 1908 with 95% confidence interval 1884–1924 [27]. If evolution has occurred between the founding strain and current infections then it has occurred over a period of ∼100 years.
We modelled the dynamics of putative genotypes of HIV-1 which differ from one another in their mean log10 SPVL. SPVL was assumed to vary as a result of both host and virus factors. These genotypes differ in their reproductive success as a result of the dependency of duration of asymptomatic infection and transmission rate on SPVL. Their prevalences change over time through competition for susceptible individuals in a constant population.
The model is formulated as a standard HIV epidemic model in which different viral strains or genotypes compete. Virulence is considered as a one-dimensional trait, with each genotype represented by a point on the one-dimensional spectrum of increasing virulence. When a person is infected by a virus of a given genotype, the infection is characterised by a SPVL which reflects the virulence, but also other non-viral factors. When transmitted, the virus can also mutate to higher or lower levels of virulence.
The model encodes the natural history of infection. After infection, individuals experience a brief period of highly infectious acute stage, after which they progress to chronic asymptomatic infection. Their SPVL determines both the duration and infectiousness of this asymptomatic stage, after which their viral load and infectiousness increases again as they progress to AIDS and death. Individuals are assumed to engage in serially monogamous partnerships; a realistic description of the sexual network was not an aim of this study.
For the sake of parsimony, we focused on relatively simple mathematical models with minimal sets of parameters, and thus left some important questions open for further study. In particular, we did not explore the effect of population structure, stochastic fluctuations, differences between subtypes, superinfection, and founder effects, and we considered only the situation of natural, untreated infection, thus appropriate to describing the evolution of the virus prior to the widespread adoption of antiretroviral therapy. We also did not address the question of conflicting directions of selection at the within and between host level, describing in-host changes in virulence instead as random drift. We hope to address these important questions in future work.
A useful practical and conceptual approach to interpreting various influences acting on SPVL is to decompose the total observed variance, σP2, into its components, genotypic, mutational and environmental variance (σG2, σM2, and σE2) [28].(1.1)Genotypic variance σG2 refers to differences in SPVL between infected individuals caused by viral factors which are preserved from one infection to the next. Environmental variance, σE2, refers to any source of SPVL variance external to the virus. Host factors e.g. age [29], sex [30] and host genotype [31], in particular HLA type [2] contribute significantly to variation in SPVL between individuals, and there may be other human and non-human covariates of SPVL e.g. antigenic stimulation [32]. All of these factors, extrinsic to the virus, contribute to σE2 in our terminology.
Mutational variance, σM2, accounts for changes in the viral virulence genotype which result from mutation of the virus between one generation and the next (i.e. one infected host and the next) as a result of within-host replication and selection of the virus. Since the viral determinants of SPVL are not currently known, this cannot be related to the nucleotide substitution rate.The mutational standard deviation, σM, is simply the expected difference in the viral component of SPVL between an index and a secondary infection.
Heritability, h2, which has been quantified in previous studies, was defined as the fraction of variance explained by shared viral factors within a transmitting couple [6], [33]. We estimate h2 as the proportion of variance in SPVL explained by heritable viral genetic factors:(1.2)Alternative definitions of heritability, including the proportion of variance in SPVL explained by the SPVL of the index case, and the proportion explained by viral factors, are discussed and estimated in Text S1.
In this study, we aim to separately estimate σM2 and σE2, and thus gain a better estimate of the extent of viral factors in individual infections, and the parameters needed to predict evolution.
The primary aim of the analysis was to quantify the effects of host and virus on variation in SPVL. The values of the environmental and mutational standard deviations (σE and σM) were estimated using a maximum likelihood approach. Since the model predicts not just the distribution of SPVL, but how they change from one infection to the next, the model could predict the observed SPVL in both index and recipient partners in transmitting couples.
Figure 1 shows the likelihood surface for the environmental and mutational standard deviations (σE and σM), and the bivariate confidence bounds. The maximum likelihood estimates are σM = 0.12 (95% confidence interval 0.00 to 0.39) and σE = 0.66 (95% confidence interval 0.47–0.94). The estimates with highest mutational standard deviation within the 95% confidence bounds are σM = 0.39 and σE = 0.55 referred to later as the most mutable plausible scenario. Further details of the likelihood surface are given in Figure S2. The diagonal nature of the region of high likelihood in Figure 1 (or better viewed in Figure S2) indicates a trade-off between the two parameters in terms of the quality of model fit.
Figure 2 shows the quality of fit of the model to the distribution of SPVL in index partners and recipients in transmitting couples, and the estimated heritability was 26% (compared to 27% in a previous statistical analysis of these couples [6]). We conclude that the model describes the data well. The distribution and heritability of set-point viral load is well described by a multi-strain model of HIV-1 virulence evolution.
Having derived maximum likelihood estimates of parameters from an equilibrium solution to the model, the dynamics of genotype competition were then simulated numerically in order to assess whether or not convergence would occur under those parameter values, and on what timescale the convergence would occur.
The evolution of the SPVL distribution is shown in Figure 3. Regardless of whether the virulence of the founding genotype was high or low, the SPVL evolved towards an intermediate level with a mean log10 SPVL of 4.5.
This convergence on intermediate SPVL values also occurred when other combinations of parameter values in the region of high likelihood (Figure 1) were used instead. The rate of convergence was positively related to σM, as shown in Figure 4(a), where the maximum likelihood prediction is compared to the most mutable plausible scenario. Convergence towards intermediate virulence occurred in approximately 150 years under the maximum likelihood values. There was still change in the mean after this time but runs beginning with high or low virulence converge around this time point. The same point was reached in 50 years under the most mutable plausible scenario.
The heritability was also calculated over time (Figure 4(b)) and under maximum likelihood values of σE and σM this reached equilibrium at 26%, which is consistent with previous studies [3], [5], [6], [9], [10]. Further details of the heritability and variance at equilibrium are given in Figures S3 and S4.
In order to examine how changes in mean log10 SPVL are related to the stage of the epidemic, we examined the effect of proportion infected over time. The effect was most evident when the founding virulence closely matched the equilibrium virulence (Figure 5(b)). During the epidemic growth phase the mean virulence increased to levels above the optimum, and then returned to the optimum as the proportion infected reached equilibrium.
We varied the founding virulence to investigate its effect on rate of convergence (Figure 6(a)). This had a marked effect on how quickly the mean log10 SPVL reached equilibrium (4.52 log10 SPVL). When the founding genotype had mean 4.5 log10 SPVL, equilibrium with regard to the mean was reached very quickly, and the more different the SPVL of the founding genotype, the longer the time to convergence. A similarly rapid convergence is seen if all genotypes had equal prevalence at the start of the run. The mean underwent little change (data not shown) but the variance rapidly decreased as the most successful genotype, already present in the population, began to dominate (Figure 6(b)).
Finally, we investigated the sensitivity of our findings to the choice of parameter values determining the dependencies of infectiousness and duration of asymptomatic infection on SPVL. These parameters were previously estimated from datasets from Amsterdam and Zambia [19]. Here, we tested the sensitivity to those estimates by bootstrapping these datasets, refitting the parameters each time and calculating the corresponding maximum likelihood estimates of σE and σM. Details of the method are in Text S1 and Table S2. The resulting maximum likelihood estimates (Table S3 and Table S4) are similar to those from the principal analysis (Figure 1).
In this paper, we developed a multi-strain evolutionary epidemiological model of HIV-1 virulence, and showed that it could accurately reproduce observations on the distribution of viral load and its heritability in transmitting couples (Figure 2). We were able to estimate the proportion of variance in set-point viral load explained by viral genetic factors (26%, 1−(σE2+σM2)/σP2), and separately how much these factors change (‘mutate’) from one infection to the next. Our best estimate is that virulence changes slowly towards an evolutionary optimum over decades, but we cannot rule out faster changes (Figure 4 and Figure 6).
Our aim here was to develop a simple, parsimonious ‘broad-brush’ model to understand the principles of HIV-1 virulence evolution in a generalised epidemic using data currently available. Most of the parameters were derived from Sub-Saharan African studies (Table S1), suggesting that the model has most direct relevance for this context. This is our intention, as this is where most of the adaptation of HIV-1 to the human population has occurred. The parameters determining the curve of survival from disease progression were derived from European data, and since these data predate antiretroviral therapy they are not expected to differ substantially from parameters derived from Sub-Saharan Africa.
We do not expect the epidemic in other contexts to differ drastically. Two studies which have observed a change in virulence in the Netherlands [20] and Italy [21] appear to support our hypothesis as the virulence in both situations has risen from a sub-optimal level towards equilibrium, as predicted in our model. The same trend was not seen in Switzerland [22], however, and further work is required to apply the model rigorously to the European context with a view to explaining these trends. More realistic predictions will require more detailed models, and by necessity more data. We list some factors that could be included in a more detailed analysis.
Describing the differences between subtypes of HIV-1 seems like one of the biggest challenges to the model presented here. We considered virulence evolution on a single dimension of low-to-high, with single functions describing the relationship between viral load, infectiousness and duration of asymptomatic infection. HIV-1 subtypes in fact differ in their transmission parameters independently of their differences in SPVL [4], [7], [8]. Subtype A shows a slower disease progression when compared to other subtypes [34]. More specifically, data from the Rakai study showed that subtype A infection results in slower disease progression than subtype D even though the distribution of SPVL is the same [4], [7]. From the same cohort it was shown that subtype A is also more transmissible than subtype D even when viral load and other confounding variables are controlled for in a regression [35]. Subtype A is therefore fitter than D in both duration and transmissibility, and the evolutionary hypothesis would predict the gradual replacement of subtype D by subtype A, which has been observed in Uganda [36] and Greece [37]. Other noteworthy trends include the dominance of subtype C in southern Africa [38], which may be a result of an extended period of high viraemia in primary infection [39]. Taken together, these findings strongly suggest that HIV-1 virulence can change in ways not fully reflected by set-point viral load, and thus that more data are needed to identify other appropriate surrogate measures (or determinants) of virulence. More generally, the theoretical challenge is then to explain in terms of these other determinants of infectiousness and survival, how differences in virulence are maintained in different viral subtypes.
There are a number of other directions in which our model could be developed. In this study the mutational variance, the extent to which the viral genotype changed from one infection to the next, was considered independent of the age of infection (AOI). At first, this may seem a paradoxical choice, since mutation which occurs between hosts must be the result of mutations and selection occurring within the infected host. It would reasonable to suggest that the size of between-host mutation is positively related to the AOI, since nucleotide divergence from the founding strain has been shown to occur at a constant rate during infection [40]. If this were the case, the between-host mutation rate would be the same regardless of the generation time and consequently of the virulence of the virus. However, a study of within-host evolution over time found that the rate of divergence from the founding genotype was positively correlated with viral load [41], suggesting that higher virulence infections diverge more rapidly. A model with a mutational variance independent of the AOI allows for this, as a higher virulence virus will have more generations in a given amount of time and therefore more between-host mutation events.
An accurate functional representation of mutational variance as a function of AOI thus requires more detailed understanding than seems currently possible. To resolve this, and for the sake of parsimony, we assume that the two effects described above cancel each other out, and thus that the mutational variance is independent of AOI. To test the sensitivity to this assumption, we changed the model to include AOI-dependent mutational variance (linearly increasing as a function of time), and the results were qualitatively and quantitatively similar (data not shown).
An additional problem with this model is that the data to which the model is fitted consists of transmission pairs, for most of whom the age of infection at which transmission occurs is unknown. Assuming an AOI-independent mutational variance considerably reduces the complexity of the analysis. There is however little doubt that extending the model to include a more detailed description of within-host processes and also resolving the effects of conflicting selection at the within and between host levels will be enlightening.
The pattern of mutation was modelled as a log-normal distribution. It may be reasonable to assume that the distribution is negatively skewed because deleterious mutations are much more frequent than beneficial ones, for example in the case of protease gene [42]. However, it is misleading to compare the between-host mutation process to the mutation of individual viral genomes because deleterious mutations may be counterbalanced by within-host selection for viable viruses and there is no evidence for asymmetry in the net effect.
The host effect in this study was also modelled by a log-normal distribution which is justified if there are a large number of host effects and they are assumed to each have a multiplicative effect on SPVL. Host effects are known to account for a certain quantity of SPVL variation [2], [29]–[31], [43] and a very low estimate of the environmental variance would not be consistent with these studies. The maximum likelihood estimate of σE was encouragingly high (σE = 0.66, Figure 1), contributing 71% of the total variance in SPVL. As more is understood about how the host contributes to variation in SPVL, this source of variance may be further decomposed [31].
The epidemiological component of this model could be made more realistic. The model could for example be structured by age, sex, location, sexual activity, HLA type and include stochastic effects. It is not clear to us what effect on virulence these heterogeneities will have, but they might help for example explain the persistence of diversity between subtypes and help provide reasonable initial conditions, since a stochastic model could elucidate which viruses are more likely to have started the epidemic. The analysis could be further developed by relaxing the assumption that the SPVL is at an evolutionary optimal equilibrium, though we note that this assumption provides good agreement with data (Figure 2). We note that the mean log10 SPVL and its heritability do not change substantially in the later stages of the epidemic (Figure 4a–c), and the mean log10 SPVL of the Ugandan data (4.51) is close to the predicted equilibrium value (4.52), suggesting that even if the observed data do not represent an equilibrium, they represent something close enough to render the maximum likelihood parameter estimations reasonable.
Despite being simple and parsimonious rather than detailed, our model provides a general framework that makes use of the most recent data on the heritability of set-point viral load, and that can be used to interpret past and predict future trends in SPVL.
One interesting trend is that the mean log10 SPVL can be observed to increase above the equilibrium value for a short while during the early stages of the epidemic. Epidemic growth is expected to favour a higher virulence than at equilibrium as a result of the cumulative advantage of rapid transmission when hosts are abundant [19], [44]. This is better demonstrated in Figure 5(c) which shows the evolution of the mean log10 SPVL from a founding virulence very close to the equilibrium mean. At this level of resolution the temporary spike in virulence can be seen, and this corresponds to the period of epidemic growth. As the number of susceptible individuals grows and the epidemic begins to slow, the virulence decreases in response towards equilibrium as longer-lived genotypes are favoured.
This suggests that if SPVL can evolve at the between-host level then a growing epidemic could select for higher virulence viruses. Bolker et al. [44] model this phenomenon and suggest that the peak of this transient virulence is likely to occur late within the first exponential growth phase of the epidemic, so if this were observable the virulence is likely still to be in this transient state above the equilibrium. Whether this phenomenon has contributed to the recent increase in virulence in Italy and the Netherlands [20], [21] cannot be distinguished from an increase in virulence as a result of the founder having sub-optimal virulence. A future slight decrease in virulence as an epidemic saturates would provide evidence for this hypothesis, if it could be identified [44]. The optimum virulence could also be shifted by a widespread intervention which affects the nature of transmission such as circumcision, vaccination, or antiretroviral therapy. In the current study we introduced a model which may be used to predict such effects on virulence.
Recently published studies reporting the development of a reasonably effective vaccine [45] and a protective vaginal gel [46] are promising in the fight against HIV transmission. Hypothetically, a vaccine may offer more protection against lower virulence genotypes and select for more virulent ones, or vice versa. Gandon et al. [47] produced simple models which suggested that vaccines which target infection or transmission should have a negligible or negative effect on virulence as reducing the rate of transmission benefits pathogens which keep their host alive longer. However they also modelled vaccines which reduce the growth or the toxicity of the pathogen and suggest that this would select for pathogens which have higher virulence which would have a negative effect when unvaccinated individuals were infected.
Antiretroviral therapy during asymptomatic infection reduces transmission rate [48], [49], presumably by reducing viral load [50], [51]. Antiretroviral therapy would therefore modify the relationship between SPVL, transmission and duration of asymptomatic infection, and it is possible to construct hypothetical scenarios that could select for either increased or decreased SPVL. In summary, our model could be used to predict (in general terms) the effects different interventions would have on virulence. These changes are expected to be relatively modest compared to gains obtained by curtailing transmission, but nonetheless some consideration should be given to the possibility of increased virulence and whether it could be mitigated.
Our results support the hypothesis that the distribution of SPVL, and by implication of HIV-1 virulence, can plausibly be explained by selection for increased transmission in populations, though differences between viral subtypes needs to be elucidated in future work. Our method disaggregates the effects of viral factors acting to determine SPVL, the effect of mutation (and thus indirectly within-host evolution), and other environmental and host factors. The best estimates indicate a relatively high proportion of SPVL explained by viral factors (26%), as well as a modest rate of evolution of putative viral virulence factors. Reconciling these findings with data on within-host viral evolution may yet shed further light on the role of viral factors in HIV-1 pathogenesis.
In order to simplify simulations, we modelled a discrete finite set of viral strains (‘genotype’), each capable of producing a finite range of possible SPVL (‘phenotype’).
Each infected host in the model carries a viral genotype, i, and has a phenotype, j. Hosts were not explicitly described in the model, rather the model specified the dynamics of relative prevalences of hosts infected with a virus of genotype i and phenotype j. In other words, we used a compartmental multi-strain epidemic model.
Each genotype is defined by a predisposition to give rise to higher or lower SPVL. Following the decomposition given by equation (1.1), viral loads can be given as:(2.1)where ej is the environmental component (with mean zero and variance σE2) and μi is the component attributed to viral factors. For a population of individuals infected with viral genotype i, the mean log10 SPVL will be given by μi, which is therefore a natural measure of the virulence of genotype i. For two viral genotypes i and k such that i is more virulent than k, i.e. μi>μk, not all individuals infected with genotype i will have higher SPVL than individuals infected with genotype k, but on average they will.
The means log10 SPVL for the viral genotypes, μi, are in the range 2.0–7.0, and SPVL phenotypes, Vj, are in the range 0.0–9.0, discretised with step 0.05 and 0.025 respectively. An individual carrying genotype i, will have a phenotype j with a probability denoted by fij which is taken from a normal distribution with mean μi and variance σE2 (2.2), normalised to sum to one for each genotype i.(2.2)
The prevalence of infections with viral genotype i, SPVL phenotype j, and age of infection a is represented by Yij,a(t) at time point t. The age of infection is the time since the individual was infected. During the course of an infection each host passes through three stages, primary, asymptomatic and disease (AIDS) (P, A and D) as the age of infection a increases.
Primary and disease stages have equal duration (DP and DD) and rate of transmission (βP and βD), regardless of SPVL. Duration of and rate of transmission during asymptomatic infection are dependent on SPVL and the relationships were modelled as Hill functions as fitted in Fraser et al. [19], from which the parameter values relating to these functions were also taken (Table S1). The mean duration of the asymptomatic stage of infection for a given SPVL j is given by:(2.3)The progression from asymptomatic to disease stage is governed by a survival function in Text S1 equation (5.1), in which SPj,a is the probability of an individual with SPVL Vj remaining AIDS free at age of infection a. This is illustrated in Figure S1.
The unadjusted rate of transmission during this stage is given by:(2.4)Rates of transmission are adjusted for duration and partner change rate, c, in order to apply to a serial monogamy model (5.2).
The rate of transmission, βj,a, is given in equation (5.3) which incorporates the different stages of infection and the curve for survival during asymptomatic infection. The force of infection for genotype i at time t, is calculated in equation (2.5) where Δt is the size of the time-step.(2.5)
Between generations a between-host mutation step occurs, so the force of infection for genotype k seeds a distribution of genotypes. The probability mik of an infection with genotype mean μk mutating so as to seed a new infection with genotype mean μi is taken from a normal distribution with mean μk and variance σM2 (2.6), normalised to sum to one for each genotype k.(2.6)Note that this is not mutation in the genetic sense, but rather a measure of the change in the distribution of viral genotypes that occurs over the course of infection within the host.
This model for the change that occurs from one infection to the next, defined by equation (2.6), represents the simplest possible model of the effect of within-host evolution on the distribution of transmitted viruses. More complex models, with directional and host-dependent selection, could feasibly be encoded in more complex mutational matrices.
The total number of infections for a given genotype in the next time step, t+Δt, is calculated by the sum of the elementwise product of each FOIk and the probability that it will mutate into genotype i, mik. This is scaled according to X(t), the proportion of susceptibles in the population at time t, meaning that the genotypes are competing for the available pool of susceptibles. To give the prevalence for each genotype and its SPVL category in the next set of new infections (where a = 0), this value is multiplied by the probability of genotype i producing SPVL category j, fij.(2.7)
The prevalent infections are updated as in equation (2.8). The term SPj,a is the function of survival from progression to AIDS, given in equation (5.1). Since AIDS is a stage of determined length, DD, the function of survival from death at age of infection a is given by , the probability of surviving progression to AIDS at a time DD years previously.(2.8)
The terms Xout(t) and Xin(t) refer to new infections and deaths, respectively.(2.9)(2.10)These are used to update the susceptible pool, with new infections being removed and individuals who die of AIDS being replaced in the population.(2.11)
The basic reproductive rate, R0, can be calculated for each genotype, and this can be used to calculate the genotype distribution at equilibrium using the next-generation formalism. The R0 of each genotype is calculated in two steps. Firstly the transmission potential is calculated for an infection with SPVL category j by multiplying the rate of transmission in each of the three stages of infection by the length of that stage. The duration of asymptomatic infection DA(Vj) is the mean of the survival curve.(3.1)Secondly, the basic reproductive rate, R0i, for each genotype i, is then calculated by taking the weighted average transmission potential, TPj, weighted by the probability that infection with genotype i results in infection with SPVL category j.(3.2)
The R0 for each genotype k (3.2) and the probability that genotype k mutates into genotype i (2.6) can be used to calculate the next-generation matrix, K.(3.3)The distribution of genotypes at equilibrium is the eigenvector ε corresponding to the dominant eigenvalue, λ, of K.(3.4)The prevalence of SPVL category j, pj, at equilibrium in the population is then calculated as follows.(3.5)This value can then be directly compared with the observed distribution of SPVL.
The likelihood of each run of the model is calculated by comparison with data from a previous study reporting the SPVL of phylogenetically confirmed transmission pairs [6] selected from a cohort in Rakai, Uganda [52], [53]. The likelihood is given by the probability of observing the index SPVL, Vd, and the recipient's SPVL, Vr. This is calculated using conditional probabilities and is given as follows. The mean log10 SPVL of the genotypes infecting the recipient and index case are given by μx and μy. As these are unknown, all possible combinations of genotypes are considered.(3.6)in which C is a constant:(3.7)and the following have been previously defined in equations (2.2), (2.6) and (3.4):(3.8)(3.9)(3.10)The total log likelihood is calculated for each couple c in which the direction of transmission is known, and for each couple u where the direction is unknown the log likelihood is worked out for each direction and the mean is taken (in this case, Vm and Vf refer to SPVL of males and females, respectively).(3.11)
Heritability is the proportion of total variation which is determined by genetic variation in the viral population. It was measured previously by calculating the proportion of the total variance which was explained by carrying genetically similar virus [6]. This can be measured for the modelled distribution in a similar fashion. The non-heritable component is the variance in SPVL in individuals infected by an index partner with a particular SPVL, as a proportion of total variance. This is weighted according to each possible SPVL of the index.(3.12)(3.13)
The likelihood was estimated by calculating the total likelihood, ℓtotal, for each combination of values of σE (range 0–1.2, step 0.005) and σM (range 0–1.0, step 0.005). Outside of these ranges the likelihood of observing the data is very low, as the variance of the equilibrium distribution becomes vastly higher than is observed. These values were used instead of their squares, σE2 and σM2, because they are on the same scale as log10 SPVL and are therefore directly related to the size of the host effect and of between-host mutation. Furthermore, using σE and σM gives greater resolution at lower values in the range of interest.
The values of Y0 and μî were not included in this analysis as they are not relevant to the equilibrium distribution since they serve only as starting points in the model. All other parameter values were taken from the literature (Table S1).
The maximum likelihood combination of these two parameters was estimated and the 95% confidence bounds were identified using a likelihood ratio test (5.4).
The next-generation formalism solution described above is sufficient for analysing the equilibrium distribution of SPVL as the end results are identical. However, the model must be run in full to determine the rate at which SPVL evolves in real time.
To run the model in continuous time, the infection is initialised at time t = 0 for the starting genotype î with mean μî and a proportion Y0 of the population are infected. The total number of infected individuals at the start of the epidemic all enter genotype category î, and are divided up between all the SPVL categories according to fîj.(4.1)All other genotype categories begin at zero, (4.2), as do all ages of infection greater than zero (4.3).(4.2)(4.3)The model was run for 500 years in discrete time-steps corresponding to one month for each set of the parameter values.
Parameter values, listed in Table S1, were taken from the literature [19], [54], [55]. Analyses were conducted using C++, MATLAB and R [56]–[58], the latter of which was also used to produce the figures [59].
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10.1371/journal.pgen.1008121 | Chaperonin TRiC/CCT supports mitotic exit and entry into endocycle in Drosophila | Endocycle is a commonly observed cell cycle variant through which cells undergo repeated rounds of genome DNA replication without mitosis. Endocycling cells arise from mitotic cells through a switch of the cell cycle mode, called the mitotic-to-endocycle switch (MES), to initiate cell growth and terminal differentiation. However, the underlying regulatory mechanisms of MES remain unclear. Here we used the Drosophila steroidogenic organ, called the prothoracic gland (PG), to study regulatory mechanisms of MES, which is critical for the PG to upregulate biosynthesis of the steroid hormone ecdysone. We demonstrate that PG cells undergo MES through downregulation of mitotic cyclins, which is mediated by Fizzy-related (Fzr). Moreover, we performed a RNAi screen to further elucidate the regulatory mechanisms of MES, and identified the evolutionarily conserved chaperonin TCP-1 ring complex (TRiC) as a novel regulator of MES. Knockdown of TRiC subunits in the PG caused a prolonged mitotic period, probably due to impaired nuclear translocation of Fzr, which also caused loss of ecdysteroidogenic activity. These results indicate that TRiC supports proper MES and endocycle progression by regulating Fzr folding. We propose that TRiC-mediated protein quality control is a conserved mechanism supporting MES and endocycling, as well as subsequent terminal differentiation.
| Endocycle, a cell cycle variant consisting of DNA replication and Gap phases without a mitotic phase, is widespread in nature. Endocycling cells arise from proliferating cells through a switch of the cell cycle mode called mitotic-to-endocycle switching (MES). While the molecular mechanisms regulating progression of the mitotic cell cycle and endocycle have been well studied, the regulatory mechanism of MES and its impact on cell differentiation processes remains poorly understood. Here we used the Drosophila steroidogenic organ to uncover the regulatory factors of MES, and our genetic analyses identified the evolutionarily conserved chaperonin TRiC as a novel regulator of MES and subsequent steroidogenesis. Given that TRiC supports proper folding of numerous proteins including cell cycle regulators, TRiC-mediated protein quality control will be a fundamental mechanism supporting a switch of the cell cycle mode that promotes terminal differentiation.
| A tightly controlled cell cycle is a fundamental system for survival in every organism. The best-known cell cycle mode is mitotic cell cycle, which is achieved through a sequence of distinct phases including genome DNA synthesis (S), mitotic (M), and intervening Gap (G) phases. Moreover, endocycle, a cell cycle variant without M phase, is commonly observed in protozoa, plants, and animals [1, 2]. Endocycling cells undergo repeated rounds of S and G phases without a M phase, which gives rise to polyploidy of genome DNA. Endocycle and polyploidy are closely associated with cell growth; in some cell types, progression of endocycling is required for their terminal differentiation [2–4]. Moreover, polyploid genomic DNA has been observed in approximately 37% of all human tumors [5], and several lines of evidence point to the importance of endocycle in tumor development and survival [6–8]. Thus, elucidation of the underlying mechanisms regulating initiation and progression of endocycle is a key step to understanding the role of endocycle in normal and pathological cellular processes.
The important question in endocycle regulation is how the transition from cell division to endocycle is achieved. Endocycling cells arise from diploid cells through a switch of the cell cycle mode, called the mitotic-to-endocycle switch (MES) [1]. At the molecular level, MES is accomplished by downregulation of mitotic cyclin-dependent kinases (M-Cdks), which leads to the M phase being bypassed. M-Cdk is suppressed through degradation of its binding partners, called mitotic cyclins, including Cyclin A (CycA) and B (CycB). Mitotic cyclins are recognized by Fizzy-related [Fzr, a.k.a. CDH1 (CDC20 Homologue 1)], an activator of a multi-subunit ubiquitin ligase anaphase promoting complex/cyclosome (APC/C), to be polyubiquitinated, and then degraded through the ubiquitin-proteasome pathway [1, 9]. Fzr triggers MES to support the progression of numerous biological events including morphogenesis, growth, and tissue repair in insects, plants, and mammals [1, 10–18]. Fzr-mediated degradation of mitotic cyclins is commonly observed during MES [1, 11, 16, 18], whereas identified upstream regulators of Fzr are diverse among species [1]. It therefore remains unclear whether there are evolutionary conserved regulatory mechanisms of Fzr expression. In addition, it is largely unknown how mitotic exit and endocycle progression are cooperatively regulated.
In this study, we focused on the Drosophila prothoracic gland (PG), an endocrine organ composed of polyploid endocycling cells, for dissecting the molecular mechanisms supporting MES and the progression of endocycle. The PG produces ecdysone, the primary insect steroid hormone that triggers initiation and progression of metamorphosis [19, 20]. In Drosophila and other higher Diptera, the PG is part of the ring gland (RG), an endocrine organ complex that comprises the PG, corpus cardiacum (CC), and corpus allatum (CA) [19]. The PG expresses a set of ecdysone biosynthetic genes including Neverland (Nvd), Spookier (Spok), Shroud (Sro), Phantom (Phm), Disembodied (Dib), and Shadow (Sad), whose expression is upregulated before the initiation of metamorphosis to enhance the ecdysteroidogenic activity [21–23]. Ecdysone secreted from the PG is converted into its active form, 20-hydroxyecdysone (20E), in peripheral tissues [19]. In Drosophila, PG cells undergo repeated rounds of endocycling during the larval stage, and endocycle progression is essential for activating ecdysone biosynthesis in the PG [22]. After at least three rounds of endocycle (when the C value reaches 32), biosynthesis of ecdysone is initiated to induce the larval-to-pupal metamorphic transition [22]. When endocycle progression is impaired in the PG, ecdysone biosynthesis is not upregulated, and the larva cannot transit into the pupal stage [22]. Because this ‘larval arrest’ phenotype is readily recognized, PG-selective genetic analysis can be a potentially effective approach to screen novel regulators of MES and endocycle progression. Its relatively large cell size and low cell number (its width is about 200 μm at the late larval stage and the cell number is around 50) also make the PG an attractive model for investigating MES and endocycle progression.
Here we show that PG cells undergo MES through downregulation of mitotic cyclins mediated by Fzr. Fzr protein expression is increased during the MES period in the PG, and knockdown of fzr in the PG causes a block of MES and subsequent ecdysone biosynthesis owing to upregulation of mitotic cyclins. Furthermore, we performed a PG-selective RNAi screen to further elucidate the regulatory mechanism of MES, and identified TCP-1 ring complex (TRiC), a molecular chaperonin complex, as a novel MES regulator. TRiC-deficient PG cells showed a prolonged mitotic period, probably due to impaired nuclear translocation of Fzr, which also caused loss of ecdysteroidogenic activity. Our genetic study provides an important basis for understanding the regulatory mechanisms of endocycle initiation and progression.
In wild type strain Oregon R, the PG increases its cell number during the 1st instar larval stage [L1, from 0 to 24 hours after hatching (hAH)] [22]. During the 2nd (L2, from 24 to 48 hAH) and the 3rd (L3, from 48 to 96 hAH) instar larval stages, in contrast, the PG cell number does not increase while the DNA content continues to increase and the C value reaches 32–64C by the late L3 stage, 84–96 hAH (Fig 1A) [22]. This suggests that PG cells undergo cell division during L1, execute MES at around 24 hAH, and undergo 3–4 rounds of endocycle during the L2 and L3 stages (Fig 1A). First we determined that the mean C value in the PG of Oregon R at 84 hAH was 58 (Fig 1B and 1C). In addition, the C values in two transgenic control lines at 96 hAH were 53 and 54, respectively (S1A and S1B Fig). These results indicate that PG cells undergo approximately four rounds of endocycling. Considering that these C values are less than 64, which is achieved by four complete rounds of endocycling, it is suggested that replication of genomic DNA in PG cells is incomplete, which is known as under-replication [1]. We next tested whether DNA replication was suppressed in the heterochromatic region, in which under-replication is commonly observed [1]. 5-Bromodeoxyuridine (BrdU), an analog of thymidine used as a marker of replication activity, was incorporated into the DNA dense-core heterochromatic region of PG cells at 84 hAH, but its frequency was less than the early and middle S-phases (S1C and S1D Fig). This suggests that the genomic DNA of PG cells exhibits under-replication but the replication in the under-replicated region is not severely impaired.
Next, we investigated the cell number, the DNA content, and expression of the mitotic marker pH3 (histone H3 phosphorylated at serine 10) in the PG of Oregon R during larval development. The PG cell number was increased during 12 to 18 hAH (Fig 1D and 1F), suggesting that mitotic cell cycle in the PG is active approximately during 12 to 18 hAH. In contrast, no significant increase in the PG cell number was observed at 24 hAH and thereafter (Fig 1D and 1F), and the C value started to increase at 18 hAH (Fig 1D and 1G). These results suggest that some, but not all, PG cells start MES from 18 hAH. In contrast, pH3-positive PG cells were detectable at 18 hAH (Fig 1D and 1H), but we could not detect a statistically significant increase in the percentage of pH3-positive PG cells at 18 hAH (Fig 1H). We also failed to detect pH3 expression in the PG at 12 hAH (Fig 1D and 1H), probably reflecting transient pH3 expression owing to rapid mitotic cycles. To clarify cell cycle phase in PG cells during 0 to 24 hAH, we used Fly Fluorescent Ubiquitin-based Cell Cycle Indicator (Fly-FUCCI) driven by PG-selective phantom-22-Gal4 (phm-Gal4) [24, 25]. The Fly-FUCCI components GFP-fused E2F11-230 (GFP.E2F1) and mRFP1-fused CycB1-266 (mRFP1.CycB) are expressed in patterns consistent with the presence of G1, S, and G2/M cells [24] (S1E and S1F Fig). The percentage of mRFP.CycB-positive/GFP.E2F1-negative cells (= S phase) reached the maximum level at 6 hAH (S1G and S1H Fig), indicating that S-phase of mitotic cell cycle is active at around 6 hAH. In contrast, the percentage of both mRFP.CycB and GFP.E2F1-positive cells (= G2/M phases) was increased at 12 and 18 hAH (S1G and S1H Fig), confirming that mitosis is active between 12 and 18 hAH. Actually, a small number of mRFP.CycB and GFP.E2F1-positive cells showed a metaphase-like nuclear shape at 12 and 18 hAH (arrows in S1G Fig). Furthermore, mRFP.CycB-positive PG cells were dramatically reduced at 24 hAH, and the majority of PG cells was mRFP.CycB-negative/GFP.E2F1-positive at 24 hAH (S1G and S1H Fig), indicating that mitotic cell cycle is downregulated by 24 hAH. Taken together, these observations indicate that mitotic cell cycle is active during a narrow time window, approximately between 12 and 18 hAH, and that PG cells execute MES by 24 hAH. Moreover, Fzr expression, which was visualized by GFP-fused Fzr (Fzr-GFP), was upregulated at 18 hAH and reached the maximum level at 24 hAH (Fig 1E and 1I), supporting the idea that Fzr-mediated MES in the PG starts at around 18 hAH and that PG cells execute MES by 24 hAH. In addition, Fzr-GFP was detectable at lower levels during later stages (Fig 1E and 1I), suggesting that Fzr blocks mitosis continuously during endocycling in the PG.
In contrast to the PG, pH3 expression was not observed in the CA (Fig 1D; CA cells are indicated by the arrowheads), another endocrine organ in the RG complex. This suggests that CA cells do not undergo cell division during the larval stage. However, the mean C value in CA cells reached around 8 at 84 hAH (Fig 1B and 1C), indicating that CA cells undergo a round of endocycle. Actually the DNA content in CA cells seems to be increased during 24 to 84 hAH (arrowheads in Fig 1D), suggesting that CA cells undergo endocycle during the L2 and L3 stages. To test this possibility, expression of S-phase activator Cyclin E (CycE) and incorporation of BrdU were observed in CA cells labeled by red fluorescent protein (RFP) expressed under the control of jhamt-Gal4. CycE-positive CA cells were detected at 48, 72, and 96 hAH (S2A and S2B Fig), and BrdU was incorporated into CA cells in these stages (S2C and S2D Fig), indicating that CA cells undergo endocycle during the L2/L3 stages. Because the percentage of BrdU-positive CA cells was reduced at 96 hAH (S2C and S2D Fig), CA cells are likely to terminate endocycle by the end of the larval stage.
To investigate whether Fzr regulates MES in the PG, PG-selective phm-Gal4 was used to overexpress RNAi construct against fzr in the PG. The nuclei of PG cells were labeled by mCherry carrying the nuclear localization signal (mCherry.nls), and RNAi efficiency was enhanced by overexpression of dicer2. In accordance with the result of ploidy measurement, the mean C value of the controls (phm>dicer2 mCherry.nls) was set to 54 (see S1A and S1B Fig). In the control animals, the PG cell number was slightly increased only during 0 to 12 hAH (Fig 2A and 2B), whereas the C value in the PG started to increase from 24 hAH and continuously increased until 96 hAH (Fig 2A and 2C). In contrast, the PG of fzr RNAi animals (phm>dicer2 mCherry.nls fzr-RNAi) continuously increased its cell number while its C value was around 4C during larval development (Fig 2A–2C). Consistent with these observations, pH3 was continuously detected in the PG of the fzr RNAi animals throughout larval development (Fig 2D and 2E). In addition, we confirmed efficient knockdown of Fzr, visualized by Fzr-GFP, along with upregulation of pH3 in the PG of fzr RNAi at 24, 48, 72, and 96 hAH (S3 Fig). Furthermore, hypomorphic fzrG0418 mutant clone PG cells, introduced by FLP/FRT recombination induced by heat-shock just after hatching (Fig 2F and 2G), showed a reduced DNA level and, in some mutant clones, metaphase-like DNA distribution (Fig 2H). Taken together, these results indicate that fzr is required for MES in the PG. Considering that mitotic cell cycle was not arrested in fzr RNAi, we suggest that fzr is unnecessary for progression of mitotic cell cycle, which has been described in a previous study [11].
We also confirmed that, as shown in a previous study [22], knockdown of fzr in the PG caused L3 arrest (S4A–S4C Fig), reduction in ecdysteroidogenic gene expression (S4D Fig), and a low ecdysteroid level (S4E Fig). Furthermore, 20E administration to fzr RNAi animals rescued their defects in pupariation (S4F and S4G Fig). These results indicate that fzr-mediated MES in the PG is required for activation of ecdysone biosynthesis and pupariation. However, fzr RNAi larvae did not show any defects in the L1-L2 and L2-L3 molting (S4A and S4B Fig), which are also triggered by ecdysone. This confirms that Fzr-mediated MES in the PG is required for pupariation but not for the L1-L2 and L2-L3 molting.
Next, we investigated whether Fzr triggers MES through downregulation of mitotic cyclins in the PG. In the controls (phm>dicer2 mCD8::GFP), neither CycA nor B expression was observed in the PG at post-MES stages, including 24, 48, 72, and 96 hAH (Fig 3A–3D, S5 Fig), whereas CycA was upregulated in the PG of fzr RNAi animals (phm>dicer2 mCD8::GFP fzr-RNAi) at the same stages (Fig 3A–3D, S5 Fig). CycB was also detectable in the PG of fzr RNAi at 48, 72, and 96 hAH (Fig 3A–3D, S5 Fig). These results suggest that fzr-deficient PG cells cannot undergo MES, and ecdysone biosynthesis is inhibited owing to highly expressed mitotic cyclins. To test this possibility, we examined whether knockdown of cycA and B rescues impaired MES and ecdysteroidogenesis in fzr RNAi animals. In contrast to fzr RNAi animals (phm>dicer2 mCherry.nls fzr RNAi) showing a cell number increase and reduced DNA content in the PG (Fig 3E–3G), the C value reached 32–64 and the cell number was restored to around 50 at 96 hAH in the PG of both fzr RNAi + cycA RNAi (phm>dicer2 mCherry.nls fzr-RNAi cycA-RNAi) and fzr RNAi + cycB RNAi animals (phm>dicer2 mCherry.nls fzr-RNAi cycB-RNAi) (Fig 3E–3G). Consistent with this, the pH3-positive PG cell number was reduced in both fzr RNAi + cycA RNAi and fzr RNAi + cycB RNAi animals (Fig 3H). These observations indicate that fzr induces MES through inactivation of mitotic cyclins in the PG. Moreover, knockdown of cycA or B in the PG of fzr RNAi animals rescued the developmental arrest at L3 (Fig 3I and 3J) and restored the expression level of ecdysone biosynthetic genes and the ecdysteroid concentration (Fig 3K and 3L). Taken together, these results indicate that the Fzr-mediated reduction of the mitotic cyclin protein level induces MES and subsequent ecdysone biosynthesis in the PG.
To further dissect the regulatory mechanisms of MES, we performed a genetic screen using the Gal4/UAS system and RNAi. A previous study carried out a genome-wide PG-selective RNAi screen and identified 701 genes whose knockdown cause L3 or L1/L2/L3 arrest (630 and 71 genes, respectively) [26]. We therefore focused on these genes as novel MES regulator candidates and knocked them down in the PG to observe their potential effects on developmental transitions as well as the cell number and the DNA content in the PG (Fig 4A). In our screen, females carrying two copies of phm>mCherry.nls were crossed with UAS-RNAi males to drive a dsRNA or shRNA construct selectively in the PG of their offspring. In addition, RNAi lines not used in a previous genome-wide RNAi screen were used whenever available to exclude potential off-target effects (see S3 Table). Because knockdown of target of rapamycin (tor) and β3-octopamine receptor (Octβ3R) in the PG causes an L3 arrest phenotype [22, 27], these two genes were used as positive controls (Fig 4A). In this screen, we used standard cornmeal/yeast Drosophila culture medium, whereas all other experiments in this study were performed using nutrient-rich German Food (GF).
In the first step analyzing the developmental phenotype (indicated as ‘Step 1’ in Fig 4A), a larval arrest phenotype was confirmed in 442 genes; more specifically, L1/L2, L1/L2/L3 and L3 arrest were observed in 77 (11%), 18 (3%), and 347 genes (49%), respectively (Fig 4B). Other phenotypes, including delayed pupariation (7 genes) and embryonic lethality (1 gene), were observed in 8 genes (1%). In contrast, 253 genes (36%) showed no obvious phenotype (NOP; Fig 4B), which were excluded from further analysis. Next, we observed PG cells of 449 RNAi lines that showed either larval arrest or delayed pupariation (indicated as ‘Step 2’ in Fig 4A). The schematic diagram in Fig 4C shows the developmental change of the cell number and the DNA content in PG cells. Normal PG cells undergo mitotic cell cycle during the early larval stage (i.e. L1), then undergo MES and several rounds of endocycling to increase the DNA content during the L2 and L3 stages (Fig 4C). By contrast, as in the case of fzr RNAi, animals with MES-deficient PG are expected to be arrested at the larval stage with the PG showing an increased cell number and reduced DNA content (Fig 4C). In addition, it is expected that a defect in endocycle progression results in a reduced DNA content, whereas blocking the downstream pathway of endocycle does not cause a severe reduction in the DNA content (Fig 4C). Based on these criteria, the cell number and the DNA content were examined in the PG of each RNAi line using histochemistry at day 6 after crossing (Fig 4A). Fig 4D shows the mean value of the DNA intensity and the cell number in the PG of 449 RNAi lines, as well as the controls (phm>mCherry.nls) (green plot in Fig 4D, indicated as “Control” in the legend), raised on standard Drosophila medium. We also observed the PG of control animals cultured in nutrient-rich GF at 0, 24, 48, 72, and 96 hAH as a references (black plots in Fig 4D, indicated as “Control cultured on GF” in the legend). Of the 449 genes tested in Step 2, knockdown of 210 genes caused morphological or physiological defects in PG cells, including an abnormal distribution of DNA and nucleolus, apoptotic nuclear condensation, and a nontransparent cytoplasm (S3 Table, S6 Fig). The remaining 239 genes were statistically analyzed to reveal which RNAi animals showed a significant increase in the PG cell number compared with the controls, indicated by green plot in Fig 4D. With this analysis, we identified 31 genes whose knockdown caused a significant cell number increase in the PG (called “MES-related genes” hereafter) (magenta and purple plots in Fig 4D; summarized in S4 Table). To reveal biological processes important for MES, Gene ontology (GO)-term enrichment analysis was performed for MES-related genes, and we found that chaperonin containing tcp1 (cct) genes were significantly enriched in the MES-related gene group. CCT proteins are subunits of the evolutionary conserved molecular chaperon complex, TRiC, which supports proper folding of cytoskeletal proteins and cell cycle regulators [28, 29]. Generally, TRiC is a hetero-oligomeric double-ring complex with eight subunits (CCT1–8) per ring [30]. Six cct subunit genes (cct1, 2, 4, 5, 6, and 8) were included in 31 MES-related genes, and knockdown of these genes caused not only an increased cell number but also a severe reduction in the DNA content (magenta plots in Fig 4D). Taken together, our RNAi screen raised the possibility that TRiC is a novel MES regulator in the PG.
To investigate the role of TRiC in the PG, each cct subunit gene was knocked down in the PG. In contrast to the controls (phm>mCherry.nls) whose cell number and C value in the PG were around 50 and 53, respectively (Fig 5A–5C, S1A and S1B Fig), the PG cell number of cct1–8 RNAi animals (phm>mCherry.nls cct-RNAi) reached 60–70, and their C value was around 8 at 96 hAH (Fig 5A–5C). These results indicate that cct subunit genes are required for proper MES. In addition, pH3 expression was detected in some, but not all, PGs of cct RNAi larvae (Fig 5D), but we could not observe statistically significant difference in pH3 expression between control and cct RNAi (Fig 5D). This suggests that cct genes are also required for proper progression of mitotic cell cycle. We next confirmed that development was mainly arrested at the L3 stage in cct RNAi animals (phm>cct-RNAi) (Fig 5E and 5F), that ecdysteroidogenic gene expression was significantly reduced in cct RNAi (Fig 5G), and that 20E administration restored larval-to-pupal transition in 20%–30% of animals (Fig 5H and 5I). The explanation for why only 20%–30% of cct RNAi was rescued is that the 20E concentration used in this rescue experiment may have been too high to trigger proper pupariation in cct RNAi animals. To test this possibility, we have administrated 20E against cct8 RNAi animals at a concentration of 0.5 mg/g (used mainly in this paper), 0.05 mg/g, 0.005 mg/g, 0.0005 mg/g, and 0 mg/g. As S7 Fig shows, approximately 30% of cct8 RNAi larvae fed on the medium with 0.5 and 0.05 mg/g of 20E undergo pupariation, but there was no significant difference in timing and the percentage of pupariation between these two groups. This suggests that lower efficiency of this rescue experiment is not explained by the concentration of 20E used. Considering that PG produces other humoral factors, including monoamines [27], one potential mechanism is that knockdown of ccts perturbs production of hormones other than ecdysone, which may cause developmental defects. Overall, these results indicate that TRiC is required for ecdysone biosynthesis in the PG to induce the larval-to-pupal transition.
Further observation of PG cells during larval development revealed that individual knockdown of cct4 and 8 caused a delay in both the onset of the DNA content increase and cessation of the cell number increase: In the controls (phm>mCherry.nls), the PG cell number reached around 45 at 24 hAH and its C value was continuously elevated after 24 hAH (Fig 6A–6C). By contrast, in cct4 and 8 RNAi animals (phm>mCherry.nls cct4/8-RNAi), the PG cell number reached around 60 by 48 hAH, and the C value of their PG cells did not increase after 48 hAH (Fig 6A–6C). Consistently, pH3-positive cells were detected in the PGs of cct4 and 8 RNAi animals even after 24 hAH, although not statistically significant (Fig 6D and 6E). These results indicate that mitotic cell cycle is prolonged (i.e., MES is delayed) in the PG of cct RNAi. Furthermore, we found that the rate of the DNA content increase was suppressed in the PGs of cct4 and 8 RNAi animals even after cessation of the cell number increase (Fig 6C). This result indicates that TRiC also regulates endocycle progression in the PG, as well as mitotic cell cycle and MES. To confirm this possibility, the cct4 mutant line named cct4KG09280, carrying a P-element insertion on the cct4 coding region that causes loss of cct4 mRNA expression and developmental arrest at the L1/L2 stages (S8 Fig), was used for FLP/FRT-based clonal analysis. FLP-out clones carrying a cct4KG09280 homozygous mutation in the PG showed decreased DNA content (Fig 6F), confirming the importance of TRiC in endocycle progression.
The above observations raised the question of how TRiC regulates MES and endocycling. To investigate whether TRiC controls MES via Fzr and mitotic cyclin regulation, expression of Fzr-GFP and mitotic cyclins was observed in the PGs of cct4 and 8 RNAi animals. In the controls (phm>mCherry.nls, fzr-GFP), Fzr-GFP was detected in both the cytoplasm and nuclei of PG cells (Fig 7A–7C). In contrast, localization of Fzr-GFP into the nuclei was suppressed in the PGs of cct4 and 8 RNAi larvae (phm>mCherry.nls cct4/8-RNAi, fzr-GFP) at 24 and 48 hAH (Fig 7A–7C), suggesting that TRiC regulates nuclear translocation of Fzr in the PG. Furthermore, probably due to the misregulation of Fzr nuclear translocation, CycA expression in the PG of cct4 RNAi (phm>mCD8::GFP cct4-RNAi) was significantly increased than those in the controls (phm>mCD8::GFP) at 24 hAH (Fig 7D and 7F). CycA was also detectable in the PG of cct8 RNAi (phm>mCD8::GFP cct8-RNAi) although this was not statistically significant ((Fig 7D and 7F). However, we could not observe enhanced CycB expression in cct4 and 8 RNAi (Fig 7E and 7G). These results suggest that TRiC-deficient PG cells cannot undergo proper MES owing to CycA upregulation. To test this possibility, we investigated whether knockdown of cycA restores MES in cct RNAi animals. As Figs 5 and 6 show, the PG cell number in cct4 and 8 RNAi larvae was around 60 at 96 hAH (S9A and S9B Fig), whereas the PG cell number was reduced to 40 when cycA RNAi was introduced into cct4/8 RNAi (phm>mCherry.nls cct4/8-RNAi cycA-RNAi: referred to hereafter as cct4/8 RNAi + cycA RNAi) (S9A and S9B Fig). In addition, pH3-positive cells were not detectable in the PGs of cct4/8 RNAi + cycA RNAi animals (S9A and S9D Fig). These observations indicate that TRiC-mediated downregulation of CycA is required for proper MES in the PG.
Moreover, knockdown of cycA in the PGs of cct4/8 RNAi animals resulted in a DNA content increase to 16C (S9A and S9C Fig), indicating that TRiC-mediated CycA downregulation promotes endocycle up to 16C. However, cycA knockdown was not sufficient to restore the third round of endocycle, from 16 to 32C (S9C Fig), which is required for upregulation of ecdysone biosynthesis in the PG. As a result, developmental arrest was not rescued in cct4/8 RNAi + cycA RNAi animals (without UAS-mCherry.nls: phm>cct4/8-RNAi cycA-RNAi) (S9E Fig). These results suggest that downregulation of CycA is not sufficient to rescue rounds of endocycle completely in the PG of cct RNAi.
Based on our findings, here we propose a working model of TRiC-mediated control of MES and endocycle progression: TRiC downregulates CycA by regulating Fzr nuclear translocation to promote MES and endocycling (Fig 8).
MES is an essential cellular process that changes cell states from proliferation to growth and initiates terminal differentiation in multicellular organisms. Here we used the Drosophila steroidogenic organ PG to study regulatory mechanisms of MES and found that PG cells undergo MES in a Fzr-dependent manner to activate ecdysteroid biosynthesis. Furthermore, our RNAi screen identified the evolutionary conserved chaperonin TRiC as a novel regulator of MES and endocycle progression. Further genetic analysis showed that TRiC downregulates CycA at least in part by regulating Fzr nuclear translocation to induce MES and subsequent endocycling. Based on these results, we propose that TRiC-mediated protein quality control is a fundamental mechanism supporting MES and subsequent endocycling that promotes terminal differentiation.
We investigated the role of TRiC in regulating Fzr and mitotic cyclin expression in the PG, and found that TRiC is required for nuclear translocation of Fzr (Fig 7). This result suggests that TRiC supports Fzr folding to facilitate its translocation into the nuclei. Furthermore, knockdown of cct subunit gene resulted in increased CycA expression (Fig 7), and knockdown of cycA along with cct subunit genes prevented PG cell number increase (S9 Fig), indicating that TRiC promotes CycA inactivation, which allows PG cells to undergo MES. Because nuclear translocation of Fzr was blocked in cct RNAi, increased CycA expression in the nuclei of cct RNAi is likely due to decreased Fzr translocation into the PG cell nuclei. However, in contrast to CycA, CycB expression was not disturbed in cct subunit RNAi (Fig 7). Thus, we speculate that TRiC is unnecessary for Fzr to recognize CycB.
Although the PG cell number was significantly increased in cct RNAi, we could not observe a statistically significant increase in the percentage of pH3-positive PG cells in cct RNAi (Figs 5 and 6). The explanation for why pH3 was not detected frequently is that cct is also required for proper progression of mitotic cell cycle in the PG. Indeed, the PG cell number was not continuously increased in cct RNAi (Fig 6). Actually, it has been reported that TRiC regulates the disassembly of mitotic checkpoint complex [31] and mitotic cell cycle events such as sister chromatid separation [32]. Further elucidation of the regulatory mechanism of TRiC-mediated mitotic cell cycle is an important step to understanding the role of TRiC in cell cycle control.
In addition to the importance of TRiC in MES, we revealed that TRiC also has a critical role in regulating endocycle progression: inhibition of cct subunit genes caused endocycle arrest at around 8C (Figs 5 and 6), and knockdown of cycA together with cct in the PG partially restored endocycle up to 16C (S9 Fig). These results indicate that TRiC-mediated CycA downregulation is also required for progression of endocycling in the PG, perhaps due to reduced nuclear translocation of Fzr. Indeed, it has been reported that CycA as well as Fzr controls endocycle progression in the Drosophila [33–35]. However, cycA knockdown was not enough to restore the third round of endocycle in the PG of cct RNAi animals (S9 Fig). This suggests that other downstream factor(s) of TRiC or Fzr promote the third endocycle independently of CycA downregulation. Because Fzr also regulates Geminin degradation, a DNA replication inhibitor, to promote endocycle progression [33], one possible mechanism is that Fzr facilitates entry into the S-phase through suppression of Geminin to execute proper progression of endocycle in the PG. Furthermore, given that TRiC supports numerous proteins’ folding, including tubulin and actin [28, 29], we propose that TRiC-mediated protein quality control is a fundamental mechanism supporting MES and subsequent endocycling, which leads to the terminal differentiation.
In this study, we used the PG as a model organ to study MES and endocycle regulatory mechanisms because of the organ’s simple structure and the correlation between endocycling and ecdysone biosynthesis. PG cells undergo MES at the end of L1 and carry out repeated rounds, at least three times, of endocycle during the L2 and L3, which is essential for activation of ecdysone biosynthesis (Figs 1–3, S1 and S4 Figs). In contrast to PG cells, CA cells do not seem to undergo mitotic cell cycle and perform only one round of endocycle during the larval stage (Fig 1 and S2 Fig). These two types of endocrine cells originate from homologous ectodermal cells, and homeobox (Hox) gene expression controls PG and CA specification during embryogenesis [36]. Because CA and PG cells originate from deformed (Dfd)- and sex comb-reduced (Scr)-positive ectoderms, respectively [36], one possible mechanism is that distinct downstream genetic programs induced by Dfd and Scr determine the timing of MES and the activity of endocycle in these cells.
Our PG-selective RNAi screening identified not only the cct subunit genes required for proper MES but also other MES-related genes (summarized in S3 Table). Considering that enhancement of fzr transcription is a common step to triggering MES in Drosophila and other organisms [1, 10–18], our results will provide a solid basis for further investigating regulatory mechanisms of how fzr expression is upregulated to initiate MES. In Drosophila ovarian follicle cells, for example, the Notch signaling pathway promotes fzr transcription during the MES period [10, 11]. However, because core components of Notch signaling were not included in our list of MES-related genes (S3 Table), the regulatory mechanisms of MES seem to be distinct between the PG and follicle cells. Thus, other signaling pathways are likely involved in transcriptional regulation of fzr in the PG. Furthermore, we identified a group of genes whose knockdown causes a significant decrease in the DNA content (Fig 4 and S3 Table), suggesting that these genes regulate endocycle progression. Endocycling in the PG is controlled by nutrient signaling, including the insulin/TOR signaling pathway regulating the third endocycle [22]. However, upstream signaling pathways of the first, second, and fourth endocycle, as well as MES, have not been identified. Thus, detailed and systematic analysis of both MES-related and endocycle-related genes will shed light on the molecular mechanisms of how environmental and genetic cues are integrated into MES and endocycle progression in the PG to determine the onset of ecdysone biosynthesis.
In summary, we have demonstrated the genetic evidence showing the importance of TRiC in the regulation of MES and endocycle. Considering that TRiC suppresses accumulation of mitotic cyclins through the generation of functional Cdh1 protein in yeast [32], we propose that TRiC-mediated regulation of Cdh1/Fzr is an evolutionary conserved mechanism that promotes exit from mitotic cell cycle, including MES. Moreover, several lines of evidence have shown that some cct subunit genes are involved in the survival and proliferation of cancer cells [37]. Considering that cancer tissues possess endocycling cells at a high frequency and that endocycle is considered crucial for tumorigenesis [6–8], elucidating the role of TRiC in MES and endocycle progression is a fundamental step to revealing TRiC-mediated control of oncogenesis. This study thus provides a solid basis for revealing genetic programs that control initiation and progression of endocycle.
Genotypes of the flies used in this study are summarized in S5–S7 Tables, and UAS-RNAi lines used for PG-selective RNAi screen and its phenotype were summarized in S3 Table. Fly stocks were maintained on standard Drosophila cornmeal/yeast medium at 18 or 25°C under a 12-hour light/dark cycle.
To obtain larvae just after hatching, parent flies were maintained in the bottle and allowed to lay eggs for 24 hours on grape juice agar plates supplemented with yeast powder. Newly hatched larvae were transferred to vials with nutrient-rich medium named as “German food (GF)” (https://bdsc.indiana.edu/information/recipes/germanfood.html). Larvae were cultured at 25°C under a 12-hour light/dark cycle, and developmental stages and lethality were scored periodically.
Total RNA was extracted from whole larvae using TRIzol (Thermo). Reverse-transcription was performed using SuperScript III (Invitrogen). cDNA was used as a template for qPCR using Quantifast SYBR Green PCR kit (QIAGEN) and Rotor-Gene Q (QIAGEN). The expression level of target gene was normalized using an endogenous control, ribosomal protein 49 (rp49), and the relative expression level was calculated (relative expression level = expression value of the gene of interest/expression value of rp49). Primer sets used for qPCR are shown in S8 Table.
Ten larvae were rinsed with distilled water, and collected in a 1.5 ml microcentrifuge tube. The larvae were homogenized in 400 μl of methanol with a plastic pestle at room temperature. The samples were centrifuged at 15,000 g for 5 min at 4°C, and 60 μl of the supernatant (equivalent to 1.5 larvae) was subjected to vacuum desiccation. Dried extract was re-dissolved in 50 μl of EIA buffer (Cayman Chemical). Ecdysteroid was quantitated by enzyme-linked immunosorbent assay (ELISA) using 20E EIA antiserum, 20E AchE tracer, and Ellman’s reagent (Cayman Chemical) according to manufacturer’s protocol. Standard 20E was purchased from Sigma.
To rescue developmental arrest in RNAi animals, larvae were transferred to GF with 0.5 mg/g 20E at 48 hAH. Larvae transferred to GF without 20E at the same time point were used as control. Developmental stages were scored at 24-hour intervals.
Larvae were dissected in phosphate buffered saline (PBS) and fixed for 25 min with 4% paraformaldehyde (PFA) in 0.1% PBT (0.1% Triton X-100 in PBS). Tissues were washed with 0.1% PBT three times for 10 min each, permeabilized with 1% PBT (1% Triton X-100 in PBS) for 5 min, blocked with 2% goat serum (Sigma, G9023) in 0.1% PBT for 30 min, and then incubated at 4°C overnight with primary antibodies diluted in blocking solution. Tissues were washed with 0.1% PBT three times for 10 min each, and incubated at 4°C overnight with Alexa 488- or Alexa 546-conjugated secondary antibodies (Thermo) in 0.1% PBT. Together with the secondary antibody, Hoechst 33342 (Thermo, H3570) was added at a 1:1500 dilution to detect DNA. After washing with 0.1% PBT three times for 10 min each, tissues were mounted in mounting medium.
Fly-FUCCI probe expressed in the PG was observed as follows: Larvae were dissected in phosphate buffered saline (PBS) and fixed for 25 min with 4% PFA in 0.1% PBT; Tissues were washed with 0.1% PBT three times for 10 min each, and incubated with Hoechst at a 1:1500 dilution at 4°C overnight; We did use neither anti-GFP nor anti-mRFP antibody; After washing with 0.1% PBT three times for 10 min each, tissues were mounted in mounting medium.
The following primary antibodies were used at indicated dilutions: rabbit polyclonal anti-Dib (a gift from M. B. O’Connor), 1:500; guinea pig polyclonal anti-Sro (a gift from R. Niwa), 1:500; rabbit polyclonal anti-pH3 (Merck, 06–570), 1:500; mouse monoclonal anti-CycA (DSHB, A12), 1:25; mouse monoclonal anti-CycB (DSHB, F2F4), 1:25; mouse monoclonal anti-GFP (Thermo, A11120), 1:1000; and chicken polyclonal anti-GFP (Abcam, ab13970), 1:500.
Images were taken with a Zeiss LSM700, and the pictures’ properties including the cell number and Fzr-GFP expression level were analyzed using Image J/Fiji [38]. Cell counting was performed using the plugin named Cell Counter. Fzr-GFP signal intensity in the PG was obtained from stacked slices of PG, and normalized by the signal intensity in the CA. Measurement of the DNA content was performed as described below.
Ploidy measurement was performed as previously described [39] with minor modifications. The C value in the PG and CA was determined using the following methods. Larvae and adult male flies were dissected in 0.7% NaCl. Brain-ring gland complex and testes were treated with 0.5% sodium acetate for 10 min and then fixed for 30 min with 4% paraformaldehyde in 0.1% PBT. After washing twice with 0.1% PBT, tissues were squashed on an APS-coated slide and submerged in liquid nitrogen to remove coverslip. Tissues were dehydrated in ethanol for 15 min and washed with PBS three times for 10 min each. Slides were stained with Hoechst (1:5000) for 15 min, washed with PBS three times for 10 min each, and mounted in mounting medium. Testes within each slides were imaged at the same gain and settings, in order to use the sperm cells as an internal control. Images were taken with a Zeiss LSM700, and analyzed using Image J/Fiji [38]. Regions were drawn around each nucleus using the trace function and the fluorescence intensity was measured within each region. The mean DNA staining intensity of sperm cells (1C) on the same slide was analyzed and set to 1. The average intensities of PGs were calculated on the basis of the C value in sperm cells.
To measure and quantify DNA signal intensity in the PG, a series of images obtained by immunostaining/histochemistry were processed using Image J/Fiji as follows. DNA signal overlapped with binarized and filled Sro signal (Oregon R) or phm>mCherry.nls signal (transgenic lines) was obtained using a function named “Image Calculator”. Processed images were stacked, DNA signal in the PG was drawn around their nuclei using the trace function, and the fluorescent intensity was measured within the region. DNA staining intensity in the PG was adjusted using average DNA staining intensity obtained from z-stacked images of the brain lobe. Normalized DNA staining intensity was further divided by the PG cell number to obtain mean DNA intensity per PG cell. In accordance with the result of ploidy measurement, the mean C value in the PG of Oregon R at 84 hAH was set to 58 (see Fig 1B and 1C), and the mean C values in the PG of phm>mCherry.nls and phm>dicer2 mCherry.nls at 96 hAH were set to 53 and 54, respectively, (see S1A and S1B Fig).
Larvae were dissected in Ringer’s solution, and the tissues were incubated for 30 min with 100 mM BrdU (Sigma, B5002) diluted in Ringer’s solution and then fixed in 4% PFA for 20 min. Fixed tissues were briefly washed twice in 0.01% PBT (0.01% Triton X-100 in PBS), washed in 0.1% PBT twice for 10 min each, and treated with 2N HCl for 30 min. The tissues were briefly washed twice in 0.01% PBT, washed in 0.1% PBT twice for 10 min, and incubated with blocking solution for 30 min. The tissues were incubated with primary antibody against BrdU diluted 1:20 in blocking solution overnight at 4°C. The tissues were washed in 0.1% PBT for 10 min three times, and incubated 4°C overnight with Alexa 488 fluor-conjugated secondary antibody (mouse IgG, Thermo Fisher, A-11001) and Hoechst diluted at 1:1000 and 1:1500, respectively, in 0.1% PBT. The tissues were washed in 0.1% PBT for 10 min three times and mounted on a slide glass with mounting medium. Images were taken with a Zeiss LSM700, and their analysis was performed using Image J/Fiji [38].
Ten virgins carrying two copies of PG-selective phm-Gal4 and UAS-mCherry.nls were crossed with five UAS-RNAi males to obtain the offspring in which gene of interest is knocked down in the PG. Parent flies were cultured on standard Drosophila medium in plastic vials for 2 days. Developmental phenotype of their progenies was observed to confirm developmental defects at day 10 after crossing. The PG of RNAi animals showing developmental defect were observed using histochemistry at day 6 after crossing as follows. Larvae at day 6, in which most of control larvae (phm>mCherry.nls) are in wandering stage, were dissected in PBS and fixed for 25 min with 4% PFA in 0.1% PBT. Tissues were washed with 0.1% PBT three times for 10 min each, and stained with Hoechst 33342 (1:1500 in 0.1% PBT) to observe the DNA content and distribution and morphology of PG cells. Images were taken with a Zeiss LSM700, and their analysis was performed using Image J/Fiji [38]. DNA quantification was performed as described above (see Ploidy measurement).
Statistical analyses were performed using R (http://www.R-project.org/). Exact P-values of Steel Dwass and Tukey’s multiple comparison test in main and supplemental figures are shown in S1 and S2 Tables. All numerical data except for the data obtained in a RNAi screen are shown in S1 Data. Numerical data in the RNAi screen are shown in S3 Table. In our RNAi screen, a significant increase in the PG cell number was determined using Dunnet’s multiple comparison test. In all statistical analysis, P < 0.05 was considered to represent a statistically significant difference. GO-term enrichment analysis was performed to evaluate which biological process term is enriched in a group of genes using Reactome resource in PANTHER (http://pantherdb.org/).
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10.1371/journal.pntd.0007607 | Cryptosporidium infection in rural Gambian children: Epidemiology and risk factors | Cryptosporidium is a major pathogen associated with diarrheal disease in young children. We studied Cryptosporidium diarrhea in children enrolled in the Global Enteric Multicenter Study (GEMS) in rural Gambia.
We recruited children <5 years of age with moderate-to-severe diarrhea (MSD) for 3 years (2008–2010), and children with either MSD or less severe diarrhea (LSD) for one year (November 2011-November 2012) at sentinel health centers. One or more randomly selected controls were matched to each case. Stool samples were tested to identify Cryptosporidium by immunoassay. A subset of randomly selected case-controls pairs were tested for Cryptosporidium species. We investigated the epidemiology of, and evaluated possible risk factors for, Cryptosporidium-positive diarrhea.
We enrolled 1938 cases (1381 MSD, 557 LSD) and 2969 matched controls; 231/1929 (12.0%) of diarrhea cases and 141/2962 (4.8%) of controls were positive for Cryptosporidium. Most Cryptosporidium diarrhea cases (85.7%, 198/231) were aged 6–23 months, and most (81.4%, 188/231) occurred during the rainy season. Cryptosporidium hominis (C. hominis) was the predominant (82.6%) species. We found associations between increased risk of Cryptosporidium-positive MSD or LSD, or both, with consumption of stored drinking water and certain animals living in the compound—cow, cat (MSD only) and rodents (LSD only). Larger households, fowl living in the compound, and the presence of Giardia infection were associated with decreased risk of Cryptosporidium MSD and LSD.
Cryptosporidium-positive diarrhea is prevalent in this setting, especially at 6–23 months of age. The preponderance of Cryptosporidium infection in the rainy season and increased risk of Cryptosporidium-positive diarrhea with consumption of stored drinking water suggest water-borne transmission. Further investigation is needed to clarify the role of animals and contamination of stored drinking water in Cryptosporidium transmission.
| Cryptosporidium, a protozoan parasite, is one of the most common diarrheal pathogens in young children living in developing countries. We describe the prevalence and risk factors for Cryptosporidium diarrhea in under-five children in The Gambia using data from the Global Enteric Study (GEMS), conducted in seven developing countries in Asia and Africa (2008–2012). We enrolled 1938 diarrhea cases and 2969 matched controls. We found that 12.0% of diarrhea cases and 4.8% controls were positive for Cryptosporidium. Most (85.7%) Cryptosporidium diarrhea cases were aged 6–23 months, and most (81.4%) occurred during the rainy season. Cryptosporidium hominis was the predominant species (82.6%). We found that consumption of stored drinking water and animals (cow, cat, rodents) living in the compound are potential risk factors for Cryptosporidium diarrhea. Improved drinking water storage may reduce the burden of Cryptosporidium diarrhea in a resource poor hygienic and sanitation setting.
| Diarrhea is the second leading cause of morbidity and mortality in children less than 5 years old, causing approximately 600,000 annual deaths, mostly in developing countries [1]. Cryptosporidium, a protozoan parasite, is transmitted like other diarrheal pathogens by the fecal-oral route and is more prevalent in patients with HIV/AIDS, in whom it causes severe and prolonged gastrointestinal illness [2–5]. However, the recent Global Enteric Multicenter Study (GEMS) found that Cryptosporidium was the third most common pathogen contributing to moderate-to-severe diarrhea (MSD) in children aged less than 5 years, irrespective of HIV prevalence [6, 7]. A multisite birth cohort community-based study (MAL-ED) also detected a high burden of Cryptosporidium associated mild and severe infectious diarrhea among children aged 0–24 months [8, 9]. In addition to the substantial burden of gastroenteritis, Cryptosporidium with or without concomitant diarrhea has been associated with growth faltering and weight loss [10–13], and with lowered physical fitness, decreased cognitive function [14] and increased mortality [2, 6, 7]. Cryptosporidium is associated with an estimated 48,000 global deaths per year in children aged less than five years [12].
In sub-Saharan Africa, the prevalence of, and risk factors for Cryptosporidium infection have been studied mostly through cross-sectional surveys over limited time periods with relatively few Cryptosporidium-positive cases [15–18]. The MAL-ED study documented risk factors of Cryptosporidium only in diarrhea cases of any severity and the effect of co-infection with other enteric pathogens were not assessed [19]. In contrast with the global burden and severity of diarrheal disease with Cryptosporidium, information on transmission in developing countries is limited [20, 21]. GEMS was a comprehensive study conducted for several years and using standardised diagnostic methods to determine the disease burden, epidemiology and risk factors for a wide variety of pathogens that may cause diarrhea (23–26). The GEMS study detected that Cryptosporidium was among the leading etiologies of MSD in eastern Gambia, accounting for 12% and 8% of MSD in 0–11 months and 12–23 months, respectively [6]. In this paper we report secondary analyses of GEMS data in The Gambia in order to characterize the epidemiology of Cryptosporidium infection in diarrhea cases and their controls and to identify risk factors for Cryptosporidium-associated diarrhea.
The study was approved by The Gambia Government/MRC joint Ethics Committee (SCC # 1054) and IRB of University of Maryland, Baltimore (HM-HP-00040030). Written informed consent was obtained from parents or guardians of study participants.
Analysis was done using SAS version 9.4 (SAS Institute, Cary, NC, USA), Stata SE 12.0 (StataCorp. 2011, College Station, Texas, USA), R 3.5.1 [28] and Microsoft Excel. Results with p < 0.05 were generally considered statistically significant; we used p < 0.10 in evaluating differences between associations with Cryptosporidium-positive MSD and LSD in multivariable modeling (i.e., in evaluating interaction terms).
Proportions were measured for categorical data. Differences in proportions were assessed by chi-square test. Continuous data were described by means (SD) or medians (range). Data on building materials and household possessions (e.g., electricity, television, radio, phone, bicycle, car, boat, refrigerator and finished floor and number of sleeping rooms in the dwellings) were used to construct a wealth index [24, 29] as a measure of socio-economic status.
Length or height was determined as the median of three repeated measurements. A height-for-age Z-score (HAZ) was calculated according to WHO guidelines (31), with HAZ < -2 used to define low height-for-age or stunting. Extreme HAZ values of (<-6 or >+6) were excluded from analysis.
The attributable fraction in the exposed (AFE) was calculated for the age range (0–23 months) for which Cryptosporidium was a significant pathogen for the entire GEMS study. From the population attributable fractions and total estimated cases with Cryptosporidium identified at the Gambia site [6, 30], the number of cases attributable to Cryptosporidium was estimated for the age groups 0–11 months and 12–23 months, separately for MSD and LSD. AFE is the ratio of the number of cases attributable to Cryptosporidium to the total cases positive for Cryptosporidium.
Associations of Cryptosporidium with diarrhea overall and within strata of age, sex, season, and type of diarrhea (MSD or LSD) were evaluated using conditional logistic regression.
Analysis of associations with Cryptosporidium-positive diarrhea was restricted to Cryptosporidium-positive cases and their matched controls. First we fit univariate conditional logistic regression models for any Cryptosporidium-positive diarrhea with single factors as covariates. Starting with variables associated with Cryptosporidium-positive diarrhea with a p ≤ 0.2 threshold, we fit multivariable conditional logistic regression models using a backward elimination stepwise process to identify a set of factors, each associated with p < 0.05. We then developed separate models for MSD and LSD by including the interaction of each factor with an indicator variable for MSD or LSD. We could not include a “main effect” for MSD/LSD because cases and controls were matched on MSD/LSD. We evaluated associations with socio-demographic variables, breastfeeding status, water source and hygiene variables, animals living in the compound, HAZ score, and presence of other potential pathogens (rotavirus, Shigella, norovirus, adenovirus 40/41, ETEC-ST, Giardia, Entamoeba histolytica, and Ascaris lumbricoides).
We enrolled 1938 cases (1381 MSD and 557 LSD) and 2969 matched controls (Fig 1). Of the study participants tested for Cryptosporidium, 231 of 1929 cases (12.0%) and 141 of 2962 controls (4.8%) were positive (p = <0.001); data for Cryptosporidium were missing for 9 cases and 7 controls.
Table 1 shows the prevalence of Cryptosporidium in cases and controls within categories of age, sex, season, and type of diarrhea (MSD or LSD). Prevalence was higher in cases than in controls for both MSD (12.2% vs. 4.8%, p<0.001) and LSD (11.5% vs. 4.6%, p<0.001). Cryptosporidium prevalence was similar in children with MSD (167/1381, 12.1%) and LSD (64/557, 11.5%) (p = 0.71 by z-test for proportions).
AFE was estimated as 76% and 70% for MSD at ages 0–11 months and 12–23 months, respectively. The corresponding estimates for LSD were 65% and 64%. For the age range 0–23 months, AFE was 73% for MSD and 65% for LSD.
Cryptosporidium prevalence was higher in cases than in controls within all age groups and for both males and females (Table 1). Prevalence in cases was much higher in children aged 6–23 months than in younger infants or children aged 24–59 months, and similar between males and females. Odds ratios for Cryptosporidium prevalence in cases versus controls were also highest at ages 6–23 months. Prevalence in cases was similar for males and females.
Cryptosporidium prevalence was higher in cases than in controls in both the dry and wet seasons (Table 1). Furthermore, relative to dry season, Cryptosporidium is much more prevalent in both cases and controls during the wet and rainy season (May-October). The overall prevalence of Cryptosporidium in both cases and controls usually started to rise in May, with a peak between July and October (Fig 2). Cryptosporidium diarrhea cases peaked in September (28.3% of all cases) and October (28.2% of cases); prevalence was low between January and April (range, 0.64%-2.8%).
Cryptosporidium results from the TaqMan assay were available for 1506 stool samples (759 cases and 747 controls); 280 (18.6%) of these were positive for Cryptosporidium by TaqMan PCR, 121 (8.0%) were positive by EIA, and 104 (6.9%) were positive by both assays. The TaqMan assay was used to identify samples positive for C. hominius or C. parvum. Of the 280 positives by Taqman, 119 (42.5%) were positive for C. hominis, and 20 (7.1%) were positive for C. parvum and 5 (1.8%) were positive for both. Among 144 samples with both species identified, only C. hominis was in 119 (82.6%), only C. parvum in 20 (13.9%), and both C. hominis and C. parvum was in 5 (3.5%). C. hominis was more frequently positive in cases than in controls (78/753, 10.4% vs. 46/741, 6.2%; p = 0.004). The frequencies of C. parvum were similar in cases and controls (12/746, 1.6% vs. 13/739, 1.8%; p = 0.82).
Analysis of risk factors included 231 Cryptosporidium-positive diarrhea cases and 349 matched controls; 36 (10.3%) of the controls were positive for Cryptosporidium infection (Table 2). One hundred sixty-seven (72.3%) of the cases had MSD and 64 (27.7%) had LSD. Since controls were matched to cases by age and sex, no analysis of these variables was done. Results of univariable conditional regression analyses are summarized in Table 2 for socioeconomic characteristics, animals living in the compound, water source and hygiene, breast feeding status, nutritional status, and the presence of other putative pathogens.
In Table 2, the median numbers of household members for Cryptosporidium-positive diarrhea cases and their matched controls were 25 (range, 3–112) and 34 (range, 3–118), respectively. The mean number of household members and the proportion households with more than 20 members were significantly higher in controls than in cases.
Children with Cryptosporidium-positive diarrhea were significantly more likely than their matched controls to live in a compound with a cat, cow or rodents. Fowl (chicken, duck or other birds) living in the compound was more common in controls than cases.
Cryptosporidium-positive diarrhea was significantly associated with drinking stored water at home in the last two weeks and drinking water usually filtered through a cloth. The proportions of cases and controls currently being breast fed, as well as the proportions of cases and controls with stunting, were similar and not significantly different.
The proportion of Cryptosporidium-positive diarrhea cases co-infected with Giardia, the most common protozoal co-infection, was significantly lower than the proportion in their matched controls. No other co-infection evaluated was significantly associated with Cryptosporidium-positive diarrhea.
Table 3 summarizes the results of multivariable conditional logistic regression modeling for associations with Cryptosporidium-positive diarrhea. Inclusion of interactions with an MSD/LSD indicator variable allowed development of separate models for Cryptosporidium-positive MSD and Cryptosporidium-positive LSD. All factors in the table were significantly associated (95% confidence interval for odds ratio did not include 1) for either MSD, LSD, or both. There was an increased risk of Cryptosporidium-positive MSD and/or LSD for certain animals (cow, cat, rodents) in the household, as well as the child drinking stored drinking water at home in the last two weeks. There were significant differences (p < 0.05 for interaction term) between MSD and LSD for odds ratios for a cow or cat living in the compound and a suggestion of a difference (p = 0.069) for rodents in the compound. There was no evidence of a difference between MSD and LSD in the association with number of people living in the household (in units of 5 individuals), child consuming stored drinking water at home in the last two weeks, fowl living in the compound or mixed infection with Giardia.
Our findings in this comprehensive population-based epidemiological study of endemic Cryptosporidium infection in young Gambian children show a striking association of Cryptosporidium-positive diarrhea with age, with nearly all cases (86%) occurring in children 6–23 months of age. A similar trend with a lower overall prevalence of Cryptosporidium-positive diarrhea, was observed in a study conducted in urban Gambia during 1991–1992 [18]. Other studies in sub-Saharan Africa have shown roughly similar prevalence but may lack the representativeness of long-term population-based studies with improved diagnostic techniques [21]. The low prevalence in infants 0–5 months of age may be explained by exclusive breast feeding [31] or transfer of immunity from mother to child. The high prevalence at 6–23 months of age may be due to exposure to contaminated food and/or water in the weaning period. Stenberg et al. demonstrated in a sero-prevalence study in Guatemala that the prevalence of antibody to Cryptosporidium parvum increased at older ages compared to those aged 6–12 months [32]. Furthermore, a study in healthy adult volunteers showed that higher anti-Cryptosporidium IgG antibody levels were associated with a reduced chance of infection and illness when challenged with low Cryptosporidium oocyst doses [33]. Thus, the low prevalence of Cryptosporidium in children 24–59 months of age may relate to the development of immunity following earlier infection. A decreased risk of infection in older children due to the development of partial immunity from earlier exposure would suggest that a vaccine may protect against Cryptosporidium infection.
The population HIV prevalence is low in our study area (<2%) [22]. Our findings show that Cryptosporidium is an important infection in young children even in a population with low HIV prevalence.
C. hominis is the predominant among identified species of C. hominis and C. Parvum (82.6% vs. 13.9%) in the Gambia. This finding is consistent with results from other studies in Kenya, Malawi, Uganda, Bangladesh and India [5, 11, 16, 34, 35]. It suggests that the primary mode of transmission in The Gambia is anthroponotic transmission, although there may also be zoonotic transmission. Either may occur through contact with contaminated drinking water [19]. Accordingly, we found consumption of stored drinking water to be associated with increased risk of Cryptosporidium diarrhea.
In our study, 81% of Cryptosporidium-positive diarrhea cases and 79% of Cryptosporidium-positive controls occurred during the rainy season. Earlier studies in The Gambia [18], Madagascar [36], Guinea-Bissau [37], Brazil [38, 39] and India [40] found associations between Cryptosporidium diarrhea and rainy season. The association of rainfall and Cryptosporidium infection could be due to domestic use of surface water, contamination of unprotected wells, and poor hygiene practices. In the rainy season, surface water may be more often contaminated with human and animal feces, so that children playing in the contaminated surface or stagnant water could facilitate the transmission of Cryptosporidium. However, Cryptosporidium infection has also been observed predominantly in the dry season in Kenya and Guatemala, when drinking water is limited [34, 41].
In our study area, drinking water is usually stored in wide-mouth containers. Household members and children use their hands to dip a cup into the water storage container to obtain drinking water, which may further contaminate the water. The chance of contamination may be increased with prolonged storage and handling if the storage container is not frequently cleaned. Further exploration of storage practices, sanitation and hygiene measures for collection of water and removing water from the container, as well as further laboratory analysis of potable water from the source to storage and point of consumption may all help to establish the source of infection.
Contrary to the findings of an association of household overcrowding with increased risk of cryptosporidiosis, our study found the opposite. The reasons for this negative association are not clear. In an urban area, close proximity of houses, overcrowding, close personal contract and lack of sanitary facilities may contribute to the spread of C. hominis infection [42]. Perhaps in a rural setting, the greater physical space available per person leads to less close contact between children in the household.
We found that the presence in the compound of domestic animals (cattle and cats) and the presence of rodents in the compound were potential risk factors for Cryptosporidium diarrhea. Association of Cryptosporidium diarrhea with the presence of animals was also found in Guinea Bissau and Guatemala [41, 43]. We believe that our study is the first to suggest an association between rodents living in a household and the presence of Cryptosporidium diarrhea. Human carriage of Cryptosporidium muris, a predominantly rodent pathogen, and rodents have been identified as reservoirs of C. parvum and C. hominis [44, 45].
The presence of Giardia was inversely associated with Cryptosporidium diarrhea. Similarly, a longitudinal analysis in another study showed no evidence of an association between Giardia infection and an increased risk of diarrhea [46]. In a systematic review, giardiasis was associated with decreased risk of acute diarrhea in children in developing countries [47]; however, the same review found that Giardia infection was positively associated with persistent diarrhea and suggested that initial Giardia infections early in infancy may be positively associated with diarrhea. Giardia may secrete mucins and glycoproteins in the intestinal mucosal layer, which may protect against attachment of other pathogens including Cryptosporidium, and such a mechanism may protect against Cryptosporidium-positive diarrhea [48].
A limitation of the study is that only about 68% of Cryptosporidium-positive diarrhea cases can be attributed to Cryptosporidium. Thus, some of the risk factor associations that we found could be at least partially due to factors other than the presence of Cryptosporidium. However, it is clear both from the present study and other studies that infants and young children in developing countries with Cryptosporidium-positive diarrhea are at risk of negative health consequences and that reducing the level of Cryptosporidium infection is an important public health concern in these countries.
Our study establishes that Cryptosporidium is an important cause of childhood diarrhea in The Gambia. Data from the ongoing rotavirus vaccine impact study will help us understand the burden of Cryptosporidium infection in Africa after introduction of rotavirus vaccine in routine immunization programs. The drinking stored water and animals living in the household are associated with Cryptosporidium-positive diarrhea. The predominance of C. hominis suggests anthroponotic transmission of Cryptosporidium infection. Associations of Cryptosporidium-positive diarrhea with drinking of stored water and animals living in the household suggest there may also be zoonotic transmission. Thus, general improved hygienic practices to store drinking water may reduce transmission of Cryptosporidium. The role of animals in the transmission of Cryptosporidium, the methods of drinking water storage, and sanitation and hygiene measures used for taking water from the water storage containers merit further study.
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10.1371/journal.pntd.0000211 | An Analysis of Genetic Diversity and Inbreeding in Wuchereria bancrofti: Implications for the Spread and Detection of Drug Resistance | Estimates of genetic diversity in helminth infections of humans often have to rely on genotyping (immature) parasite transmission stages instead of adult worms. Here we analyse the results of one such study investigating a single polymorphic locus (a change at position 200 of the β-tubulin gene) in microfilariae of the lymphatic filarial parasite Wuchereria bancrofti. The presence of this genetic change has been implicated in benzimidazole resistance in parasitic nematodes of farmed ruminants. Microfilariae were obtained from patients of three West African villages, two of which were sampled prior to the introduction of mass drug administration. An individual-based stochastic model was developed showing that a wide range of allele frequencies in the adult worm populations could have generated the observed microfilarial genetic diversity. This suggests that appropriate theoretical null models are required in order to interpret studies that genotype transmission stages. Wright's hierarchical F-statistic was used to investigate the population structure in W. bancrofti microfilariae and showed significant deficiency of heterozygotes compared to the Hardy-Weinberg equilibrium; this may be partially caused by a high degree of parasite genetic differentiation between hosts. Studies seeking to quantify accurately the genetic diversity of helminth populations by analysing transmission stages should increase their sample size to account for the variability in allele frequency between different parasite life-stages. Helminth genetic differentiation between hosts and non-random mating will also increase the number of hosts (and the number of samples per host) that need to be genotyped, and could enhance the rate of spread of anthelmintic resistance.
| During the last decade, there has been a substantial increase in the use of mass drug administration to reduce the disease caused by parasitic worms. With so many people regularly receiving treatment, there is a risk that drug resistance may develop. As a result, the number of studies looking for genetic markers of drug resistance has increased noticeably. In this paper we analyse the results of one such study that investigated the presence of genes associated with drug resistance in parasites responsible for elephantiasis. This study, like many other studies of human parasitic infections, relies on analysing parasite immature stages (such as eggs) because the adult worms are often inaccessible within the human body. Using computer models we show how the gene frequency in the immature stages may vary from that in the adult worm population. Parasites with these markers for drug resistance might also be unevenly distributed across the host population even prior to treatment. This may increase the spread of drug resistance and make it harder to detect. We suggest that studies conducted only on parasite immature stages should be interpreted with caution and should carefully consider the number of people and the number of parasites they analyse.
| In recent years there has been a substantial increase in the use of mass drug administration (MDA) to reduce the morbidity associated with helminth infections of humans [1], increasing the probability that anthelmintic resistance may become a public health concern in the future. One such annual MDA programme is the Global Programme to Eliminate Lymphatic Filariasis (GPELF) which, in 2005, treated over 145 million people with albendazole (a broad spectrum benzimidazole anthelmintic) in combination with either ivermectin or diethylcarbamazine [2]. GPELF targets mainly Wuchereria bancrofti, the most widely distributed of the filarial parasites of humans.
Sensitive molecular assays are required to detect the presence of anthelmintic resistance before widespread treatment failure is apparent, drug resistance becomes disseminated and disease control is jeopardised [3]. Surveys of helminth parasites of humans are being conducted to establish whether genetic changes at certain polymorphic loci (associated with resistance to the same or related drugs used against veterinary helminths), are present in these populations and subject to detectable selection under chemotherapeutic pressure [4]–[13]. A phenylalanine to tyrosine substitution at position 200 on the β-tubulin isotype 1 molecule has been identified in a number of helminth parasites of farmed ruminants including Haemonchus contortus [14],[15], Cooperia oncophora [16], and Teladorsagia circumcincta [17] and is associated with benzimidazole (BZ) resistance in these species. Worryingly, this genetic change has also been identified in W. bancrofti [13], though the phenotypic studies relating the substitution to a decreased albendazole efficacy have not been undertaken in this species. To aid clarity the two alleles at position 200 on the β-tubulin isotype 1 molecule shall be referred to as allele F (phenylalanine) for susceptibility and allele Y (tyrosine) for putative resistance.
Inbreeding, the mating of related individuals, influences parasite genotype distribution and can affect the selection of adaptive traits. Facets of a species' biology may cause parasite inbreeding, such as population structure or assortative mating (when mate choice is determined by phenotype). Parasite allele frequency can differ between infrapopulations (the populations of parasites within individual hosts) due to the ecology of the infection or through the random nature of infection events (all groups may have an equal probability of having a rare allele, but actual numbers may vary between groups by chance). Helminth parasites have a particularly subdivided population structure as adult worms are confined within their definitive host, and only able to mate with other worms that belong to the same infrapopulation. The population genetic structure of most helminth species remains unknown. The few studies that have been undertaken indicate that whilst some species appear to have no apparent genetic structure others exhibit a high degree of parasite genetic differentiation between hosts [18]. The degree of genetic differentiation in the parasite infrapopulation can shed insight into the microepidemiology of parasite transmission [19]–[23]. Infrapopulation genetic differentiation will also influence helminth population genetics as it causes a reduction in the frequency of heterozygote offspring, a phenomenon known as the Wahlund effect [24].
Studies investigating the inheritance of benzimidazole resistance are lacking, though evidence indicates that thiabendazole resistance in H. contortus may be a semi-dominant trait [25]. Other authors have postulated that alleles conferring anthelmintic resistance, including allele Y, are likely to be recessive [17],[26], which would make heterozygote worms susceptible to treatment. If an allele conferring drug resistance is recessive, excess parasite homozygosity will increase the probability that a resistance allele will survive treatment. This has been shown using genetic metapopulation models investigating nematodes of grazing animals; these models indicate that the spread of rare recessive genes is promoted by hosts accumulating multiple related infections simultaneously [27],[28]. The degree of parasite genetic differentiation among hosts can be quantified using FST (or related analogues; see [18] and references therein).
The adult stages of the majority of parasitic helminths of humans cannot be obtained routinely for direct investigation, so genetic surveys (including those investigating drug resistance) resort to sampling transmission stages, i.e. those (immature) life-stages that gain access to the environment to be transmitted to and from hosts or through vectors [13], [29]–[32]. However, the results of these surveys should be interpreted with caution, as the underlying allele frequency of the adult worm population may differ from the allele frequency of the sampled transmission stages. Variations in transmission stage allele frequency and genotype distribution could be generated randomly or be a product of the parasite's spatial structure and life-history traits. For example, population subdivision will cause random variation in adult worm allele frequencies between hosts at low parasite densities. Filarial parasites have separate sexes and are thought to be polygamous [33], which may accentuate the variability in microfilarial allele frequency, e.g. a rare allele may be highly over-represented in the subsequent generation if, by chance, a male worm with this allele inhabits a host harbouring females but no other males. In addition, the inherent random sampling of gametes during sexual reproduction [34], and the overdispersed distribution of parasite numbers among hosts [35] may cause the allele frequency and genotype distribution to vary by chance from generation to generation.
This paper analyses population genetic data collected for a study by Schwab et al. [13] who identified the presence of the β-tubulin allele Y in populations of W. bancrofti. Firstly, the extent of parasite inbreeding is estimated from W. bancrofti microfilarial samples taken from patients in Burkina Faso, West Africa. Samples were obtained from different villages, some of which had received a single round of MDA with ivermectin and albendazole, under the auspices of the GPELF. Secondly, an individual-based stochastic model is presented which simulates microfilarial genetic diversity from adult worm allele frequencies. The model generates sample allele and genotype frequencies using the same number of hosts, and the same number of microfilariae per host as in Schwab et al. [13]. This model is then used to assess whether the observed level of parasite inbreeding is the result of a sampling artefact or a true biological phenomenon. Finally, the model is used to assess the likely range of adult worm allele frequencies which could have given rise to the observed microfilarial data, providing some insight into how genetic surveys which sample transmission stages should be interpreted. We discuss the implications of our results in terms of the development and detection of anthelmintic resistance.
Table 1 summarises the data collected for the study by Schwab et al. [13] and indicates the number of microfilariae and hosts sampled. The village of Gora was removed from the F-statistic analysis since only one host was sampled in this village. In some hosts it was possible to genotype only a few microfilariae, increasing the uncertainty associated with estimation of underlying infrapopulation allele frequencies in these hosts. Results are grouped according to parasite treatment history. The average frequencies of allele Y in microfilarial samples from untreated and treated hosts were 0.26 and 0.60, respectively [13]. The degree of parasite heterozygosity (the proportion of microfilariae with the heterozygote genotype) is estimated for each village. The table also indicates the deviation of each population from the Hardy-Weinberg Equilibrium (HWE), which gives the proportion of heterozygote microfilariae that would be expected in a randomly mating population. This reveals a strong deficit of heterozygotes in all three populations.
In this paper, we refer to two different types of allele frequency: (1) the underlying frequency of the allele putatively associated with BZ resistance, with ql denoting the allele frequency of the entire parasite population of a given locality, and (2) the parasite allele frequency within the host population that is sampled, denoted by Hql. The superscript l denotes the parasite life-stage under investigation, be it microfilariae (l = M) or adult worms (l = W), and H denotes definitive host. The allele frequency estimated from the sample, , may not correspond to the true underlying allele frequency, ql, either because the hosts sampled are not representative of the whole host population, or because the parasites genotyped do not represent adequately the allele frequency within the host.
By genotyping transmission stages before they leave the definitive host prior to the introduction of mass chemotherapy, insight can be gained into the different causes of microfilarial excess homozygosity. If it is assumed that the number of microfilariae produced, their survival, and their probability of being sampled are independent of their genotype (as we do in the null model), it can be assumed that deviation from the HWE may be the result of non-random mating. If the locus being investigated is not under selection, the excess microfilarial homozygosity will most likely be the result of either infrapopulation genetic differentiation or non-random parasite mating within hosts. Genotyping transmission stages would allow the relative contributions of each of these two sources of inbreeding to be estimated. The variation in the allele frequency between hosts will account for some of the excess homozygosity whilst deviation from the HWE in the microfilariae within an individual host will indicate possible non-random mating within the infrapopulation.
The Wright's hierarchical F-statistic is used to investigate the correlation of parasite genes within and between human hosts [29]–[31],[36]. It is assumed that the infrapopulation is the first hierarchical group in the parasite population, and FIS is defined as the correlation of genes between microfilariae within the infrapopulation; , as the correlation of microfilarial genes between different hosts living in the same village; , as the correlation of microfilarial genes between different villages within the overall microfilarial population; and FIT, as the correlation of genes between individual microfilariae relative to the overall microfilarial population of the region. The different inbreeding terms introduced are summarized in Table 2. A value of FIS is significantly greater than zero points towards adult worm non-random mating, indicates variation in worm allele frequency between hosts, and suggests differences in the worm allele frequency between villages. The same statistical frameworks used to estimate Wright's F-statistic were employed here, taking into account variable sample sizes [34]. Estimates of the 95% confidence intervals for FIS, and FIT, were generated by bootstrapping simultaneously worms within each host and bootstrapping over hosts within each village [37]. F-statistics, and their associated uncertainty, were calculated for each village.
A dioecious adult worm helminth population with a 1:1 male to female ratio was randomly generated for a given mean number of worms per host and degree of parasite overdispersion (as determined by the k parameter of the negative binomial distribution, parameterized following [35]). Each adult worm infrapopulation was randomly allocated an allele frequency, as analysis of pre-treatment data did not detect any significant relationship between the host's frequency of allele Y and microfilarial burden. The adult worm allele frequency of each host was randomly selected according to the given underlying allele frequency, qW, and the degree of parasite genetic differentiation between hosts, . For a description of a method for generating the distribution of allele frequencies in a subdivided population using the beta distribution [38], see Porter [39].
It is again assumed that microfilarial production and survival is independent of genotype, allowing a microfilarial population for each host i to be generated according to the size and allele frequency of the adult worm infrapopulation. Worms were assumed to be polygamous; implying that if only one male parasite were present within a host, all fertile females within that infrapopulation would be mated. The number of microfilariae produced by each parasite infrapopulation was assumed to be proportional to the number of fertilised females within that host. It was also assumed that gametes separate independently and re-assort according to the degree of non-random mating (FIS). The probability with which a microfilaria within host i, will be of genotype j is denoted , and given by the equations,(1)(2)(3)where and are, respectively, the frequency of allele Y in the male and female adult worms within host i, and and are the corresponding susceptible allele F frequencies. To allow random stochastic fluctuations in genotype distribution, the actual number of microfilariae in host i with genotype j follows a binomial distribution, with the number of trials being equal to the number of microfilariae produced by host i, with genotype probability equal to .
Microfilarial allele frequencies and genotype distributions were generated by sampling a specific number of microfilariae from the generated hypothetical population according to the sampling scheme used in Schwab et al. [13]. The exact number of samples taken from each of the 30 hosts was: 11, 10, 15, 9, 11, 9, 13, 10, 10, 7, 10, 10, 7, 1, 11, 9, 1, 7, 4, 1, 10, 9, 8, 6, 4, 6, 9, 10, 10, 8, for a total of 246 microfilariae. Analysis of pre-treatment data had indicated that the number of samples taken from each host by Schwab et al. [13] was independent of host microfilaraemia and host allele frequency, allowing the number of microfilariae sampled per host to be randomly allocated. The program code for the simulations implemented was written in C++ and run 100,000 times, with each run generating a new helminth population and genotype distribution from which 95% confidence limits (95% CL) were calculated.
The model was parameterised for the untreated villages of Tangonko and Badongo, Burkina Faso, which had an initial prevalence of microfilaraemia of 25%. The mean adult worm burden was estimated from observed microfilarial counts using the functional relationship given in the deterministic model EPIFIL (see original formulation and parameter values in Norman et al. [40]), giving a mean adult worm burden of 13.5 host−1. The degree of adult worm overdispersion was estimated from the recorded microfilarial prevalence (taken here as a proxy for the prevalence of adult worms producing microfilariae) and the mean adult worm burden, using the prevalence vs. intensity relationship that derives from assuming a negative binomial distribution of worms among hosts [35], yielding a k value of 0.07. The model outlined above will only be valid for comparisons against the pre-treatment data, since chemotherapy is known to impede microfilarial production and / or survival [41].
The null model assumes that mating is random between male and female worms within each infrapopulation and that allele Y is randomly distributed across hosts, i.e. . Results of the inbreeding analysis can be incorporated into the individual-based model described in equations (1) to (3) to explore the range of adult worm allele frequencies which can give rise to the observed microfilarial data.
The observed microfilarial genotype distribution was found to deviate from HWE. Villages with no history of mass anthelmintic chemotherapy had an overall inbreeding coefficient of FIT = 0.44 (95% CL = 0.17, 0.68), indicating strong inbreeding. Fifteen percent of the microfilariae were found to be homozygous for allele Y, an estimate 2.3 times higher than would be expected in a random mating parasite population. Results indicate the occurrence of a significant degree of genetic differentiation in worm allele frequency among the host population . Infrapopulation allele Y frequency, , varied from 0 to 0.77 in the villages with no history of treatment, indicating an increase in microfilarial homozygosity of 60% above HWE. The results also suggest a degree of non-random mating within hosts measured by FIS = 0.29 (−0.09, 0.54), which is however is not significantly greater than zero. No difference was observed in the microfilarial allele frequency between the two treatment-naïve villages .
The data from the two treatment-naïve villages of Tangonko and Badongo were analysed separately. Both showed a high level of microfilarial homozygosity, with overall inbreeding coefficient of FIT = 0.51 (0.16, 0.76) and FIT = 0.33 (−0.10, 0.78), respectively (Figure 1). The degree of parasite genetic differentiation between hosts varied between the two villages, though the difference was not statistically significant (p = 0.38, calculated from the square of the normalized difference in FST estimates [42]). For the purpose of the following analysis the two treatment-naïve villages have been grouped together to increase the study sample size. A similar degree of parasite inbreeding was observed in the village of Perigban which had received one round of MDA.
Parasite inbreeding increases the range of underlying adult worm allele Y frequencies, qW, which can give rise to the observed microfilarial allele Y frequency of 0.26 (Figure 2). Results from the null model, where mating was assumed to be random and allele Y is randomly distributed amongst hosts, indicate that qW in the untreated villages of Tangonko and Badongo could range from 0.21 to 0.32. If we use the excess inbreeding estimate reported in pre-treatment villages (FIT = 0.44), then model simulations suggest that qW could range from 0.18 to 0.37.
The microfilarial genotype diversity model indicates that the observed homozygosity is unlikely to be solely a result of genetic sampling, demographic stochasticity, population subdivision, or the sampling scheme employed, suggesting that true biological mechanisms are operating in the parasite population even before the introduction of anthelmintic therapy. Figure 2 indicates the range of likely microfilarial genotype distributions that can be generated from a given qW value using the null (random) model. The observed excess homozygosity in the untreated villages was greater than the 95% confidence interval estimates generated by the null model (Figure 3). It is interesting to note the wide range of microfilarial genotype distributions that can be generated by the null model.
Despite the large increase in microfilarial homozygosity attributable to parasite inbreeding, there is only a modest increase in the prevalence of hosts who have microfilariae that are homozygous for allele Y (and therefore putatively resistant if the allele confers drug resistance were recessive, Figure 4). Parasite overdispersion reduces the number of hosts who are microfilaria-positive and concentrates allele Y into a small proportion of the host population. A high degree of parasite non-random mating and infrapopulation genetic differentiation increases the number of hosts (and the number of samples per host) that need to be sampled, in order to detect or quantify reliably parasite genetic diversity (Figure 4). The model is used to investigate how parasite inbreeding may influence the sampling scheme of genetic surveys seeking to identify the presence of a known marker for drug resistance (Figure 5). Results indicate that the observed level of parasite inbreeding markedly increases the minimum number of hosts, and the overall number of samples necessary to be 95% confident of detecting a rare allele. The sampling scheme used within Figure 5 assumes that the number of parasites genotyped per host is weighted by the host's microfilarial load. This improves the accuracy of allele frequency estimates by allowing heavily infected hosts to have a greater contribution to the sampled microfilarial population, something which is particularly important in overdispersed parasite populations.
To date there is no phenotypic evidence that allele Y causes albendazole resistance in W. bancrofti. However, if an allele conferring drug resistance existed in populations of this parasite then the consequences on the spread of such an allele of parasite non-random mating and genetic differentiation between hosts will depend on the frequency and the relative dominance of the resistance allele. If the resistance allele were recessive, helminth inbreeding would greatly increase the probability that a parasite survives anthelmintic treatment. This is evident from Figure 6 which shows the influence of parasite inbreeding on the relative proportion of resistant genotypes for a given allele frequency. With a recessive resistance allele at a frequency of 0.05, the degree of inbreeding within the W. bancrofti population reported here, would on average increase the number of worms with the homozygote resistance genotype nine-fold. Conversely, if the resistance allele was dominant, inbreeding would reduce the probability that a parasite survives chemotherapy, as fewer worms would have the resistant allele (the deficiency of heterozygous parasites caused by parasite inbreeding will be greater than the increase in resistant homozygous worms).
The genotype distribution of W. bancrofti microfilariae varied dramatically from the HWE prior to the introduction of MDA. The degree of excess homozygosity reported falls outside the range of values generated by the null model described in this paper, indicating a significant degree of parasite non-random mating. This may be caused, in part, by parasite genetic differentiation between hosts. The null model generates a wide range of microfilarial allele frequencies and genotype distributions indicating that caution should be exercised when interpreting results obtained by sampling solely transmission stages. Significant changes in the genetic diversity of microfilarial populations over time may not reflect a significant change in the underlying adult worm population. This result highlights the crucial importance of developing sound theoretical null models that enable helminth population genetics data to be interpreted adequately [43]. These models should take into account the uncertainty in outcomes, given the sampling scheme employed and the life-history traits of the parasite. A combination of sampling transmission stages and parasite inbreeding could cause estimates of the underlying adult worm allele frequency to be highly variable, increasing the number of samples that need to be genotyped in order to detect significant changes in the adult worm genome with time after introduction of chemotherapeutic pressure.
Producing a null model to assess the range of adult worm allele frequencies that could give rise to the microfilarial genetic diversity observed in villages having received treatment is complex and beyond the scope of this paper. A dynamic, full transmission model would be required that takes into account the pharmacodynamic properties of the drugs in combination and separately, as the effects of chemotherapy will influence microfilarial genetic diversity for a number of years after chemotherapy. As a result it is not possible to conclude whether adult worm genetic diversity differs between the villages that have and have not received MDA, even though their microfilarial populations differ significantly in their genetic diversity.
The results presented within this paper regarding the metapopulation dynamics of bancroftian filariasis stem from the analysis of a single nucleotide polymorphism in one gene. Further surveys, using multiple neutral polymorphic loci, are required to distinguish demographic and sampling effects from selective pressures [34]. If the allele of interest has been under selection then the observed genotype distribution could have been generated without the need for non-random parasite mating. The accuracy of the model developed here to derive microfilarial genetic diversity is limited by uncertainties regarding the biology of W. bancrofti. Results are dependent on our current ability to mimic adult worm burden and its distribution among hosts. Limitations inherent in the EPIFIL model, the presence of amicrofilaraemic yet circulating filarial antigen-positive infections, and possible heterogeneity in host immune responses could make adult worm burden estimates highly uncertain from microfilarial prevalence and intensity data. The relationship between the number of adult filariae and the rate of microfilarial production is likely to be complex and may depend on the immune responses elicited during the infection. The null model assumes a mean parasite intensity of 13.5 adult worms per host, though sensitivity analysis indicated that model results were relatively insensitive to small changes in parasite intensity around this value (sensitivity analysis ranged from 8.5 to 18.5 adult worms host−1, results not shown). Our conclusions are based on the adequacy of the null model, which may be improved by the inclusion of further biological detail. For example, recent evidence suggests a possible association between β-tubulin genotype in the related filarial parasite, Onchocerca volvulus, and female worm fertility [9],[10], suggesting a cost of resistance. Whilst the same gene has been analyzed in the current study, it is not known whether a similar relationship between genotype and fertility applies to W. bancrofti. If this were the case then the conclusions drawn regarding the causes of the observed genotype distribution should be treated with caution. Although no differences were seen in genotype frequency between the two pre-treatment villages studied, additional baseline surveys (prior to the start of MDA) would be required before firm conclusions regarding the true underlying frequency of allele Y in pre-treatment W. bancrofti populations can be drawn.
Notwithstanding the fact that the F-statistic provides a phenomenological tool rather than a mechanistic measure of inbreeding (and therefore does not describe the biological processes generating excess homozygosity), we proceed to propose some likely causes for the strong degree of non-random mating identified in W. bancrofti, as well as the implications that this may have for the development and detection of anthelmintic resistance.
Our results suggest that adult W. bancrofti worms do not mate randomly within the infrapopulation. This is in agreement with ultrasonography studies that show adult parasites congregating in ‘worm nests’ along lymphatic vessels, which remain stable over time [44]. Spatial heterogeneity within the host may produce multiple reproducing populations within each infrapopulation, which would increase host microfilarial homozygosity. Evidence of an apparent relationship between β-tubulin genotype, the same gene analyzed by Schwab et al. [13], and female worm fertility in the related filaria O. volvulus has been reported by Bourguinat et al. [10]. If such a relationship exists in W. bancrofti, the excess within-host homozygosity reported above may result from the increased fertility of homozygous adult worms. Anthelmintic treatment, prior to the introduction of MDA for lymphatic filariasis, may also have increased non-random mating depending on the selective advantage that allele Y may confer to the parasite at the time of treatment.
The degree of genetic differentiation in the parasite infrapopulation can shed insight into the microepidemiology of parasite transmission [19]–[23]. The metapopulation transmission dynamics of W. bancrofti will depend on the transmission efficiency and biting behaviour of the mosquito vector. Anopheles gambiae sensu stricto and An. funestus are thought to be the main vectors of W. bancrofti in Burkina Faso [45]. Hosts can acquire multiple L3 larvae during the same bite. Although density-dependent processes are known to operate on the uptake and development of W. bancrofti in An. gambiae, infective vectors will regularly transmit multiple related L3 larvae simultaneously [46]. Other mosquito vectors of W. bancrofti have even greater vector competence. For example, up to 32 L3 larvae were recovered from an experimental host after it was bitten by a single Culex quinquefasciatus [47], a main vector in East Africa. Mark-recapture studies and bloodmeal analysis indicate that various mosquito species appear to have high site fidelity, regularly biting multiple members of the same household [48],[49]. These aspects of W. bancrofti transmission increase the likelihood that a host will be infected with closely related parasites and will contribute to the observed genetic differentiation.
More generally, drug treatment may increase infrapopulation genetic heterogeneity, as those parasites within treated hosts which survive treatment may have a higher resistance allele frequency than those harboured within untreated hosts. In Burkina Faso, lymphatic filariasis is treated with albendazole and ivermectin. Evidence indicates that the albendazole plus ivermectin combination has some macrofilaricidal and reproductive effects (mainly associated with albendazole [41]), as well as the microfilaricidal effect (mainly associated with ivermectin). It is possible that a degree of the genetic differentiation between hosts observed in the untreated villages may have resulted from individual members of the community seeking, for instance, treatment for geohelminth infection prior to the introduction of GPELF.
Population subdivision and non-random mating will influence the outcomes of selection under chemotherapeutic pressure in different ways, depending on the initial frequency of the allele under selection and the ecology of the infection. Before the rate of spread of drug resistant parasites can be predicted reliably and accurately, greater knowledge would be required regarding the number, linkage, dominance, and possible negative pleiotropic effects of putative resistance allele(s), as well as regarding the pharmacodynamic properties of the drugs administered singly and in combination. However, useful biological insights can be obtained from mathematical models that make reasonable assumptions concerning the above [50],[51].
If the resistance allele is recessive and it has a low initial frequency, inbreeding will increase parasite homozygosity and as a result, the spread of drug resistant worms across the parasite population (see Figure 6 and [50]). If drug resistance is a semi-dominant trait then parasite inbreeding will either increase or decrease the spread of drug resistance, depending on the efficacy of the drug against heterozygote parasites. Parasite genetic differentiation between hosts will also increase the spread of resistance even when the resistance allele is initially present at a very low frequency, as it increases the probability that male and female resistant worms will inhabit the same infrapopulation. This work is consistent with mathematical models of veterinary helminths which indicate that spatial heterogeneity and aggregated infections between hosts increase the spread of rare recessive genes [27],[28].
The operation of a strong degree of parasite genetic differentiation between hosts reduces the prevalence of infection with drug resistant parasites and would therefore increase the number of hosts and parasites that should be sampled to detect and quantify the frequency of resistance-conferring alleles reliably. Even at high resistance allele frequencies, some hosts will have no phenotypic signs of resistance, particularly if the resistance allele is recessive, and therefore hosts respond to treatment. In practice the number of parasites that can be genotyped will be restricted, so surveys should carefully consider the sampling scheme they employ in order to maximise the accuracy of allele frequency estimates. Repeatedly sampling from the same host increases the chance of detecting a resistance mutation if it is present in that infrapopulation. However, sampling transmission stages from as many hosts as possible should be considered the optimum strategy, even in a population with low parasite genetic differentiation between hosts, as it reduces the chance of repeatedly sampling offspring of the same adult worm. Prior to the introduction of chemotherapy, studies investigating the presence and frequency of putative resistance markers through genotyping transmission stages alone should weight the number of samples they take per host by the host's infection intensity. However, after the start of chemotherapy the best sampling scheme will depend on the pharmacodynamics of the drug and the nature of the questions under investigation.
For human helminth infections, the importance of parasite genetic differentiation between hosts stretches beyond population genetics and will influence the outcomes of parasite elimination campaigns such as the GPELF. The ability of a parasite species to persist in a host population following prolonged MDA will depend in part on the metapopulation dynamics of helminth transmission, the patterns of host compliance with treatment regimes and the pharmacodynamic properties of the drugs used. The aggregated nature of the passage of transmission stages between hosts will make parasite elimination harder to achieve by lowering the breakpoint density (the unstable equilibrium below which the parasite population will tend naturally to local extinction [52]), as overdispersion of parasites will result in fewer hosts with a single-sexed infection. |
10.1371/journal.pgen.1004672 | Sensors at Centrosomes Reveal Determinants of Local Separase Activity | Separase is best known for its function in sister chromatid separation at the metaphase-anaphase transition. It also has a role in centriole disengagement in late mitosis/G1. To gain insight into the activity of separase at centrosomes, we developed two separase activity sensors: mCherry-Scc1(142-467)-ΔNLS-eGFP-PACT and mCherry-kendrin(2059-2398)-eGFP-PACT. Both localize to the centrosomes and enabled us to monitor local separase activity at the centrosome in real time. Both centrosomal sensors were cleaved by separase before anaphase onset, earlier than the corresponding H2B-mCherry-Scc1(142-467)-eGFP sensor at chromosomes. This indicates that substrate cleavage by separase is not synchronous in the cells. Depletion of the proteins astrin or Aki1, which have been described as inhibitors of centrosomal separase, did not led to a significant activation of separase at centrosomes, emphasizing the importance of direct separase activity measurements at the centrosomes. Inhibition of polo-like kinase Plk1, on the other hand, decreased the separase activity towards the Scc1 but not the kendrin reporter. Together these findings indicate that Plk1 regulates separase activity at the level of substrate affinity at centrosomes and may explain in part the role of Plk1 in centriole disengagement.
| Centriole disengagement in telophase/G1 is the licensing step for centrosome duplication in the subsequent S phase. Recent data suggest that separase, together with polo-like kinase Plk1, is essential for the centriole disengagement and individual depletion of either separase or Plk1 alone fails to suppress the centriole disengagement. This raises the question of how separase activity is regulated at the centrosome. By generating a series of separase sensors, we show that separase at centrosomes becomes active already in mid metaphase, well before its activity can be detected at the chromosomes. Depletion of the previously published inhibitors of centrosomal separase, astrin or Aki1, did not promote separase activity at the centrosomes. This indicates that morphological criteria like the formation of multipolar spindles are insufficient criteria upon which to base predictions about separase regulation. Finally, the ability of Plk1 to promote cleavage of the Scc1-based reporter but not of the kendrin reporter reveals regulation of separase activity at the substrate level. These results provide partial explanation of the role of Plk1 in centriole disengagement.
| Centrosomes are the main microtubule organizing centers of animal cells that consist of the organizing centrioles and pericentriolar material. Centrosomes, like DNA, duplicate exactly once per cell cycle. From S phase to the end of mitosis centrosomes are composed of a pair of centrioles, the mother and the daughter centrioles, which lie perpendicular to one another [1]. Separation of the mother and daughter centrioles, also referred to as “centriole disengagement”, takes place in telophase/G1 and is the licensing step for centriole duplication in the next S phase [2]–[4]. Following the centriole disengagement, a flexible linker containing the proteins C-Nap1 and rootletin assembles between the separated centrioles [5]. The C-Nap1/rootletin linker connects the two centrosomes (also named centrosome cohesion) until G2 or the beginning of mitosis when the linker is disassembled by the activity of the kinase Nek2 [6]–[9]. The disjoined centrosomes each containing two orthogonally engaged centrioles then become the poles of the mitotic spindle [9]. Thus, centriole engagement and centrosome cohesion are two distinct processes that are regulated by different mechanisms.
Separase (Espl1), a cysteine protease, is best known for its role in relieving sister chromatid cohesin during the metaphase-anaphase transition by cleaving the cohesin subunit Scc1/Rad21 [10], [11]. The function of separase in centriole disengagement has been established in Xenopus egg extracts [3]. Consistently, centriole disengagement was partially inhibited in human separase knockout cells. However, centriole disengagement was only blocked completely when the activities of both separase and the polo-like kinase Plk1 were simultaneously repressed [12].
Both cyclin B1 and securin have been shown to inhibit separase at chromosome until the end of anaphase [11], [13]. On the other hand, the regulation of separase at centrosomes is poorly understood. The proteins astrin and Aki1 have been proposed to act as inhibitors of centrosomal separase activity [14], [15]. Depletion of either astrin or Aki1 induces multipolar spindles in mitosis with disengaged centrioles, which would be consistent with premature separase activation [14], [15]. Furthermore, shugoshin (Sgo1) is the “guardian” of the chromosomes and prevents the prophase-dependent removal of cohesin from centromeres by recruiting PP2A-B56 to the centromere to counteract Plk1 kinase activity [16], [17]. Interestingly, a smaller version of Sgo1, called sSgo1, associates with the centrosomes. Depletion of Sgo1 promotes centriole disengagement in human cells in a manner that requires Plk1 activity [18].
Kendrin, a splice variant of pericentrin, is a coiled-coil motif containing protein which localizes to the centrosomes, where it recruits the γ-tubulin ring complex and modulates centrosome cohesion through the regulation of Nek2A kinase activity [19]–[21]. Localization studies identified also the subunits of cohesin at the centrosomes [22]. Moreover, siRNA depletion experiments showed that not only kendrin, but also cohesin is important for the integrity of the centrosome [23]. Strikingly, the cohesin subunit Scc1/Rad21 and kendrin/pericentrin B, here referred to as Scc1 and kendrin, respectively, are both cleaved by separase at the centrosome [24], [25]. Subsequent biochemical analyses support the notion that cohesin is the “glue” that connects the mother to the daughter centrioles, and that cleavage of this pool of cohesin by separase promotes centriole disengagement [24]. Furthermore, expression of a non-cleavable version of kendrin blocks centriole disengagement [25].
A fluorescence-based method was used to measure the separase activity on chromosomes [26], [27]. This separase activity sensor comprises the separase cleavage sites of Scc1142-467 flanked by N-terminal mCherry and C-terminal eGFP fluorescent molecules. Cleavage of the sensor releases the eGFP moiety to diffuse throughout the cytoplasm while the mCherry remains anchored at chromosomes because it is fused to C-terminus of H2B. As a result, the color of the sensor at chromosomes switches from yellow to red [26].
Here we investigated the regulation of separase activity at the centrosomes using Scc1- and kendrin-based separase sensors, which were targeted to centrosomes via the PACT domain of AKAP450 [28], [29]. Both sensors changed their fluorescent signal in a manner that required separase activation and the integrity of the separase cleavage site. Centrosomal separase activity strongly increased midway through metaphase ahead of chromatin-associated separase activity. We also tested whether astrin, Aki1 and sSgo1 regulate centrosomal separase activity and show that morphological criteria are insufficient indicators for separase activation at centrosomes.
Separase localizes to centrosomes during mitosis where it regulates the centriole disengagement [12], [30]. However, it remains to be established how this centrosomal separase activity is regulated. To address this important question, we have developed two distinct “separase sensors” that measure separase activity at the centrosomes of individual cells in real time. The sensors contained the separase cleavage sites (SCS) of either Scc1 (142–467 aa; cleavage sites at R172 - cleavage site 1, R450 and R460 - cleavage site 2) or kendrin (2059–2398 aa; cleavage site at R2231), the two known centrosomal separase substrates [26], [31]. mCherry was fused to the N-terminus and eGFP to the C-terminus of each SCS element. Each mCherry-SCS-eGFP module was joined to the N-terminus of the PACT domain of AKAP450 (aa 3643–3808) (Figure 1A, Figure S1A). The PACT domain is a high affinity centrosomal targeting domain, and so will target each of these two reporters to the centrosome [28]. To control for separase cleavage dependent changes in fluorescent signal, we also used reporters in which critical residues within the separase cleavage sites (ExxR) were mutated (RxxE). These mutations inhibit the ability of separase to cleave the fusion protein (separase sensorNC) (Figure S1A) and so this modified reporter serves as an important internal control for separase dependent cleavage [10], [26].
The reporters were stably integrated into the FRT sites of HeLa T-REx cells to render their expression dependent upon the addition of doxycycline [32]. The first version of the Scc1-derived sensor accumulated in the nucleus during interphase and failed to bind to the centrosomes even after nuclear envelope breakdown in mitosis (Figure S1B. However, inactivation of the first nuclear localization sequence (ΔNLS-1, 319–323 aa of Scc1) in the mCherry-Scc1(142-467)-ΔNLS-eGFP-PACT reporter (named Scc1(142-467)-ΔNLS)) promoted its binding to the centrosomes (Figure S1C). Nonetheless a fraction of the sensor was still detected in the cytoplasm and nucleus. Most likely the number of centrosomal binding sites for the PACT based reporter is limited as this has been observed for other PACT fusion proteins [29]. Fluorescence recovery after photobleaching (FRAP) revealed that the mCherry-Scc1(142-467)-ΔNLS-eGFP-PACT reporter stably associated with centrosomes (Figure S1D). The kendrin-based sensor was also enriched at the centrosome (Figure S1C, right panel) with additional signal in the cytoplasm. Thus, both reporters are targeted to centrosomes via their PACT domain.
At chromosomes, separase becomes active just before anaphase onset [26]. In contrast, the exact timing of separase activation at centrosomes is unknown. Real time fluorescence analysis showed that the yellow mCherry-SCS-GFP-PACT signal at centrosomes switched to green GFP-PACT before cells entered anaphase (Figures 1B, C and Figure S1E). This was indicative of reporter cleavage to release the mCherry moiety into the cytoplasm, while eGFP-PACT was retained at centrosomes. The yellow fluorescent signal from the corresponding non-cleavable separase sensorNC persisted at centrosomes throughout the cell cycle (Figures 1B, D and Figures S1E, F). It has been shown that both Scc1 and kendrin are substrates of separase, and they are not cleaved upon Espl1 siRNA [10], [25]–[27], [33], [34]. For proof of principle analyses, we interfered with separase activity via Espl1 siRNA and showed that reporter activation was prevented even when cells passed into G1 phase (Figures S2A–E). Thus, the cleavage of the separase reporters at the centrosome was indeed dependent on separase activity and occurred before anaphase onset.
We next addressed whether the timing of separase activation on chromosomes coincided with its activation on centrosomes. For this, we compared cleavage of the centrosomal mCherry-Scc1(142-467)-ΔNLS-eGFP-PACT and mCherry-Kendrin(2059-2398)-eGFP-PACT reporters with the chromatin-associated sensor. Anaphase onset was used as the internal reference point (t = 0) for both reporters. In line with published data, separase became active at chromosomes between −6 and 0 min before anaphase onset [26]. Both centrosomal reporters, however, were activated between −12 and −6 min, clearly before separase activation on chromatin (Figure 1B, Figure S3). The delayed activation of separase at chromatin might reflect a preferred spatial activation of separase at centrosomes, as has been reported for Cdk1-cyclin B1 [35]. The degradation of cyclin B1 in Drosophila also starts at spindle poles and from there spreads to the metaphase plate [36]. This behavior of the separase inhibitor cyclin B is consistent with the earlier activation of separase at centrosomes. In such a model, separase activity spreads from centrosomes to the cytoplasm. However, it is also possible that separase is activated with the same timing at both locations but cleaves cohesin and kendrin at the centrosomes faster than the chromatin bound cohesin because of topological restrains.
A number of proteins are transported to the centrosomes along microtubules [37], [38]. We used the Scc1-based reporter to ask whether activation of separase at the centrosomes requires microtubules or the activity of the minus-end directed, microtubule-based motor protein dynein. Cells were first synchronized in prometaphase with the drugs STLC ((+)-S-trityl-L-cysteine) or nocodazole that arrest the cells due to spindle checkpoint activation (Figure 2A) [39]. In STLC-treated cells, dynein was subsequently inhibited by the Ciliobrevin D [40]. Cells were then driven from prometaphase to G1 by Cdk1 inhibition with RO-3306, and as previously reported this treatment does not interfere with the activation of separase during mitotic exit (Figure 2A) [41], [42]. Moreover, in vivo experiments revealed that centrioles still disengage in response to Cdk1 inhibition [12].
Upon dynein inhibition or microtubule depolymerization the Scc1-based reporter was cleaved with similar efficiency as in the control (Figure 2B, C) or during a normal mitotic exit (Figure 1B, 24 min). Thus, separase activity at centrosomes is independent of polymerized microtubules or dynein activity. Consistently, the level of Espl1 at the centrosome did not decrease by nocodazole-induced microtubule depolymerization when compared with STLC treated control cells (Figure 2D). Instead, nocodazole slightly increased the centrosomal Espl1 signal (Figure 2D).
Scc1/Rad21 is also a substrate of caspase-3 during apoptosis [43]. We analyzed the level of PARP cleavage in order to eliminate the involvement of caspase-3 in sensor cleavage in our experimental approaches (Figure S4A). Although there was no significant change in apoptotic cleavage of PARP, in order to be completely sure that the apoptotic cleavage of Scc1-based sensors did not interfere with the experimental setup, the experiments were repeated with either a non-cleavable version of the separase sensor (Figure S4B) or with the wild-type sensor in the presence of the apoptosis inhibitor Z-VAD-FMK (Figure S4C). As expected, the non-cleavable sensor was not subject to cleavage and the wild-type sensor was still cleaved in the presence of apoptosis inhibitor. In conclusion, centrosomal activation of separase is independent of microtubule integrity and reporter cleavage is a direct consequence of separase activity at centrosomes.
Activation of separase midway through metaphase prompted us to analyze the localization of separase to discriminate between two possibilities: First, separase is targeted to the centrosomes after initial activation at the centrosome. Alternatively, binding to the centrosome and local activation are two distinct steps. Although we detected a cytoplasmic separase signal during interphase as previously reported [44], no centrosomal signal was seen. From pro-metaphase onwards until telophase/G1 separase was detected at centrosomes (Figure 2E). Furthermore the localization of separase-GFP, expressed in HeLa BAC cells, confirmed the timing of separase recruitment to centrosomes (Figure S5). Thus, separase localizes to centrosomes from prometaphase to G1 but is only active at this location 6–12 min before anaphase onset. This means that separase targeting to centrosomes and separase activation are two independent processes.
The proteins astrin (Spag5) and Aki1 have been implicated in the regulation of separase activity at centrosomes [14], [15]. Depletion of astrin causes premature separase activation and sister chromatid disjunction [14]. Moreover, depletion of either astrin or Aki1 promotes premature centriole disengagement in mitosis [14], [15]. To test whether astrin and Aki1 directly regulate separase activity at centrosomes, each protein was depleted from HeLa cells carrying either the Scc1- or the kendrin-based separase sensors by siRNA (Figure 3A, Figure S6A). Consistent with published data, astrin or Aki1 depletion gave rise to cells with separated sister chromatids (Figure 3B) [14]. However, the Scc1 and kendrin sensors indicated that separase was either not or only very weakly activated at centrosomes (Figure 3C). Furthermore, there was no increase in the number of separated centriole pairs upon astrin depletion since always a mother and daughter centriole pair closely associated with one mitotic spindle pole (Figures S6B, C). It is therefore unlikely that astrin regulates centrosomal separase activity. Similarly, Aki1 depletion did not activate separase (Figure 3C).
Disengaged centrioles remain connected by centrosomal linker proteins including rootletin until onset of mitosis [4]. To eliminate the possibility that in our depletion experiments mitotic centrioles are joined together by the C-Nap1/rootletin linker, we asked whether the protein rootletin was associated with centrosomes [45]. Rootletin was absent from the mitotic centrosomes upon astrin and Aki1 depletion while it was associated with interphase centrioles (Figure S6B). This provides further evidence that mitotic centrioles remain together when astrin and Aki1 are depleted.
Thus, how does astrin or Aki1 depletion cause sister chromatid disjunction without separase activation? Recent reports have implicated the role of astrin in kinetochore-microtubule attachments [46]. Aki1 or astrin depletion probably causes mitotic arrest through activation of the spindle assembly checkpoint that eventually leads to loss of sister chromatid cohesion without separase activation through a mechanism called “cohesion fatigue” [47].
A smaller splice variant of Sgo1, named sSgo1, associates with centrosomes [18]. It has been reported that Sgo1/sSgo1 depletion leads to both the premature disjunction of sister chromatids and centriole disengagement. sSgo1 might counteract Plk1 activity through the recruitment of PP2A-B56, as demonstrated for centromeric Sgo1 [16], [48], [49]. Alternatively, sSgo1 may regulate centrosomal separase activity. To discriminate between these two possibilities, Sgo1 and sSgo1 were co-depleted from our reporter cell line by siRNA (Figure 3A). Depletion of sSgo1 co-depletes Sgo1 because the coding region of this splice variant overlaps with Sgo1. Chromosome spreads revealed that Sgo1/sSgo1 depletion arrested mitotic progression at metaphase with disjoined sister chromatids (Figure 3B). However, Sgo1/sSgo1 depleted cells did not separate the closely associated centrioles prematurely as judged by the persistence of paired centrin signals (Figures S6B, C). Measurements of the distance of the distal centriole marker GFP-centrin also confirmed the tightly association of mother and daughter centrioles in Sgo1/sSgo1 depleted cells (Figure S6D). The centriole pairs in siRNA Sgo1/sSgo1 depleted cells had the same close distance as wild type cells arrested at prometaphase by STLC. In contrast, STLC treated cells that were driven into G1 by Cdk1 inhibition and therefore had disengaged centrioles showed much larger GFP-centrin distances (Figure S6D) [3]. Moreover, we failed to see a significant increase in the percentage of multipolar spindles during mitosis, which is normally caused by premature centriole disengagement (Figure 3D) [50].
Sgo1/sSgo1 depletion did not activate separase at centrosomes or chromosomes as indicated by the co-localization of eGFP and mCherry at centrosomes and chromosomes (Figure 3C, Figure S6E). However, in ∼20% of Sgo1/sSgo1 siRNA cells, we observed unfocused γ-tubulin signals at centrosomes that sometimes contained extra γ-tubulin foci (Figure S6F). The majority of these additional γ-tubulin signals did not contain GFP-centrin suggesting that they arose from disruption of centrosome structure rather than from centriole disengagement. Indeed, loss of tension at kinetochores provokes centrosome fragmentation [51]. Thus, Sgo1/sSgo1 depleted cells fragment the centrosomes due to the loss of kinetochore tension.
Plk1 has been suggested to promote centriole disengagement in a pathway overlapping with separase [12]. This function of Plk1 directed us to test whether Plk1 regulates centrosomal separase activity. We first arrested cells with the kinesin-5 (Eg5) inhibitor STLC in prometaphase. Subsequently, Plk1 activity was inhibited using the specific, small molecule inhibitor BI2536. Cdk1 inhibition (RO-3306) then drove cells out of mitosis (Figure S7A). Plk1 inhibition reduced cleavage of the Scc1 based separase sensor at centrosomes to ∼50% even when cells were incubated for 3 h with the Cdk1 inhibitor (Figure 4A, B). After Cdk1 inhibition the nuclear envelope reformed in 100% of the cells and a fraction of the Scc1 sensor was targeted to the nucleus due to its nuclear localization signal indicating that cells exited mitosis (Figure 4C and Figure S1C). Moreover, inhibition of Plk1 decreased the activity of separase not only at the centrosome but also at the chromatin (Figure S7B).
The impact of Plk1 towards the activity of separase at the centrosome could be due to the decrease in the phosphorylation of the anaphase promoting complex APC/C. Plk1 phosphorylation of APC/C complex in G2 cells maintains the APC/C in an inactive state, such that inhibition of Plk1 induces premature APC/C activation in G2 [52]. In order to test, whether we see a similar effect during mitosis, we analyzed the steady state levels of the APC/C substrate cyclin B1. Cyclin B1 levels of prometaphase arrested cells (nocodazole) did not increase in repeated experiments following Plk1 inhibition (Figure 5A, lines 1 and 2). However, kendrin was still efficiently cleaved when cells were driven out of mitosis by the Cdk1 inhibitor RO-3336 as indicated by Plk1/Cyclin B1 degradation and H3S10 dephosphorylation (Figure 5A, lanes 3 and 4). These findings argue against the possibility that Plk1 inhibition during mitosis activates the APC/C that then would degrade the separase inhibitors securin and cyclin B1 to promote separase activation.
Alternatively, Plk1 phosphorylation of Scc1 might increase its cleavage. Such a model has been proposed for the chromosomal Scc1 [49]. Scc1 has two separase cleavage sites (R172 and R450/R460) with neighboring Plk1 phosphorylation sites (e.g. S175 and S454). At chromosomes Plk1 mainly regulates the Scc1 cleavage site at R450/R460. Cleavage at R172, moreover, is only moderately stimulated by Plk1 [49]. To test whether Plk1 stimulates Scc1 cleavage at centrosomes through substrate phosphorylation, we first inactivated the separase cleavage at R172 (mutation from “ExxR” to “RxxE”). Additionally, we mutated the Plk1 phosphorylation site Ser454 to Ala to prevent phosphorylation near the second separase cleavage site. Inactivation of the first separase cleavage site by the R172E mutation strongly reduced cleavage of the Scc1 reporter indicating that the first separase cleavage site around R172 is preferentially cleaved over the second site at R450/R460 (Figure S8A, B).
We next asked whether the R450/R460 site is regulated through Plk1 phosphorylation at S454. The R172E/S454A double mutation completely abolished separase cleavage of the Scc1 reporter (Figure S8A, C). Cleavage of the first site at R172 was independent of the Plk1 phosphorylation site S175 as the double phospho-dead mutant (S175A/S454A) was still cleaved by separase (Figures S8A, D) as reported before [49]. However, Plk1 inhibition by BI2536 in S175A/S454A mutant cells prevented the complete cleavage of this mutant sensor (Figure S8E). This implies that additional Plk1 sites in Scc1 might be important for the separase activity at the centrosome. Additional Plk1 phosphorylation sites close to the fist separase cleavage site of Scc1 have been reported [49]. Mutating these sites (T144A, S153A, S175A, S185A, T186A, T187A, T188A, S189A) together with inactivating the second separase cut site (R450E and R460E, Scc1(142-467)-8A-ΔNLS-ERRE) strongly reduced cleavage efficiency of the Scc1 separase reported at centrosomes (Figures S9A, B). Thus, separase cleavage of both cut sites of Scc1 at centrosomes is promoted by Plk1 phosphorylation.
Interestingly, the same Plk1 inhibition experiment with the kendrin sensor did not reveal a dependency of sensor cleavage on Plk1 activity (Figures 5B and C). This result excludes a role for Plk1 in separase targeting to the centrosomes or in separase specific activity since both reporters would be equally affected if this were the case. Consistently, separase's localization on centrosomes was not affected by Plk1 inhibition when compared with nocodazole arrested prometaphase cells (Figure 5D). In contrast, Plk1 inhibition affected localization of γ-tubulin at centrosomes implying that Plk1 inhibition worked as expected (Figure 5D) [53]. Taken together, a likely explanation of our results is therefore that Plk1 activates the Scc1 substrate whereas such an activation step is not required for kendrin.
In conclusion, we have constructed reporter proteins that measure the activity of separase at centrosomes. With these sensors in hand, we have tested putative regulators of centrosomal separase. This analysis indicates that astrin and Aki1 do not activate separase at centrosomes. Instead, we propose that the centriole separation phenotype that arises from astrin and Aki1 depletion is a secondary consequence of the loss of sister-chromatid cohesion [14], [18]. This demonstrates the importance of using separase sensors to analyze separase activity at centrosomes. Morphological criteria such as multipolar spindles or multiple centrosomes do not support conclusions about separase activity at centrosomes as these phenotypes may arise from the loss of sister chromatid cohesion during prolonged mitotic arrests. The sensors we have constructed are excellent tools to find the regulators of separase at the centrosome in screening-based studies.
Our data suggest that separase localizes to centrosomes from prometaphase until the end of mitosis with continued accumulation to enhanced levels during anaphase. Separase was active at centrosomes before it was activated at chromosomes, which may be explained by the early loss of Cdk1-cyclin B1 activity at the centrosomes ahead of the general wave of cyclin B1 degradation [36], [54], [55]. The APC/C complex may be initially activated at centrosomes before diffusing throughout the cell. We also found that Plk1 promotes cleavage of a subset of substrates by separase at centrosomes, while kendrin does not require Plk1 activation for separase cleavage. This finding at least in part explains the role of Plk1 in centriole disengagement.
The following antibodies directed against the indicated proteins were used in this study: Sgo1 (1∶1000, Thermo Scientific PA5-30869), astrin (1∶1000, Bethyl Laboratories A301-512A), tubulin (1∶1000, Sigma T9026), pericentrin (1∶2000, Abcam ab4448), cyclin B1 (1∶200, CR UK V152), PARP (1∶1000, Cell Signaling #9532), separase (1∶500, Abcam ab16170) for WB, separase (1∶500, Abcam ab3762) for IF and γ-tubulin (1∶1000, Sigma).
HeLa Centrin2-GFP cells, HeLa FRT cells, Separase-GFP expressing HeLa BAC cell line and U2OS cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM) Glutamax (Gibco) supplemented with 10% FBS, 1% P/S and 1% Na-Pyruvate. Stable HeLa FRT cells were created and sustained as described previously [56].
At least two different siRNAs were used for depletion experiments of astrin, Aki1 and sSgo1. siRNA oligos that were directed against the same mRNA gave identical phenotypes in depletion experiments. Therefore, the results of only one siRNA oligo (marked with *) per mRNA are shown in this manuscript. Cells were transfected with cDNA or siRNA according to the manufacturer's protocol via Lipofectamine 2000 or RNAiMax, respectively (Invitrogen). Sgo1 siRNAs (s45599*: 5′-CAUCUUAGCCUGAAGGAUAtt-3′ and s45600: 5′-GGCAAACGCAGGUCUUUUAtt-3′, Ambion; #L-015475-00-0005 pool of: 5′-GUGAAGGAUUUACCGCAAA-3′, 5′-AAACGCAGGUCUUUUAUAG-3′, 5′-GUUACUAUCUCACAUGUCA-3′, 5′-CAGCCAGCGUGAACUAUAA-3′, Dharmacon) were used at concentration of 100 µM. Aki1 siRNA (#s226792*: 5′-AACAAAGACAUCCAGAUCGCCAGGG-3′, Ambion; #GS54862 pool of: 5′-CACGAGCGCATCGTCAAGCAA-3′, 5′-CAGCGCCAAGATGCGGCGCTA-3′, 5′-CAAGTTCGAAGTGGTTCACAA-3′ and 5′-CCCGGCGTCCACGCCTACCTA-3′, Qiagen), astrin siRNA (#SI02653938: 5′-AAAUUAGCUCUACUCCUAAtt-3′ and #SI02653945*: 5′-CCGACAACUCACAGAGAAAtt-3′), Espl1 siRNA (#s121651: 5′-GCUUGUGAUGCCAUCCUGAtt-3′, Ambion) were used at concentration of 50 µM. Sgo1 depleted cells were checked after 24 h whereas astrin, Aki1 and Espl1 depleted cells were checked 48 h after transfection via microscopy and immunoblotting.
For live cell imaging, cell cycle progression was blocked with 2.5 mM thymidine (Sigma, #T1895) for 24 h. After three washes with PBS, cells were released into fresh media for 10 h. For inhibitor experiments, cells were arrested in prometaphase with 5 µM S-trityl-L-cysteine (STLC, Sigma #164739) for 15 h, and Plk1 inhibitor BI2536 or 50 µM dynein inhibitor Ciliobrevin D (#250401, Millipore) was added for 1 h more. 5 µM of Cdk1 inhibitor RO-3336 (Millipore, #217699) was used to trigger mitotic exit. Z-VAD (OMe)-FMK was used for apoptosis inhibition (Millipore, #627610).
Cells, grown on coverslips, were fixed with ice-cold methanol for 5 min, and rehydrated with PBS. Coverslips were incubated with 10% FBS (fetal bovine serum) for 1 h, and washed with PBS and then re-incubated with primary antibodies in 3% BSA (Sigma, #05470) for 1 h. Following three washes with PBS, the coverslips were further incubated in 1∶500 dilution of 2 mg/ml Alexa-488/Alexa-555/Alexa-647 (Molecular Probes) conjugated secondary antibodies, which were diluted in 3% BSA plus 5 µg/ml Hoechst 33342 (Molecular Probes), for 30 min. The coverslips were mounted in Prolong Gold Antifade (#P36930, Molecular Probes).
For Espl1-GFP localization, HeLa cells were pre-extracted in 0.1% TX-100 plus 20 µg/ml Alexa-647 conjugated nanobody (Chromotek) for 6 min, following 3 washes with PBS, the cells were directly observed in PBS without fixation.
For immunofluorescence analysis of separase in U2OS cells and co-localization of separase and centrosomal markers in HeLa Espl1-GFP cells, cells were pre-extracted with 0.1% TX-100 for either 1 min or 2 min, respectively, before fixation in ice-cold methanol for 5 min. Subsequent procedures were as above.
The images were quantified using Fiji (ImageJ, http://fiji.sc/Fiji). The onset of anaphase was marked by the separation of sister chromatids. The mean fluorescence intensities of GFP and mCherry at the centrosomes were quantified using raw data without projection since the Scc1-based sensor, which also localizes to the nucleus, disperses to the cytoplasm after nuclear envelope breakdown to create a background signal. This makes quantifications of Z-projections of the sensor incomparable. The centrosome signals are usually observed in only 1 stack, as the distance between each stack was 1 µm (double the size of a centrosome). For the kendrin-based sensor, quantifications were made with Z-projected images as the sensor specifically localizes to the centrosome. For background correction, the cytoplasmic signal was subtracted from the measured centrosomal signal. The mCherry/eGFP (RR) ratio was used to represent sensor cleavage efficiency. In order to assess the rate of cleavage, each RR value was normalized to the average of two measured RR values. If the ratio was negative, it was considered to be zero. The graphs were plotted using Prism 6 software. For the H2B sensor, eGFP/mCherry ratio was used as the sensor was put C-terminally. For each quantification, n represents the number of centrosomes quantified.
The centriole distance in 3D has been measured using Fiji.
Stable HeLa FRT cells were seeded onto Labtek Chambers (Thermo Scientific, #155411) and induced with 2 µg/ml doxycyclin (Sigma, #D9891) for 24 h before being observed in Live Cell Imaging Media (Gibco) using DeltaVision Olympus IX71 microscope (Applied Precision) equipped with DAPI, FITC, TRITC, and Cy5 filters (Chroma Technology) and CoolSNAP HQ camera (Photometrics). Images were taken every 6 min with 14 z-stack (1 µm/stack) using Plan Apo 60× NA 1.4 oil-immersion objectives (Olympus) with 2×2 binning.
Cells were grown in 6 cm dishes. 24 or 48 h after siRNA transfection, 100 ng/ml colcemid was added before a further incubation for 1 h (Kryomax, Invitrogen). The experiment was continued as before with a slight modification [57]. In brief, cells were collected and suspended in 0.8% sodium citrate (Sigma, #W302600), and incubated for 10 min. After sedimentation at 1,000 rpm for 10 min, cells were fixed with freshly prepared fixation solution (75% methanol+25% acetic acid) and incubated for 10 min. This process was repeated 3 times before final resuspending the cells in 300 µl of fixation solution and dropping them onto Fisher Superfrost/Plus slides (Thermo Scientific) followed by drying. Finally, the slides were incubated in 1 µg/µl Hoechst solution for 15 min and mounted with number 1.5 coverslips using Prolong Gold (Molecular Probes).
Cells were collected by scraping and washed with PBS. After lysis in 10 mM Tris-Cl pH 7.5; 150 mM NaCl; 5 mM EDTA; 0.1% SDS; 1% Triton X-100; 1% deoxycholate supplemented with 1 mM PMSF (Sigma) and Roche protease inhibitor cocktail for 30 min cell pellets were boiled with Laemmni buffer.
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10.1371/journal.pgen.1007940 | The Drosophila fussel gene is required for bitter gustatory neuron differentiation acting within an Rpd3 dependent chromatin modifying complex | Members of the Ski/Sno protein family are classified as proto-oncogenes and act as negative regulators of the TGF-ß/BMP-pathways in vertebrates and invertebrates. A newly identified member of this protein family is fussel (fuss), the Drosophila homologue of the human functional Smad suppressing elements (fussel-15 and fussel-18). We and others have shown that Fuss interacts with SMAD4 and that overexpression leads to a strong inhibition of Dpp signaling. However, to be able to characterize the endogenous Fuss function in Drosophila melanogaster, we have generated a number of state of the art tools including anti-Fuss antibodies, specific fuss-Gal4 lines and fuss mutant fly lines via the CRISPR/Cas9 system. Fuss is a predominantly nuclear, postmitotic protein, mainly expressed in interneurons and fuss mutants are fully viable without any obvious developmental phenotype. To identify potential target genes or cells affected in fuss mutants, we conducted targeted DamID experiments in adult flies, which revealed the function of fuss in bitter gustatory neurons. We fully characterized fuss expression in the adult proboscis and by using food choice assays we were able to show that fuss mutants display defects in detecting bitter compounds. This correlated with a reduction of gustatory receptor gene expression (Gr33a, Gr66a, Gr93a) providing a molecular link to the behavioral phenotype. In addition, Fuss interacts with Rpd3, and downregulation of rpd3 in gustatory neurons phenocopies the loss of Fuss expression. Surprisingly, there is no colocalization of Fuss with phosphorylated Mad in the larval central nervous system, excluding a direct involvement of Fuss in Dpp/BMP signaling. Here we provide a first and exciting link of Fuss function in gustatory bitter neurons. Although gustatory receptors have been well characterized, little is known regarding the differentiation and maturation of gustatory neurons. This work therefore reveals Fuss as a pivotal element for the proper differentiation of bitter gustatory neurons acting within a chromatin modifying complex.
| Ski/Sno proteins have been discovered as proto-oncogenes transforming chicken fibroblasts into cancer cells. They have been found to be ubiquitously expressed in embryonic and adult tissues and to interfere with TGF-ß/BMP signaling. More recently, a group of proteins has been discovered which belongs to the same protein family, the functional Smad suppressing elements (Fussel). They have a highly restricted, mainly neuronal expression pattern suggesting different functional importance compared to Ski/Sno. We have used Drosophila as a model organism to characterize the highly specific neuronal expression pattern and created knock-out mutations within the Drosophila fuss gene. Surprisingly, fuss mutants are fully viable, but they show defects in bitter taste perception, and indeed, we could prove that Fuss is expressed specifically in bitter sensing neurons, where it affects their terminal differentiation making these cells insensitive for bitter compounds. To understand the molecular process involved in Fuss function we started protein interaction studies and could show, that Fuss forms part of a chromatin modifying complex, which seems to be important for the proper differentiation of neurons in the adult nervous system, therefore, assigning Drosophila as an indispensable model to study the molecular function of the Fuss protein family.
| During development, the TGF-ß superfamily plays an important role in cell proliferation, differentiation, apoptosis, cell adhesion, wound healing, bone morphogenesis and cell motility [1]. Accordingly, there are multiple inhibitory factors taking care of proper regulation of TGF-ß pathways. Besides inhibitory Smads and Smurfs, which act by preventing the activation of TGF-ß receptors, another group of negative regulators of the TGF-ß pathway exists: The Ski/Sno protein family [2–4]. Although Ski/Sno proteins are classified as proto-oncogenes, their exact role in cancer progression is not fully understood. Various experimental approaches have identified pro- and anti-oncogenic features, where the tumor promoting function of Ski/Sno proteins seems to be mainly linked to their ability to counteract the anti-proliferative effects of TGF-ß signaling [5,6]. Physiologically, Ski and Sno have both been implicated in axonal morphogenesis, myogenesis and mammary gland alveogenesis [7–9]. Proteins of the Ski/Sno family are characterized by a Ski/Sno homology domain and a SMAD4 binding domain. These domains, although resembling DNA binding domains, mediate protein-protein interactions enabling binding of mSin3a, N-CoR, the histone deacetylase HDAC1, SMAD4 and different regulatory SMADs, thus leading to the recruitment of a repressive transcription complex, binding to target genes of the TGF-ß signaling pathway [10–12].
Whereas Ski and Sno are expressed mainly ubiquitously, two additional members of the Ski/Sno protein family, the functional smad suppressing elements (Fussel) 15 and 18 (Skor1 and Skor2 in mouse, respectively), are highly restricted to postmitotic neurons such as Purkinje cells [13–15]. Previous analysis showed that Skor1 interacts with Smad3 and acts as a transcriptional corepressor for LBX1, whereas Skor2 inhibits BMP signaling in overexpression assays and is required for the expression of Sonic Hedgehog in Purkinje cells. In addition, Skor2 is needed for proper differentiation of Purkinje cells and knockout mice die prematurely within 24 h after birth. However, there is no further insight into the functional mechanisms of Skor1 in mice [15–17].
In contrast to vertebrates, Drosophila melanogaster has only two Ski/Sno proteins: The Ski novel oncogene Snoo and the functional Smad suppressing element Fussel (Fuss). Snoo is the homologue of Ski and Sno and Fuss the homologue of Skor1 and Skor2. As in mice, Snoo is expressed broadly during development and adulthood, whereas Fuss expression is limited to a subset of cells in the nervous system during development [18]. Snoo has been found to be involved in eggshell patterning and in wing and tracheal development in Drosophila melanogaster [19–21]. Recent findings of Fuss function are highly controversial due to the lack of a reliable Drosophila fussel mutation. Loss of function experiments with a chromosomal deletion suggested, that Fuss acts as a cofactor for Smox signaling enabling ecdysone receptor (EcrB1) expression in developing mushroom bodies in the brain. In addition, a severe malformation of the adult mushroom bodies was detected [22]. Contrary to these results, in overexpression assays Fuss leads to an inhibition of BMP signaling via its interaction with Medea, the SMAD4 homologue in Drosophila melanogaster [18].
To clarify the molecular and physiological function of Fuss, we generated a complete loss of function allele via CRISPR/Cas9 editing. Interestingly, fuss mutants are fully viable, suggesting a modulatory function during development or/and adulthood, rather than an essential role for cell survival. To be able to better analyze Fuss expressing cells, we generated specific antibodies and reporter lines, which enabled us to further clarify the expression pattern of Fuss. During development, Fuss is expressed postmitotically in a highly restricted number of interneurons of the central nervous system (CNS). In our fuss mutant, we could show that Fuss, in contrast to previous studies, is neither acting as a negative regulator of BMP signaling endogenously, nor involved in mushroom body development. A targeted DamID (TaDa) experiment could not only confirm our findings molecularly, but also revealed, that Fuss is expressed in bitter sensory neurons. In consequence, fuss mutant flies lack the ability to sense bitter compounds and show reduced expression of bitter gustatory receptors. Furthermore, interaction studies show that Fuss can form a protein complex with Rpd3, a homologue of HDAC1, and indeed, downregulation of rpd3 in bitter gustatory neurons resembles loss of Fuss. Thus, we propose that the Fuss/Rpd3 complex is required for proper cell fate determination in gustatory neurons either by direct or indirect control of the expression of gustatory bitter sensing receptors.
The fuss gene is localized on the fourth chromosome and therefore, due to the limited genetic resources for this chromosome, fuss mutations have escaped discovery in genetic screens for developmental mutations and previous research on this gene focused either on overexpression studies or on a chromosomal deletion covering multiple genes [18, 22]. However, it was shown recently that genes of the fourth chromosome can be CRISPR/Cas9 edited via homology directed repair and thus, we decided to generate a fuss null allele using this system [23]. A prerequisite for proper mutant generation is a detailed analysis of the genomic organisation of the gene. The fuss gene locus is fairly complex as it overlaps N-terminally with the RNA gene sphinx and C-terminally with the RNA gene CR44030. In addition, the Pax6 homologue twin of eyeless (toy) lies downstream of fuss and is transcribed in the opposite direction. Although the two genes are over 10 kb away from each other, regulatory sites of toy are located in the vicinity of the transcription start site of fuss [24].
Three different transcriptional start sites of fuss are annotated, which lead to three transcripts fussB, fussC and fussD. FussB and fussD have an identical amino acid sequence in contrast to the fussC transcript, which differs in 25 amino acids N-terminally from fussB and fussD. FussC uses an artificial promoter sequence due to a transposon insertion, leading to the assumption that this transcript is rather insignificant as it is of very low abundance (see below). To reduce side effects by deleting a silencer/enhancer structure of sphinx or toy and to maximise the effect on fuss, we removed an 855bp fragment, which is shared by all three transcripts (Fig 1A). This fragment contains the conserved Ski/Sno/Dac homology and SMAD4 binding domains, which are important for protein interactions and function of the Ski/Sno proteins [25,26]. Simultaneously, an attP site was introduced in the open reading frame of all fuss transcripts, which additionally results in a premature stop codon. Successful deletion of the two domains and the presence of the attP site was confirmed by PCR and subsequent DNA sequencing (S1A Fig). We termed this deletion fussdelDS and it is a null allele. Due to the deletion and the premature stop codon no functional proteins can be made, which could be shown by anti-Fuss antibody stainings of heterozygous and homozygous fussdelDS embryos (S1B Fig and S1C Fig). As a second mutant allele, we used the MiMIC-line fussMi13731, which is a gene trap insertion leading to the expression of GFP under the fussB and fussD promotor and a premature transcriptional stop of the fussB and fussD transcripts (Fig 1A). qPCR revealed that transcript levels of fussB and fussD in homozygous fussMi13731 flies are reduced to ten percent in contrast to WTB flies (S1D Fig). With an anti-GFP and anti-Fuss antibody staining, we could confirm that heterozygous fussMi13731/+ flies express GFP in the correct Fuss expression pattern, whereas Fuss staining in homozygous fussMi13731 flies is reduced to background levels (S1E Fig and S1F Fig). We conclude that fussMi13731 is at least a strong hypomorph for fuss, which further suggests that fussC is not specifically expressed or only at very low levels. In addition to fussMi13731, which can be used as a GFP reporter line, we created a Gal4 line from fussMi13731 by recombination mediated cassette exchange [27]. This Gal4 line was named fussBD-Gal4 and it was edited with the CRISPR/Cas9 system following the same strategy as for the fussdelDS allele. This resulted in a line called fussdelDS-Gal4, which allows Gal4 expression in Fuss expressing cells in a mutant background enabling us to analyze the presence and integrity of these cells. At last, we generated a UAS-t::gRNA-fuss4x line, which allows cell specific gene disruption of fuss via the UAS-Gal4 system [28]. The gRNAs target four CRISPR target sites located in the DNA sequence of the Ski/Sno homology domain (S1G Fig). We could detect a strong loss of GFP signal in adult brains of flies overexpressing GFP tagged Fuss, Cas9 and t::gRNA-fuss4x by fussBD-Gal4 compared to adult brains only expressing GFP tagged Fuss and Cas9 by fussBD-Gal4 (S1H and S1I Fig).
In a previous study by Takaesu et al. [22], a 40 kb spanning genomic deletion including the fuss gene (among several other genes) was used for functional studies of the fuss gene. They observed a strongly reduced survivability during development and a decreased lifespan, which was attributed to the loss of Fuss expression alone. In contrast to their results, we did not observe a reduced survivability during larval or pupal stages with our fuss mutant flies. Therefore, we conducted longevity experiments. Neither homozygous fussMi13731 nor fussdelDS–flies showed a significant reduction in lifespan compared to their controls (Fig 1B and Fig 1C).
Next, we compared the CD8-GFP expression pattern of heterozygous fussdelDS-Gal4/+ and mutant fussdelDS-Gal4/fussdelDS flies. We did neither observe an evident loss of GFP positive cells in the CNS of third instar larvae (Fig 1D and Fig 1E) nor in three to five-day old adult flies (Fig 1F and Fig 1G). Therefore, loss of fuss does neither lead to cell death, nor to a reduced survival during development or to a shortened lifespan.
Due to the absence of any clear visible phenotype, we created specific polyclonal antibodies against a 16 kDa nonconserved fragment localized at the C-terminus of Fuss to characterize Fuss expressing cells and to draw conclusions about its function (S2A Fig). These anti-Fuss antibodies clearly detect a Fuss-GFP fusion protein on western blots and stainings mirror previously conducted RNA in situ hybridisations (S2B Fig) [18, 22].
In a first overview of Fuss staining during embryonic development, Fuss expression is mainly observed in the embryonic brain (Fig 2A, circles), the developing stomatogastric nervous system (Fig 2A, arrowhead), single cells lying anterior to the CNS (Fig 2A arrows), which will develop to inner gustatory neurons as shown later and the ventral nerve cord (VNC, Fig 2B). As Fuss is characterized by its conserved domains as a member of the Ski/Sno protein family, which are all considered to be transcription regulators, we observe Fuss protein, as expected, exclusively localized in the nucleus. During embryonic development, Fuss protein appears first at stage 13 and the number of Fuss positive cells increases continuously from early to late embryonic stages as previously observed (S2C Fig [22]). At embryonic stage 16, expression can be observed in two to five cells per hemineuromer with ascending numbers from posterior to anterior (Fig 2B and S2C Fig).
The late appearance of the Fuss protein during development suggested, that Fuss might be expressed only postmitotically. We confirmed this hypothesis by visualizing ganglion mother cells in the embryo with anti-Prospero and Fuss cells with anti-Fuss antibodies and no overlapping stainings were detected (Fig 2C). As shown by colocalization studies with the glia marker Repo and the neuronal marker Elav, the staining pattern is exclusively neuronal (Fig 2D). To further identify neuronal subpopulations in hemineuromers of the VNC, prominent neuronal markers such as Engrailed (En), Even skipped (Eve), Apterous (Ap), Hb9, Dachshund (Dac) and Twin of eyeless (Toy) were utilised. No colocalization of Fuss with the interneuron marker En or with the motoneuron markers Eve or Hb9 was observed (S2D Fig, S2E Fig and S2F Fig). Because Eve and Hb9 label most of the embryonic motoneurons, Fuss is unlikely to be expressed in motoneurons [29,30]. We were especially interested if Dac and Fuss colocalize, because the interneuron marker Dac shares sequence similarity with Ski and Sno and consequently is a related protein to Fuss [31,32]. Interestingly, Dac and Fuss are partially coexpressed, which emphasizes that at least some Fuss neurons are interneurons (arrowhead, Fig 2E and Fig 2F). As the toy gene lies only 11 kb downstream of fuss and is transcribed in the opposite direction, it is reasonable that they partially share enhancer/silencer regions. Remarkably, we only found one Toy positive Fuss neuron per hemineuromer in the VNC (arrow, Fig 2E and Fig 2F) excluding extensive overlap of regulatory regions. Ap is expressed in three cells per abdominal hemineuromer. These cells are subdivided into one dorsal Ap and two ventral Ap interneurons [33]. Using the ap-tau-LacZ reporter, which only labels one ventral Ap cell and the ap-Gal4 driver line we showed, that both ventral AP interneurons are Fuss positive (Fig 2G and S2G Fig). Due to the location of the Toy positive Fuss neuron, we assume that it is one of the ventral Ap cells and therefore also an interneuron.
Taken together we could show that Fuss is expressed only postmitotically in interneurons in the developing CNS, which will be further confirmed later.
Fuss is expressed in heterogenic neuronal populations, which are represented by differentially expressed markers and by their projection patterns. To develop new approaches to identify and study viable phenotypes in fuss mutants, it was of upmost importance to identify genes, which are regulated by Fuss. Therefore, we performed a targeted DamID (TaDa) experiment by expressing a Dam-PolII fusion protein with the fussdelDS-Gal4 driver line. RplI215, the large subunit of the RNA Polymerase II, is fused with the Dam methylase and thus this so called Dam-PolII fusion protein enabled us to detect the binding sites of the RNA Polymerase II similar to an RNA PolII ChIP and to detect transcribed genes in these neurons without cell sorting [34]. As a control, the unfused Dam protein was expressed with the fussdelDS-Gal4 driver line. Expression of UAS-Dam or UAS-Dam-PolII was inhibited by Gal80ts during development and expression of these proteins was allowed for 24 h at 29°C in one to three-day old flies. Next generation sequencing libraries were generated from three different biological replicates expressing Dam-PolII and from three replicates expressing Dam alone. Each experiment was compared to each control leading to nine individual datasets. Because the binding patterns of all nine files were highly similar, individual datasets were averaged to reduce the amount of false positive hits of expressed genes. Genes with a false discovery rate (FDR) lower than 0.01 were accounted as expressed resulting in 2932 genes (S1 Appendix). The TaDa data is represented as a log2 ratio of Dam-PolII/Dam. As expected, fuss was one of the genes with the lowest FDR and highest PolII coverage (Fig 3A). This clearly indicates that the approach was carried out successfully. Furthermore, genes already identified by antibody stainings such as elav, dac or toy, were also detected by the TaDa experiment. Toy was also expressed in some Fuss neurons in adult brains (Fig 3B). This again underlines, like already observed during embryonic development, that fuss and toy might share common silencer/enhancer elements with Fuss. To further verify the TaDa data, colocalization experiments were conducted. Two cell fate markers atonal (ato) and acj6 were enriched in our dataset and we could also detect the expression of these two proteins via immunofluorescense stainings in Fuss neurons (Fig 3B). Furthermore, we analyzed genes which show no or low PolII coverage e.g. pale (ple) and Insulin-like peptide 2 (Ilp2) via immunofluorescence and could not detect any staining in Fuss positive neurons (S3 Fig). In particular, the absence of Fuss in insulin producing cells is in disagreement with recent published results using enhancer/reporter constructs (S3B Fig, [35,36]). In summary, we can conclude, that using this strategy, we have successfully generated an adult Fuss neuron specific transcriptional profile.
In the next step, we wanted to search for potential target genes of Fuss using the same strategy and conditions as above, but this time fussdelDS-Gal4 was kept over the fussdelDS allele to profile transcription of fuss mutant neurons. Again, individual datasets were averaged and genes with an FDR lower than 0.01 were accounted as expressed resulting in 3150 genes (S2 Appendix). The comparison of the log2(DamPolII/Dam) data of heterozygous fussdelDS/+ and homozygous fussdelDS flies showed, that there is not a strong deviation (coefficient of deviation R2 = 0.889) of the mutant transcriptional profile from the control (Fig 3C). Because Fuss is only expressed in a small number of CNS neurons, the acquired data can only be confirmed by antibody staining and not by semiquantitive qPCR or western blots from whole heads. There were three genes which attracted our attention: Eaat2, Ir76b and especially Gr66a as they provided a possible link to Fuss expression in gustatory sense neurons (Fig 3D). These genes could be found in both datasets, although only Eaat2 had an FDR lower than 0.01 in both datasets. The PolII coverage of Eaat2 and Ir76b was only slightly different between homozygous and heterozygous flies, whereas Gr66a, which is exclusively expressed in bitter gustatory sense neurons (GRNs), showed a significant reduction in mutant flies (S1 Appendix and S2 Appendix).
It has been shown that the glutamate aspartate transporter Eaat2 is expressed in sensory neurons [37]. The ionotropic receptor Ir76b is expressed in gustatory neurons and the gustatory receptor Gr66a is specifically expressed in bitter GRNs, where Gr66a is a very important component for bitter taste sensation [38,39]. We already observed Fuss expression in cells outside of the larval CNS, therefore, to confirm the TaDa datasets, we analyzed gustatory neurons in larval and adult stages. In larvae, Fuss expression cannot be observed in the terminal or dorsal organ, but it can be found in the inner gustatory sense organs. We found Fuss expression in two pairs of neurons in the dorsal pharyngeal sensilia (DPS, Fig 4A), one neuron pair in the dorsal pharyngeal organ (DPO, Fig 4A) and two neuron pairs in the posterior pharyngeal sensilia (PPS, Fig 4A). None of the GRNs in the ventral pharyngeal sense organ (VPS) express Fuss. These cells have been already characterized by expression of different gustatory receptors and we found that larval Fuss expressing GRNs show a colocalization with a marker for bitter sensing neurons Gr33a [40]. In addition, one neuron pair in the DPS also shows an overlap with Gr93a which has been shown to be important for caffeine response in larvae (Fig 4B, [41,42]).
Later, in adulthood, Fuss expression continues in GRNs of the proboscis. In the adult labellum three different types of sensilla can be found divided into short (S-type), intermediate (I-type) and long sensilla (L-type). Intermediate sensilla are innervated by two GRNs and short and long sensilla by four GRNs [43]. Interestingly Fuss expression is observed in one GRN per gustatory sensilla and is consistently colocalized with the bitter GRN marker Gr66a in neurons innervating short and intermediate sensilia (Fig 4C [38]). Long sensilla do not contain a Gr66a positive GRN, therefore, all Gr66a neurons in the labellum are Fuss positive, but not vice versa. Another gustatory receptor which is broadly expressed and labels sweet GRNs is Gr5a, but no overlap with Fuss positive neurons was observed (Fig 4D). Besides Gr66a our TaDa dataset revealed that the ionotropic receptor Ir76b is expressed in Fuss neurons. Ir76b has been shown to be expressed by one GRN per L-type sensillum, which plays a role in attractive salt tasting [44]. We found that in L-type sensilla Fuss is coexpressed with Ir76b (Fig 4E–4G). Besides the expression in GRNs of the proboscis we found Fuss being expressed in two GRNs each in the last two tarsal segments in every leg (S4A Fig). In conclusion, we integrated Fuss expression into the GRN model from Freeman and Dahanukar (Fig 4H, [45]) and demonstrate that Fuss is expressed in bitter neurons in S- and I-type sensilla and in salt attracting neurons in L-type sensilla.
By its gustatory system Drosophila melanogaster can discriminate between valuable food sources for foraging or egg laying and toxic compounds which could harm the fly or its offspring [46]. To address if Fuss is required for the proper development of GRNs, we focused on the impact of Fuss mutation on differentiation of bitter GRNs, because Fuss is expressed in all bitter GRNs of the proboscis. To detect if fuss mutant flies display an impaired bitter taste sensation, we tested one to three-day old flies in a two-choice feeding assay. In our standard test, flies had to choose between 1mM sucrose or 5mM sucrose plus 10mM caffeine. We calculated a preference index ranging from zero to one, where zero indicates complete avoidance of the bitter compound and one a complete preference for it, due to the higher sugar concentration. First, Fuss expressing neurons were ablated by UAS-rpr expression with fussBD-Gal4 to show their importance in bitter sensing and indeed, these flies showed a strong impairment of bitter discrimination (Fig 5A). Furthermore, homozygous fussMi13731, fussdelDS and transheterozygous mutants (fussMi13731/fussdelDS) as well as their appropriate controls were tested. All mutant genotypes showed an increased preference for 5mM sucrose mixed with caffeine and by overexpression of Fuss in fuss mutant neurons we could revert preference to wildtype levels (Fig 5A). To show that the behavioural phenotype of fuss mutants is due to defects in GRNs and not derived from other higher order Fuss neurons in the CNS we specifically disrupted fuss in all GRNs with the Poxn-Gal4-13-1 driverline and our UAS-cas9; UAS-t::gRNA-fuss4x flies. Poxn-Gal4-13-1 expresses Gal4 early in development in all GRNs and in ellipsoid body neurons as well as interneurons of the antennal lobe of the brain (S4B Fig, [47]), therefore the only common neuronal populations between Fuss and Poxn-Gal4-13-1 are the GRNs and indeed, as shown in Fig 5A, these flies show the same bitter sensing deficits. We also tested different concentrations of caffeine as well as another bitter compound (denatonium benzoate) and fussMi13731 flies always displayed a higher preference towards the 5mM sucrose mixed with the bitter compound than controls except when the concentration of the bitter compound was too high (S4C and S4D Fig). Thus, not only detection of caffeine but more general bitter sensation is disturbed, because different GR multimers are needed for the detection of different aversive compounds, e.g. Gr93a which is expressed in a subset of S-type sensilla is needed for caffeine but not for denatonium benzoate sensation [48]. The gustatory receptor Gr66a showed a strong reduction in PolII coverage in mutant flies in contrast to control flies and is only expressed in a proportion of Fuss positive GRNs. The gustatory receptor GR33a has been found to be coexpressed with Gr66a in bitter GRNs and both are involved in bitter sensation, particularly together with Gr93a in caffeine sensation [40,48]. To validate GRN results from the TaDa experiment, we extracted RNA from adult proboscises and analysed the expression levels of those GRs via semiquantitative RT-PCR. In homozygous fussMi13731-flies Gr33a and Gr66a expression were strongly reduced as compared to WTB and heterozygous fussMi13731-flies. Gr93a expression levels of homozygous fussMi13731-flies were similar to WTB levels but reduced when compared to heterozygous fussMi13731-flies (Fig 5). The observed effects were enhanced in fussdelDS-flies. Gr33a, Gr66a and Gr93a expression levels were all reduced in fussdelDS-flies in contrast to both controls (Fig 5C). A similar downregulation of Gr33a, Gr66a and Gr93a expression levels was observed in transheterozygous fussMi13731/fussdelDS flies in contrast to WTB flies (S4E Fig). Next, we tested if the number of Gr33a and Gr66a positive GRNs is reduced in fuss mutant flies. We counted Fuss positive and Gr33a positive neurons in flies of the genotypes Gr33a-Gal4/UAS-LacZ; fussMi13731/+ and Gr33a-Gal4/UAS-LacZ; fussMi13731/fussdelDS. In this genetic combination, we counted 2.5 less Fuss positive cells and surprisingly 7.2 less Gr33a positive cells in transheterozygous mutants than in control flies (Fig 5D). Furthermore, we analysed the number of Fuss positive and Gr66a positive neurons in flies of the genotypes UAS-LacZ/+; Gr66a-Gal4/+; fussMi13731/+ and UAS-LacZ/+; Gr66a-Gal4/+; fussMi13731/fussdelDS. We found the same reduction in overall number of Fuss positive GRNs in transheterozygous mutants. But the number of Gr66a positive GRNs is decreased at the same level as the number of overall Fuss positive GRNs (Fig 5E). Thus, the total number of bitter GRNs is slighty reduced in fuss mutant flies, but interestingly Gr33a expression is completely abolished in some bitter GRNs, whereas the reduction of Gr66a expression found in qPCR experiments does not result in a reduced number of Gr66a positive GRNs. So, upon the loss of Fuss expression, bitter GRN differentiation is highly disturbed, which renders these flies inable to detect bitter compounds.
In mammals there are two homologues of Fuss, Skor1 and Skor2, which display a high sequence conservation within the Ski/Sno/Dac homology domain and the SMAD4 binding domain. In contrast, the conservation in the C-terminal region is very low, which shows a high degree of evolutionary divergence (S4F Fig). Although the I-loop of the SMAD4 binding domain, which has been implicated as an important structure for SMAD4 binding, is not very well conserved in Fuss and its homologues, we and others have detected an interaction between SMAD4 with Fuss and Skor2, respectively [11,14,18]. The repressive action of Ski/Sno proteins is generally exerted by the recruitment of a protein complex containing HDAC1 [10]. Skor1 and Skor2 also interact with HDAC1 and interestingly, it has been shown that the residues important for this interaction are localized in a segment reaching from amino acid 385–592 in mouse Skor2 [16,17]. Similar to the lack of the I-loop sequence, this segment is highly diverse between Fuss and Skor2 challenging if Fuss nevertheless is able to interact with Rpd3, the HDAC1 homologue in Drosophila melanogaster (S4F Fig). Therefore, we performed Co-Immunoprecipitations (CoIP) and transfected S2R+ cells with Fuss and Rpd3 tagged with FLAG or HA. Interaction between Fuss and Rpd3 could be shown independent of the type of the tags (Fig 6A). Skor1 and Skor2 have also been described to interact with Smad2 and Smad3, homologues of the Drosophila Smox, which executes the same function as Mad, but in the TGF-ß like signaling pathway [13,14,22]. Using the same methological approach as for the Fuss and Rpd3 interaction, we could not detect any interaction between Fuss and Smox, independent of the tags used (Fig 6A). Interestingly Smox is one of the genes specifically enriched in our TaDa datasets for Fuss neurons, so there would be a possibility for interaction in these cells.
If Fuss is acting within a protein complex in concert with Rpd3, we should be able to mimic fuss mutant phenotypes with rpd3 depletion. Therefore, UAS-rpd3-IR was specifically expressed in Fuss neurons using the fussBD-Gal4 driver to reduce rpd3 expression throughout development. Adult flies were then tested again in a two-choice feeding assay for bitter sensing. Rpd3 knockdown flies showed a significant higher preference towards caffeine than control flies (fussBD-Gal4 x UAS-cherry-IR; Fig 6B). Because Rpd3 is involved in many different chromatin complexes, we analyzed again the expression levels of bitter gustatory receptors. Expression of all three tested GRs Gr33a, Gr66a and Gr93a was again diminished (Fig 6C) and therefore we conclude, that the Fuss/Rpd3 complex plays a key role in the final cell fate determination of gustatory neurons.
In overexpression experiments, Ski/Sno proteins have often been identified as negative regulators of TGF-ß or BMP-signaling [14,17]. In Drosophila, Dpp is the main homologue to vertebrate BMPs and it is involved in multiple developmental signaling events, in particular in the Drosophila wing [49]. We have previously shown, that an overexpression of Fuss during wing development indeed results in diminished expression of Dpp target genes and, concomitantly, induces a phenotype, which resembles loss of Dpp signaling, despite the fact, that we could only detect a physical interaction with the Co-Smad Medea but not with the R-Smad Mad [18]. In Dpp signaling, Mad gets phosphorylated by the type I receptors Saxophon and/or Thick veins and thus phosphorylated Mad is an excellent marker for active Dpp signaling and also for motoneurons or Tv neurons [50,51]. To analyse a possible role of Fuss in Dpp signaling, we used fussMi13731-flies, in which GFP is expressed under the fuss promotor to label Fuss expressing cells and we counterstained 3rd instar larval brains with an antibody against phosphorylated Mad (pMad) (Fig 7A and Fig 7B). These results clearly showed that Fuss expression is not overlapping with pMad in heterozygous fussMi1373/+ conditions. As there is a possibility that Fuss is acting upstream of Mad phosphorylation, we compared pMAD staining of heterozygous (Fig 7C) with homozygous fussMi13731-flies (Fig 7D). Again, there is no overlap of pMAD and GFP stainings in both genotypes, indicating that there is no increase of pMAD in fuss mutant neurons in the absence of Fuss. Importantly, this is in agreement with our overexpression studies, where Fuss had no influence on Mad phosphorylation [18]. Therefore, we conclude, that endogenously Fuss is not involved in Dpp signaling inhibition and it also emphasizes previous results, that Fuss is expressed in interneurons and not in motoneurons, which require pMad activity [51].
Previously, the only loss of function data of fuss was generated using a genomic deletion of 40 kb including the fuss locus and additional genes [22]. This deletion lead to a reduced survivability during development, a shortened lifespan of the escapers and an impaired mushroom body development. All these phenotypes were attributed to the loss of Fuss expression. As we did not observe an impact on survivability or lifespan upon the loss of Fuss (see above), we wondered if Fuss is indeed involved in mushroom body development. Based on RNA in situ hybridisations Takaesu et al. assumed that Fuss is expressed in Kenyon cells during development and is required for the proper formation of the mushroom body [22]. Having now specific antibodies, gene trap constructs and fuss mutations in hand, we decided to carefully reevaluate this data on mushroom body expression and function during development. In a first step, we used OK107-Gal4 driven nuclear GFP as a marker for developing Kenyon cells and colabeled larval brains with EcRB1 and Fuss. We found that Fuss is not expressed in the developing mushroom body Kenyon cells, but it shows a partial overlap with EcRB1 expression outside of the Kenyon cell domain (Fig 7E–7E´´´). Next, we analysed adult mushroom body structures of fuss mutant flies using a FasII-antibody. As expected, due to the lack of Fuss expression in Kenyon cells, no deformation or loss of any of the lobes of the mushroom body was observed in homozygous fussMi13731 or fussdelDS-flies (Fig 7F–7I). In addition, the expression of rpr with the fussdelDS-Gal4 line lead to a complete ablation of Fuss neurons, but did not result in a malformation of adult mushroom bodies (Fig 7J). Furthermore, expression of CD8-GFP with fussBD-Gal4 in adult brains shows that Fuss neuron clusters are also localized distal to the mushroom body (Fig 7K). In fact, Fuss neuronal projections are localized outside of the mushroom body lobes in the adult brain and some Fuss neurons are targeting the optic lobe including different layers of the medulla, lobula and lobula plate but not the lamina (Fig 7L). From these results, we conclude that fuss has no impact on mushroom body development and that most of these neuronal populations such as the Fuss/Atonal positive neurons are higher order neurons of the visual system.
The molecular and cellular functions of the fuss genes, which are members of the Ski/Sno protein family, are still poorly understood. The fact that Drosophila contains only one single fuss gene offers a great opportunity for a thorough analysis. However, this has been restrained due to its location on the 4th chromosome, where only limited genetic tools were available. As a consequence, previous reports have been focusing on the analysis of either overexpression studies or by using a multi-gene deficiency with contradictory results [18,22]. In the meantime, more recent methodological advances like the CRISPR/Cas9 genome editing [52] and the MiMIC gene trap technique [27] have expanded the Drosophila genetic toolbox and provided an appropriate genetic environment allowing a thorough and in-depth study of such genes. The availability of the fussMi13731 fly line, which is a gene trap of fuss, allowed us to study the expression pattern of Fuss. This line perfectly matches our Fuss-antibody stainings and was used to create a Gal4 line via RMCE as previously described [27]. A second independent mutant fuss allele, fussdelDS was created by CRISPR/Cas9 editing by deletion of the main functional protein domains. Although fussMi13731 and fussdelDS alleles are generated by different genetic approaches they share the same phenotypes, underlining that despite the complex genomic organization of fuss the observed phenotypes are due to the loss of fuss. Surprisingly, fuss mutant flies are fully viable and do neither show developmental lethality or reduced lifespans nor any other apparent phenotypes.
By means of our new tools, we could show that Fuss is expressed postmitotically in a small subset of neurons. All Fuss neurons in the CNS are interneurons, but they express different cell fate markers, suggesting that they represent a rather diverse group of neurons. These results were confirmed molecularly by a targeted DamID experiment, which, in addition, indicated a highly specific expression of gustatory receptor genes and indeed, Fuss is expressed in one GRN per sensillum. In S and I-type sensilla it is expressed in bitter GRNs and in L-type sensilla, which lack bitter GRNs, it is expressed in salt attracting GRNs. We investigated how the bitter GRNs react to the loss of Fuss and interestingly, this leads to an impairment of bitter sensation. Remarkably, this phenotype is correlated with a downregulation of bitter gustatory receptors Gr33a, Gr66a and Gr93a and in some bitter GRNs of fuss mutant flies no Gr33a expression can be observed anymore. The expression of Fuss in sensory neurons during development, and the adult phenotype, suggest that Fuss is needed for the proper maturation of these neurons and therefore is essential for bitter GRN differentiation. As there is a possibility, that the bitter sensation phenotype might be due to some higher order interneurons within the CNS, we generated a specific UAS-t::gRNA-fuss4x line to be able to perform cell type specific gene knockouts. Indeed, using an independent driver line (Poxn-Gal4-13-1) expressed in all GRNs, faithfully reproduced this phenotype indicating a direct association of bitter sensation and GRN defects. In fuss mutant flies morphology of bitter GRNs was not altered and cell number was just slightly changed compared to controls, while Gr33a expression was completely lost in 40% of all bitter GRNs and Gr66a expression was reduced in all GRNs, but was never completely absent from a bitter GRN. Therefore, in fuss mutant flies bitter GRNs are correctly specified but the terminal differentiation of this neurons is disturbed, which ultimately results in impaired bitter taste sensation. This is comparable to Fuss neurons in the larval and adult CNS, where loss of Fuss expression also did not have an impact on axonal projections or cell numbers and thus not on initial specification of these neurons. This supports the idea, that Fuss is required for fine tuning individual subgroups of neurons during development, a phenotype, which resembles loss of Skor2 in mice, where it is dispensable for initial Purkinje cell fate specification but is required for proper differentiation and maturation of Purkinje cells [15]. It is very likely that other genes will also be affected by the loss of Fuss, and the reduction of these gustatory receptors could lead to a cumulative effect, as it has been shown that they act in heteromultimers where a multimeric receptor consists of at least Gr66a, Gr33a and Gr93a, which are all required for caffeine sensation [53,54]. Whereas over the years many studies have dissected the function of single gustatory receptors, the complexes they establish, and genes which are involved in more common topics like sensory neuron formation, less is known about the differentiation and specification of subsets of GRNs [55–57]. To find further genes involved in differentiation of bitter GRNs and to clarify the molecular consequences of the fuss mutation in bitter GRNs we will conduct transcriptional profiling experiments specifically in Fuss positive GRNs.
Using the TaDa method, we were curious to see if this method is sensitive enough to pick up significant differences between fuss mutant and wildtype flies. This was indeed the case for GR66a. However, in general, the performed TaDa experiments showed only slight differences between mutant and control flies. This could be a consequence of Fuss being expressed in heterogenic neuronal clusters. We showed, that Fuss interacts with Rpd3, a histone deacetylase, and therefore, a chromatin modifier, which is preferentially associated with inhibitory gene regulating complexes [58]. This could be a common mechanism for Fuss in all Fuss expressing neurons. However, different neuronal populations have different open and closed chromatin and probably the Fuss/Rpd3 complex regulates different genes in different neuronal populations, which could lead to the masking of differential gene expression by individual neuronal cell groups. Additionally, although the TaDa technique functions very well to generate transcriptional profiles without cell isolation, data is nondirectional and at GATC fragment resolution, which decreases overall resolution. To overcome these limitations experiments are on the way to unravel the function of specific neuronal clusters as well as the function of fuss in these neuronal clusters, and to specifically profile transcription of these clusters and changes upon loss of fuss.
A careful analysis with our newly generated antibodies shows, that there is no expression of Fuss in larval or adult Kenyon cells as has been postulated recently [22]. To unequivocally show, that there is no requirement for Fuss in mushroom body development, neither autonomously nor non-autonomously, Fuss expressing neurons were ablated using a fuss-GAL4 line driving Reaper. Again, these flies, even without any fuss expressing cells, are fully viable and do not show mushroom body defects. Lastly, we also did not find any evidence of Fuss being expressed in insulin producing neurons by our antibody staining or DamID experiments as shown recently [36]. These discrepancies are most likely explained by the use of the specific knockout line fussdelDS, and the gene trap line fussMi13731 in our case, whereas a 40 kb genomic deletion Df(4)dCORL was used in Takaesu et al. [22] and Tran et al. [36]. This deletion covered the fuss locus as well as two more protein coding genes, 4E-T and mGluR, and three noncoding RNA genes, CR45201, CR44030 and sphinx. Any of these, or a combination of them, could be responsible for premature lethality or mushroom body defects. One additional possible explanation for their mushroom body defects in the deletion is an inappropriate fusion of a new transcriptional start site or enhancer region from the mGluR upstream to the toy gene creating a weak overexpression phenotype of toy in mushroom bodies, a phenotype, which has been described already [59]. Indeed, very recently Tran et al. [35] described a slight overexpression of Toy in their deficiency allele Df(4)dCORL.
We and others have shown that Ski/Sno proto-oncogenes have an inhibitory effect on TGF-ß or BMP signaling in overexpression assays [18,60]. This is often associated with the ability of Ski/Sno proteins to inhibit the antiproliferative effects of TGF-ß signaling in cancer and to promote their progression [61]. However, in an endogenous situation, Fuss is not expressed in cells, where the BMP/Dpp signaling pathway is active. This is displayed by the absence of the motoneuron marker pMad in Fuss neurons. Later in adulthood, Mad itself is also not specifically enriched in Fuss expressing neurons according to the TaDa dataset, clearly pointing against a function in BMP signalling. We also tested if Fuss is involved in the Activin signaling cascade, but we could not detect an interaction between Fuss and Smox in CoIP assays. However, we cannot rule out the possibility that the phosphorylated form of Smox is interacting with Fuss or the Fuss/Med complex. But since both, phosphorylated Smox and Fuss interact with Medea, we would potentially also get an artificial interaction [18,62]. At least according to the TaDa dataset, Smox is expressed in Fuss neurons. Unfortunately, there is currently no good marker available to test for an activated TGF-ß signaling pathway in Drosophila cells, like an antibody against phosphorylated Smox. What might be the main molecular mechanism for Fuss? Although the Ski/Sno/Dac homology domain and the SMAD4 binding domain in Ski have DNA binding character, they mainly have been shown to be involved in protein-protein interactions [11,63]. Furthermore, Ski/Sno proteins do not possess an intrinsic catalytic activity, they rather act as recruiting proteins [2]. In agreement, we could show that this is also the case for Fuss. Not only that Fuss binds to Medea, which is a DNA binding protein and therefore mediates the DNA binding, Fuss also interacts with Rpd3, a histone deacetylase. Thus, the Med/Fuss/Rpd3 complex is involved in chromatin silencing and plays a key role in terminal differentiation. Interestingly, the loss of bitter sensation and downregulation of bitter GRs could also be phenocopied by a knockdown of rpd3 in Fuss expressing gustatory neurons. One current hypothesis of Fuss/Rpd3 function in GRNs, which we propose, is, that this protein complex is inhibiting a repressor of GR genes and in the absence of either fuss or rpd3, the complex is inactivated and this repressor will inhibit bitter GR genes.
For Ski and Sno, the transcriptional repressor complexes have been reasonably well characterized [10,64], but for the Fuss-type proteins, very little is known about their complexes. It would be highly interesting if Fuss proteins act through repressor complexes identical to the complexes of Ski or Sno or a rather unique one. The most exciting question to solve regarding protein interaction will be, if the Fuss/Rpd3 complex plays a role in TGF-ß signalling, or if in contrast to its mammalian homologues, it is not only acting BMP independent, but also independent from the TGF-ß signalling cascade. Besides identifying further protein-protein interactions and investigating DNA-protein interactions more precisely, it will be very important to describe the exact function of the Fuss/Rpd3 complex. In mammals, Skor2 is thought to activate Sonic Hedgehog expression in Purkinje cells from direct binding to the Sonic Hedgehog promotor and this might be achieved by inhibition of the BMP pathway or by cooperation with the RORalpha pathway, a nuclear orphan receptor [15,17]. In contrast to that, Skor1 interacts with Lbx1, a homologue of the ladybird early or ladybird late in Drosophila, and acts as a transcriptional corepressor of Lbx1 target genes [16]. Our TaDa datasets strongly point towards another function for Fuss in Drosophila, as neither hedgehog nor the homologues of Lbx1, ladybird late and ladybird early, are enriched in Fuss expressing cells. Therefore, identifying target genes, interacting proteins, binding motifs of the Fuss complex and subsequent comparison with established models for other transcription factor complexes will elucidate the role of this complex in cell fate determination.
Flies were kept under standard conditions (25°C, 12 h/12 h LD cycle). Flies from RNA interference crosses were kept at 29°C. Fly lines obtained from the Bloomington Stock Center were fussMi13731 (#60860), UAS-CD8-GFP (#5137), UAS-CD8-RFP (#32218), UAS-LacZ (#8529), tubulin-Gal80ts (#7108), UAS-Stinger (#65402), UAS-rpd3-IR (#33725), UAS-cherry-IR (#35785), ap-Gal4 (#3041), Gr33a-Gal4(#31425), Gr66a-Gal4 (#57670), Gr93a-Gal4 (#57679), Hb9-Gal4 (#32555), Gr5a-Gal4 (#57591), Ir76b-Gal4 (#51311), UAS-cas9 (#58985) and ato-Gal4 (#6480). UAS-Dam and UAS-Dam-PolII stocks were a gift from Andrea Brand. Poxn-Gal4-13-1 was a gift from Markus Noll. UAS-fussB, ap-tau-LacZ and UAS-rpr were from our stock collection. To generate the fussdelDS line two sgRNAs (GTAAGCTCCGTTTTGCTGTA and GGTGTTCCCTTTAACTTACA) were employed and cloned into pU6-BbsI-chiRNA. Homology arms were cloned into pHD-DsRed-attP and coinjected with pU6-BbsI-chiRNA as described in Gratz et al. [52]. The fussBD-Gal4 and the fussMi-cherry lines were created via RMCE with the vectors pBS-KS-attB1-2-GT-SA-GAL4-Hsp70pA and pBS-KS-attB1-2-GT-SA-mCherry-SV40, respectively [27]. To generate the mutant fussdelDS-Gal4 line, the fussBD-Gal4 line was additionally targeted with the same sgRNAs via CRISPR/Cas9, which were used for the fussdelDS line. Genomic DNA of CantonS and fussdelDS flies was extracted with QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). Successful indel mutation was confirmed by PCR with Cr1seqfw (CAAATCGACTGGGTAAATGGT) and Cr2seqrv (GTAGTCCACTACAAAGTTCCTG) oligonucleotides und subsequently sequenced (GATC Biotech, Konstanz, Germany). hs-fussB-GFP was generated by cloning the ORF of fussB-GFP into pCaSpeR. hs-fussB-GFP flies were generated via P-element integration of pCaSpeR-hs-fussB-GFP vector into w; +/Δ2–3, Ki and subsequent crossed to W1118 flies and transformants were balanced. For generation of UAS-t::gRNA-fuss4x flies we followed the protocol from Port et al. [28] and used primers, which allow the targeting of the CRISPR target sites GTAAGCTCCGTTTTGCTGTACGG, ATTGTATCCCTGCACATTGAAGG, CCAGTGAGTTCCCGACGATGTGG and TTGAAATTTGCGCCAAGCAAAGG. The pCFD6-t::gRNA-fuss4x was injected into y[1],M{vas-int.Dm}ZH-2A,w[*]; M{3xP3-RFP.attP}ZH-86Fb flies to generate UAS-t::gRNA-fuss4x flies. All Drosophila strains generated in this publication are available upon request.
Fulllength fuss ORF was codon optimized at GeneArt, Regensburg, Germany. An appropriate fragment of the codon optimized fuss gene was cloned into pQE60 resulting in a 16 kDa 6xHis tagged Fuss fragment called Fuss16-6xHis (S1 Fig). Transformed Rosetta2 cells were grown to an OD 0.6 and protein expression was induced with 0.5 mM IPTG. Cells were incubated for 2.5 h at 37°C, harvested, resuspended in PBS supplemented with Protein Inhibitors (Roche, Switzerland) and lysed via sonication. Fuss16-6xHis was purified with an Äktapurifier10 (GE Healthcare, Life sciences) and was used for immunization of two rabbits at Davids Biotechnologie, Regensburg, Germany. The resulting antiserum was purified against Fuss16-6xHis to reduce nonspecific binding. Before using the anti-Fuss antibodies for immunostainings or western blots they were preabsorbed using 0–6 h embryos treated with 4% PFA in PBST 0.1% as follows: The antibody was diluted to 1:50 in 500ml PBST 0.1%, NGS 5% and incubated with 100 μl fixed embryos on a rotator at 4°C over night. Anti-Fuss antibody was further diluted to 1:200 in PBST 0.1%, NGS 5% for immunostainings and 1:1000 in TBST 0.1% for western blots.
Sixty proboscises from each genotype (equal number of males and females) per biological replicate were dissected on ice and snap-frozen in liquid nitrogen. RNA was extracted by adding lysis buffer from the MicroSpin Total RNA Kit (VWR) and the tissue was extracted with a bead mill and it was proceeded according to the manufacturer’s protocol. cDNA was generated with the QuantiTect Reverse Transcription Kit (QIAGEN). For subsequent real time PCR ORA qPCR Green ROX L Mix (HighQu, Kralchtal, Germany) was employed. RP49 was used as a housekeeper control, with the primers RP49fw (CCAAGCACTTCATCCGCCACC) and RP49rv (GCGGGTGCGCTTGTTCGATCC). Primer sequences for Gr33afw (CCACCATCGCGGAAAATAC), Gr33arv (ACACACTGTGGTCCAAACTC), Gr66afw (ACAGGAATCAGTCTGCACAA), Gr66arv (AATGTTTCCATGTCCAGGGT), Gr93afw (CCACGTCACAAACTCATTCC), Gr93rv (GCCATCACAATGGACACAAA), fussBDfw (TGGCTTCTATATCTGTGGCTCA) and fussBDrv (CAAAGGCGCTCTTGACCTTC) were generated with PrimerBlast. For relative quantification, we applied the ΔΔCT method. Every experiment has been repeated at least four times.
Developmental studies Hybridoma Bank (DSHB) antibodies were: Acj6 (1:50), Dac (Mabdac1-1, 1:20), EcRB1 (AD4.4, 1:50), LacZ (JIE7, 1:20), Pros (MR1A, 1:10), Elav (7E8A10, 1:50), Engrailed (4D9, 1:20), Even skipped (3C10, 1:20) Repo (8D12, 1:20), and Fas2 (1D4 1:10). Additional antibodies were: Pale (AB152, 1:500, Millipore), Ilp2 (1:400, gift from Pierre Leopold), Toy (1:200, gift from Uwe Walldorf), GFP (goat 1:100, Rockland; rabbit 1:1000, ThermoFisher), RFP (rabbit 1:20, ThermoFisher) and anti-phospho-SMAD1/5 (1:50, Cell signaling). Secondary antibodies were goat anti-mouse, anti-rabbit, anti-rat and anti-guinea pig Alexa Fluor 488, 555 and 594 (ThermoFisher). Samples were analysed with a Leica SP8 microscope. To confirm functionality of anti-Fuss antibodies hs-fussB-GFP third instar larvae were heatshocked for one hour at 37°C and were allowed to recover for another hour at room temperature. RIPA buffer was added to ten larvae and they were mechanically disrupted. Insoluble fragments were removed by centrifugation and supernatant was incubated at 95°C for five minutes. Supernatant was analysed via SDS-Page and Western blotting. As a housekeeper mouse anti-tubulin (B-5-1-2, MERCK) was utilised and secondary antibodies were goat anti-mouse 680nm and goat anti-rabbit 800nm (Li-Cor, Lincoln, USA). Signals were detected using an Odyssey infrared imaging system (Li-Cor, Lincoln, USA).
S2R+ cells were cultured in Schneider’s Drosophila Medium (Pan Biotech, Aidenbach, Germany) supplemented with 10% Fetal Bovine Serum (Pan Biotech, Aidenbach, Germany). The coding regions of fussB, smox and rpd3 were inserted into pFSR11.58 3xHA and pFSR12.51 4xFlag (Frank Sprenger, Regensburg, Germany). Cells were transfected in 6 well plates at 70% confluency with 2 μg of pFSR11.58 Fuss-HA and pFSR12.51 Rpd3-Flag (or Smox-Flag), or pFSR11.58 RPD3-HA (or Smox-HA) and pFSR12.51 Fuss-Flag, respectively, using Lipofectamine 3000 (Thermo Scientific, Waltham, MA, USA) according to the manufacturer’s protocol and incubated for another 24 h. Transfected cells were harvested using a plastic scraper. For Rpd3 and Fuss interaction experiments nuclear extracts were prepared using the NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific, Waltham, MA, USA) and only nuclear fraction was used. For Fuss and Smox interaction whole cell extracts were prepared with 400 μl lysis buffer (50 mM HEPES pH 7.5, 150 mM NaCl 150, 1% Triton X-100, 10% Glycerol, 1 mM EGTA, 10 mM NaF) supplemented with cOmplete Mini Protease Inhibitor Cocktail (Roche, Switzerland). After preclearing the extracts with 30 μl Protein A-Agarose beads (Santa Cruz, Dallas, TX, USA) and conjugating 1.5 μl Anti-Flag M2 antibody (Sigma, St. Luis, Mo, USA) to 30 μl Protein A/G Plus beads (Santa Cruz, Dallas, TX, USA), the volume of the nuclear extract was brought up to 400 μl using RIPA buffer (50 mM Tris (pH 7.5), 150 mM NaCl, 1% (v/v) NP-40, 0.5% (w/v) Deoxycholat) supplemented with cOmplete Mini Protease Inhibitor Cocktail (Roche, Switzerland). 5% of the precleared extracts were saved for input analysis. Immunoprecipitation was conducted for 2 h at 4°C. Following three washing steps with RIPA buffer, the precipitated proteins, as well as the precleared nuclear extracts, were analyzed by SDS-PAGE and western blotting. As primary antibodies Anti-Flag M2 and Anti-HA.11 (Covance Inc., USA) were used. Secondary antibody was goat anti mouse 680 (Li-Cor). Signals were detected using an Odyssey infrared imaging system (Li-Cor, Lincoln, USA).
Targeted DamID to profile transcription in Fuss expressing neurons was performed as previously described [34,65,66]. UAS-Dam, UAS-DamPolII, UAS-Dam; fussdelDS or UAS-DamPolII; fussdelDS flies were crossed to tubulin-Gal80ts; fussdelDS-Gal4 flies. Three biological replicates of DamPolII expressing flies and three biological replicates of Dam expressing flies were conducted. Per replicate 100 one to three-day old flies (50 females and 50 males) were incubated for 24 h at 29°C and snap-frozen in liquid nitrogen. Heads were detached by vortexing and separated with sieves. Processing of genomic DNA from heads and data analysis were performed as described and NGS libraries libraries were prepared with NEBNext UltraII DNA Library Prep Kit for Illumina [34,65,66]. Sequencing was carried out by the Biomedical Sequencing Facility at CeMM. For aligning reads, dm6 release from UCSC was used. Data tracks from same genotype were averaged with the average_tracks script and 3150 genes were called with an FDR < 0.01 for mutant flies and 2932 genes for control flies. log2(Dam-PolII/Dam) ratio datasets were visualized with the Integrative Genomic Browser.
For life span determination, male flies were collected within 24 h after eclosion and were raised at 25°C under a 12 h∶12 h light/dark cycle. These flies were transferred to fresh food vials every two to three days.
Feeding behaviour was analysed as previously described at 25°C [38]. Fly age at time of testing ranged from one to three days and experiments were only accounted if at least 30% of all flies showed clear evaluable coloured abdomen. As bitter compounds caffeine and denatonium benzoate were utilised at the indicated concentrations. Because feeding behaviour was influenced by temperature, fussBD-Gal4 x UAS-cherry-IR and fussBD-Gal4 x UAS-rpd3-IR flies were shifted to 25°C two hours prior testing. Every experiment has been repeated at least four times.
All figures were assembled with Adobe Photoshop CC (Adobe Systems) by importing microscopy images from Fiji and graphs from Prism.
Survival data were analyzed using the Log-rank (Mantel-Cox) and Gehan-Breslow-Wilcoxon tests. Significance was determined by two-tailed t-test or by One-way ANOVA with post hoc Tukey Multiple Comparison Test (****p<0.001; ***p<0.001; **p<0.01 and *p<0.05). Statistical analysis was carried out using Prism version 7.0a for MacOs, GraphPad Software, La Jolla, CA, USA.
Raw sequencing data are accessible via Gene Expression Omnibus: GEO Series GSE115347.
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10.1371/journal.ppat.1001044 | The Pneumococcal Serine-Rich Repeat Protein Is an Intra-Species Bacterial Adhesin That Promotes Bacterial Aggregation In Vivo and in Biofilms | The Pneumococcal serine-rich repeat protein (PsrP) is a pathogenicity island encoded adhesin that has been positively correlated with the ability of Streptococcus pneumoniae to cause invasive disease. Previous studies have shown that PsrP mediates bacterial attachment to Keratin 10 (K10) on the surface of lung cells through amino acids 273–341 located in the Basic Region (BR) domain. In this study we determined that the BR domain of PsrP also mediates an intra-species interaction that promotes the formation of large bacterial aggregates in the nasopharynx and lungs of infected mice as well as in continuous flow-through models of mature biofilms. Using numerous methods, including complementation of mutants with BR domain deficient constructs, fluorescent microscopy with Cy3-labeled recombinant (r)BR, Far Western blotting of bacterial lysates, co-immunoprecipitation with rBR, and growth of biofilms in the presence of antibodies and competitive peptides, we determined that the BR domain, in particular amino acids 122–166 of PsrP, promoted bacterial aggregation and that antibodies against the BR domain were neutralizing. Using similar methodologies, we also determined that SraP and GspB, the Serine-rich repeat proteins (SRRPs) of Staphylococcus aureus and Streptococcus gordonii, respectively, also promoted bacterial aggregation and that their Non-repeat domains bound to their respective SRRPs. This is the first report to show the presence of biofilm-like structures in the lungs of animals infected with S. pneumoniae and show that SRRPs have dual roles as host and bacterial adhesins. These studies suggest that recombinant Non-repeat domains of SRRPs (i.e. BR for S. pneumoniae) may be useful as vaccine antigens to protect against Gram-positive bacteria that cause infection.
| Serine-rich repeat proteins (SRRPs) are a family of surface-expressed proteins found in numerous Gram-positive pathogens, including Staphylococcus aureus, Streptococcus pneumoniae, Group B streptococci, and the oral streptococci that cause infective endocarditis. For all of these bacteria, SRRPs have been demonstrated to play pivotal roles in adhesion to tissues and the development of invasive disease. It is now known that biofilm formation is an important step for bacterial pathogenesis. Bacteria in biofilms have been shown to have differences in metabolism, gene expression, and protein production that contribute to enhanced surface adhesion and the persistence of an infection. Herein we describe a novel role for PsrP, the S. pneumoniae SRRP, as an intra-species bacterial adhesin that promotes bacterial aggregation in the lungs of infected mice during pneumonia. In vitro we show that the Basic Region domain of PsrP promotes self-interactions that result in denser biofilms, greater biofilm biomass, and altered architectures of surface grown cultures; these interactions could be neutralized by antibodies to PsrP that are protective against pneumococcal infection. We also demonstrate that the SRRPs of S. aureus and Streptococcus gordonii also function as intra-species bacterial adhesins. Therefore we conclude that SRRPs have dual roles as host-cell and intra-species bacterial adhesins.
| Streptococcus pneumoniae is a leading cause of otitis media (OM), community-acquired pneumonia, sepsis and meningitis. Primarily a commensal, S. pneumoniae typically colonizes the nasopharynx asymptomatically, however in susceptible individuals such as infants, the elderly, persons who are immunocompromised, and those with sickle cell anemia, the pneumococcus is often able to cause opportunistic diseases [1], [2], [3], [4]. Worldwide, S. pneumoniae is responsible for up to 14.5 million episodes of invasive pneumococcal disease (IPD) and 11% of all deaths in children [5], [6]. In the elderly the mortality-rate associated with IPD can exceed 20% and for those in nursing homes may be as high as 40% [7]. Thus, the pneumococcus has been and remains a major cause of morbidity and mortality.
psrP-secY2A2 is a S. pneumoniae pathogenicity island whose presence has been positively correlated with the ability to cause human disease [8]. Analyses of the published S. pneumoniae genomes has demonstrated that psrP-secY2A2 is present and conserved in a number of globally distributed invasive clones, in particular those belonging to serotypes not covered by the heptavalent conjugate vaccine [9]. To date, numerous studies have shown that deletion of genes within psrP-secY2A2 attenuated the ability of S. pneumoniae to cause disease in mice. psrP-secY2A2 mutants were shown to be unable to attach to lung cells, establish lower respiratory tract infection, and were delayed in their ability to enter the bloodstream from the lungs. Importantly, the same studies found that psrP-secY2A2 did not play an important role during nasopharyngeal colonization or during sepsis following intraperitoneal challenge [10], [11], [12], [13]. Thus psrP-secY2A2 is currently understood to be a lung-specific virulence determinant.
In TIGR4, a virulent serotype 4 laboratory strain, psrP-secY2A2 is 37-kb in length and encodes 18 proteins. These include the Pneumococcal serine-rich repeat protein (PsrP), which is a lung cell adhesin, 10 putative glycosyltranferases, and 7 proteins homologous to components of an accessory Sec translocase [14]. To date, the latter 17 genes remain uncharacterized; however, based on their homology to genes found within the Serine-rich repeat protein (SRRP) locus of Streptococcus gordonii, the encoded proteins are putatively responsible for the intracellular glycosylation of PsrP and for its transport to the bacterial surface [8], [15], [16], [17], [18]. PsrP in TIGR4 is composed of 4,776 amino acids, has been confirmed to be glycosylated, and separates at an apparent molecular mass of 2,300 kDa on an agarose gel [13]. It is one of the largest bacterial proteins known. PsrP is organized into multiple domains including a cleavable N-terminal signal peptide, a small serine-rich repeat region (SRR1), a unique non-repeat region (NR), followed by a second extremely long serine-rich region (SRR2), and a C-terminal cell wall anchor domain containing an LPXTG motif (Figure 1A). The SRR1 and SRR2 domains of PsrP are composed of 8 and 539 serine-rich repeats (SRR) of the amino acid sequence SAS[A/E/V]SAS[T/I], respectively, and are the domains believed to be glycosylated. The NR domain of PsrP has a predicted pI value of 9.9, for this reason it is called the Basic Region (BR) domain.
S. pneumoniae is surrounded by a polysaccharide capsule that protects the bacteria from phagocytosis but also inhibits adhesion to epithelial cells [19]. Based on the size and domain organization of PsrP we have previously hypothesized that the extremely long SRR2 domain serves to extend the BR domain through the capsular polysaccharide to mediate lung cell adhesion (Figure 1B) [12], [13]. Consistent with this model, we have previously shown that PsrP is expressed on the bacterial surface, that the BR domain, in particular amino acids 273–341, was responsible for PsrP-mediated adhesion to Keratin 10 (K10) on lung cells, and that complementation of psrP deficient mutants with a truncated version of the protein (having only 33 SRRs in its SRR2 domain) restored the ability of uncapsulated but not capsulated PsrP mutants to adhere to A549 cells, a human type II pneumocyte cell line [13].
It is now recognized that biofilms play an important role during infectious diseases. Briefly, bacteria in biofilms are more resistant to host-defense mechanisms including phagocytosis and serve as a recalcitrant source of bacteria during antimicrobial therapy [20], [21]. For S. pneumoniae, pneumococcal biofilms have been shown to occur in the middle ears of children with chronic otitis media and is thought to contribute to its refractory nature [22]. Likewise, biofilms have been detected in the nasopharynx of infected chinchillas [23]. However, until now biofilm structures have not been described in the lungs during pneumococcal pneumonia. This is in contrast to other respiratory tract pathogens, such as Pseudomonas aeruginosa and Bordatella pertussis, for which in vivo biofilm production is now recognized to be an important pathogenic mechanism [21]. Herein, we demonstrate for the first time that S. pneumoniae forms biofilm-like aggregates in the lungs. We show that this phenomenon is PsrP-dependent and mediated by its BR domain. Using recombinant protein and SRRP mutants, we show that the SRRPs of S. gordonii and Staphylococcus aureus, GspB and SraP, respectively, also promote bacterial aggregation, thus describing a previously unrecognized role for members of the SRRP family. Collectively, these findings suggest an important dual role for PsrP and other SRRPs during infection, host cell and intra-species bacterial adhesion, both of which may be targeted for intervention with antibodies against recombinant (r)NR.
To test whether PsrP contributed to biofilm or microcolony formation in vivo mice were infected with TIGR4 and its isogenic psrP deficient mutant, T4 ΔpsrP, and whole lung sections were examined using scanning electron microscopy (SEM). As would be expected for both wild type and the mutant, the majority of bacteria present were in the form of diplococci. However, for TIGR4 we also observed the presence of large bacterial aggregates attached to ciliated bronchial epithelial cells as well as to alveolar epithelial cells (Figure 2). For quantitative analysis of this phenomenon, nasal lavage fluid and bronchoalveolar lavage (BAL) fluid from mice was collected two days post-challenge. Aliquots from each biological sample were heat-fixed to glass slides, Gram-stained, and examined with a microscope (Figure 3A). In all, the number of bacterial aggregates composed of 2–9, and ≥10 diplococci were significantly greater for mice infected with TIGR4 than T4 ΔpsrP in both the nasopharyngeal and BAL elute fluids (Figure 3B,C). Moreover, the largest aggregates, those composed of >100 bacteria, were observed only in mice infected with TIGR4. Fluorescent imaging of bacteria in frozen lung sections confirmed this phenotype; large bacterial aggregates were only detected in the lungs of TIGR4 infected mice (Figure S1). Thus we determined that PsrP promoted the formation of biofilm-like aggregates in vivo, including in the nasopharynx, a site previously shown not to require PsrP for bacterial colonization [12].
Given the previous results, moreover to develop an in vitro model that was amendable to manipulation, the ability of TIGR4 and T4 ΔpsrP to form early biofilms was tested using microtiter plates [24]. As shown in Figure 4A, no differences were observed between wild type and the mutant, suggesting that PsrP does not play a role in pneumococcal attachment to polystyrene or the formation of early biofilm structures, in particular the bacteria lawn. The role of PsrP was next tested in 3-day old mature biofilms using the once-through continuous flow cells as described previously by Allegrucci et al. [25]. In this system, a stark difference in the architecture of TIGR4 and T4 ΔpsrP biofilms was observed (Figure 4B). Wild type biofilms displayed a dense cloud-like morphology with extremely large aggregates that covered the glass surface. Closer inspection revealed that these aggregates were composed of tightly clustered pneumococci. In contrast, T4 ΔpsrP biofilms displayed a less intimate phenotype characterized by smaller aggregates, gaps, and the formation of columns, resulting in an overall patchier phenotype. Quantitative analysis of the biofilm structures using COMSTAT software confirmed that TIGR4 biofilms had significantly greater total biomass and average thickness than those formed by the T4 ΔpsrP (Figure 4C). No differences in either the maximum thickness of the biofilms or the roughness coefficient (a measure of biofilm heterogeneity) were observed (Figure 4C; data not shown, respectively), indicating that T4 ΔpsrP could still form biofilms, although with distinct architecture. Importantly, T4 ΩpsrP-secY2A2, a mutant deficient in the entire psrP-secY2A2 pathogenicity island, behaved identically to T4 ΔpsrP, forming patchy biofilms with small aggregates and less intimate associated bacteria (Figure S2).
Bacterial biofilms were also grown under once through conditions in silicone tubing. After a designated time, the biofilms were extruded from the line and examined for biomass both visually and quantitatively. After 3 days of growth, differences between TIGR4 and T4 ΔpsrP in opacity of the exudates were visible to the eye (Figure 5A) and could be confirmed using a spectrophotometer which showed a >3-fold difference in optical density (Figure 5B). Microscopic visualization of the line exudates following crystal violet (CV) staining revealed that TIGR4 had formed large aggregates whereas T4 ΔpsrP exudates were composed of small clusters or of individual diplococci (Figure 5C). Increased biofilm biomass was supported by measurement of total protein concentrations that showed TIGR4 biofilm exudates had 2–3 fold more protein than those corresponding to T4 ΔpsrP (Figure 5D).
Of note, during planktonic growth TIGR4, T4 ΔpsrP, and T4 ΩpsrP-secy2A2 were indistinguishable, growing either as short chains or diplococci with a marked absence of aggregates (data not shown). This led us to examine psrP transcription using Real-Time PCR and the finding that TIGR4 expressed psrP at levels 47-fold greater during biofilm versus planktonic culture (P = 0.04 using a Student's t-test). Thus low expression of psrP may be one reason TIGR4 did not form aggregates during liquid culture.
To date a number of groups, including our own, have shown that SRRPs mediate bacterial adhesion to host cells primarily through their NR domain [13], [26], [27]. For this reason we sought to test whether the BR domain of PsrP was also involved in biofilm/bacterial aggregation. To do this we first utilized a pre-existing collection (described in Figure S3) of encapsulated (T4 ΩpsrP) and unencapsulated (T4R ΩpsrP) S. pneumoniae mutants deficient in PsrP that either expressed a truncated version of PsrP with 33 SRRs in its SRR2 domain (PsrPSRR2(33)), a similar truncated version lacking the BR domain (PsrPSRR2(33)-BR), or carried the empty expression vector pNE1 [13]. These strains were tested for their ability to form biofilms in silicone lines under once through conditions.
Complementation of T4 ΩpsrP with PsrPSRR2(33), but not PsrPSRR2(33)-BR or the empty pNE1 vector, partially restored the ability of T4 ΩpsrP to form large aggregates in the lines when examined microscopically (Figure 6A). However, measurement of other biofilm markers such as optical density and total protein concentration showed no differences between any of the complemented mutants and the negative controls (Figure 6B–C). Complementation of T4R ΩpsrP with PsrPSRR2(33), also partially restored the ability of T4R ΩpsrP to form aggregates (Figure 6A). In this instance, line exudates from T4R ΩpsrP with PsrPSRR2(33) had significant more biofilm biomass than the negative controls (Figure 6B–C). Importantly, the truncated version of PsrP lacking the BR domain failed to restore, even partially, T4 ΩpsrP or T4R ΩpsrP suggesting that the BR domain was responsible for the intra-species aggregation. This was subsequently confirmed by Far-Western blot analyses that showed that Gst-tagged recombinant BR (Gst-BR) bound only to S. pneumoniae cell lysates that contained a truncated PsrP with the BR domain (Figure 6D) and a control experiment showing that a Gst-tagged Chlamydia trachomatis protein did not interact with these lysates (Figure S4).
To further explore the role of the BR domain in the observed bacteria to bacteria interactions, the ability of His-tagged BR constructs (rBR; Figure 7A), purified from Escherichia coli and Cy3 labeled, were tested for their ability to bind to the surface of TIGR4 and T4 ΔpsrP. Full-length rBR interacted with TIGR4 but not with T4 ΔpsrP (Figure 7B), confirming not only that PsrP bound to pneumococci, but also suggesting that its ligand was another PsrP. Furthermore, only rBR.A retained the ability to attach to PsrP on the pneumococcal surface. This suggested that the binding domain of PsrP was possibly located within AA 122–166, the section not shared between rBR.A and rBR.B.
Hereafter, BR to BR interactions were tested for by Far Western and co-immunoprecipitation. Far Western blot experiments using assorted E. coli cell lysates from bacteria expressing assorted rBR constructs, confirmed that only lysates containing PsrP constructs with AA 122–166 bound successfully to Gst-BR (Figure 7C). This was also observed in co-immunoprecipitation experiments, whereby Gst-BR was tested for its ability to bind whole cell lysates from E. coli expressing versions of PsrP (Figure 7D). Far Western blots using purified proteins showed that Gst-BR had affinity to purified rBR, rBR.A, and a synthesized peptide corresponding to AA 122–166, but not rBR.B, BR.C, or the control his-tagged Streptolysin O (Figure 7E). Hence, using numerous assays it was determined that the BR domain, most likely AA 122–166, had self-interacting properties that might be responsible for the observed bacterial aggregation.
Of note, because the BR constructs were purified from E. coli and PsrP is normally glycosylated, the above observations may have been an artifact of the unglycosylated constructs used. To address this possibility a glycosyated truncated PsrP construct was purified from S. pneumoniae (PsrPSRR2(33)-HIS; Figure S5) and tested for its ability to bind S. pneumoniae cell lysates containing either native PsrP or assorted constructs. As shown in Figure 7F, it was determined that a glycosylated PsrP probe maintained specificity for the BR domain even in the context of glycosylated recipient protein. A finding that supports the notion that PsrP to PsrP interactions occur in natural setting when PsrP is always glycosylated.
To determine whether the BR aggregation (AA 122–167) and the K10 binding subdomains (AA 273–341) of BR had functionally independent roles, competitive inhibition assays were performed using rBR constructs. Bacterial adhesion to A549 cells was tested following incubation of cells with the AA 122–166 peptide, rBR, and rBR.C (Figure 8A). Pre-treatment of A549 cells with AA 122–167 had no impact on adhesion. In contrast and consistent with the location of the K10 binding domain within BR.C: 1) TIGR4 adhered significantly less to cells treated with rBR or rBR.C, 2) TIGR4 adhered to BSA treated cells better than T4 ΔpsrP. In complementary biofilm experiments the opposite result was observed. Addition of 1 µM peptide AA 122–167 to media reduced the aggregation phenotype observed for TIGR4 (Figure 8B) and modestly lowered the optical density of the biofilm exudate and the total biomass collected from the continuous flow lines versus addition of BR.C (Figure 8C–D). Thus these findings suggested that the aggregation and K10 subdomains of PsrP had distinct roles that did not overlap during host cell adhesion or biofilm formation.
Finally we sought to determine a biological effect for the aggregation phenotype. We observed that after 1 hour, 69±2% of J477 macrophages incubated with planktonically grown TIGR4 were associated with FITC-labeled bacteria whereas only 51±5% of macrophages mixed with biofilm grown TIGR4 were positive (P = 0.024). Macrophages exposed to biofilm grown TIGR4 also took up less bacteria than macrophages mixed with planktonic (74±1%; P = <0.001) and biofilm (60±1%; P = <0.001) cultures of T4 ΔpsrP. Interestingly, a 10% reduction in macrophage uptake was observed for the biofilm versus planktonic grown T4 ΔpsrP cultures (P = 0.077); and no difference was observed between macrophage uptake of TIGR4 and T4 ΔpsrP when taken from planktonic cultures. These findings suggest, that in addition to PsrP, other bacterial factors expressed during growth in a biofilm also affect opsonophagoyctosis.
Previously we had shown that antibodies against the SRR1-BR domains of PsrP neutralized the ability of S. pneumoniae to attach to lung cells and that vaccination with rBR protected mice against pneumococcal challenge [12], [13]. For this reason we tested the ability of polyclonal antiserum against rBR and against a SRR motif peptide to block bacterial aggregation in the biofilm line model. Todd Hewitt Broth (THB) supplemented with a 1∶1000 dilution of antiserum against the BR domain inhibited the formation of bacterial aggregates as observed by microscopic visualization of the biofilm line exudates. In contrast, bacteria in media supplemented with antiserum to the SRR motif peptide or that from naïve animals, formed aggregates similar to wild type bacteria grown under serum free conditions (Figure 9A). Biofilm exudate optical density and protein concentrations supported these microscopic observations (Figure 9B–C). To determine whether the effect of the BR antiserum on biofilm formation was specific for TIGR4, we tested the ability of antibodies to the BR domain to block biofilm formation in unrelated clinical isolates (Figure S6). Antiserum against rBR from TIGR4 inhibited biofilm formation in two unrelated clinical isolates that carried PsrP. The same sera had no effect on biofilm formation by an invasive serotype 14 isolate that lacked PsrP. Therefore these studies confirmed previous observations that increased bacteria aggregation in biofilm models can occur independently of PsrP, but that if present, antiserum against BR can block the contribution of PsrP to these processes.
To determine whether other SRRPs also mediated intra-species aggregation we tested the effect of gspB and sraP deletion on S. gordonii and S. aureus biofilm architecture, respectively. Deletion of gspB and sraP negatively impacted biofilm formation in the microtiter biofilm model at 24 hours (Figure 10A,B). Growth of wild type and mutant bacteria in the line models also demonstrated that both proteins contributed to the formation of large aggregates during surface attached growth; although this property was much more dramatic for S. gordonii than for S. aureus which did not show a significant difference in the optical densities of the exudates (Figure 10C,D). Of note, S. aureus biofilm experiments were stopped after 1 day due to bacteria overgrowth and blockage of the lines.
Subsequent Far Western analysis using Gst-BR from S. pneumoniae as well as recombinant SRR1-NR from SraP and recombinant NR from GspB showed that these proteins have affinity for cell lysates from their parent strain but not for cell lysates from isogenic SRRP deficient mutants (Figure 10E). This supports the notion that these proteins might be involved in intra-species aggregation. For PsrP BR from S. pneumoniae, no affinity was observed for cell lysates from either S. gordonii or S. aureus suggesting that PsrP does not play a role as an inter-species adhesin (Figure 10E). In contrast, the NR constructs from S. aureus and S. gordonii bound to cell lysates from the other bacteria, even in the absence of the SRRP (Figure 10E). The discrepancy between PsrP and the other SRRPs might be explained by the fact that certain SRRPs have been described to have lectin activity [26], [27]. In contrast PsrP adhesion has been shown to be independent of lectin-activity [13].
To date, SRRPs have been described in at least 9 Gram-positive bacteria and have been shown to function as adhesins that contribute to virulence. For example, deletion of sraP and gspB in S. aureus and S. gordonii, respectively, decreased the ability of these bacteria to bind to platelets and form vegetative plaques on heart valves of catheterized rats [27], [28]. Similarly, Srr-1 of Streptococcus agalactiae has been shown to bind human Keratin 4, mediate adherence to mucosal epithelial cells, and promote invasion of bacteria through human brain microvasculature endothelial cells [29], [30]. SRRPs also mediate acellular attachment, a role important for colonization of the dental surface by oral streptococci. Froelinger and Fives-Taylor showed that Streptococcus parasanguis containing mutations of Fap1 failed to attach to saliva-coated hydroxyapatite [31]. Likewise, deletion of srpA significantly diminished the ability of Streptococcus cristatus to attach to glass slides [32]. Thus, while it was well established that SRRPs play an important role in bacterial attachment to cells or surfaces, until this report their role as intra-species adhesins remained unrecognized.
A dual role, host and bacterial adhesin for bacterial surface proteins is not unprecedented. For example, in Streptococcus pyogenes and S. agalactiae, the pilus proteins mediate adhesion to epithelial cells and promote microtiter biofilm formation [33], [34]. Likewise, for Neisseria meningitidis, PilX, also a pilus-associated protein, mediates adhesion to epithelial cells and facilitates bacterial aggregation [35]. For the pneumococcus, some evidence existed that bacterial adhesins may also have dual roles. In 2008, Munoz-Elias et al. found that the pneumococcal adhesins Choline binding protein A and the pilus protein RrgA were both required for robust biofilm formation on microtiter plates and efficient nasopharyngeal colonization [36]. However, the attenuated biofilm phenotype was observed only with unencapsulated bacteria and encapsulated mutants formed biofilms normally. Other pneumococcal proteins shown to affect biofilm formation in vitro include Neuraminidase A, which possibly alters the extracellular matrix [37], [38], [39], competence proteins, which suggest an altered protein profile [40], [41], and capsule synthesis enzymes, which were determined to be down regulated in biofilms [36], [42], [43]. Unlike PsrP, which would be expected to bridge cells directly, these proteins most likely act indirectly by altering gene expression, the extracellular milieu, or the surface availability of other adhesins, including possibly RrgA and CbpA.
Our studies determined that the self-aggregating subdomain of PsrP was located in the BR domain and involves amino acids 122–166. Recombinant BR constructs containing these amino acids were able to bind S. pneumoniae carrying PsrP, had an affinity for the BR domain in other PsrP constructs, and could modestly inhibit biofilm formation when added to media. Importantly, adhesion assays using pretreated cells and biofilm assays with rBR.C showed that the AA 122–166 was not responsible for adhesion to lung cells and that the K10 binding subdomain (AA 273–341) was not involved in bacterial aggregation. Thus these subdomains appeared to have independent roles during the conditions tested. Further studies are warranted to delineate the specific AAs responsible for these adhesive properties, also to determine the structure of the BR domain and clarify how these subdomains interact with PsrP on other pneumococci and K10 on lung cells.
GspB and SraP have been previously shown to bind platelets [27], [28]. While the ligand for SraP is unknown, it has been determined that GspB binds to Sialyl T-antigen on platelet membrane glycoprotein Ibα [26], [27]. The observation that the NR domains of GspB and SraP bound to cell lysates containing their respective SRRPs but not to their mutants and that the mutants had diminished aggregative properties suggests that SRRPs in other bacteria might also mediate aggregation in vivo. One could imagine that SraP on S. aureus or GspB on S. gordonii mediating attachment to platelets and cells in an endocarditic lesion while at the same time mediating adhesion of individual bacteria to each other. Similarly, one could envision a microcolony of the pneumococcus in the lungs with some bacteria attached to host cells via PsrP/K10 interactions and other bacteria attached to these bacteria through PsrP/PsrP interactions. Presumably, this is what was observed in the lungs of the infected mice. Interestingly, the finding that GspB and SraP NRs bound to cell lysates from other bacteria suggests that these proteins may also mediate inter-species biofilm formation. For S. gordonii, this would be relevant as the dental plaque is now recognized to be a multi-species biofilm. Importantly, neutralization of pneumococcal aggregation in biofilms with BR antiserum suggests that SRRPs might have utility as vaccine antigens. One caveat is that SRRPs would have to be one-component of a multi-valent vaccine because not all strains of S. pneumoniae, S. aureus, or the oral streptococci carry these proteins.
In previous studies we had found that the length of the SRR2 domain was important for adhesion to K10 when capsule was present. Consistent with these findings, the inability of truncated PsrP to fully complement capsulated mutants supports our hypothetical model that the SRR2 domain serves to extend the BR domain away from the cell to mediate bacterial interactions. This model is also indirectly supported by Munoz-Elias et al., who showed that down-regulation of capsule allowed CbpA and RrgA to contribute to biofilm production [36]. It is also noteworthy to state that Munoz-Elias et al. did not identify PsrP in their screen for biofilm mutants although they used TIGR4 which carries PsrP. This can be explained by the fact that we observed no contribution for PsrP in the microtiter plate early biofilm model.
We observed that PsrP-mediated bacterial aggregation occurred in the nasopharynx, despite earlier studies demonstrating that K10 was absent from this site and that PsrP was not required for nasopharyngeal colonization. Aggregation of S. pneumoniae in the nasopharynx may serve as a mechanism to resist opsonophagocytosis as shown herein, or we speculate a way to resist desiccation during transmission of infectious particles. The observation that aggregates were present at an anatomical site that lacked K10, further supports an independent role for these PsrP subdomains. In regards to opsonophagoyctosis, one important consideration is that the pneumococcus most likely has different gene expression profiles in vivo as an aggregate attached to a cell versus in vitro as a biofilm [44]. Thus caution is warranted in applying our vitro observations, such as resistance to opsonophagoyctosis or enhanced PsrP expression during biofilm growth, with events that occur in vivo.
Polyclonal antibodies against the BR domain, but not the SRR motif, neutralized the ability of TIGR4 and clinical isolates carrying PsrP to form aggregates in the line model. These findings were consistent with previous studies showing that antibodies against BR also neutralized its ability to mediate adhesion to host cells and protected mice against pneumonia [12], [13]. One possible reason that antibodies against the SRR motif peptide failed to have a neutralizing effect is that PsrP is glycosylated and antibodies against the peptide failed to recognize the native version of the protein. Alternatively, antibodies to the SRR motif may bind away from the BR domain and therefore do not inhibit the ability of the BR domain to self-interact. Interestingly, polyclonal antibodies to surface proteins often promote aggregation. This did not occur for unknown reasons. Finally, our finding that antibodies against rBR neutralized bacterial aggregation in the biofilm line model suggests that the same antibodies might also neutralize bacterial aggregation in vivo. This remains to be tested, however, the protection that was observed in mice following immunization with rBR [13], may have been in part due to inhibition of bacterial aggregation in addition to blocking interactions with K10.
Importantly, because rNR domains produced in E. coli are not glycosylated, yet for the tested SRRPs were able to aggregate, immunoprecipitate, and bind to native protein in cell lysates, it seems that the BR domain does not require glycosylation to function as a self-adhesin. This is supported by the observation that addition of antibodies against unglycosylated rBR and that synthetic peptide AA 122–166 both inhibited bacterial aggregation in the biofilm line. In contrast to the latter concept, Wu et al. demonstrated that monoclonal antibodies specific for the glycan motifs of the serine-rich repeat motifs of Fap1 were capable of blocking attachment to saliva coated hydroxyapatite by Streptococcus parasanguis [45]. Importantly, Fap1 is the most divergent of the SRRPs and has 2 NR domains. Fap1 adhesion to saliva coated hydroxyapatite is mediated by glyconjugates on the serine-rich repeat domain [46]; as evidenced by the fact that inactivation of one of the glycosyltranferases known to modify the glycan moieties of Fap1, drastically altered the ability of S. parasanguis to form biofilms [45]. Thus Fap1 is interesting because it suggests an NR-independent mechanism for SRRP adhesion, which is distinct from those discussed for GspB, SraP, or PsrP. Future studies need to further examine the differences between these diverse SRRPs and to determine if the two NRs of Fap1 play a role in bacterial aggregation. This is especially true given that the NR domain of SraP has a pI of 5.6, in contrast to the basic NRs of GspB (9.5 pI) and PsrP (9.9 pI) [47].
In summary, we have described for the first time the presence of a pneumococcal biofilm-like structure in the lungs of infected mice. We have determined that PsrP mediates a more intimate bacterium to bacterium interaction that contributes to the presence of large bacteria aggregates in vivo and increased biofilm biomass and aggregates in vitro. This property appears to be shared among other SRRPs including those of medically relevant bacteria such as S. aureus and S. gordonii, suggesting that it is a conserved function for this class of proteins. How these interactions contribute to pathogenesis remains to be fully determined, however, studies with other bacteria indicate that biofilms serve to inhibit phagocytosis, protect against defensin-mediated killing, and serve as a focal point of infection during early stages of disease. Future experiments will be required to determine the extent to which this may apply for SRRP-mediated aggregates in vivo.
Wild type strains used in this study included S. pneumoniae strain TIGR4 and the previously described clinical isolates IPD-5, TNE-6012, and TBE-6050 [8], [12], [14]. T4R is an unencapsulated derivative of TIGR4 [48]. S. aureus ISP479C and S. gordonii M99 and their corresponding isogenic mutants ISP479C ΔsraP, and M99 ΔgspB have also been previously described [17], [27]. All of the S. pneumoniae mutants used in this study including T4 ΔpsrP, T4 ΩpsrP-secY2A2, T4 ΩpsrP, and T4R ΩpsrP have been shown not to have polar effects on upstream and downstream gene transcription [12], [13]. S. pneumoniae and S. gordonii were grown in Todd-Hewitt broth (THB) or on blood agar plates at 37°C in 5% CO2. S. aureus were grown in Tryptic-Soy Broth (TSB) or on blood agar plates at 37°C. Stocks for the PsrP mutants were grown in media supplemented with 1 µg/mL of erythromycin, those complemented with the expression vector pNE1 were grown on media supplemented with 250 µg/mL of spectinomycin. SraP and GspB mutant stocks were grown in media supplemented with either 15 µg/mL of erythromycin or 5 µg/mL chloramphenicol respectively. E. coli strain DH5α (Invitrogen, Carlsbad CA) expressing recombinant PsrP constructs were grown with 50 µg/mL of kanamycin. Recombinant proteins were purified as previously described [13], [26]. To avoid stress effects on the bacteria, no antibiotics were added to the media during any of the experiments.
Female BALB/cJ mice, 5–6 weeks old, were obtained from The Jackson Laboratory (Bar Harbor, ME). Mice were anesthetized with 2.5% vaporized isoflurane prior to challenge. Exponential phase cultures of S. pneumoniae were centrifuged, washed, and suspended in sterile phosphate buffered saline (PBS). For each experimental cohort at least 6 mice were instilled with either 107 cfu of TIGR4 or T4 ΔpsrP in 20 µL of PBS into the left nostril. After two days mice were sacrificed for tissue collection. For imaging experiments the intact lungs were collected and processed as described below. For enumeration of bacterial aggregates, nasal lavage fluid was collected from anesthetized mice by instillation and retraction of 20 µl PBS. The same mice were subsequently asphyxiated with compressed CO2, and BAL fluid collected by flushing the lungs twice with 0.5 ml of PBS using a sterile catheter. All animal experimentation was conducted following the National Institutes for Health guidelines for housing and care of laboratory animals. Animal experiments were reviewed and approved by the Institutional Animal Care and Use Committee at The University of Texas Health Science Center at San Antonio.
Lungs were cut in a sagital orientation, fixed for 2 hours with 2.5% glutaraldehyde in PBS, and then rinsed twice for 3 min in 0.1 M phosphate buffer (pH 7.4). Lungs were submerged in 1% osmium diluted in Zetterquist's Buffer for 30 minutes then washed with the same buffer for 2 minutes [49]. This was followed by step-wise dehydration with ethanol (i.e. 70%, 95%, and 100%); the first two steps for 15 minutes, the last for 30 minutes. Samples were treated with hexamethyldisilizane for 5 minutes prior to drying in a desiccator overnight. The next day samples were sputter coated with gold palladium and viewed with a JEOL-6610 scanning electron microscope.
From each mouse BAL and 1∶10 PBS diluted nasopharyngeal lavage elutes were smeared onto glass slides, heat fixed, and Gram-stained. Since the nasopharyngeal samples were mucoid, dilution of the samples was warranted. Bacteria were visualized using a CKX41 Olympus microscope at 200× magnification. For each biological sample 100 CFU were randomly selected, taking note of the approximate number of diplococci composing each CFU, either 1, 2–10, or >10. Images of the bacteria were acquired at 400× magnification to better show the multiple bacteria composing the aggregates.
Lung tissues were excised and frozen in Tissue Tek O.C.T solution (Miles Scientific). 5 µm thick lung sections were cut at the University of Texas at San Antonio Histopathology Core and stored at −80° C. Bacteria in the lung sections were detected by immunofluorescence using antibody against the capsular polysaccharide. Sections were thawed, fixed with ice-cold acetone for 20 minutes, and then rehydrated with 70% ethyl alcohol and then PBS. Samples were permeabilized with 0.1% Triton-X-100 for 5 minutes then blocked with 10% fetal bovine serum (FBS) in F12 media for 1 hour. Sections were incubated with 1∶1,000 rabbit anti-serotype 4 pneumococcus antiserum (Statens Serum Institut, Denmark) overnight at 4°C. After washing for three times with 0.5% Tween-PBS, sections were covered with FBS-F12 containing goat anti-rabbit FITC conjugated antibody (Invitrogen) at 1∶2,000 and DAPI (5 µg/ml; for DNA) and the sections incubated for 1 hour at room temperature. Tissue sections were washed and mounted with FluorSave (Merck Biosciences). Images were acquired at 1,000× using a Nikon AX-70 fluorescent Microscope and images processed with SimplePCI software.
Early biofilm formation was examined by measuring the ability of cells to adhere and accumulate biomass on the bottom of a 96-well (flat-bottom) polystyrene plates (Costar, Corning Incorporated, Lowell MA) [24]. Microtiter wells with 200 µl THB were inoculated with 106 CFU of S. pneumoniae taken from cultures at mid-logarithmic phase growth (OD620 = 0.5). Plates were incubated at 37° C in 5% CO2. S. aureus and S. gordonii biofilm formation on microtiter plates was done in a similar manner, with the exception that TSB was used for S. aureus [50], [51]. Bacteria were grown for 2, 4, 6, 8, 18, and 24 h, after which the biofilms were washed gently with PBS and stained with 100 µL of 0.1% CV. Biofilm biomass was subsequently quantified by image capture using an inverted microscope at 15× and 100× magnification and measuring the corresponding optical density (A540) of the supernatant following washing of the bacteria and solubilization of CV in 200 µL of 95% ethanol.
Mature S. pneumoniae biofilms were grown under once through conditions in a glass slide chamber using a continuous-flow through reactor [25]. The flow cell was constructed of anodized aluminum containing a chamber (4.0 mm by 1.3 cm by 5.0 cm) having two glass surfaces, one being a microscope slide and the other being a glass coverslip serving as the substratum. S. pneumoniae cells grown to mid-logarithmic phase served as the inoculum and were injected into a septum 4 cm upstream from the flow cell. Bacteria were allowed to attach to the glass substratum for 2 hours prior to initiating flow. The flow rate of the system was adjusted to 0.014 ml/min. Flow through the chamber was laminar, with a Reynolds number of <0.5, having a fluid residence time of 180 min. Biofilms were grown at 37°C in 5% CO2 for 3 days under once through conditions. Biofilms were then visualized by confocal laser microscopy as described below.
Biofilms were also grown on the interior surface of a 1-meter long, size 16 Masterflex silicone tubing (0.89mm Internal Diameter, Cole Parmer Inc.) using once-through continuous flow conditions. The line was inoculated with 5 mL of a mid-logarithmic culture and the bacteria were allowed to attach for 2 hours. The flow rate of the system was adjusted to 0.035 ml/min and bacteria were grown for 3 days at 37°C in 5% CO2. Bacterial cells were harvested from the interior surface by pinching the tube along its entire length, resulting in removal of the cell material from the lumen of the tubing. Following extraction, exudates were gently suspended in 1 ml of PBS and the optical density (OD620) was measured. For light microscopy pictures, 50 µl of line exudate in saline was stained by the addition of 50 µL of 1% CV. A volume of 5 µl of stained line exudates was applied to glass slides, coverslipped, and images taken at 200× magnification using a light microscope. Viable cell counts were determined by plating serial dilutions of exudates following the disruption of each sample by vortexing. Biofilm biomass was determined by measuring the total protein concentration of the exudates by BCA following the complete lysis of S. pneumoniae with saline containing 0.1% deoxycholate and 0.1% sodium-dodecyl sulfate, which activates the murein hydrolase autolysin, or use of French press for S. gordonii and S. aureus cultures. For studies testing whether antibodies or recombinant protein inhibited bacteria aggregation media was supplemented with BR antiserum at 1∶1,000 or spiked with recombinant protein at a final concentration of 1.0 µM.
Confocal scanning laser microscopy was performed with an LSM 510 Meta inverted microscope (Zeiss, Heidelberg, Germany). Images were obtained with an LD-Apochrome 40×/0.6 lens and the LSM 510 Meta image acquisition software (Zeiss). To visualize the biofilm architecture of 3-day-old biofilms, biofilms were stained using the Live/Dead BacLight stain from Invitrogen (Carlsbad, CA). Quantitative analysis of epifluorescence microscopic images obtained from flow cell-grown biofilms at the 6-day time point was performed with COMSTAT image analysis software [52].
Recombinant full-length BR and truncated versions (BR.A, BR.B, BR.C) were expressed and purified from E. coli as previously described [13]. Glycosylated PsrPSRR2(33)-HIS was purified in the same manner from TIGR4 (Figure S3), with the exception that cultures were induced with 1% fucose and lysed with 1% SDS in PBS. Far Western analysis was carried out as described by Takamatsu et al. with minor modifications [53]. Nitrocellulose membranes were spotted with either 1 µg of whole cell lysate of S. pneumoniae, S. gordonii, S. aureus or E. coli expressing various PsrP constructs or with purified recombinant proteins in PBS. Membranes were incubated overnight in PBS with 4% bovine serum albumin and 0.1% Tween 20 (T-PBS) at room temperature. The next day, membranes were washed with T-PBS three times for 5 minutes, and incubated overnight at 4°C on an orbital platform rocker with T-PBS containing 1% bovine serum albumin (TB-PBS) with 1 µg/mL of Gst-BR, PsrPSRR2(33)-HIS, or the designated NR constructs from S. gordonii and S. aureus. Membranes were washed and incubated with monoclonal mouse anti-Gst antibody (1∶5,000 dilution) (Proto-Tech) overnight at 4°C in TB-PBS. Antibody binding was detected by incubating the membranes for 1 h with HRP-conjugated anti-mouse IgG (1∶10,000 dilution) (Sigma), followed by development with the Super Signal chemiluminescent detection system (Thermo Scientific). As a control for inadvertent interactions with the Gst tag, Far Western blots were also performed using an unrelated Gst-tagged Chlamydia trachomatis protein (TC0109; Figure S4). No interactions were observed.
Co-immunoprecipitation of Gst-BR with the truncated versions of rPsrP was carried out as previously described by Shivshankar et al. [13]. Protein G Sepharose beads (Amersham) were incubated overnight at 4°C with mouse monoclonal penta-His antibody (1∶50; Qiagen) in 500 ml of F12 media supplemented with 10% fetal bovine serum. Beads were incubated with 400 µl of whole bacterial lysates from E. coli expressing penta-His tagged recombinant versions of PsrP spiked with 200 µg of recombinant Gst-BR full length and incubated overnight at 4°C with gentle agitation. Beads were washed with RIPA buffer, then boiled in sample buffer for 10 min [54]. Samples were separated on 12% SDS-PAGE gels and electrophoretically transferred to nitrocellulose membranes. Membranes were blocked with T-PBS containing 4% bovine serum for 30 min at room temperature. Membranes were then incubated overnight at 4°C with mouse anti-Gst (1∶7500; Proto-tech) in blocking buffer. Following incubation, membranes were washed with T-PBS three times for 5 minutes. HRP-conjugated goat anti-rabbit Immunoglobulin G (1∶10 000; Sigma) was used as the secondary antibody, followed by development with the Super Signal chemiluminescent detection system (Thermo Scientific).
For labeling of bacteria, TIGR4 and T4 ΔpsrP were pelleted and suspended in 1 ml of carbonate buffer (pH 9.0) containing FITC (1 mg/ml) and incubated in the dark at room temperature with constant end-to-end tumbling. FITC-labeled bacteria were washed with PBS (pH 7.4) and centrifuged, until the supernatant became clear. rBR fragments were labeled using a FluorLink-Ab Cy3 labeling kit (Amersham) using the instructions provided by the manufacturer. Labeled bacteria were suspended in serum-free F12 media containing the labeled constructs for 1 hour and gently mixed. Subsequently, pneumococci were washed and suspended in F12 medium. Labeled bacteria and bound recombinant protein were visualized using an AX-70 fluorescent microscope and the images were captured at 0.1112–0.8886 ms exposure time for Cy2 and Cy3 filters. The magnification used for capture of digital images was 1000×. Captured images were processed using Simple PCI software.
A549 cells (human alveolar type II pneumocytes; ATCC CRL-185), were grown to 90% confluence on 24-well plates (∼106 cells/well). Prior to use, cells were washed with cell F12 media to remove serum. For competitive inhibition binding assays, A549 cells were incubated with 1µM of either rBR, rBR.C, a synthesized peptide corresponding to AA 122–167, or BSA for 1 HR. Following incubation, cells were exposed to media that contained 107 cfu/mL of bacteria and incubated for 1 h at 37°C in 5% CO2. Nonadhering bacteria were removed by washing the cells 3 times with T-PBS and the number of adhering bacteria was determined by lysis of the monolayer with 0.1% Triton X-100 and plating wells per experiment.
Bacterial cultures were centrifuged and suspended in 0.1M sodium carbonate buffer (pH 8.0) at an OD620 of 0.2. Care was taken to cause minimal disruption of the biofilm aggregates. The diluted cultures were labeled with fluorescent isothiocyanate (1mg/ml) for 30 min at room temperature in the dark. Following labeling, cultures were gently washed three times with sterile PBS to remove free FITC and suspended in PBS. FITC-labeled bacteria were opsonized with 3% control rabbit serum for 30 minutes at 37°C with mild periodical tapping. Mouse J774.1, macrophage cultures maintained in 10% FBS containing DMEM were used for phagocytosis of the opsonized pneumococci. Macrophages were harvested, washed and diluted with opsonophagocytosis buffer (PBS containing 0.2% BSA). FITC-labeled bacteria in 100 µl were added to 106 macrophage cells in 400 µl and incubated for 1 hour at 37°C with periodic shaking. Afterwards, the macrophages were pelleted and washed twice in the assay buffer. Cells were suspended in 400µl of 2% paraformaldehyde until flow cytometric analysis. A2-Laser BD FACSCaliber Analyzer (Becton Dickinson, NJ; Institutional Flow Cytometry Core Facility at the Health Science Center) was employed to analyze percent phagocytic uptake of the labeled bacteria by the macrophages. A minimum of 20,000 events were counted for each sample at 480 nm excitation and 530nm detection wavelengths. Background fluorescence was nullified by subjecting negative control macrophages in assay buffer without any fluorescent bacteria to FACS analysis. Data were processed using CellQuest software.
For pair-wise comparisons of groups statistical analyses were performed using a Student's t-test. For multivariate analyses a 1-Way ANOVA followed by a post-priori test using Sigma Stat software was used.
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10.1371/journal.pcbi.1002939 | A Dynamical Role for Acetylcholine in Synaptic Renormalization | Although sleep is a fundamental behavior observed in virtually all animal species, its functions remain unclear. One leading proposal, known as the synaptic renormalization hypothesis, suggests that sleep is necessary to counteract a global strengthening of synapses that occurs during wakefulness. Evidence for sleep-dependent synaptic downscaling (or synaptic renormalization) has been observed experimentally, but the physiological mechanisms which generate this phenomenon are unknown. In this study, we propose that changes in neuronal membrane excitability induced by acetylcholine may provide a dynamical mechanism for both wake-dependent synaptic upscaling and sleep-dependent downscaling. We show in silico that cholinergically-induced changes in network firing patterns alter overall network synaptic potentiation when synaptic strengths evolve through spike-timing dependent plasticity mechanisms. Specifically, network synaptic potentiation increases dramatically with high cholinergic concentration and decreases dramatically with low levels of acetylcholine. We demonstrate that this phenomenon is robust across variation of many different network parameters.
| The function of sleep is one of the greatest mysteries in contemporary neuroscience. Nearly every species of animal requires it, yet we do not know why. One idea, known as the synaptic renormalization hypothesis, suggests that waking results in a global increase in the strengths of connections in the brain, a phenomenon which is unsustainable because stronger connections consume more energy and take up more space. The function of sleep, according to this hypothesis, is to downscale or “renormalize” connection strengths. While mounting experimental evidence confirms that sleep-dependent synaptic downscaling does occur, we still do not know what biophysical mechanism causes it. In this paper, we show computational results which indicate that the neuromodulator acetylcholine may have a key role to play in sleep-dependent synaptic downscaling. If confirmed experimentally, these findings will help to unravel the mystery of sleep.
| Sleep is crucial for normal cognitive function as evidenced by the many cognitive impairments associated with chronic sleep loss [1], [2]. A leading proposal for the function of sleep, called the synaptic renormalization hypothesis, posits that sleep is required to maintain synaptic balance in the brain [3], [4]. According to this hypothesis, waking experiences result in the net potentiation of many brain circuits, leading to both increased energy consumption and heightened demand for space by the potentiated synapses. In order to conserve energy and space, sleep induces a period of large-scale synaptic downscaling. Sleep is therefore “the price we pay for plasticity” [5].
Multiple lines of empirical evidence supporting the synaptic renormalization hypothesis have recently emerged [6]–[10], including in vivo studies finding increased slope of evoked LFP/EEG responses after wakefulness and decreased slope following sleep in rats [11] and humans [12]. Furthermore, increasing evidence supports a link between synaptic depotentiation during sleep and slow wave activity (SWA) [13], which is the pattern of electroencephalograph (EEG) activity observed during non-rapid eye movement (NREM) sleep in mammals and birds which features increased power in the delta band (0.5 to 4 Hz). Various studies have shown that SWA in NREM sleep locally increases in brain areas that exhibit potentiation during prior wakefulness [14]–[16], suggesting that SWA may function to maintain synaptic homeostasis.
Exactly how synaptic downscaling is induced during sleep is an open question. One suggestion is that the repeated alternation of depolarized “up” states, reflecting the simultaneous activity of many neurons, and hyperpolarized “down” states, reflecting fewer active neurons, observed to occur at approximately 1 Hz during SWA may induce long-term depression (LTD) of synapses [17], [18]. Another possibility is that the reduction of brain-derived neurotrophic factor (BDNF) during sleep [5], [6] might enable synaptic depression. Similarly, it is not clear exactly why synapses might exhibit net potentiation during wakefulness, though it has been suggested that the processing of sensory signals or the formation of new memories may inevitably lead to synaptic upscaling [4].
A further hypothesis is that differences in the neuromodulators available during waking and NREM sleep states may contribute to the opposing effects of wakefulness and NREM sleep on neuronal potentiation levels [5]. Waking is characterized by high levels of noradrenaline, serotonin, histamine and acetylcholine in cortex, while all these neurotransmitters are at low levels during NREM sleep [19], [20]. The low levels of these neuromodulators during sleep has led to the idea that this alters molecular mechanisms underlying spike-timing dependent plasticity (STDP) so that sleep favors synaptic depotentiation [21]. Although some investigation has been done into the effects of various neuromodulators on STDP [22], these mechanisms remain poorly understood. The effects of neuromodulators upon other forms of plasticity may also contribute to synaptic renormalization [23], [24].
In the present study, we build upon previous work to develop a new theory for synaptic downregulation during NREM sleep that highlights a role for differing cortical network dynamics during wake and NREM sleep states. This theory relies upon previous findings showing that acetylcholine (ACh) modulates the phase-dependence of neural responses in cortex [25], [26]. When ACh is more available, as in the awake state, most cortical neurons display phase-independent firing in response to synaptic input: they fire soon after receiving excitatory input regardless of their activity when the input arrives (Type I). In contrast, when ACh is less available, as during NREM sleep, cortical neurons display phase-dependent firing in response to synaptic input: whether they fire sooner or later after receiving an excitatory input depends on how long it has been since they last fired (Type II). As we and others have shown previously, the increased flexibility of exact firing times in response to input that occurs with low ACh concentration better enables pre- and post-synaptic cells to synchronize their activity, thereby increasing synchronized activity in cortical networks [27]–[29]. While ACh has many diverse effects in the brain [30], [31], here we focus on these dynamical effects of cholinergic modulation.
Our new theory concerns the effect of increased synchronized network activity during NREM sleep on the strength of synaptic connections. In particular, we posit that although this increase in synchronized network activity strengthens some individual synaptic connections, it weakens others. Further, and critically, this weakening is more pronounced when an animal is experiencing NREM sleep (more synchronized activity) than when an animal is awake (less synchronized activity). Supporting this novel hypothesis, we show that a computational model employing these dynamic, physiologically-plausible mechanisms is fully able to account for synaptic renormalization during NREM sleep.
We simulated the effects of ACh on synaptic potentiation in cortical networks consisting of 1000 neurons (20% of which were inhibitory). Each neuron was described by a recently-developed cortical pyramidal cell model [26] that was motivated by experimentally measured effects of ACh [25]. In this model, simulated cholinergic modulation blocks a slow, low-threshold M-type potassium current that induces spike frequency adaptation. Blockade of this current modulates the response properties of modeled neurons as measured by the phase response curve (PRC). With low ACh levels, the neuronal PRC displays phase regions where spike timing is delayed and where it is advanced, categorized as Type II PRC [28], [29]. High ACh levels produce only advances in spike timing regardless of the phase of perturbation, resulting in Type I PRC (see Fig. 1).
Switching PRCs of synaptically coupled neurons from Type II to Type I has been shown to dramatically affect the synchronization of neuronal networks. Specifically, simulated large-scale neuronal networks whose cells have Type II PRCs have been shown to synchronize much better than neuronal networks composed of cells with Type I PRCs [32]. This effect can be explained heuristically by the fact that neurons with Type II PRC are in some sense “more flexible” than those with Type I PRC, since neurons with Type II PRC can advance and delay their spike firing in response to synaptic input [28], [29]. More rigorous mathematical analysis has shown that in the weak coupling limit, the emergence of stable synchronous dynamics depends upon a stability criterion known as the H-function, which is constructed from the odd part of the neuronal PRC [33]. Such analysis has shown that while the emergence of a phase delay region in the PRC is sufficient to promote stable synchrony, it is not necessary–a PRC which is entirely positive but skewed toward late phase can also elicit highly synchronous dynamics [34]. The designations “Type I” and “Type II” therefore constitute two poles of a spectrum of neuronal response properties. The PRC framework has been used to explain why cholinergic modulation has a dramatic effect upon the synchronization of simulated cortical networks, with low ACh concentration (which induces more Type II-like PRC) leading to much higher network synchrony than high ACh concentration (which induces more Type I-like PRC) [25]–[27].
We investigated how the differential effects of ACh on network synchrony influenced overall network synaptic potentiation when synaptic strengths evolved according to a spike-timing dependent plasticity (STDP) rule. In our network simulations, synaptic strength values were initialized to an intermediate value and then allowed to evolve, according to the STDP rule, over the interval (see Materials and Methods for simulation details). We quantified the steady state distribution of synaptic strength values with a measure of “network potentiation,” calculated as a scaling of the mean equilibrium synaptic weight. The values of this network potentiation measure range from −1 for maximally weakened networks (all synaptic strength values go to 0) to +1 for maximally strengthened networks (all synaptic strength values go to ). We investigated the effects of network connectivity by varying synaptic connection architecture using the Watts-Strogatz small-world paradigm [35]. With this method, each neuron was initially connected to a fixed number of its nearest neighbors, and then a certain proportion of these connections were re-wired to synapse onto randomly-selected cells in the network. The proportion of connections which were re-wired was specified by the re-wiring probability. Since both maximum synaptic strength and network connectivity structure are known to dramatically influence neuronal network dynamics, we explored a wide range of values for and the re-wiring probability to ensure the robustness of our results.
High simulated cholinergic modulation switched neuronal PRCs from Type II to Type I (Fig. 1 a,b), inducing a decrease in network synchronization (Fig. 1 c,d) that affected the steady state distributions of synaptic strengths (Fig. 1 e,f). The synaptic strength distribution of the high-ACh network was heavily skewed toward maximal synaptic weight, reflecting higher network potentiation. On the other hand, the distribution of the low-ACh network was more symmetric, with about half the synapses at the maximal value and the majority of remaining synapses at zero strength. These results were robust to variations in maximal synaptic strength and network connectivity architecture (Fig. 2 a,b). Network potentiation values for high-ACh networks exceeded those for low-ACh networks for almost all combinations of re-wiring probability and .
Differences in network potentiation were especially pronounced for , at which values the network potentiation dropped to approximately zero in low-ACh networks for all values of the re-wiring probability (Fig. 2b). Interestingly, this drop in network potentiation coincided with the transition from asynchronous to synchronous activity in low-ACh networks (Fig. 2d). On the other hand, the robustly high levels of potentiation observed in high-ACh networks (Fig. 2a) corresponded to completely asynchronous activity for all network parameters (Fig. 2c). Our simulations therefore counterintuitively showed that synchronous network dynamics led to relatively lower network potentiation than asynchronous network dynamics.
Since STDP requires correlated firing to potentiate the connection between two neurons, one might expect that asynchronous network activity should induce no net change in network potentiation, rather than the overall increased potentiation we observed. Further analysis of pre- and post-synaptic cell pairs uncovered an important statistical structure of the neuronal firing patterns in the cholinergically-modulated networks: post-synaptic neurons throughout the network were more likely to fire shortly after their pre-synaptic neurons rather than shortly before (Fig. 3a). Thus, pre-post spike time differences landed in the positive portion of the STDP curve more frequently than in the negative portion of the STDP curve, resulting in increased potentiation of the network as a whole.
On the other hand, the relatively lower network potentiation observed in networks with low cholinergic modulation was due to post-synaptic neurons firing right before their pre-synaptic partners much more frequently (Fig. 3b). This effect occurred because the bursts of activity in low-ACh networks constrained all neurons to fire within very short time windows, forcing pre-synaptic neurons to directly compete with one another to induce common post-synaptic partners to fire. As a result, roughly half the pre-post spike time differences fell in the positive portion of the STDP curve, and the other half fell in the negative portion, leading to nearly symmetric and highly polarized final distributions of synaptic strengths (as in Fig. 1f).
It should be noted that we tested this result for robustness against noise by adding Gaussian-distributed noise with a temporal correlation of 100 ms (the approximate inter-spike interval of the slowest-firing neurons) to the external constant current driving individual neurons. We found that even for a noise amplitude as high as , we still observed much greater potentiation in high-ACh networks than in low-ACh networks for a large range of network parameters (Fig. 4a,b). This noise amplitude was large relative to the driving currents for both high-ACh networks () and low-ACh networks (). Furthermore, we found that if we chose one set of network parameters and progressively increased the noise amplitude, the difference between network potentiation in high- and low-ACh networks did not disappear until the noise amplitude reached (Fig. 4c).
Since acetylcholine levels vary dramatically in cortex, we investigated how sensitively our results depended upon acetylcholine levels, which dramatically influence PRC shape. Cholinergic modulation was modeled by varying the slow potassium conductance which decreases with increasing levels of acetylcholine. Fig. 5 depicts the dependence of network potentiation upon (in all other plots, is set to to simulate high ACh concentration and to simulate low ACh concentration). Figs. 5a,b show examples of the network potentiation plotted as a function of network parameters for two different values. Note how results in much greater network potentiation than for most network parameters. Fig. 5c shows that for representative network parameters, network potentiation and network synchrony undergo sharp phase transitions as increases. The phase transition in synchrony (which induces the phase transition in network potentiation) is well explained by the transition in PRC shape depicted in Fig. 5d. As increases, the neuronal PRC is shifted to the right and, crucially, the positive slope at phase zero is attenuated while the negative slope at later phase is not. This is consistent with the idea that network synchrony stabilizes when the odd part of the PRC, known as the H-function, switches the sign of its slope at phase zero [29], [36].
We also tested our results for robustness to connectivity density by increasing the radius of connectivity in our network simulations (see the description of the Watts-Strogatz small world network paradigm detailed in Materials and Methods). High-ACh networks showed greater overall potentiation than low-ACh networks for a wide range of connectivity densities (0.8% to 4.0% connectivity), though sparser connectivity led to greater differences in network potentiation (Fig. 6).
We tested the results for robustness to frequency modulation by varying the duration of the STDP window, . We used this approach rather than directly modulating neuronal frequency because network effects made it difficult to elicit a wide range of average firing frequencies. In Fig. 7, was varied from 1 ms to 100 ms (the default value throughout this study was 10 ms). High-ACh networks exhibited much higher network potentiation than low-ACh networks for all values of .
Finally, several studies have shown that the equilibrium distribution of synaptic weights in a network subject to STDP strongly depends upon the mathematical form of the STDP rule. For example, some have suggested that the integral of the LTD portion of the STDP curve should be greater than the LTP portion of the curve in order to maintain network potentiation at reasonable levels [37], [38]. We explored this STDP formulation by using an asymmetric STDP rule in which the integral of the LTD curve was ten percent greater than the integral of the LTP curve. The results of these simulations, shown in Fig. 8, are qualitatively similar to our main results in Fig. 2. Others have pointed out that “multiplicative” (weight-dependent) STDP rules tend to produce qualitatively different synaptic weight distributions than “additive” STDP rules [39]. Indeed, the polarized synaptic weight distributions shown in Fig. 1 are the typical result of an additive STDP rule [40], [41], and when we switched to a multiplicative rule we obtained more unimodal distributions (Fig. 9). For both STDP rules, we observed that high ACh led to significantly greater network potentiation than low ACh (Figs. 2 and 9), though the effect was more pronounced for the additive rule (Fig. 2a,b) than for the multiplicative rule (Fig. 9a,b).
The above results pertained to networks with homogeneous connectivity distributions in the sense that all synapses could achieve the same maximal strength, and long-range network connections did not preferentially target any particular neurons. Such homogeneity certainly does not exist in the brain [42], [43]. Therefore, we explored effects of cholinergic modulation on synaptic potentiation in the presence of network connectivity heterogeneities. A question of particular interest was whether ACh-induced changes in synaptic plasticity affect all connections in the network to the same extent. To address this question, we considered a network of 1000 neurons with an embedded cluster of 50 neurons. The maximal synaptic strength values () of connections originating from cells within the cluster were two times greater than for the surrounding network. Additionally, while the number of outgoing connections per neuron was the same for both the cluster and the rest of the network, a fixed fraction of out-going synaptic connections from surrounding cells preferentially targeted the cluster and vice versa. Thus, in the network, a small number of connections originated within the cluster and projected outside the cluster, while a larger number of connections originated outside the cluster and projected to the cluster (see Materials and Methods for more details).
In this heterogeneous network, we alternately switched between the high and low acetylcholine concentration (simulating waking and NREM sleep, respectively), and found that such switching induced immediate and dramatic changes in network synchrony and potentiation (Fig. 10a). As in the homogeneous networks, we found that the asynchronous dynamics induced by high cholinergic modulation resulted in relatively high network potentiation (Fig. 10b,c), but we found that the depotentiating effects of low acetylcholine levels were even more pronounced than in homogeneous networks. Fig. 10a shows that the network potentiation measure actually dipped below zero for two low-ACh intervals, implying that the number of connections whose synaptic strength went to 0 exceeded the number that reached (Fig. 10d).
This enhanced depotentiating effect resulted from the dynamical interplay between the cluster and the rest of the network. As shown in Fig. 10e, under low levels of acetylcholine the cluster tended to fire in synchronized bursts, which drove the rest of the network to respond by firing noisy bursts. The relative firing times of the surrounding network relative to the cluster resulted in potentiation of connections originating in the cluster and projecting outside the cluster, and depotentiation of connections originating outside the cluster and projecting to the cluster (see the “low Ach” intervals in Fig. 10f). Since there were more connections originating outside the cluster and projecting into the cluster than vice versa, strong overall network de-potentiation occurred.
Fig. 10f demonstrates another striking feature of this network: the small subset of connections projecting from the cluster to the surrounding network remains at very high potentiation levels throughout cholinergic switching. Furthermore, this set of connections collectively increases in strength during epochs when ACh is low, in contrast to the collective weakening exhibited by connections in the rest of the network.
We have proposed a novel physiologically-plausible mechanism, based on cholinergic modulation of neural membrane excitability, that can account for synaptic renormalization during NREM sleep. We have shown that the dramatic changes in membrane excitability induced by cholinergic modulation, and the resulting changes in network firing patterns, lead to upscaling and downscaling of mean synaptic efficacy. Thus, our results propose a dynamical mechanism for synaptic renormalization that provides a bottom-up framework linking changes in the neuromodulator environment during waking and NREM sleep to changes in neuronal excitability, network activity patterns, and overall renormalization of network connectivity. Simulations of networks with heterogeneous synaptic connection distributions also provided evidence for selective rescaling of particular network connections.
Our simulations showed that high levels of acetylcholine in cortical networks led to asynchronous dynamics, which in turn led to relatively high network potentiation. On the other hand, low levels of acetylcholine resulted in more synchronous network activity and relatively lower overall potentiation. These results are consistent with the prediction of the synaptic renormalization hypothesis that wakefulness (during which ACh is present at high levels in cortex) is associated with global synaptic upscaling, while NREM sleep (during which ACh is present at much lower levels in cortex) is associated with global synaptic downscaling. These results were also robust to noise, changes in network frequency, different network topologies, and various STDP parameters, and they were strengthened by network heterogeneities. Additionally, Fig. 5 shows that extreme concentrations of ACh (either high or low) do not appear necessary to induce the transition from low to high network potentiation–large intervals of accommodated both states.
The desynchronization of neuronal activity that resulted from high concentration of ACh in our model is expected from PRC theory, since higher ACh induces more Type I-like PRC [25]. Some studies, however, have associated increased ACh with elevated neuronal synchrony. For example, Rodriguez et. al. showed that ACh promoted gamma synchronization in response to light stimuli in cat visual cortex [44]. There have been other studies, however, which have shown the opposite effect. Kalmbach et. al. showed that optogenetically-induced release of ACh by nucleus basalis axons led to an immediate desynchronization of afferent cortical neurons [45], and Metherate et. al. demonstrated that electrical stimulation of the nucleus basalis desynchronized cortical EEG [46]. Thus it seems unclear from the literature exactly how ACh affects neuronal synchronization. One possibility is that ACh enhances synchrony in response to attended stimuli, but has a desynchronizing effect in regions of cortex which are not actively processing attended stimuli. In that case, our model would emphasize endogenous network dynamics over stimulus-evoked activity.
On the other hand, ACh is known to be down-regulated during NREM sleep, when slow wave activity dominates EEG recordings. Such activity is associated with the slow oscillation of thalamocortical neuron membrane potential that results from thalamocortical bistability [47]–[49]. In addition, multiple lines of evidence suggest that slow waves involve the persistent synchronous bursting of cortical neuron populations [5], [50]–[52]. Similar activity patterns were produced in our simulations of low-ACh networks (see Fig. 1d), suggesting that low cholinergic concentration may work in tandem with underlying slow oscillations to facilitate bursting activity. As shown in Fig. 10, this highly synchronous activity resulted in synaptic downscaling relative to the asynchronous activity induced in high-ACh networks.
Fig. 10f also shows how a subset of connections that were highly potentiated following waking (high ACh) remained strong–and were actually even further strengthened–during simulated NREM sleep (low ACh). This effect was obtained through the introduction of a small subset of connections which had larger maximum synaptic strength values than in the rest of the network, providing a possible mechanism for sleep-dependent memory consolidation within the framework of spike-timing dependent plasticity.
While our theory focuses on possible dynamical underpinnings of the renormalization hypothesis, there are many other factors which may contribute to synaptic renormalization. Incoming sensory signals may promote upscaling during wakefulness [4], while downscaling during sleep might be facilitated by the endogenous low-frequency rhythms of slow-wave sleep, which share similar frequency content with the low-frequency stimulation known to induce long-term depression [17], [18]. One recent study suggested that elevated levels of neuromodulators such as noradrenaline and acetylcholine during waking may promote overall synaptic potentiation, while the absence of these same neuromodulators during sleep may modify spike-timing dependent plasticity to favor synaptic depression [21], [22]. Our simplified model focuses upon spike-timing dependent plasticity because we are interested in how network potentiation is affected by alterations in network synchrony, and STDP is the form of plasticity which is most relevant for changes in synchrony. There are, however, many plasticity mechanisms in the brain other than STDP which may also contribute to synaptic renormalization, including the many varieties of homeostatic plasticity [53], [54]. Investigating the interaction between STDP and these other forms of homeostatic plasticity is beyond the scope of this paper.
Our theory hinges on the result that synchronous network activity leads to synaptic downscaling, while asynchronous network activity generates synaptic upscaling. Our analysis of the structure of spike times in pre- and post-synaptic cell pairs indicates that downscaling was due to timing competition between arriving excitatory post-synaptic potentials (EPSPs) within the brief period of synchronous spiking activity. This competition within such a short time window resulted in about half the pre-post pairings falling in the negative portion of the STDP curve and therefore leading to lower network potentiation relative to asynchronous network activity. It has previously been shown that asynchronous neuronal activity leads to increased network potentiation while synchronous activity leads to decreased network potentiation in simulated networks incorporating STDP with propagation delays [55]. Our results show that similar effects can be obtained in networks where synaptic delays are negligible. Additionally, these effects are obtained for completely different and counterintuitive reasons, namely through altered statistics of spike arrival times at post-synaptic cells.
In summary, we have shown that cholinergic modulation can lead to changes in overall network potentiation, and that these changes may be understood in terms of the altered cellular and network dynamics induced by ACh. Further experimental investigation into the possible role of cholinergic modulation in the dynamical underpinnings of synaptic renormalization is clearly required.
The cortical pyramidal model neuron we employed was motivated by a recent experimental study which showed that in slices of mouse visual cortex, the presence of acetylcholine (ACh) modulated the response properties of cortical neurons as measured by the phase response curve (PRC) [25]. The neuronal PRC tracks the changes in spike timing in response to perturbations of the membrane potential as a function of the phase of the spike cycle at which the perturbation occurs. The presence of ACh and its effects upon neuronal PRCs were shown to be well modeled by varying the maximum conductance of a slow, low-threshold -mediated adaptation current from to in a Hodgkin-Huxley based neuronal model [26], [56]. We used this model in the current study, and modulated only to model the presence or absence of ACh. The model also featured a fast, inward current. The model also includes an inward current, a delayed rectifier current, and a leakage current. The current balance equation for the cell was(1)with , in millivolts, and in milliseconds. was an externally applied current that was constant for each neuron but Gaussian-distributed across neurons in the network, with a variance set to induce a spread of 1 Hz in the instrinsic neuronal frequencies in the neurons for both high and low levels of cholinergic modulation. The mean of the distribution of values was for high-ACh networks and for low-ACh networks (different values were necessary to account for different firing thresholds and frequency-current curves). was a Gaussian noise term supplied to each neuron in our study of noise robustness (Fig. 4). This noise was independent from neuron to neuron, but for each individual neuron the noise was correlated over a time scale of 100 ms (the typical inter-spike interval of the slowest-firing neurons). was the synaptic current received by neuron .
Activation of the current was instantaneous and governed by the steady-state activation function . Dynamics of the current inactivation gating variable were given by(2)with and . The delayed rectifier current was gated by , whose dynamics were governed by(3)with and . The slow, low-threshold current targeted by cholinergic modulation was gated by , which varied in time according to(4)where .
The slow, low-threshold current loosely modeled the muscarine-sensitive M-current observed in cortical neurons. Setting modeled high levels of ACh in cortical networks, and setting modeled low ACh levels. All other parameter values were the same for both high-ACh and low-ACh networks: , , , , , and .
To obtain the phase response curves displayed in Fig. 1, was set to a fixed value to elicit repetitive firing in a single, synaptically isolated neuron, and the model equations were time evolved using a fourth-order Runge-Kutta numerical scheme until the oscillatory period stabilized. Then, using initial conditions associated with the spike peak, brief current pulses were administered at different phases of the oscillation, and the perturbed periods were used to calculate the corresponding phase shifts. The current pulses were administered at 100 equally-spaced time points throughout the period of the neuronal oscillation. The current pulses had a duration of 0.06 ms and an amplitude of for the high-ACh cortical pyramidal neuron, and a duration of 0.06 ms and an amplitude of for the low-ACh cortical pyramidal neuron.
We simulated networks with 800 excitatory neurons and 200 inhibitory neurons. The network connectivity pattern was constructed using the Watts-Strogatz architecture for “small world networks” [35]. Starting with a 1-D ring network with periodic boundary conditions, each neuron was at first directionally coupled to its nearest neighbors, and then every connection in the network was rewired with probability to another neuron selected at random. In this way, resulted in a locally-connected network and in a randomly connected network. The radius of connectivity therefore determined the density of connections in the network, while the re-wiring parameter determined the network connectivity structure. Network connectivity was set to 4 in all simulations except those in Fig. 10 and Fig. 6.
Synaptic current was transmitted from neuron following times when its membrane voltage breached −20 mV. The synaptic current delivered from neuron to a synaptically connected neuron at times was given by , where we used and for excitatory synapses and for inhibitory synapses. The total synaptic current to a neuron was given by , where was the set of all neurons presynaptic to neuron . Excitatory synaptic strengths evolved according to an additive STDP rule in which the change in synaptic strength between postsynaptic neuron and presynaptic neuron was given by(5)where represents the spike time of postsynaptic neuron minus the spike time of presynaptic neuron . We set in all our simulations, except in Figs. 7 and 8. We also confined synaptic strength values to the interval , where was a parameter that we varied in our simulations. The maximum amount the strength of a synapse could change due to one spike pairing was set by the parameters and , which we set to (except for the simulations in Fig. 8). We intentionally chose this value to be rather large so that synaptic strength distributions would equilibrate in a reasonable amount of time.
Simulations were initialized with all synaptic strengths set to , after which the strengths of excitatory synapses evolved freely according to the dynamics of the network (strengths of inhibitory synapses were fixed). After the distribution of synaptic weights had equilibrated (which required longer for low-ACh networks because they fired at lower rates than high-ACh networks; high-ACh network simulations were run for 5,000 ms and low-ACh network simulations were run for 20,000 ms), the overall network potentiation was quantified using the measure(6)where designates the mean of all equilibrium excitatory synaptic strengths. This measure, which is just a scaling of mean synaptic strength, attributed a network potentiation value of +1 to maximally potentiated final synaptic distributions, and a network potentiation value of −1 to maximally depotentiated final synaptic distributions. All simulations were numerically integrated in Matlab using a fourth-order Runge-Kutta method with a time step of 0.05 ms.
We quantified phase-synchronization of neuronal firing in our simulations using the mean phase coherence (MPC) measure [57]. This measure quantified the degree of phase locking between neurons, assuming a value of 0 for completely asynchronous spiking and 1 for complete phase locking. Note that high MPC could be attained for locking of phases at any value, not just zero. The MPC between a pair of neurons, , was defined by:(7)(8)where was the time of the spike of neuron 2, was the time of the spike of neuron 1 that was largest while being less than , was the time of the spike of neuron 1 that was smallest while being greater than or equal to , and was the number of spikes of neuron 2. The MPC of the entire network was calculated by averaging the mean phase coherence of all neuron pairs, discounting the first half of network activity, in order to capture steady-state network synchronization.
In our simulations exploring network heterogeneity, the network was composed of 1000 neurons (800 excitatory, 200 inhibitory), of which 50 comprised a cluster in which was two times greater than in the rest of the network ( for connections originating from neurons within the cluster, and for connections originating from neurons outside the cluster). Connectivity was constructed by initially segregating the cluster from the rest of the network, so that the cluster and the rest of the network formed two disjoint Watts-Strogatz networks, each with a radius of connectivity of 4 and a re-wiring probability of 0.60. The two networks were then coupled by sending three outgoing connections from each cluster neuron to randomly-selected neurons in the rest of the network. Similarly, three outgoing connections were also sent from each neuron in the rest of the network to randomly-selected neurons within the cluster. Simulations were then run in which the network was repeatedly switched between high-ACh and low-ACh states, and the effects on network potentiation were explored. We quantified the network potentiation for all excitatory connections, as before, but also for just the connections which linked the cluster and the rest of the network.
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10.1371/journal.pgen.1008259 | Attenuating the emergence of anti-fungal drug resistance by harnessing synthetic lethal interactions in a model organism | Drug resistance is a rapidly emerging concern, thus prompting the development of novel therapeutics or combinatorial therapy. Currently, combinatorial therapy targets are based on knowledge of drug mode of action and/or resistance mechanisms, constraining the number of target proteins. Unbiased genome-wide screens could reveal novel genetic components within interaction networks as potential targets in combination therapies. Testing this, in the context of antimicrobial resistance, we implemented an unbiased genome-wide screen, performed in Saccharomyces cerevisiae expressing a Candida glabrata PDR1+ gain-of-function allele. Gain-of-function mutations in this gene are the principal mediators of fluconazole resistance in this human fungal pathogen. Eighteen synthetically lethal S. cerevisiae genetic mutants were identified in cells expressing C. glabrata PDR1+. One mutant, lacking the histone acetyltransferase Gcn5, was investigated further. Deletion or drug-mediated inhibition of Gcn5 caused a lethal phenotype in C. glabrata cells expressing PDR1+ alleles. Moreover, deletion or drug-mediated inactivation of Gcn5, inhibited the emergence of fluconazole-resistant C. glabrata isolates in evolution experiments. Thus, taken together, the data generated in this study provides proof of concept that synthetically lethal genetic screens can identify novel candidate proteins that when therapeutically targeted could allow effective treatment of drug-resistant infections.
| Life threatening infections are an increasing reality. Multi-drug resistant bacteria e.g. MRSA are present in nearly all hospitals, and community acquired TB is often recalcitrant to treatment. Less well known, but causing four to five times more deaths in the UK than MRSA, are fungi in the genus Candida. These are commonly associated with mucosal infections such as “thrush”, but are responsible for > 400,000 life-threatening infections worldwide every year, especially in the immunosuppressed patient population. One of the principle pathogens is Candida glabrata. This fungus grows as a single celled yeast, and alarmingly is highly resistant to both host innate defences and many clinically used antifungal drugs. Even with the best possible medical care, infections with this yeast cause ~50% mortality and alarmingly the incidence of C. glabrata infections is steadily climbing. This demands that novel antifungal therapies are developed that can target drug resistant fungal infections such as C. glabrata. We have identified genes that prevent the growth of fungal cells that express gain-of-function mutations in Pdr1, a key mediator of antifungal resistance in C. glabrata. We also provide proof of concept that targeting a protein essential for Pdr1 function significantly inhibits the emergence of antifungal drug resistance. Thus such an approach could provide a powerful tool in developing treatments for drug resistant infections.
| Drug resistance has emerged as a huge problem in many areas of medicine from cancer to infectious diseases [1, 2, 3, 4]. This is leading to the development of novel therapeutic strategies. Multi-target therapies are gaining ground, where combinations of drugs targeting different components of disease networks are deployed with the expectation of reduced toxicity, emergence of resistance, and off-target effects [5, 6, 7]. Combinatorial therapies involving an antibiotic and a second drug either targeting the same pathway, another cellular function, and/or specific mechanisms of antimicrobial drug resistance have shown promise as therapeutic regimens to treat antimicrobial drug resistant infections [8]. A major impediment to this approach is the characterization of the adjunctive targets. To date most adjunctive therapy targets have been selected based on previous biological knowledge of drug mode of action and/or mechanisms of resistance. This severely constrains the number of proteins that can be targeted for adjunctive therapy. In this study, we hypothesized that unbiased genome-wide screens can reveal previously unknown proteins that could be targeted for adjunctive therapy. This has recently been demonstrated in the context of cancer, where Cas9 mediated genome editing was used to target chromatin regulatory domains in a murine acute myeloma cell line, identifying six known drug targets, and a further 19 genes that are essential in this cancer cell line [9]. In relation to antimicrobial resistant infection, we rationalised that the characterization of mutations that genetically interact with alleles conferring drug resistance could reveal novel proteins that could be therapeutically targeted to allow effective treatment of antimicrobial drug resistant infections.
Fungi are important agents of infectious disease, causing more deaths annually than either malaria or TB [10]. In this context, Candida species are the fourth most commonly isolated species from nosocomial blood stream infections, causing life threatening disease in individuals with AIDs, patients recovering from surgical procedures or major burns, and those undergoing chemotherapy and organ transplant. Systemic fungal infections are currently very difficult to diagnose, and even with best practice management mortality rates are generally higher than for bacterial disease [11]. Furthermore, the effectiveness of the drugs used to treat fungal infections is decreasing, as antifungal drug resistance is rapidly emerging. Antifungal resistance has been reported in environmental fungal isolates suggesting a reservoir of resistant strains [12,13]. There is an urgent clinical and economic need for new cost effective treatment options, including novel therapeutics.
Candida glabrata ranks second after Candida albicans as the most common yeast pathogen of humans. It is responsible for many opportunistic infections in immunocompromised individuals, which are associated with a high mortality rate. The incidence of C. glabrata infections has grown rapidly over the last 20 years, and is responsible for ~25% of systemic candidiasis cases [14,15]. The reason for this increasing incidence of C. glabrata infection is not fully understood, but it is well established that this species has a higher innate tolerance to commonly administered azole antifungals, in particular fluconazole (FLZ), the principle therapeutic option for Candida infections. For instance, C. glabrata populations have an average MIC to fluconazole (FLZ) of 4 μg/ml compared to 0.125 μg/ml for C. albicans populations [16–18]. Alarmingly, C. glabrata is also adept at rapidly acquiring drug resistance [19]. MIC values of 64 μg/ml are found in up to 30% of C. glabrata isolates, and thus are often resistant to FLZ therapy [20,21].
One of the principle mediators of FLZ resistance and acquired resistance are gain of function mutations in the PDR1 (CAGL0A00451g) gene (PDR1+), which encodes a transcriptional activator of genes encoding drug efflux pumps [22]. To date, many PDR1+ mutations have been described that mediate azole resistance (Table 1). These PDR1+ mutations cause amino acid changes across all four functional domains of the transcription factor: the transcriptional activation domain, the regulatory domain, the middle homology region, and the activation domain (Fig 1). PDR1 is up-regulated during systemic infections [23,24], and is induced in response to combinatorial stresses encountered in vivo. C. glabrata strains harbouring PDR1+ mutations exhibit increased virulence [18] implying adaptation within the host to antifungals may itself enhance the ability of C. glabrata to cause disease. In this study, we have performed an unbiased genetic screen to identify mutants that are synthetically lethal with PDR1+ fluconazole resistant C. glabrata cells, having adopted the approach of a combination of genome-wide screens [25] and mutant construction to identify C. glabrata loss of function mutations that interact to impact negatively on the growth in combination of with specific FLZ resistant alleles. We then used prior knowledge to identify which of the proteins encoded by these genetic interactors can be targeted therapeutically, either using known or newly discovered small molecule inhibitors, to treat FLZ resistant C. glabrata.
Mutations are termed ‘synthetically lethal’ if either mutation alone has no impact on cellular viability, but in combination result in cellular death. Our hypothesis, was that by inhibiting the product of a gene whose deletion is synthetically lethal with PDR1+ alleles will allow targeting of FLZ resistant C. glabrata. To test this prediction we set to address two questions; what are the synthetic lethal/synthetic sick interaction partners of PDR1+ alleles, and, as proof of concept, can any of these be targeted to prevent the emergence of azole resistant C. glabrata? Therefore the primary aim of this work is to identify pathways that could be targeted to prevent the emergence of antifungal drug resistance. In this study we have taken this methodology to identify conserved synthetic genetics interactions across a PDR1+ allele, then used this data to identify small molecule inhibitors of these synthetic interactors (namely GCN5 inhibitor ɣ-butyrolactone) and determined if they can be used to treat FLZ resistant C. glabrata.
Synthetic Genetic Array (SGA) screening is not currently possible in C. glabrata, as the technique relies on high throughput mating. Hence to identify PDR1+ synthetic genetic interactions, we used the model yeast S. cerevisiae as a surrogate with a view that key synthetic lethal interactions would subsequently be confirmed in C.glabrata. To initiate the characterization of C. glabrata PDR1+ synthetic genetic interaction network, we performed a synthetic dosage lethal (SDL)-SGA experiment [26], to identify synthetic interactions with a PDR1+L280F allele described in a clinical C. glabrata isolate DYS565 (Fig 2) [19,21]. This particular allele was chosen due to its poor clinical outcomes and high FLZ MIC [27]. The DYS565 strain has a FLZ MIC of 128 μg/ml and a G840C (L280F) mutation (PDR1+L280F), whereas the parental strain, DYS562, obtained from the same patient is relatively FLZ sensitive (MIC 8 μg/ml) and contains a wild-type PDR1 allele. The PDR1+L280F was amplified by PCR, sequence verified, cloned and transformed into an S. cerevisiae MATa SGA starter strain, with the endogenous copy of PDR1 deleted and then mated to the entire MATα knock-out collection. This genome-wide SGA screen was performed in triplicate and all double mutants were visually scored for growth. A total of 144 negative genetic interactions were identified with the C. glabrata PDR1+L280F allele (Fig 2, S1 Table), of which 22 were synthetically lethal (S2 Table) and the remainder caused significant reductions in growth. Of the 22 synthetic lethal interactions four were also lethal with wild-type PDR1 i.e. elp2, elp4, elp6 and pdr5. Elp2, Elp4 and Elp6 are all components of the elongator complex, while Pdr5 is a multidrug transporter involved in pleiotropic drug responses. Thus 18 strains had specific lethal interactions with C. glabrata PDR1+L280Fand included genes with functions related to drug transport (ERG5, EAF1), and others transcription factors (e.g. PDR3, PDR8, STE12 and UME6). In the case of the synthetic sick interactions identified in both screens, CgPDR1 (104 interactions) and PDR1+L280F (105 interactions), 90 were common to both screens with 14 unique to CgPDR1 and 15 unique to PDR1+L280F (S1 Table and Fig 2).
To determine if these genetic interactions were maintained with different PDR1+ gain of function alleles, we performed tailored SGA screens. Specifically, the previous interactions identified from the PDR1+L280F screen were sub-arrayed to determine if the synthetic lethal interactions were common to other gain of function alleles. Four other gain-of-function alleles were selected; S316I, L1391I, E555K and F817S, covering the four main functional domains of Pdr1 (S3 Table, S1 Fig and Fig 1). From this refined screen, we were confident in following GCN5 as our proof of principle gene of interest for chemical inhibition, due to its synthetically lethal interaction with the additional gain of function mutants screens and when chemically inhibited in a series of gain of function mutants we were able to induce lethality in the strains (Fig 3).From these additional screens, we identified 9 SL interactions that were common to all gain of function genes tested; DUG1, EA1, ELP4, GLC5, HEK2, PDR5, PDR8, STE12 and STE2. These genes offer potential further targets for chemical inhibition in future studies, across a variety of molecular functions. In S. cerevisiae GCN5 encodes a component of the ADA and SAGA histone acetyltransferase complexes.
To test our hypothesis that drug targeting of lethal interactors, identified above, would abolish survival of drug resistant PDR1+ cells, we focussed on GCN5 for two reasons. Firstly, deletion of GCN5 was synthetically lethal with the five gain-of-function PDR1+ alleles tested and, secondly, there is a well-characterized specific inhibitor of the Gcn5 HAT, γ-butyrolactone. Thus, if our hypothesis is correct, prevention of Gcn5 function through γ-butyrolactone treatment, should kill C. glabrata cells harbouring the drug resistant PDR1+L280Fallele.
As a first step, we confirmed that expression of PDR1+L280F in a C. glabrata gcn5 null mutant background was lethal (Fig 4). To achieve this PDR1+L280F was placed under the control of the methionine repressible promoter in pCU-MET3 [28] and transformed into a C. glabrata gcn5 pdr1 double null mutant. The induction of PDR1+L280F in this strain resulted in a loss of viability, thus confirming that the synthetic lethal interaction identified in S. cerevisiae is conserved in C. glabrata.
Once we had confirmed that loss of Gcn5 function is lethal in C. glabrata cells expressing the PDR1+L280F fluconazole resistant allele, we then determined the impact of chemically inhibiting Gcn5. Notably, the addition of 2mM γ-butyrolactone, the chemical inhibitor of Gcn5, prevented the growth of S. cerevisiae pdr1Δ strains expressing C. glabrata PDR1L280F, the clinical FLZ resistant C. glabrata strain DYS565 expressing PDR1L280F, and an engineered C. glabrata lab strain (BG2 derivative) in which the wild-type PDR1 allele was replaced with PDR1L280F (Fig 4). Collectively, these data demonstrate that targeting Gcn5, a synthetically lethal interacting partner of PDR1L280F identified in S. cerevisiae, renders both this species and the orthologous C. glabrata mutant inviable, strongly supporting the proposition that targeting synthetic lethal interactions offers a new paradigm for the treatment of drug resistant infection.
To further explore this concept, we investigated whether the addition of ɣ-butyrolactone would prevent and/or inhibit the growth of addition FLZ-resistant clinical isolates with different gain of function mutations in PDR1 (Table 1). Notably, ɣ-butyrolactone prevented the growth of 20/31 clinical isolates screened (Fig 4). This demonstrates that the chemical inactivation of the Gcn5 protein is synthetically lethal in approximately 65% of the PDR1+ FLZ resistant alleles tested.
Finally, we examined whether targeting synthetic lethal interactions could minimise the emergence of FLZ resistance utilising an experimental evolution approach [29]. Using such an approach allowed for the observation of the impact of their deletion on the emergence of FLZ resistance. C. glabrata wild-type and gcn5 null strains, together with wild-type cells in which the function of Gcn5 was chemically inhibited, were exposed to doubling dilutions of FLZ and the emergence of resistance monitored. Our working hypothesis was that FLZ resistance would emerge at a much-reduced rate and to a lower level in strains that had synthetic lethal genes deleted or chemically inactivated (in this case GCN5), compared to wild-type C. glabrata.
As each propagation was made to the next round of selection, the PDR1 gene was sequenced to determine in each condition, and at which cycle, gain of function mutations started to arise in the populations and to what region of the gene they mapped to. For the wild type strain BG2, after going through three rounds of exposure to FLZ and up to a concentration of 8μg/ml, we identified the appearance of the first gain of function mutation in the PDR1 allele (Fig 5A). This mutation was located in the activation domain of the gene. As the exposure to FLZ continued for a further 7 cycles, there was a noted increase in the number of gain of function mutations arising in the wild type strain. This was also linked to the increase in FLZ concentration. Following the 10 cycles of propagation in FLZ, we had identified 50 previously described gain of function mutations in PDR1 (Fig 5B).
In the case of the gcn5 null strain (Fig 5C), and the chemically inhibited gcn5 strain (Fig 5D), a gain of function mutation was not observed in PDR1 until 7 rounds of propagation in FLZ. In the gcn5 null mutant, the T2450C (F817S) mutation in the activation domain (Table 2) was the first observed mutation, whereas T2575 (F859L) was the first mutation identified in the chemically inhibited strain. From this data, it is possible to determine that the evolution of C. glabrata Δgcn5 mutants in the presence of FLZ inhibits the emergence of gain of function mutations in PDR1.
In this proof of concept study we have demonstrated that the identification of synthetic lethal genetic interactions with alleles that confer antifungal drug resistance is a valuable approach to identify pathways that could be targeted to prevent the emergence of drug resistance. By employing SGA analysis we identified a number of genetic mutations that were synthetically lethal with PDR1+ gain-of-function alleles. Focussing on one specific mutation, that in the histone acetyltransferase Gcn5, we could show that deletion or chemical inactivation of Gcn5 significantly inhibited the emergence of PDR1+ gain-of-function alleles in evolution experiments. Histone modifications modulate the packing of chromatin, this level of packing is critical for gene transcription, as the cellular machinery must have access to promoters to allow for transcription. As previously stated GCN5 in S. cerevisiae is known to be a component of the ADA and SGA complexes, therefore we propose that in C. glabrata clinical isolate with gain of function mutations in PDR1, it is acting as a gene silencer thus resulting in the synthetic lethal phenotype. The combination of the interaction between GCN5 and PDR1gain of function may be resulting in the inhibition of histone acetyltransferases and DNA damage events resulting from drug exposure leading to cell death. This control of chromatin remodelling processes may provide a target for novel drug therapies. Future work employing similar genetic approaches could be powerful in identifying additional targets that could halt the emergence of drug resistant strains.
PDR1 genes were PCR amplified from their relevant strains, wildtype from BG2 (i.e. no point mutations) and DSY565, the clinical isolate containing the PDR1+ gain of function mutation L280F. PCR products were sequence verified prior to cloning into the Gateway system. Final destination plasmids were transformed into S. cerevisiae strain Y7092[30], using standard LiAc transformation protocols[31].
The deletion mutant array was manipulated using a Singer RoTor HAD (Singer Instruments). For the genome wide PDR1 and PDR1+L280F synthetic genetic screens, the MATα query strain Y7092 [30] was transformed with either pDEST426-ccdB-GPD-PDR1 or pDEST426-ccdB-GPD- PDR1+L280F. The resulting query strains were mated to the entire MATa deletion mutant array and the SGA methodology was used as previously described to maintain the plasmid [26]. All genome-wide screens were performed in triplicate at 30°C with growth visually scored for lethality (SL), slow growth (synthetic sick SS) or suppression (SUP). Putative genetic interactions were identified in a minimum of two out of three replicates. These putative interactions were then confirmed in S. cerevisiae and C. glabrata. (Confirmed genetic interactions are listed in Supporting information, Fig 2).
To confirm the SGA screens performed in S. cerevisiae, we recapitulated the SL phenotypes in C. glabrata. The gcn5 pdr1 null mutant was generated using standard deletion protocol [29], followed by transformation of the plasmid containing the PDR1 or PDR1+L280F under the control of the MET3 promoter [31].
Dot assays were performed by spotting 5 μl of 10-fold serial dilutions (OD600 = 0.1, 0.01, 0.001, 0.0001) onto specified media, and sealed plates were incubated at 37°C. All dot assay experiments were repeated using three different isolates of each strain. FLZ at concentrations 16–64μg/ml and ɣ-butyrolactone at 2mM were used in screening plates.
To examine the effect of FLZ treatment on the genome of C. glabrata strains, we performed a series of control evolution experiments. We took C. glabrata strains; BG2, Δgcn5, and chemically inhibited BG2, and inoculated into doubling dilution of FLZ from 0-256 μg/ml, in synthetic complete medium at 37°C for 24 hours. The culture at the highest FLZ dose with obvious growth was used to propagate the next FLZ gradient. This was performed for ten cycles with each strain being tested in ten technical replicates. The same workflow was followed for the chemically inhibited gcn5 strain to determine the impact on the emergence of resistance via the addition of γ-butyrolactone to the media. The chemical inhibitor was combined with FLZ. For both experimental regimens the level of FLZ was inferred, for each strain, as the concentration from which propagation was made to the next round of selection. The driving hypothesis for this section of work was that FLZ resistance would emerge at a reduced rate and to a lower level in the strains that have had gcn5 deleted or chemically inhibited compared to wild-type C. glarbata.
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10.1371/journal.pgen.1004641 | The Association of the Vanin-1 N131S Variant with Blood Pressure Is Mediated by Endoplasmic Reticulum-Associated Degradation and Loss of Function | High blood pressure (BP) is the most common cardiovascular risk factor worldwide and a major contributor to heart disease and stroke. We previously discovered a BP-associated missense SNP (single nucleotide polymorphism)–rs2272996–in the gene encoding vanin-1, a glycosylphosphatidylinositol (GPI)-anchored membrane pantetheinase. In the present study, we first replicated the association of rs2272996 and BP traits with a total sample size of nearly 30,000 individuals from the Continental Origins and Genetic Epidemiology Network (COGENT) of African Americans (P = 0.01). This association was further validated using patient plasma samples; we observed that the N131S mutation is associated with significantly lower plasma vanin-1 protein levels. We observed that the N131S vanin-1 is subjected to rapid endoplasmic reticulum-associated degradation (ERAD) as the underlying mechanism for its reduction. Using HEK293 cells stably expressing vanin-1 variants, we showed that N131S vanin-1 was degraded significantly faster than wild type (WT) vanin-1. Consequently, there were only minimal quantities of variant vanin-1 present on the plasma membrane and greatly reduced pantetheinase activity. Application of MG-132, a proteasome inhibitor, resulted in accumulation of ubiquitinated variant protein. A further experiment demonstrated that atenolol and diltiazem, two current drugs for treating hypertension, reduce the vanin-1 protein level. Our study provides strong biological evidence for the association of the identified SNP with BP and suggests that vanin-1 misfolding and degradation are the underlying molecular mechanism.
| Hypertension (HTN) or high blood pressure (BP) is common worldwide and a major risk factor for cardiovascular disease and all-cause mortality. Identification of genetic variants of consequence for HTN serves as the molecular basis for its treatment. Using admixture mapping analysis of the Family Blood Pressure Program data, we recently identified that the VNN1 gene (encoding the protein vanin-1), in particular SNP rs2272996 (N131S), was associated with BP in both African Americans and Mexican Americans. Vanin-1 was reported to act as an oxidative stress sensor using its pantetheinase enzyme activity. Because a linkage between oxidative stress and HTN has been hypothesized for many years, vanin-1's pantetheinase activity offers a physiologic rationale for BP regulation. Here, we first replicated the association of rs2272996 with BP in the Continental Origins and Genetic Epidemiology Network (COGENT), which included nearly 30,000 African Americans. We further demonstrated that the N131S mutation in vanin-1 leads to its rapid degradation in cells, resulting in loss of function on the plasma membrane. The loss of function of vanin-1 is associated with reduced BP. Therefore, our results indicate that vanin-1 is a new candidate to be manipulated to ameliorate HTN.
| Hypertension (HTN) or high blood pressure (BP) is common in populations worldwide and a major risk factor for cardiovascular disease (CVD) and all-cause mortality [1]. Although it is observed across ethnically diverse populations, the prevalence of HTN in the US varies from 27% in persons of European ancestry to 40% among those of African ancestry [2]. BP is a moderately heritable trait and affected by the combined effects of genetic and environmental factors, with heritable factors cumulatively accounting for 30–55% of the variance [3]. After age 20, African Americans have higher BP than other US race/ethnicities [4]–[6] and the progression from pre-HTN to HTN occurs one year eariler on average [7]. Increased rates of HTN among African Americans are the main factor contributing to their greater risk of CVD and end-stage renal disease compared to US whites [8], [9]. Given the widespread occurrence of this condition, and our as yet limited ability to reduce the disease burden, identifying the genetic variants of BP phenotypes could elucidate the underlying biology of high BP and reduce the CVD prevalence.
Identification of genetic variants of consequence for HTN remains a significant challenge, owing in large part to the complex and polygenic nature of the disorder and the imprecision with which the phenotype is measured [10]. Using admixture mapping analysis of data from the Family Blood Pressure Program, we recently identified a genomic region on chromosome 6 harboring HTN-associated variants [11]. The same region on chromosome 6 was replicated in an admixture mapping analysis based on the African Americans enrolled in the Dallas Heart Study [12]. By further genotyping the functional variants in the region of interest on chromosome 6, the VNN1 gene, in particular SNP rs2272996 (N131S) was found to account for the association with BP in both African Americans and Mexican Americans, but this association was not observed in European Americans [12]. Fava et al. [13] recently argued that rs2294757 (T26I), rather than N131S, was a more likely functional variant accounting for the effect on BP because it is located in a splicing regulation site in VNN1, but these investigators only found a weak association between T26I and both DBP and HTN in one of the two studies that they carried out. The results of this study are consistent with the lack of evidence for association observed in European Americans in the Dallas Heart Study [12].
VNN1 encodes the protein vanin-1, a glycosylphosphatidylinositol (GPI)-anchored membrane protein [14], [15]. Vanin-1 is widely expressed in a variety of tissues, with higher expression in liver, kidney and blood [16]. Vanin-1 is a pantetheinase, a member of the biotinidase branch of the nitrilase superfamily [17]. Vanin-1 hydrolyzes pantetheine to pantothenic acid (vitamin B5) and cysteamine, a potent regulator of oxidative stress. In vanin-1 null mice free cysteamine is undetectable, indicating vanin-1's indispensable role in generating cysteamine under physiological conditions [18]. Therefore, vanin-1 plays an essential role in regulating oxidative stress via cysteamine generation. A linkage between oxidative stress and HTN has been hypothesized for many years [19]–[21]. Furthermore, vanin-1 was reported to be involved in cardiovascular diseases [22], [23]. Overexpression of vanin-1 was associated with progression to chronic pediatric immune thrombocytopenia (ITP) [24], and was shown to lead to hyperglycemia [25]. Vanin-1−/− mice showed protective effects against a variety of phenotypes, such as oxidative stress [26], intestinal inflammation [27], and colon cancer [28], mostly due to higher glutathione storage to maintain a more reducing environment. As a consequence, vanin-1's pantetheinase activity may offer a physiologic rationale for BP regulation with loss of vanin-1 function.
In this study, we first investigated the association evidence of the missense variant rs2272996 (N131S) in VNN1 and BP phenotypes by performing a meta-analysis of nearly 30,000 African ancestry subjects from 19 independent cohorts from the Continental Origins and Genetic Epidemiology Network (COGENT). We next examined whether there were other variants in VNN1 associated with BP traits. Lastly, we conducted molecular experiments to establish a functional connection between N131S vanin-1 and HTN.
The study samples were the African-ancestry subjects from the COGENT, which includes 19 discovery cohorts. The details are described elsewhere by Franceschini et al [29]. Briefly, the phenotype-genotype association analysis was performed in each cohort separately. Systolic BP (SBP) and diastolic BP (DBP) were treated as continuous variables. For individuals reporting the use of antihypertensive medications, BP was adjusted by adding 10 and 5 mmHg to SBP and DBP respectively [30]. SBP and DBP were adjusted for age, age2, body mass index (BMI) and gender in linear regression models. The results of association between SNP rs2272996 and SBP or DBP for the 18 cohorts are presented in Figure 1. This SNP was not available in the GeneSTAR cohort. The corresponding allele frequencies in the different studies are listed in Supplementary Table S1. Among the 18 cohorts, 12 and 10 have positive effect sizes for SBP (P = 0.048) and DBP (P = 0.24), respectively, comparing to 9 expected under null hypothesis of no association between this SNP and BP. We next performed meta-analysis by applying both fixed-effect [31], [32] and random-effect [33] models to estimate the overall effect. SNP rs2272996 was significantly associated with SBP in both fixed-effect (P = 0.01) and random-effect (P = 0.04) models (Table 1). However, we did not observe evidence of genotype-phenotype association for DBP.
Among the individual cohort analyses, the Maywood cohort had a sample size of 743 and was the only cohort that showed significant association with rs2272996 for both SBP (P = 0.016) and DBP (P = 0.0003), however the direction of the association was opposite to what was found in the test for overall effects (Figure 1). The distributions of SBP, DBP, age and BMI did not suggest that the Maywood was an outlier in epidemiologic characteristics (Supplementary Figure S1), with the exception that the sampling strategy for this cohort was based on exclusion of persons on antihypertensive medications (antihypertensive medication rate was 0.7%). The Nigeria cohort also included a low antihypertensive medication rate but this was a result of inaccessibility to medications (Supplementary Figure S2). When analyses were repeated after exclusion of the Maywood cohort, the association of BP and rs2272996 was substantially improved (P = 0.003 for SBP, Table 1). The different association evidence between Maywood and other cohorts may suggest genetic heterogeneity or possible interaction between gene and environment factors, although further studies are needed to address this possibility.
Since additional genetic variants in VNN1 might be associated with BP, we examined available known variants in VNN1 including 10 kb up- and down-stream of the gene. A total of 105 other SNPs were available in the 19 cohorts. SNP rs7739368 had the smallest p values for association with SBP using either fixed-effect model (P = 0.004) or random-effect model (P = 0.004, Supplementary Table S2), but this was not significant after correcting for multiple comparisons. This SNP is ∼7 k bp's upstream of VNN1 adjacent to the PU.1 transcription factor binding region (306 bp's upstream).
To understand the function of N131S vanin-1 in relation to HTN, plasma samples from Nigeria HTN patients and normotensives with WT (TT) or homozygous N131S (CC) vanin-1 were collected (6 samples per group, 4 groups). The same amount of total plasma protein from each sample was subjected to Western blot analysis: a clean vanin-1 protein band appeared at 70 kD (Figure 2A), consistent with previous reports [14], [34]. The plasma vanin-1 protein in homozygous N131S vanin-1 was significantly lower than that in WT vanin-1 in both hypertensive and normotensive groups (P = 1.64×10−5 and 0.014, Figure 2A, see Figure 2B for quantification), indicating that the N131S mutation is a functional variant that is associated with substantially less steady-state vanin-1 protein. Furthermore, the plasma vanin-1 protein in normotensive groups with WT vanin-1 (samples 13–18) was significantly lower than that in HTN patients with WT vanin-1 (samples 1–6) (P = 0.042) (Figure 2A, see Figure 2B for quantification). These results demonstrated that vanin-1 expression is associated with both the genotypic N131S mutation and phenotypic HTN, with the former exerting stronger effect. Lastly, the plasma vanin-1 protein in normotensive groups with homozygous N131S vanin-1 (samples 19–24) is also lower than that in HTN patients with homozygous N131S vanin-1 (samples 7–12) although it was not statistically significant (P = 0.13), probably due to the already exceedingly low vanin-1 quantity. These results suggest that the WT vanin-1 is associated with increased plasma vanin-1 protein expression, and increased HTN risk.
We tested two variants, N131S and T26I, as regards how they influence the total vanin-1 protein levels because other investigators have suggested that T26I may be a candidate variant for BP variation as well [13]. We utilized the human embryonic kidney 293 (HEK293) cells stably expressing these vanin-1 variants because HEK293 cells have high transfection efficiency and physiologically-relevant cell environment for vanin-1 protein expression [23]. Significantly lower total vanin-1 proteins were detected in the cells expressing N131S vanin-1, whereas similar vanin-1 protein levels were detected in cells expressing T26I vanin-1 compared to cells expressing WT vanin-1 (Figure 3A, quantification shown below).
Because vanin-1 is a GPI-anchored membrane protein, it needs to traffic efficiently to the plasma membrane for its pantetheinase activity. We hypothesized that N131S substantially reduces the trafficking of vanin-1 protein to the plasma membrane, whereas T26I does not. Using a surface biotinylation assay [35] , we observed that the N131S mutation led to significantly lower plasma membrane expression, whereas in cells expressing the T26I mutation, vanin-1 surface expression was similar to that observed in WT cells (Figure 3B, quantification shown below).
We further confirmed that the variation in vanin-1 protein expression resulted in corresponding functional consequences for N131S and T26I mutations. A cell-based fluorescence assay was carried out to record the kinetics of pantetheinase activity by vanin-1 variants [36]. The cells expressing T26I vanin-1 had similar pantetheinase activity compared to cells expressing WT vanin-1; however, cells expressing N131S vanin-1 retained approximately 9% of the pantetheinase activity, by quantifying the fluorescence signals at the kinetic steady state at 57 minutes (Figure 3C). These data taken together provide evidence of less protein, less membrane trafficking, and lower enzymatic activity of the N131S protein as compared to both the wild type and the T26I variant.
To determine the mechanism of loss of surface N131S vanin-1, we sought to confirm that N131S vanin-1 is rapidly degraded. A cycloheximide (CHX) chase assay was used to quantify the half-life of vanin-1 variants in HEK293 cells: WT vanin-1 had a half-life of 240 min; T26I vanin-1, 232 min; N131S vanin-1, 76 min, respectively (Figure 4A, quantification in Figure 4B). Thus, N131S vanin-1 has a much faster degradation rate than WT vanin-1, whereas T26I vanin-1 is degraded at a rate similar to that of WT vanin-1.
To confirm that misfolded N131S vanin-1 is subjected to ERAD, we applied MG-132 to the cells, which is a potent proteasome inhibitor. MG-132 treatment resulted in the accumulation of ubiquitinated proteins and substantially more total vanin-1 proteins (Figure 4C, cf. lane 2 to lane 1), indicating that efficient proteasome inhibition prevents the degradation of N131S vanin-1. Furthermore, using immunoprecipitation against vanin-1, we confirmed that MG-132 treatment resulted in ubiquitination of N131S vanin-1 (Figure 4C, cf. lane 5 to lane 4). These data indicate that N131S vanin-1 is subjected to rapid ERAD, resulting in loss of functional vanin-1 on the plasma membrane.
We hypothesize that rapid degradation of N131S vanin-1 resulted from its misfolding in the endoplasmic reticulum (ER). The endoglycosidase H (endo H) enzyme selectively cleaves vanin-1 after asparaginyl-N-acetyl-D-glucosamine (GlcNAc) in the N-linked glycans incorporated in the ER. After the high-mannose form is enzymatically remodeled in the Golgi, endo H is unable to remove the oligosaccharide chain. Therefore, endo H-resistant vanin-1 bands (with higher molecular weight) represent properly folded, post-ER vanin-1 glycoforms, which traffic at least to the Golgi compartment. The N131S mutation resulted in much less intense endo H-resistant bands than WT vanin-1 (Figure 4D, cf. lane 6 to lane 2), whereas T26I did not (Figure 4D, cf. lane 4 to lane 2). The ratio of endo H-resistant to total vanin-1 serves as a measure of vanin-1 trafficking efficiency. The trafficking efficiency of N131S vanin-1 was less than WT vanin-1, indicating that N131S vanin-1 does not fold properly in the ER. These data support the conclusion that N131S vanin-1 is misfolded in the ER and subsequently degraded by the ERAD pathway.
To determine whether vanin-1 is a target of current anti-hypertensive drugs, we tested the effect of two commonly prescribed HTN drugs with different known drug mechanisms on endogenous vanin-1 protein level. Human monocyte THP-1 cells were used because they were derived from human blood and have high endogenous WT vanin-1 protein expression levels. Two HTN drugs used are diltiazem [37], an L-type calcium channel blocker, and atenolol, a selective β1 adrenergic receptor blocker [38]. Treatment of THP-1 cells with diltiazem (10 µM) or atenolol (10 µM) for 1d or 3d decreased the endogenous total vanin-1 protein significantly in a time-dependent manner (Figure 5A, quantification shown below). Furthermore, application of diltiazem for 3d decreased the endogenous total vanin-1 protein significantly in a dose-dependent manner (Figure 5B, quantification shown below). This indicates that vanin-1 is a molecular target of current HTN drugs, which was previously unknown and confirms the relevance of vanin-1 to the regulation of blood pressure. Therefore, exploring other compounds that decrease vanin-1 level may lead to discovery of novel antihypertensive drugs, especially those with previously unknown function in HTN.
A major methodological issue that has greatly increased the challenges faced in the genetic epidemiology of BP is the high noise-to-signal ratio in the phenotype. This problem has numerous causes, including variation in measurement protocols of SBP and DBP across studies, the dynamic nature of BP levels, and concurrent use of antihypertensive medications. In addition, as with all polygenic disorders, the effect size for any single gene variant is very small and a large number of genes/variants are involved [10]. Recent large-scale BP genome-wide association studies (GWAS) of European, Asian and African ancestry populations demonstrated that the identified genetic variants together explain only 1–2% of BP variation [29], [39], [40]. It is thus not surprising that a large sample size is often necessary to detect genome-wide significant effects.
An analysis method complementary to GWAS is admixture mapping, which has been successfully applied to detect BP loci [11], [12], [41]. Our group reported that the missense variant rs2272996 (N131S) in VNN1 was associated with BP through admixture mapping, and we conducted a follow-up association analysis in African and Mexican American samples [11], [12]. The association evidence in European-ancestry population is however less convincing [12], [13] In the current study, we performed meta-analysis using the COGENT consortium consisting of 19 studies with a total sample size of nearly 30,000 African ancestry subjects and confirmed the association evidence between rs2272996 and SBP (P = 0.01, Table 1).
However, statistical evidence alone cannot explain the role of a given variant on disease risk and drug response. Therefore, in our study we decided to analyze the functional effects of the N131S variant. Vanin-1 is a pantetheinase generating cysteamine, which regulates the glutathione-dependent oxidative stress response. We showed that the HTN-associated N131S mutation in vanin-1 significantly reduces vanin-1 total and cell surface expression. Consequently, the N131S vanin-1 only has fractional pantetheinase activity on the plasma membrane, which is associated with decreased HTN risk. Our result is consistent with the recognized link between impaired reduction-oxidation status and the development of HTN [19]–[21], and the observed protective effects in vanin-1−/− mice in a variety of diseases, including oxidative stress [26], intestinal inflammation [27], and colon cancer [28], mostly due to higher glutathione storage to maintain a more reducing environment.
We further tested the drug effects of atenolol and diltiazem in human monocyte THP-1 cells, which have high endogenous WT vanin-1 protein expression level. Atenolol is a selective β1 adrenergic receptor blocker and developed as a replacement for propranolol in treating hypertension; diltiazem is a nondihydropyridine member of calcium channel blockers used in treatment of hypertension. We found that both drugs reduce the vanin-1 protein level in the THP-1 cells. The anti-hypertensive drugs may have different and complex mechanisms leading to reduced BP, but whether vanin-1 is targeted was previously unknown. Our experiments filled this gap and showed vanin-1 is involved in the BP regulation pathway. Therefore, other potent vanin-1 inhibitors may prove to have BP reducing effects, which is especially useful given that these inhibitors have not been studied in HTN and thus may provide new therapeutics for HTN.
Our study presented the first functional studies of vanin-1 in HTN association, and provides compelling evidence for the essential role of its N131S mutation. Nonetheless, it has been demonstrated that multiple variants in a gene may contribute to a phenotypic variation [42], [43], and it is possible that other closely linked variants may have similar or analogous effects, or act in combination with N131S to regulate the vanin-1 protein expression and function. Current GWAS of HTN related traits mainly focus on testing common variants (MAF: minor allele frequency ≥5%) through pre-built chips and imputations based on HapMap [44] data; to date those findings in general have modest effect sizes [39]. Other functional and rare variants may be identified by deep sequencing, in combination with publicly available databases, such as the 1000 Genome Projects [45] and the Encyclopedia of DNA Elements (ENCODE) [46]. Identification of additional functional SNPs in VNN1 and their association with BP should provide further evidence for vanin-1 function in the regulation of BP.
Cell lines were used to determine that ERAD is the underlying mechanism for vanin-1's loss of function due to the N131S mutation. Cell lines are commonly used for the study of molecular mechanisms because they typically provide efficient transfection and a physiologically-relevant cell environment for the target protein. However, BP has a complex etiology with the involvement of a variety of organs, such as heart, brain and kidney, which cannot be recapitulated solely in cell lines. Although knowledge gained from our cell system provides essential cellular mechanistic insights into the regulation of vanin-1 and its function, the study of BP regulation by vanin-1 calls for studies in animal models. A hypertensive mouse or rat model, vanin-1 knockout mouse or rat model, and N131S vanin-1 knockin mouse or rat model would be of great interest to study the effects of vanin-1 and its mutation in the complex physiological and metabolic systems.
Vanin-1 provides a potential candidate to be manipulated to ameliorate HTN. Vanin-1 is a pantetheinase that contains the conserved catalytic triad residue–glutamate, lysine and cysteine–within the nitrilase family [47]. Based on the sequence alignment of vanin-1 with other nitrilase family members, the conserved catalytic triad of vanin-1 is composed of glutamate 79, lysine 178 and cysteine 211 [23]. A three-dimensional atomic model of vanin-1 was built using the I-TASSER server (Figure S3) [48]. Neither T26 nor N131 is in the vicinity of the catalytic sites of vanin-1. Therefore, the T26I and N131S mutations per se are not expected to change the vanin-1 enzyme activity significantly. Indeed, we showed that the T26I mutation did not influence vanin-1 maturation or enzymatic activity. The N131S mutation has much weaker pantetheinase activity, presumably due to exceedingly low concentration of N131S vanin-1 on the plasma membrane; however, the activity is still evident, implying that the catalytic triad is not disrupted by this mutation.
Loss of function of vanin-1 is caused by misfolding and rapid degradation of vanin-1 due to a single missense mutation from Asn to Ser at position 131. As a GPI-anchored protein, to function properly, vanin-1 needs to be trafficked efficiently to the plasma membrane, where it acts as a pantetheinase. In accordance with the maturation of general GPI-anchored proteins [15], vanin-1 is co-translationally translocated into the ER for folding. Because human vanin-1 has six potential N-linked glycosylation sites, its maturation is presumably dictated by glycoprotein processing machinery in the ER [49], [50]. Properly folded vanin-1 is trafficked out of the ER, through the Golgi and to the plasma membrane in a fully functional state. Misfolded vanin-1 is recognized by the ER quality control machinery and subjected to ERAD, being retrotranslocated to the cytosol, ubiquitinated and degraded by the proteasome [51]–[54]. Cells need to maintain a delicate balance between protein synthesis, folding, trafficking, aggregation and degradation for individual proteins that make up the proteome in normal physiology. This balance is dictated by the cellular protein homeostasis (proteostasis) network, composed of a variety of sub-networks, including the chaperone, degradation and trafficking networks, and cellular signaling pathways that regulate proteostasis as the core layers [55]–[57]. Therefore, further elucidation of the proteostasis network for vanin-1 should provide a valuable fine-tuning control of vanin-1 expression, function and BP.
19 cohort studies contributed to the meta-analysis of BP and genetic variants in VNN1 in African-Americans as detailed in Franceschini et al [29], including Biological Bank of Vanderbilt University (BioVU); Atherosclerosis Risk In Communities (ARIC); Coronary Artery Risk Development in Young Adults (CARDIA); Cleveland Family Study (CFS); Jackson Heart Study (JHS); Multi-Ethnic Study of Atherosclerosis (MESA); Cardiovascular Health Study (CHS); Genetic Study of Atherosclerosis Risk (GeneSTAR); Genetic Epidemiology Network of Arteriopathy (GENOA); The Healthy Aging in Neighborhoods of Diversity Across the Life Span Study (HANDLS); Health, Aging, and Body Composition (Health ABC) Study; The Hypertension Genetic Epidemiology Network (HyperGEN); Mount Sinai, New York City, USA Study (Mt Sinai Study); Women's Health Initiative SNP Health Association Resource (WHI); Howard University Family Study (HUFS); Bogalusa Heart Study (Bogalusa); Sea Islands Genetic Network (SIGNET); Loyola Maywood Study (Maywood); and Loyola Nigeria Study (Nigeria). Each study received IRB approval of its consent procedures, examination and surveillance components, data security measures, and DNA collection and its use for genetic research.
We selected 24 plasma samples from the International Collaborative Study on Hypertension in Blacks (ICSHIB), in which the study participants were recruited from Igbo-Ora and Ibadan in southwest Nigeria as part of a long-term study on the environmental and genetic factors underlying hypertension [58]. The ICSHIB included 1,188 subjects who were genotyped using Affymetrix platform 6.0 chip [59]. We selected 6 subjects per group from the high and lower SBP traits in each of TT and CC genotype groups of SNP rs2272996. For each of these 24 subjects, western blot analysis was performed by controlling the same amount of total plasma protein.
The detailed statistical analysis of each cohort can be found in Franceschini et al [29]. In brief, each study cohort received a uniform statistical analysis protocol and analyses were conducted accordingly. BP was measured in mmHg. For individuals reporting use of antihypertensive medications, BP was imputed by adding 10 and 5 mmHg for SBP and DBP, respectively. For unrelated individuals, SNP associations for SBP or DBP were assessed by linear regression assuming an additive model, adjusting for age, age2, body mass index (BMI) and gender. Population stratification was controlled by adjusting for the first 10 principal components obtained from selected ancestry informative markers [60], [61]. For family data, association was tested using a linear mixed effect model, where random effects account for family structure [62].
Meta-analysis across the 19 cohorts was performed by applying both fixed-effect [31], [32] and random-effect [33] models to estimate the overall effect. The fixed-effect model assumes that the effect size is the same for all the included studies; the only source of error is the random error within studies, which depends primarily on the sample size for each study. Because the inverse variance is roughly proportional to sample size, the fixed-effect model provides a weighted average of the effect sizes, with the weights being the estimated inverse of the variance of the estimate in each study. The random-effect model assumes that the effect sizes from studies are similar but not identical, dependent on each study protocol; the source of error includes within-study and among-study error [33]. It is more conservative and thus provides relatively wider 95% confidence intervals when heterogeneity across studies exists.
All experimental data are presented as mean ± SEM, and any statistical significance was calculated using two-tailed Student's t-test.
MG-132, diltiazem, and atenolol were obtained from Sigma-Aldrich. The pCMV6 plasmids containing human vanin-1 and pCMV6 Entry Vector plasmid (pCMV6-EV) were obtained from Origene. The human vanin-1 missense mutations, N131S and T26I, were constructed using QuickChange II site-directed mutagenesis Kit (Agilent Genomics), and the cDNA sequences were confirmed by DNA sequencing, showing the single-site mutation of these variants. The rabbit polyclonal anti-vanin-1 antibody came from Pierce antibodies, the mouse monoclonal anti-transferrin antibody from Santa Cruz Biotechnology, the mouse monoclonal anti-β-actin antibody from Sigma, and the rabbit polyclonal anti-ubiquitin antibody from Cell Signaling.
Human embryonic kidney 293 (HEK293) cells and human monocytic THP-1 cells came from ATCC. THP-1 cells were maintained in RPMI-1640 medium (Hyclone) with 10% heat-inactivated fetal bovine serum (Sigma-Aldrich) and 1% Pen-Strep (Hyclone) at 37°C in 5% CO2. HEK293 cells were maintained in Dulbecco's Modified Eagle Medium (DMEM) (Hyclone) with 10% heat-inactivated fetal bovine serum (Sigma-Aldrich) and 1% Pen-Strep (Hyclone) at 37°C in 5% CO2. Monolayers were passaged upon reaching confluency with TrypLE Express (Life Technologies). HEK293 cells were grown in 6-well plates or 10-cm dishes and allowed to reach ∼70% confluency before transient transfection using Lipofectamine 2000 (Life Technologies) according to the manufacturer's instruction. Stable cell lines expressing vanin-1 variants (WT, N131S or T26I) were generated using the G-418 selection method. Briefly, transfected cells were maintained in DMEM supplemented with 0.8 mg/mL G418 (Enzo Life Sciences) for 15 days. G-418 resistant cells were selected for follow-up experiments.
Cells were harvested and then lysed with lysis buffer (50 mM Tris, pH 7.5, 150 mM NaCl, and 1% Triton X-100) supplemented with Roche complete protease inhibitor cocktail. Lysates were cleared by centrifugation (15,000× g, 10 min, 4°C). Protein concentration was determined by MicroBCA assay (Pierce). Endoglycosidase H (endo H) or Peptide-N-Glycosidase F (PNGase F) (New England Biolabs) enzyme digestion was performed according to published procedure [35]. Aliquots of cell lysates or human plasma samples were separated in an 8% SDS-PAGE gel, and Western blot analysis was performed using the appropriate antibodies. Band intensity was quantified using Image J software from the NIH.
HEK293 cells stably expressing vanin-1 variants were plated in 10-cm dishes for surface biotinylation experiments according to published procedure [35]. Intact cells were washed twice with ice-cold PBS and incubated with the membrane-impermeable biotinylation reagent Sulfo-NHS SS-Biotin (0.5 mg/mL; Pierce) in PBS containing 0.1 mM CaCl2 and 1 mM MgCl2 (PBS+CM) for 30 min at 4°C to label surface membrane proteins. To quench the reaction, cells were incubated with 10 mM glycine in ice-cold PBS+CM twice for 5 min at 4°C. Sulfhydryl groups were blocked by incubating the cells with 5 nM N-ethylmaleimide (NEM) in PBS for 15 min at room temperature. Cells were solubilized for 1 h at 4°C in lysis buffer (Triton X-100, 1%; Tris–HCl, 50 mM; NaCl, 150 mM; and EDTA, 5 mM; pH 7.5) supplemented with Roche complete protease inhibitor cocktail and 5 mM NEM. The lysates were cleared by centrifugation (16,000× g, 10 min at 4°C) to pellet cellular debris. The supernatant contained the biotinylated surface proteins. The concentration of the supernatant was measured using microBCA assay (Pierce). Biotinylated surface proteins were affinity-purified from the above supernatant by incubating for 1 h at 4°C with 100 µL of immobilized neutravidin-conjugated agarose bead slurry (Pierce). The samples were then subjected to centrifugation (16,000×g, 10 min, at 4°C). The beads were washed six times with buffer (Triton X-100, 0.5%; Tris–HCl, 50 mM; NaCl, 150 mM; and EDTA, 5 mM; pH 7.5). Surface proteins were eluted from beads by boiling for 5 min with 60 µL of LSB/Urea buffer (2× Laemmli sample buffer (LSB) with 100 mM DTT and 6 M urea; pH 6.8) for SDS-PAGE and Western blotting analysis.
The cell-based fluorescence assay to evaluate vanin-1's pantetheinase activity was performed according to published procedure with modifications [36]. The substrate, pantothenate-7-amino-4-methylcoumarin (pantothenate-AMC) was chemically synthesized according to published method [36]. As a pantetheinase, vanin-1 catalyzed the release of AMC, giving a fluorescence signal at excitation 350 nm and emission 460 nm. HEK293 cells expressing vanin-1 variants were lysed with lysis buffer (50 mM Tris, pH 7.5, 150 mM NaCl, and 1% Triton X-100) supplemented with Roche complete protease inhibitor cocktail. Enzyme activity was performed using 10 µg of total proteins containing the substrate pantothenate-AMC (5 µM), 0.5 mM DTT, 5% DMSO in a 100 µL final volume in PBS, pH 7.5. Fluorescence signals at excitation 350 nm and emission 460 nm measuring the released AMC were recorded every 3 min at 37°C in 96-well plates (Greiner Bio-One) using a fluorescence plate reader. A 60-min kinetic assay in four replicates and three biological replicates was carried out. Buffer only and HEK293 cells transfected with empty vector (EV) were used as negative controls for non-specific pantetheinase activity.
HEK293 cells stably expressing vanin-1 variants were seeded at 2.5×105 cells per well in 6-well plates and incubated at 37°C overnight. To stop protein translation, cells were treated with 100 µg/mL cycloheximide (Ameresco) and chased for the indicated time. Cells were then lysed for SDS-PAGE and Western blot analysis.
Cell lysates (500 µg) were pre-cleared with 30 µL of protein A/G plus-agarose beads (Santa Cruz) and 1.0 µg of normal rabbit IgG for 1 hour at 4°C to remove nonspecific binding proteins [63]. The pre-cleared cell lysates were incubated with 2.0 µg of rabbit anti-vanin-1 antibody (Pierce) for 1 hour at 4°C, and then with 30 µL of protein A/G plus agarose beads overnight at 4°C. The beads were collected by centrifugation at 8000×g for 30 s, and washed four times with lysis buffer. The vanin-1 protein complex was eluted by incubation with 30 µL of SDS loading buffer in the presence of 100 mM DTT. The immunopurified eluents were separated in 8% SDS-PAGE gel, and Western blot analysis was performed.
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10.1371/journal.pcbi.1000185 | A Dual Receptor Crosstalk Model of G-Protein-Coupled Signal Transduction | Macrophage cells that are stimulated by two different ligands that bind to G-protein-coupled receptors (GPCRs) usually respond as if the stimulus effects are additive, but for a minority of ligand combinations the response is synergistic. The G-protein-coupled receptor system integrates signaling cues from the environment to actuate cell morphology, gene expression, ion homeostasis, and other physiological states. We analyze the effects of the two signaling molecules complement factors 5a (C5a) and uridine diphosphate (UDP) on the intracellular second messenger calcium to elucidate the principles that govern the processing of multiple signals by GPCRs. We have developed a formal hypothesis, in the form of a kinetic model, for the mechanism of action of this GPCR signal transduction system using data obtained from RAW264.7 macrophage cells. Bayesian statistical methods are employed to represent uncertainty in both data and model parameters and formally tie the model to experimental data. When the model is also used as a tool in the design of experiments, it predicts a synergistic region in the calcium peak height dose response that results when cells are simultaneously stimulated by C5a and UDP. An analysis of the model reveals a potential mechanism for crosstalk between the Gαi-coupled C5a receptor and the Gαq-coupled UDP receptor signaling systems that results in synergistic calcium release.
| The G protein signal transduction system transmits a wide variety of extracellular signals including light, odors, and hormones, to intracellular effectors in diverse cell types in eukaryotes. G-protein-coupled receptors are involved in many diseases including inflammation, cardiac dysfunction, and diabetes, and are the targets of 40–50% of modern drugs. Despite the physiological and pharmacological importance of this signal transduction system it is not known how the system buffers and integrates information at a biochemical level. The multiple receptors expressed by every cell pass their signals through a common set of downstream effectors distinguished by multiple isoforms with slightly different specificities and activities. The coupling among these pathways causes interactions among the signals sent by the different classes of receptors. We have developed a mechanistic model of the G protein signal transduction system from the receptor to the central intracellular second-messenger calcium. We have used statistical methods to integrate a diverse set of experimental data into our model and quantify confidence in our model predictions. We used this model, trained on single receptor data, to predict the signal processing of two G-protein-coupled-receptor signals. Validation experiments support our hypothesized mechanism for dual receptor signal processing and the predictions of the model.
| The G-protein-coupled signal transduction system integrates a wide range of intercellular signals and actuates downstream pathways. G-protein-coupled receptors (GPCRs) are composed of seven α-helices that span the plasma membrane, an extracellular domain that is activated by an agonist and an intracellular domain that binds a guanine nucleotide heterotrimer made up of different α, β, and γ subunit isoforms. This receptor system accounts for 40–50% of modern medicinal drug targets but only 10% of the known receptors are targeted by drugs [1]. Though the system is physiologically and pharmacologically important, the mechanism by which the system integrates multiple signals is not well understood [2].
We address the G-protein-mediated route to calcium release in RAW264.7 cells. When activated by a specific ligand, the G protein heterotrimer dissociates to free Gα-GTP and Gβγ. Specific Gα and Gβγ isoforms are able to bind specific isoforms of phospholipase C β (PLCβ) and catalyze the synthesis of inositol (1,4,5)-triphosphate (IP3) and diacylglycerol (DAG) from phosphatidylinositol (4,5)-bisphosphate (PIP2) [3],[4]. In addition to its catalytic activity, PLCβ acts as a GTPase for Gα-GTP [5]. IP3 binds to specific receptor-channels on the membrane of the ER to release Ca2+ into the cytosol [6]. DAG and Ca2+ bind to and activate protein kinase C (PKC) which may phosphorylate and inactivate specific PLCβ isoforms [7]. G protein receptor kinase (GRK) is activated once it is phosphorylated by PKC [8] and is localized to the plasma membrane by Gβγ [9]. Though phosphorylation has not been shown to be necessary for GRK activation, we have assumed so in our model because phosphorylation by PKC may release the inhibition of GRK2 by being bound to calmodulin [8]. Activated GRK can then phosphorylate specific GPCRs which leads to receptor inactivation—perhaps directly or by arrestin activity [8]. In this complex signal transduction network, Gα and Gβγ subunits have different patterns of specificity for PLCβ isoforms and calcium is an important cofactor in several important feedback loops [10].
The two extracellular signaling ligands we consider here are C5a and UDP. The small peptide C5a is a potent anaphylotoxin and a strong chemoattractant for many immune system components [11]. The calcium response due to stimulation by C5a is predominantly coupled through Gαi-linked heterotrimers. Macrophage cells and their precursors, monocytes, express several receptors that are specific to extracellular nucleotides and it has been shown that the P2Y6 receptor, which is sensitive to UDP, regulates the production and secretion of the chemokine interleukin 8 (IL-8) in monocytes [12]. The UDP response is mediated by Gαq-linked heterotrimers, but other receptors in the P2Y family may respond to UDP and couple the signal through other G protein isoforms [13].
Four recent models have sought to explore various aspects of the G protein coupled signal transduction system in detail. Lukas et al. compare measured calcium response over a range of bradykinin doses to their model predictions [14]. Mishra and Bhalla built a model to investigate the role of IP4 as a signal coincidence detector in the GPCR pathway [15]. The model by Lemon et al. predicts the calcium response to UTP stimulation and is the closest in focus to our model [16]. A recent model of calcium dynamics in RAW cells has been proposed that is quite similar to this model, but does not deal with crosstalk between receptors or formal statistical uncertainty in model predictions [17],[18].
Several hypotheses for the mechanism of crosstalk and synergy among GPCR-mediated pathways have been proposed. Crosstalk among GPCR-mediated pathways is important both physiologically and pharmaceutically. Quitterer et al. propose that crosstalk is mediated by Gβγ exchange between Gαi-coupled and Gαq-coupled receptors [19]. Zhu et al. speculated that PLC is under either conditional or dual regulation of Gβγ and Gα [20]. Though these hypothetical mechanisms for crosstalk among G protein coupled receptor systems are conceptually plausible we have not found these or any other of the many competing hypothetical mechanisms tested in the context of a quantitative mathematical model [2].
In this paper Bayesian statistical inference is used to provide a rigorous connection between the mathematical model derived from mass-action kinetics, prior information from in-vitro biochemical studies and heterogeneous experimental data. The prior distribution over the parameters represents our uncertainty before observing a set of experimental data. A broad, high variance, prior distribution means we are quite uncertain and a concentrated, low variance, prior means we are more certain about the parameter a priori. The objective of our inference is the posterior distribution over the parameters because it is an informed estimate of both the value of the parameter and the uncertainty in the parameter value. The posterior distribution over the parameters is then used as a tool for experiment design to estimate the model-based posterior distribution over observable quantities such as the cytosolic calcium concentration and to drive the design of new experiments. This statistical approach is possible in a model of this size because of the abundance and quality of the data collected for this study.
There are two main features of the structure of our model, shown in Figure 1, which contribute to crosstalk in the system and produce the key dynamical features in the calcium response: isoform specificity and calcium-dependent feedback. As we will show, by including multiple isoforms of PLCβ and Gα as well as the negative feedback mediated by PKC, GRK and the IP3 receptor itself, we are able to predict the synergistic interaction between C5a and UDP observed in the experimental data.
Our representation of the G-protein-coupled signal transduction system includes C5a and P2Y6 receptors, Gαi2, Gαq, Gβγ, PLCβ3, PLCβ4, PIP2, DAG, IP3, PKC, GRK2, calcium buffer, a Na2+/Ca2+ exchanger, a sarco(endo)plasmic reticulum Ca2+-ATPase (SERCA) pump, IP3 receptors and RGS. The model is composed of 53 coupled ordinary differential equations with 84 parameters and 24 non-zero initial conditions. The complete model equations are shown in Figure S7 and a more detailed model diagram is shown in Figure S6. The parameters and initial conditions are in Table S2 and Table S1, respectively. Where available, we have relied on in-vitro or in-vivo biochemical experiments for the reactions and parameter values (see Supporting Information). In cases where the biochemical parameter values were not known, we chose physically reasonable values. Twenty of the 84 parameters most relevant to the knock-down and wild-type data were estimated from cytosolic calcium measurements as described in the Methods section. Most reactions were assumed to be governed by mass-action kinetics, but for a few proteins—such as RGS—the mechanism of regulation is not known in enough detail and we have approximated with Michaelis-Menten kinetics or a phenomenological function.
We briefly discuss the reactions involving the Na2+/Ca2+ exchanger, SERCA pump, IP3 receptors, RGS and calcium buffer because they are important for the faithful representation of the system in our model. Regulators of G protein signaling (RGS) are GTPase proteins that down-regulate the extent of signaling [21]; RGS2 at least is expressed in RAW264.7 macrophage cells and therefore an RGS activity is included in our model. The mechanism of activation of RGS2 as it relates to Gαi and Gαq signaling is not entirely known and is difficult to assess because antibodies that specifically recognize RGS2 are not widely available [22]–[24]. We have assumed constitutive activity and expect as more information becomes available a more accurate model of the regulation of RGS2 and other RGS isoforms will be possible. The SERCA pump helps to bring the cytosolic Ca2+ concentration back to the resting level after stimulation. We have modeled the SERCA pump as in the Keizer and DeYoung model [25]. The IP3 dependent opening of ER calcium channels was found to be cooperative [26] and we have used the Meyer and Stryer model for the IP3-gated channel with a Hill coefficient of four [25],[27]. Finally, many other proteins such as calmodulin and the fluorescent indicator Fura-2 bind Ca2+. Because our measurements reflect these effects, we have included a general buffer for cytosolic calcium.
Complement factor 5a activates the C5a receptor which is a Gαi-coupled receptor [28]. The released Gβγ dimer activates PLCβ2 and PLCβ3 which are lumped and called PLCβ3 in our model because: (i) the activity of Gβγ-activated PLCβ3 has been shown to be greater than Gβγ-activated PLCβ2 in in-vitro studies and (ii) Gαq activates both PLCβ2 and PLCβ3 so the structural connections from Gβγ and Gαq to PLCβ2 and PLCβ3 in the model are identical [4],[29]. PLCβ1 is activated by Gβγ and Gαq, but RAW264.7 macrophage cells do not express this isoform, so we have not included it in the model. PLCβ3 then catalyzes the hydrolysis of phosphatidylinositol (4,5)-bisphosphate (PIP2) into inositol 1,4,5-trisphosphate (IP3) and diacylglycerol (DAG).
UDP stimulates the P2Y6 receptor and the associated Gαq-GTP activates both PLCβ3 [30] and PLCβ4 [31]. The GTPase rate of Gαq is increased 1000-fold when bound to PLCβ [5]. Due to this rapid hydrolysis rate, we have assumed, in our model, that PLCβ3 or PLCβ4 bound Gαq-GTP may only hydrolyze one molecule of PIP2 before releasing Gαq-GDP. Additionally, the Gβγ released by the P2Y6 receptor also activates PLCβ3 [30], but does not activate PLCβ4 [32].
Our model assumes that PLCβ3 does not simultaneously bind Gβγ and Gαq. Indeed, a biochemical study of PLCβ2 activity in reconstituted membrane fractions strongly argues that Gαq and Gβγ do not simultaneously bind this effector [33]. While this was specifically demonstrated for PLCβ2, we implicitly assume the same holds for PLCβ3 because we lump the two in our model. This is a mechanistic assumption of our model and an interesting issue for future testing with directed experiments.
Though important for response specificity, the dynamical control of calcium release is not limited to the forward pathway in this system. Calcium participates in feedback processes that both enhance and inhibit its own release at multiple points in the pathway. There are four main nodes of calcium-dependent feedback control in our model: PLCβ, IP3 receptor, protein kinase C (PKC) and G protein receptor kinase (GRK).
Calcium enhances its own release by binding to the EF-hand domain on PLCβ and is required for PLCβ to hydrolyze PIP2 into IP3 and DAG [34]. Because the dissociation constant for PLCβ-Ca2+ in our model is larger than the basal concentration of cytosolic calcium, as more Ca2+ is released from the ER, more PLCβ-Ca2+ becomes available to bind Gαq or Gβγ. This positive feedback mechanism accelerates the release of Ca2+.
In our model, Ca2+ and IP3 cooperatively open the channel between the ER and the cytosol. It is believed that Ca2+ initially stimulates the IP3 receptor with maximal stimulatory effect at 100–300 nM [6]. At higher concentrations, Ca2+ has an inhibitory effect. We use the IP3 receptor model structure in the Keizer and DeYoung model for this component [25].
Protein kinase C (PKC) has been shown to phosphorylate PLCβ3 which inhibits PLCβ3 activation due to Gαq and Gβγ [35],[36]. PKC is activated when bound to DAG and Ca2+ [7],[37]. Because the preferred order of binding is not entirely known, PKC, DAG and Ca2+ form a thermodynamic cycle of reversible reaction with only the PKC-DAG-Ca2+ form active. In our model, the dissociation constant of PKC and Ca2+ is much greater than the basal Ca2+ concentration, and upon binding DAG, the PKC-DAG complex has a higher affinity for Ca2+ making the order of binding preferentially PKC to DAG then PKC-DAG to Ca2+. It is not known whether PLCβ4 is also regulated by PKC. We have assumed, in our model, the same mechanism of PKC regulation of PLCβ3 and PLCβ4.
The final key calcium-dependent feedback loop in our model is mediated by G protein receptor kinase (GRK). GRK2 phosphorylates and inactivates ligand-bound C5a receptors when activated by PKC and Gβγ. In sequence, PKC phosphorylates GRK2 which causes translocation to the plasma membrane [8]. When properly localized, GRK2 may bind Gβγ and then phosphorylate the C5a-C5a receptor complex to inactivate it [38]. This simplified representation of the receptor desensitization mechanism does not include arrestin activity, multiple receptor phosphorylation sites and other fine grain or slower biochemical interactions that may be present in-vivo.
Having specified the structure of our model, we direct our attention to the parameters. We estimate 20 of the 84 parameters in our model using a dataset composed of 96 Fura-2 time series measurements as described in the Materials and Methods section. Each experiment consists of 3–4 samples from different wells in a 96 well plate. There are 15 experiments spanning 9 doses of C5a and 14 experiments spanning 11 doses of UDP on wild-type cells in the dataset (see Figure S3). The dataset also contains calcium measurements on 5 different shRNAi knockdown cell lines constructed by lentiviral infection (see Figure S4). The time interval between samples is approximately 3–4 seconds and each time series is approximately 100–300 seconds of post-stimulation data. Table 1 shows a summary of the knockdown data used for statistical parameter estimation for this model in addition to the wild-type experiments.
We find that our model is generally quantitatively consistent with the experimental data within measurement uncertainty. Where the model is less consistent with the data – specifically for the GRK knockdown experiment – we find the deviation has a reasonable biological explanation. The summary of the dataset and the fit of the model to each single ligand experiment are available in the Supporting Information. We briefly discuss some issues relating to goodness of fit and the Bayesian parameter estimation here.
While most optimization procedures produce a point estimate of the parameters that maximize the goodness of fit of the model to the observed data, the Bayesian procedure we have employed here estimates the entire posterior distribution of the parameters given the data. This information is valuable for qualitatively and quantitatively evaluating the precision of the parameters estimates. Figure 2 shows, as a qualitative evaluation, that while the a-priori forward and reverse binding rates for the receptors (C5aR and P2YR) are uncorrelated they are correlated in the posterior distribution. The calcium measurements have informed and constrained the posterior estimates of the dissociation constants to be approximately 5 nM and 250 nM for the C5aR and P2YR respectively. We have quantitatively computed marginal highest posterior density (HPD) confidence intervals for each of the twenty parameters we have estimated from the data. Those estimates are shown in Table S3. Those parameters with large HPD intervals are not well informed by the measurements and are candidates for directed biochemical experiments.
The calcium response to C5a adapts and returns to the basal level, but the UDP response has a sustained elevated calcium level that slowly decays. Figure 3 shows two representative experiments of the response of the wild-type cell to stimulation with C5a and UDP. We expect that the fit to this data will be good because 20 key model parameters were fit using an experimental dataset that included these experiments – the fit is indeed accurate. The point estimate curve is constructed from the maximum a-posteriori parameters from an MCMC chain. The prediction intervals are estimated by Monte Carlo sampling from the posterior parameter distribution and the measurement error distribution conditional on the parameters. The prediction confidence intervals generally cover the observed data.
Lentiviral infection is used to introduce small hairpin RNAs to interfere with the translation of the key signaling proteins GRK2, Gαi2, Gαq, PLCβ3 and PLCβ4 [39]. There are three main sources of uncertainty in the knockdown experiment model predictions: parametric uncertainty, measurement uncertainty and knockdown efficiency uncertainty. We have dealt with the first two sources in the previous section on wild-type experiments. Here we address prediction variability due to knockdown efficiency uncertainty by using nominal parameter values.
Figure 4 shows simulations and experimental data for three representative knockdown experiments. The upper-left panel of Figure 4 shows a GRK knockdown line stimulated with 250 nM C5a. Because GRK2 desensitizes the C5a receptor, we expect that by eliminating the feedback mechanism, the calcium peak will be higher and more sustained. The experimental data as well as the model indeed show that effect. Quantitatively, the model prediction shows a greater effect than the experimental data. A likely reason is that the model only considers one isoform of GRK while there are four isoforms expressed in the RAW264.7 cell line (GRK1,2,4,6). If more than one isoform can desensitize the C5a receptor, the effective knockdown in desensitization function will be less than as measured by western blot analysis on GRK2.
While GRK does not desensitize the P2Y receptor in our model, it is a buffer for Gβγ released from Gαq. Reducing the amount of GRK will shift the equilibrium towards more Gβγ bound to PLCβ3 and thus more calcium release even though GRK does not directly feed back on the P2Y6 receptor. The top-right panel in Figure 4 shows that, based on the model, the peak intracellular calcium concentration is expected to be very slightly higher in the GRK2 knockdown line when stimulated by 25 µM UDP. A comparison of the experimental peak heights of the wild-type and GRK knockdown cell line data by t-test cannot reject the null hypothesis that the peak heights are equal (p = 0.9963). The effect of the GRK knockdown is expected to be so slight that the effect size is overwhelmed by the measurement error in the data. The effect of the uncertainty in the GRK2 knockdown fraction impacts the range of the confidence intervals of the predicted C5a response much more than the confidence intervals of the predicted UDP response which is consistent with GRK2 being a more significant component of the C5a response.
Our model structure has PLCβ3 stimulated by either Gβγ or Gαq. Because the C5a response signals only through PLCβ3 the effect of the knockdown is expected to be more pronounced for the C5a response than for the UDP response. The bottom-left panel of Figure 4 confirms that the model prediction is consistent with the representative experiment. The UDP response activates PLCβ3 through Gβγ, but also activates PLCβ3 and PLCβ4 with Gαq. Therefore, we expect that the calcium response should be more robust to perturbations in just one of the PLCβ isoforms. The UDP response in the PLCβ3 knockdown line (bottom right panel of Figure 4) shows that our model predicts the knockdown effect to be small relative to the total magnitude of the response in part due to the redundancy in the use of PLCβ isoforms in the UDP response.
Because this dataset was used for parameter estimation, the fit of model to the data may overstate the accuracy of the model. Nonetheless, the good fit does suggest that the model warrants being tested in truly predictive experiments; we describe such experiments in the following section.
We examine our model response to a simultaneous stimulation by C5a and UDP because it has been shown experimentally that macrophage cells respond synergistically to such conditions [40]. To quantify the amount of synergy or non-additivity that is present in the calcium response, a synergy ratio is computed for each ligand dose pair. The numerator of the ratio is the peak offset from baseline of the intracellular calcium concentration. The denominator of the ratio is the sum of the peak offsets when the cell or model is stimulated with only one ligand. A synergy is present when the ratio is greater than one implying the peak height is greater than expected from an additive combination of ligand effects. While this is certainly not the only possible measure of synergy it is widely adopted and has been used in previous studies on calcium synergy [40].
The left panel of Figure 5 shows the results of model simulations at nominal parameters for a grid of doses of C5a and UDP. In the dose response surface, there is a ridge of synergistic calcium release for a moderate dose of UDP. We tested the model prediction with the experiment design measuring the synergy ratio at the points denoted as black open circles in the left panel of Figure 5. A χ2 goodness-of fit test comparing the model expected synergy ratio to the observed synergy ratio fails to reject the null hypothesis that the data were generated by the model mechanism (p-value≈1.0). The root-mean-squared error (RMSE) deviation between the predicted and actual experimental data is 0.492. By way of comparison, the RMSE between the data and the null model of no synergy is 1.044. We therefore conclude that the model predictions are consistent with the experimental observations. It should be noted that measurements of synergy in RAW cells are noisy and the ridge occurs at low doses of UDP. Notwithstanding, the phenomenon has been reported [40] and has been observed by us in this cell line.
The right panel of Figure 5 shows the same synergy dose response surface but for a GRK knockdown cell line. The synergy ridge observed in the wild-type cell simulation is changed in the GRK knockdown simulation indicating the C5a receptor desensitization mechanism mediated by GRK is important for the synergistic release of calcium. In the next section we pursue this conclusion in more detail, developing a conceptual explanation of the mechanism of crosstalk and synergy within our model.
G-protein-coupled receptors form a complex network of interacting proteins that generally exhibits the properties of a system in which each receptor signal is buffered from the others. For a minority of ligand combinations, however, crosstalk between pairs of receptors is apparent. Due to the complexity and importance of the system many hypothetical mechanisms have been proposed to explain the crosstalk [2]. In particular, simultaneous Gβγ and Gαq binding to PLCβ [20] and Gβγ exchange between Gαi and Gαq-coupled receptors have been proposed as potential mechanisms [19]. While our model does not eliminate these potential mechanisms, we do show that the mechanism represented in our model is consistent with a full range of experimental data including a variety of doses of C5a and UDP, C5a and UDP stimulation of five different knockdown cell-lines and double-ligand dose response experiments.
To our knowledge, this is the first multireceptor GPCR model and the first to address the complex phenomenon of crosstalk between GPCR receptor pathways that has been statistically estimated and validated with experimental data. This important phenomenon plays a role in processes as diverse as chemotaxis and perhaps drug interactions. In our model, the primary mechanism of synergy is due to the cooperative opening of the IP3 receptor. The robustness of the synergy is due to the feedback of GRK on the C5a receptor and the specificity of the synergy is due to the interaction patterns between specific Gα isoforms and PLCβ isoforms. The simultaneous binding model [20] accounts for the specificity of synergy, but not the robustness pattern of the synergy.
We observe in the model that if the Gαq-PLCβ3-Ca2+ and Gαq-PLCβ4-Ca2+ binding reactions are inhibited, the system still exhibits synergy. We conclude from this observation that the crosstalk mechanism is mediated by Gβγ. If the binding reaction of Gβγ to phosphorylated GRK2 is removed, the synergy is eliminated. Furthermore, if the GRK2-mediated phosphorylation of complexed C5a receptors is removed, the double ligand response is additive. We deduce then that the synergy mechanism involves GRK2 phosphorylation of complexed C5a receptors. However, GRK2 phosphorylation does not entirely explain the synergy mechanism.
In our model, the calcium released from the IP3 receptor is a function of the number of receptor molecules complexed to IP3 raised to the fourth power [41]. Therefore, for a small range of IP3 concentration, the amount of Ca2+ released is more than additive (see Figure S8). We conclude from our analysis of the model that the synergy ridge in Figure 5 arises because the GRK2 mediated mechanism holds the IP3 concentration in this non-additive region for most concentrations of C5a. The UDP response does not have the GRK2 mediated feedback and thus only shows a synergistic response for a small range of UDP concentration. If the GRK2 desensitization is removed from the model, the synergy ridge is removed and synergy is only present at low doses of C5a and UDP (see Figure 5).
The Bayesian method we have used for this model has several advantages for the estimation of model parameters in complex mechanistic system models. We have used an informative prior to exclude negative rate constants from the permitted parameter space. We have also used the prior distribution to center our a priori expectations of the rate constant at values obtained from in-vitro and other biochemical experiments. The Bayesian update rule allowed us to estimate parameters with our best current dataset and then update those estimates as new data became available from the calcium assay. In this way, we were able to iteratively refine and recalibrate our model with the most recent data available during data collection period for this project. The posterior distribution provides not only an estimate of the rate constants, but the entire distribution, from which we can calculate highest posterior confidence intervals and posterior correlations between parameters. For example, the posterior correlation between the binding and unbinding rates for the UDP-P2Y receptor complex were highly correlated, but those two constants were uncorrelated with the corresponding rates for the C5a-C5a receptor complex reaction even though we imposed no correlations a priori. Finally, the algorithmic methods for collecting ensembles of samples from the posterior distribution have improved considerably in recent years in terms of speed and robustness
We have shown that the signal transduction system as it is represented by our model does not require simultaneous binding of Gαq and Gβγ to PLCβ3 to cause a synergistic Ca2+ response due to simultaneous stimulation by C5a and UDP. We have shown that our representative model is consistent with this experimental dataset in RAW264.7 macrophage cells, but we have not excluded all other potential mechanisms that may be absent or regulated differently in this cell line compared to other macrophage cell lines. Indeed there are a few examples of statistical discrepancies between the model and experiments in our dataset (Table S4). These differences are substrate for further experimentation and modeling. The purpose of our model is to provide a quantitative tool to aid in reasoning about such complex interacting systems so that meaningful experiments can be designed to explore and understand the biological mechanism.
The model equations are given in Figure S7. The initial conditions and parameter values are in Table S1 and Table S2, respectively. All the data used in this work and a stand-alone implementation of the model is provided at http://genomics.lbl.gov/supplemental/flaherty-gpcr/. The model was simulated using CVODE [42] and the GNU Scientific Library. Further details on materials and methods are available in Dataset S1.
Intracellular free calcium in cultured adherent RAW264.7 cells was measured in a 96-well plate format using the Ca2+-sensitive fluorescent dye Fura-2 [43],[44]. A Molecular Devices FLEXstation scanning fluorometer was used to measure fluorescence using a bottom read of a 96-well plate. Each well was sampled approximately every 4 seconds. The measurement protocol is described in AfCS experimental protocol ID #PP00000211 (available from http://www.signaling-gateway.org). The parameters in ligand concentration model were estimated using FITC solution in the FLEXstation scanning fluorometer as described in Molecular Devices Maxline Application Note #45 and in Protocol S1 (see also Figure S5).
Twenty of the 84 parameters were chosen to be estimated from data based on relevance to the experimental hypothesis. Only those parameters that related to the knockdown experiments in the dataset were estimated and are denoted with a star in Table S2. We used data to estimate only the two forward rate constants in the enzymatic mass-action equations because the forward and reverse rate constants for a given reaction will be highly correlated in the posterior distribution making estimation by Markov chain methods computationally expensive. An analysis of the sensitivity of the model to each parameter is shown in Figure S9.
For each estimated parameter we constructed an independent Gaussian prior on a log scale with a mean chosen based on relevant literature and a standard deviation of 0.25. We found that this prior variance was sufficiently permissive to allow exploration of the space while still constraining the rates to be physically reasonable. The prior distribution over the parameters allows the incorporation of both soft and hard constraints in the parameter estimates. Parameter sets with zero measure are not permitted in the posterior distribution and parameter sets with small measure must be assigned a large likelihood in order to have a large posterior probability.
The likelihood is a function of the parameters (θ) and links the prior distribution with the posterior distribution under Bayes rulewhere y denotes the observed data.
In our model, the likelihood function is a Gaussian distribution according to the non-linear regression equation y = f(θ)+ε, ε∼N(0,σ2), where f(θ) is the deterministic model prediction. The posterior distribution is of interest because it informs us as to the most probable setting of the parameters as well as the uncertainty in the values.
The Metropolis-Hastings algorithm [45] was used to estimate the posterior density of the parameters Pr(θ|y). Three independent chains were simulated from different initial parameter values (see Figure S1). To assess convergence of the posterior distribution estimate, we used the Gelman-Rubin potential scale reduction factor (PSRF) [46]. The multivariate PSRF is 2.44 and 95% of the individual PSRFs were less than 1.5. A PSRF value of one indicates that the distribution has converged and values near one are close to converged.
Posterior prediction confidence intervals were constructed using the percentiles from the predictive distribution approximated with 2000 Monte Carlo samples from Pr(ynew|θi) at each of 100 simple random samples from Pr(θ|y) obtained fromwhere Pr(ynew|θi)∼N(f(θ),s2) and s2 is the pooled variance estimate, which is computed as an average of the variances of all the time points in each of the 29 wild-type experiments. These average variances were weighted by the number of technical replicates in each experiment and then averaged to yield the estimate s2. A small factor of 1 nM2 was added to each variance estimate to bound variance estimates away from zero.
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10.1371/journal.pbio.1001034 | A Structural and Mutagenic Blueprint for Molecular Recognition of Strychnine and d-Tubocurarine by Different Cys-Loop Receptors | Cys-loop receptors (CLR) are pentameric ligand-gated ion channels that mediate fast excitatory or inhibitory transmission in the nervous system. Strychnine and d-tubocurarine (d-TC) are neurotoxins that have been highly instrumental in decades of research on glycine receptors (GlyR) and nicotinic acetylcholine receptors (nAChR), respectively. In this study we addressed the question how the molecular recognition of strychnine and d-TC occurs with high affinity and yet low specificity towards diverse CLR family members. X-ray crystal structures of the complexes with AChBP, a well-described structural homolog of the extracellular domain of the nAChRs, revealed that strychnine and d-TC adopt multiple occupancies and different ligand orientations, stabilizing the homopentameric protein in an asymmetric state. This introduces a new level of structural diversity in CLRs. Unlike protein and peptide neurotoxins, strychnine and d-TC form a limited number of contacts in the binding pocket of AChBP, offering an explanation for their low selectivity. Based on the ligand interactions observed in strychnine- and d-TC-AChBP complexes we performed alanine-scanning mutagenesis in the binding pocket of the human α1 GlyR and α7 nAChR and showed the functional relevance of these residues in conferring high potency of strychnine and d-TC, respectively. Our results demonstrate that a limited number of ligand interactions in the binding pocket together with an energetic stabilization of the extracellular domain are key to the poor selective recognition of strychnine and d-TC by CLRs as diverse as the GlyR, nAChR, and 5-HT3R.
| Ligand-gated ion channels play an important role in fast electrochemical signaling in the brain. Cys-loop receptors are a class of pentameric ligand-gated ion channels that are activated by specific neurotransmitters, including acetylcholine (ACh), serotonin (5-HT), glycine (Gly), and γ-aminobutyric acid (GABA). Each type of cys-loop receptor contains an extracellular domain that specifically recognizes only one of these four neurotransmitters and opens an ion-conducting channel pore upon ligand binding. In this study, we investigated the poor specificity with which two potent neurotoxic inhibitors, namely strychnine and d-tubocurarine, are recognized by different cys-loop receptors. Using X-ray crystallography we solved 3-dimensional structures of strychnine or d-tubocurarine in complex with ACh binding protein (AChBP), a well-recognized structural homolog of the nicotinic ACh receptor. Based on ligand-receptor interactions observed in AChBP structures we designed mutant GlyR and α7 nAChR to identify hot spots in the binding pocket of these receptors that define potent inhibition by strychnine and d-tubocurarine, respectively. Combined, our results offer detailed understanding of the molecular recognition of antagonists that have high affinity but poor specificity for different cys-loop receptors.
| Strychnine and d-TC (Figure 1A) are alkaloids from poisonous plants. Strychnine exerts its lethal effects by antagonizing inhibitory glycine receptors (GlyR) in the central nervous system. Intoxication with strychnine causes muscle spasms, convulsions and eventually leads to death by respiratory paralysis. Clinical use of strychnine is restricted, but it is still applied as a rodenticide. Unlike strychnine, curare is not a homogenous substance but a cocktail of compounds derived from different plant families. One of the best-described active compounds is d-tubocurarine (d-TC), a quaternary head-to-tail tetrahydroisoquinoline that potently antagonizes the action of acetylcholine on muscle-type [1],[2] and neuronal [3],[4] nAChRs. Intoxication leads to complete paralysis of all skeletal muscles and death by respiratory paralysis. In the Western world d-TC analogs have been used in anesthesia as a muscle relaxant during surgery.
In addition to their clinical use d-TC and strychnine have been essential molecular tools for the pharmacological characterization of different cys-loop receptors (CLR). d-TC and strychnine act as competitive antagonists with very high affinity for nAChRs and GlyRs, respectively. However, their actions extend to other members of the CLR family. For example, d-TC antagonizes the action of serotonin on 5-HT3 receptors [5],[6]. Strychnine mainly blocks the inhibitory GlyR but also antagonizes certain GABAA receptors [7] and nAChRs [8],[9]. This mode of action strikingly differs from that of protein and peptide neurotoxins such as α-bungarotoxin and α-conotoxins, which in general bind with high affinity and specificity to distinct subtypes of nAChRs, and not to other CLRs.
Our understanding of the molecular action of d-TC and strychnine derives from decades of research including ligand competition assays, receptor labeling, electrophysiological studies, and site-directed mutagenesis [1],[2],[5],[10]–[19]. Mutational analysis of the homomeric α1 GlyR revealed several residues in the extracellular ligand-binding domain important for agonist and antagonist binding (reviewed in [20],[21]). Additional evidence for amino acids involved in strychnine binding comes from the identification of a single amino acid substitution in the neonatal-specific α2 GlyR that renders newborn rats insensitive to strychnine poisoning [22]. Recently, Grudzinska et al. described the contribution of several key residues to strychnine binding in the β-subunit of heterooligomeric α1β GlyR [23]. Mutational analysis of conserved aromatic residues of nAChRs demonstrated their importance for binding of curariform antagonists [1],[10]. Recently, Gao et al. [24] characterized an extensive set of mutants in acetylcholine binding protein (AChBP), a structural and functional homolog of the extracellular domain of the nAChR (Figure 1B) [25]. Mutagenesis experiments in AChBP [24] and muscle-type nAChR [26] were based on the ligand-receptor contacts observed in docking simulations of d-TC- and metocurine-complexes with AChBP.
In this study we addressed a question that is fundamental to CLR function and that is how the binding cavities of CLRs as diverse as the nAChR, GlyR, and 5-HT3R recognize inhibitors such as strychnine and d-TC with high affinity but low specificity. In particular, we investigated the molecular determinants of ligand recognition of these inhibitors. For this, we co-crystallized AChBP with d-TC and strychnine. These structures enabled identification of the ligand-binding modes and contacts formed in the receptor pocket and, complemented with computational simulations, revealed the dynamic effects of antagonist binding. Mutagenesis and electrophysiological recordings of human GlyRs and nAChRs were then used to test the functional relevance and predictive value of these models. Together, our study provides a blueprint for the molecular recognition of poorly selective alkaloid antagonists at different CLRs.
To investigate the validity of AChBP as a model to understand binding of strychnine and d-TC to CLRs we determined the affinity of these ligands for Aplysia californica AChBP (Ac-AChBP) [27], a preferred homolog for structural studies. From competitive binding assays with 3H-epibatidine and 3H-methyllycaconitine we calculated Ki-values for strychnine and d-TC (Table 1). The affinity of strychnine for Ac-AChBP (Ki = 38.0±3.3 nM) is more than 100-fold higher than for α7 nAChR (Ki = 4,854±133 nM) and is actually close to the high affinity of strychnine reported for the α1 GlyR (Ki = 16±2 nM). This suggests that AChBP is an appropriate model to predict binding of strychnine to the nAChR as well as to the GlyR. Similarly, we found that the affinity of d-TC for Ac-AChBP (Ki = 509.2±38.0 nM) is in the same range as the reported values for binding of d-TC at muscle nAChR [1] and the mouse 5-HT3R [28]. Together, the high affinity binding of strychnine and d-TC makes AChBP suitable for structural studies of ligand-binding modes of these ligands by X-ray crystallography.
Crystallization of Aplysia AChBP with strychnine or d-TC gave co-crystals that diffracted at 1.9 Å and 2.0 Å resolution, respectively, and provided a highly detailed view of their binding modes. Crystallographic data are reported in Table S1. The crystal structure of Aplysia AChBP in complex with strychnine contains 1 pentamer in the asymmetric unit (Figure 2A). Similar to other AChBP co-crystal structures (e.g. pdb code 2c9t) the symmetry packing in this crystal form is characterized by an interaction of neighboring pentamers through an interface formed by two C loops (Figure 2A). Inspection of simple difference electron density unambiguously revealed the ligand orientation in all five binding sites of the pentamer (Figure 2B). Remarkably, one of the two C loops involved in forming a crystal contact is in a more extended conformation and reveals electron density for a second strychnine molecule in the same binding site. The second strychnine molecule has a B-factor = 60.67 Å2 compared to an average B-factor = 26.24 Å2 for all five other strychnine molecules, indicating that the additional strychnine molecule has a more disordered binding mode. The first strychnine molecule, which contacts the (+) face, is pivoted by 48° around the N-atom relative to the strychnine molecule in all other four binding sites (Figure 2C, single occupancy in yellow and double occupancy in magenta). The second strychnine molecule, which contacts the (−) face, is stacked onto the first molecule in a mirrored upside-down orientation and both molecules are separated by a distance of 3.6 Å (Figure 2B). In the binding site with double occupancy, the tip of loop C is displaced outward by a distance of 5.6 Å relative to the binding sites with single occupancy (Figure 2C).
Together, these results show that strychnine molecules, which have a rigid structure, can adopt very distinct but fixed binding orientations in each of the five binding pockets. For the first time we observe the presence of two ligands in the binding pocket of AChBP. Because the C-loop interacting with 2 strychnine molecules also interfaces with a C-loop from a neighboring pentamer the double strychnine occupancy might be the result of a crystal contact. However, interactions between neighboring pentamers comparable to those observed for AChBP may also occur under physiological conditions for intact GlyR and nAChR since these receptors are densely clustered at the neuronal synapse [29],[30].
We also determined the co-crystal structure of Aplysia AChBP in complex with d-TC in order to verify whether multiple occupancies and distinct ligand orientations are a common property among these alkaloid antagonists. The structure of this complex was solved from diffraction data to 2.0 Å. The asymmetric unit contains two pentamers, which interact through loop C and form an interface that resembles the one seen in the strychnine complex (Figure 2D). Inspection of simple difference electron density revealed occupancy of most of the binding sites by d-TC. Remarkably, at least three different binding orientations of d-TC can be distinguished (indicated with binding mode 1–3, Figure 2D). Binding mode 1 (yellow, Figure 2E) occurs at most binding sites, but with ligand occupancies that vary between 30% and 60%. d-TC molecules were not built in binding sites if the electron density indicated partial occupancy. The binding orientation for these ligands likely corresponds to binding mode 1 but were left unlabeled in Figure 2D. In binding mode 1, the tertiary amine group of d-TC forms a hydrogen bond with the carbonyl oxygen of W145 and forms cation-π interactions with conserved aromatic residues of the binding pocket.
A second ligand orientation (mode 2, magenta, Figure 2E) occurs only once in the pentamer and is characterized by a polar interaction between the quaternary amine group of d-TC and the carbonyl oxygen of W145. In addition to the upside-down orientation relative to binding mode 1, this ligand is also rotated by 74° toward the (+) face. A superposition of the (+) face in binding mode 1 (protein shown in yellow, Figure 2E) and the (+) face in binding mode 2 (protein and ligand shown in magenta, Figure 2E) shows that the different ligand orientation in mode 2 results in an outward displacement of the tip of loop C by a distance of 4.5 Å relative to mode 1. Finally, difference electron density at one of the binding sites involved in a crystal contact between neighboring C loops indicates the occupancy by a single ligand likely adopting multiple binding orientations. This ligand could be acceptably built into the clearest part of the density and is represented as binding mode 3 (shown in green, Figure 2E). This ligand is rotated by 140° around the tertiary isoquinoline moiety relative to d-TC in binding mode 1.
Together, these data demonstrate that rigid molecules like d-TC and strychnine can adopt different binding orientations in each of the five equivalent binding sites of the receptor. Loop C, which forms part of the binding site, adopts a more contracted or extended conformational state depending on the binding orientation of the ligand, also when not involved in a crystal contact. Remarkably, this stabilizes AChBP in a structurally asymmetric state even though this pentameric protein is composed of identical subunits. These data imply that potentially a level of functional diversity of CLRs may arise, which depends on receptor occupancy, and that would add to diversity arising from homomeric and heteromeric assemblies of the α- and non-α subunits bearing intrinsic pharmacological differences.
The conformational state of loop C and its contraction around the ligand was quantified by measuring the distance between the carbonyl oxygen atom of W145 and the γ-sulfur atom of C188 in each subunit of the pentamer. In the strychnine complex this average distance is 9.90±1.59 Å, compared to 11.80±1.32 Å in the complex with d-TC (indicated with an asterisk in Figure 3A). For epibatidine, an agonist for the nAChRs, this distance is 6.88±0.16 Å and for α-conotoxin ImI, a subtype-specific antagonist for nAChRs, this distance is 14.38±0.13 Å (Figure 3A and 3B). A comparative analysis of C-loop conformations for all agonists, partial agonists, and antagonists currently co-crystallized with AChBP reveals several features. First, for most ligands a correlation exists between the extent of C-loop closure observed in AChBP structures and the ligand mode of action at nAChRs. Ligands that act as antagonists (shown in red bars) typically displace loop C outward by a distance of 10–15 Å relative to the conserved Trp of loop A, whereas agonists (shown in green bars) cause a contraction of loop C around the ligand and reduce this relative distance to <8 Å. Second, partial agonists cause an intermediate contraction of 8–10 Å and typically induce larger variations in the extent of C-loop contraction in different subunits of the pentamer when compared to full agonists and antagonists. This variation can at least in part be explained by the occurrence of different ligand orientations for partial agonists [31]. Third, the position of the C-loop is not a strict predictor for the ligand mode of action at nAChRs because the C-loop closure for some partial agonists overlaps the diffuse boundaries that define agonists versus antagonists (e.g. DMXBA and 4OH-DMXBA). Additionally, lobeline, which acts as a partial agonist at nAChRs [32], causes an exceptionally strong C-loop closure. Together, this comparative analysis rationalizes results obtained from more than 30 co-crystal structures of AChBPs determined to date and shows in general a good correlation between C-loop contraction and predicted ligand action at nAChRs. Finally, multiple ligand orientations are not an exclusive property of partial agonists as suggested by Hibbs et al. (2009) [31] because antagonists such as strychnine and d-TC also show multiple conformations.
Next, we examined the molecular contacts between ligand and receptor in more detail, and these results were compared for strychnine and d-TC. First, strychnine forms contacts with the following residues on the (+) face: Y91, S144, W145, C188, C189, Y193 and (−) face: Y53, Q55, M114, I116, D162, S165 (Figure 4A, Table S2). Three additional contacts are formed in the binding site with double ligand occupancy, namely Y186 on the (+) face and T34, R57 on the (−) face (Figure 4B). In comparison, d-TC forms contacts that are remarkably similar. In binding mode 1 (Figure 4C, Table S2), d-TC interacts with Y91, S144, W145, C188, C189, Y186, and Y193 on the (+) face. An additional hydrogen bond is formed with E191, an interaction not seen in the strychnine-complex. On the (−) face d-TC forms contacts with T34, Y53, Q55, M114, I116, and S165 (Figure 4C). In binding mode 2, d-TC forms an additional hydrogen bond with K141 on the (+) face. Due to the different ligand orientations, two hydrogen bonds are formed with Y193 and Q55 (Figure 4D). These findings demonstrate that strychnine and d-TC, which differ in chemical and three-dimensional structure, have a large overlap in molecular contacts in the AChBP binding pocket. Also, the set of interactions formed by strychnine and d-TC has a much wider range compared to the agonist nicotine (Table S2), which interacts with Y89, W143, T144, C187, and C188 on the (+) face and W53, L112, and M114 on the (−) face (residues and numbering correspond to the Lymnaea AChBP complex with nicotine, pdb code 1uw6).
Comparison with residue contacts formed by α-conotoxin ImI, which has high selectivity for distinct nAChR-subtypes (pdb code 2c9t [33]), demonstrates that this peptide antagonist forms residue contacts that overlap with interactions seen for strychnine and d-TC but extend to a much wider range of residues (Table S2). α-Conotoxin ImI forms contacts with the (+) face: Y91, S144, W145, V146, Y147, Y186, C188, C189, E191, Y193, and I194 (exclusive contacts are underlined). On the (−) face contacts are formed with Y53, Q55, R57, D75, R77, V106, T108, M114, I116, D162, and S164. Thus, the high subtype-selectivity of α-conotoxin ImI likely arises from a broad range of contacts that are only found in specific nAChR subtypes [33]. In contrast, the poorly selective recognition of strychnine and d-TC may arise from similar interactions in binding pockets of different CLRs. For example, residues of loop A and loop C show good sequence conservation among most members of the CLR family. Specifically for loop E, residues homologous to M114 and/or I116 are well conserved between the α7 nAChR (Q117) and 5-HT3R (Q123), which are both inhibited by d-TC. Good conservation also exists between the α1 GlyR (L127/S129) and α1 GABAA-R (L128/T130), which are both inhibited by strychnine.
Are the observed ligand orientations and molecular contacts in AChBP representative for our understanding of antagonist recognition in human CLRs? To address this question, we performed alanine-scanning mutagenesis of the homologous residues in the human GlyR or nAChR using structure-based sequence alignments with AChBP (see Figure S1). The two-electrode voltage-clamp technique was used to measure ligand potency on wild type and mutant GlyR or nAChR expressed in Xenopus oocytes. For strychnine, we characterized homologous contact mutants in the human α1 GlyR because it is the primary target that mediates the physiological effects of strychnine. Results of this comprehensive mutagenesis study are summarized in Table 2. We found that strychnine displays a decrease in potency for most α1 GlyR mutants by 1–2 orders of magnitude. F159A and F214A were not functional. Critical contact residues are located in loop B (S158, homologous to S144 in AChBP) and loop D (F63 and R65, homologous to Y53 and Q55 in AChBP, respectively). Mutation of residue S158 in loop B of the GlyR (this study) causes a 300-fold decrease in strychnine-potency, whereas mutation F63A and R65A in loop D [23] cause a 250- and 3-fold decrease in strychnine-potency, respectively. Additionally, mutation of contacts in loop A (A101, homologous to Y91-AChBP) and loop E (L127 and S129, homologous to M114- and I116-AChBP) cause a ∼10-fold drop in potency. Mutation of Q177 in loop F (homologous to D162-AChBP) causes an apparent increase in strychnine-potency. To investigate the relevance of two strychnine molecules occupying a single binding pocket we investigated the effect of mutations at positions homologous to residues involved in unique contacts in the double strychnine occupancy mode, namely T34, R57, and Y186 in AChBP. The IC50-values for the homologous mutants in the α1 GlyR, F44A, and Q67A and F207A, are 3-, 15-, and 119,444-fold higher than wild type receptor, respectively. One of these mutants, F207A, was previously characterized in the work from Grundzinska et al. [23]. Such a profound effect of F207A could be expected if the interactions occur as observed for the first strychnine molecule under double occupancy binding mode. The profound effects of these mutations on strychnine inhibition, in particular F207A, suggest that the double occupancy binding mode of strychnine as observed in the crystal structure is biologically relevant.
For d-TC, previous mutagenesis studies have been carried out either on the muscle-type nAChR [1] or 5-HT3R [5],[12]. The heteropentameric muscle-type nAChR contains two different binding sites for d-TC with a 100-fold difference in affinity, whereas differences between human and mouse 5-HT3R yield a more than 1,000-fold difference in affinity. Both model systems either complicate the mutational analysis or make it difficult to derive conclusions that can be generalized to other CLRs. Here, we chose the human α7 nAChR to characterize the effect of homologous contact mutations on the potency of d-TC. The α7 nAChR, which is also a target of d-TC [4], is closely related to Ac-AChBP and is an attractive model system to extrapolate the results obtained from our X-ray crystal structures. Fifteen potential contacts in α7 nAChR were mutated and seven of these yielded a non-functional receptor, most of which are mutants of loop C. Similar to strychnine, crucial contacts are localized in loop B (S148, homologous to S144-AChBP) and loop D (W55, homologous to Y53-AChBP), which display a 148-fold and 14-fold drop in d-TC-potency, respectively. Mutations in other loops of the α7 nAChR have moderate to no effect (S36A, Y93A, Q117A, L119A, and E193A—homologous to T34-, Y91-, M114-, I116-, and E191-AChBP). Two mutants, namely G167A in the α7 nAChR and Q177A in the α1 GlyR (homologous to D162-AChBP), showed a significant decrease in the IC50-values for d-TC or strychnine compared to the wild-type receptor (p<0.05). A possible explanation for this observation is that these ligand interactions are energetically unfavorable in the wild type receptors.
Together, these AChBP-directed mutagenesis analyses pinpoint crucial interactions of strychnine and d-TC to residues in loop B and loop D of the α1 GlyR and α7 nAChR. This indicates that strychnine and d-TC form similar interactions in different classes of CLRs, which parallels our observation from the X-ray crystal structures with AChBP.
X-ray crystal structures of AChBP provide static snapshots of a receptor that undergoes highly dynamic changes upon ligand binding [34]. To investigate the validity of AChBP crystal structures and their different ligand binding modes observed in this study, we simulated the dynamic behavior of AChBP complexes with strychnine and d-TC and compared the calculated ligand orientations with those observed in co-crystal structures of AChBP. Simulation of an interface occupied either by a single strychnine molecule or two strychnine molecules shows that the system quickly resolves to equilibrium, indicating that a thermodynamically equilibrated conformation is obtained. Introduction of the ligand into an equilibrated conformation of the unliganded AChBP demonstrates that an induced fit is obtained within 9 to 12 ns (available as Movie S1). Superposition of simulated conformations and X-ray crystal structures showed only subtle changes characterized by an RMSD = 1.6 Å for strychnine in single occupancy, 1.8 Å for strychnine in double occupancy, 1.4 Å for d-TC in binding mode 1, and 3.2 Å for d-TC in binding mode 2 (Figure S2, panel A, B, and C).
Additionally, the system equilibrium was evaluated for each simulation as described in [35]. The equilibrium timeframe was truncated by 3 ns from the beginning along the time coordinate to avoid boundary artifacts. Interaction energy was measured for the ligand using thermodynamic integration in one direction along the time coordinate, giving 12±1 kJ/mol for the single strychnine conformation, 2×10±1 kJ/mol for double strychnine conformation, 11±1 kJ/mol for d-TC binding mode 1, and 10±1 kJ/mol for binding mode 2.
We attributed significant differences of the d-TC ligand pose 2 from others in RMSD from crystal structure to failure of H-bond formation with residue K141 from the (+) interface, which is observed in the X-ray structure. To investigate this we performed a more precise QM/MM (B3LYP/CHARMM) simulation. Details on system setup and simulation procedure are given in the Text S1 (see also Figure S3). After a 40 ps simulation RMSD from the crystal structure for the d-TC ligand pose 2 dropped to around 1.8 Å (Figure S2D).
Finally, we measured the fluctuation properties of the equilibrium state using a direct Fourier transform. Fourier spectra and the resulting frequency characteristic (Fc) were derived for AChBP bound to d-TC and strychnine and compared to typical agonists and antagonists. The primary frequency characteristic for unliganded AChBP was located at 450±15 GHz, whereas the complex with d-TC exhibited a leftward shift to 212±30 GHz, indicative of lower oscillation energy (Figure 5). This shift can be interpreted as a decrease in the thermodynamic temperature, which leads to an increased stability of the complex. A comparable leftward shift (Fc = 105±15 GHz) was observed in a control simulation performed for AChBP in complex with a variant of α-conotoxin PnIA (pdb code 2br8). In contrast, AChBP in complex with nicotine (pdb code 1uw6) demonstrated an extreme rightward shift (Fc = 1,200±550 GHz). The partial agonist tropisetron also demonstrated a slight increase in oscillation frequency (Fc = 745±50 GHz). These data suggest a correlation between a leftward shift of Fc and antagonistic action of the ligand and a rightward shift and agonist activity. This increase in thermodynamic temperature upon agonist binding agrees well with previous findings that agonist potency is correlated with increase in domain mobility upon ligand binding [31],[36] and indicates a higher flexibility of AChBP compared to complexes with antagonists. Unexpectedly, the AChBP-complex with strychnine exhibits a small increase in oscillation frequency up to 655±30 GHz (Figure 5). This result parallels our analysis of the conformational states of loop C (Figure 3), showing that strychnine stabilizes loop C in a conformation similar to some agonists.
In summary, we found that deviations of the crystal structures from the simulated energy minimum are relatively small. All ligand conformations induce receptor fit with high energy, thereby increasing binding efficacy and reducing the probability to switch from one conformation to another. Analysis of the oscillatory movement of AChBP shows that agonist binding results in a thermodynamic destabilization of the receptor, whereas antagonist binding freezes AChBP in a state with a lower oscillation frequency. In conclusion, using molecular dynamics we observe distinct binding modes that are present in the AChBP model system and that might correspond to binding poses present in CLRs.
Acetylcholine binding protein (AChBP) has proven a valuable tool for structural studies with more than 20 prototypical ligands for the nAChR. In this study, we take advantage of the molecular recognition by AChBP of strychnine, a prototypical antagonist for the GlyR, and d-TC, which acts on the nAChR and 5-HT3R. We determined X-ray crystal structures of strychnine or d-TC complexes with AChBP and quantified the energetic contribution of observed interactions using molecular dynamic simulation and mutagenesis in α1 GlyR and α7 nAChR. Our results demonstrate that AChBP binds strychnine and d-TC with high affinity and serves as an appropriate model for mapping the binding site topography in different CLRs, including the GlyR, nAChR, and 5-HT3R.
In the AChBP complex with strychnine we observed that four binding pockets are fully occupied by a single ligand in identical binding orientations. A fifth binding pocket, which is also involved in a crystal contact, is occupied by two strychnine molecules in a different binding orientation. The relevance of the double occupancy in the binding sites of AChBP to true CLRs is not entirely clear. An intriguing possibility is that double ligand occupancy, which occurs at an interface between two neighboring AChBP molecules, may also occur at synapses where native CLRs are tightly clustered. In analogy, four molecules of epibatidine were suggested to be present in the muscle-type nAChR with its two expected binding sites [37].
In the AChBP complex with d-TC we observed that a preferred ligand orientation occurs in most binding pockets, but with varying degrees of occupancy. Two other binding pockets contain a ligand in a different orientation at full occupancy or a ligand that appears to switch between 2 orientations in a single site. Consequently, these different ligand binding modes result in varying conformations of loop C and stabilize the homopentameric AChBP in a structurally asymmetric state. This adds a new level of diversity among CLRs, whose heterogeneity is known to arise from homomeric and heteromeric assemblies of α- and non-α subunits with different pharmacological properties. Multiple orientations of the same ligand in binding sites of the same AChBP molecule revealed in our work may be present in complexes with true CLRs [38].
Our energy calculations of AChBP complexes, in combination with mutagenesis experiments on the α1 GlyR and α7 nAChR, point to crucial interactions with residues in loop A (Y91-AChBP), B (S144- and W145-AChBP), and D (Y53-AChBP). Gao et al. [24] investigated d-TC and metocurine binding modes using computational methods available at that time and proposed residues Y89 (loop A), W143 (loop B), Y192 (loop C), and L112 and M114 (loop E) in Lymnaea AChBP to be structural determinants of d-TC binding. Grudzinska et al. [23] simulated docking of strychnine into a homology model of the α1 GlyR and identified several residues crucial for strychnine-affinity, including F63 (loop D) and R131 (loop E). Some of these ligand contacts identified in both studies are confirmed by our results, but the overall ligand orientation of the strychnine and d-TC molecules modeled using computational approaches differs from those observed in our X-ray crystal structures and MD simulations. Moreover, we have systematically mutated the homologous contact residues in all loops of the α1 GlyR and α7 nAChR, based on ligand binding poses experimentally observed in X-ray crystal structures of AChBP. This allowed us to derive a common mode of action that defines poor selective recognition of strychnine and d-TC by various CLRs.
The essential residues in loop A, B, and D as identified in our study belong to the aromatic residues that are highly conserved among the CLR family, possibly explaining the wide range of CLRs that can be targeted by strychnine and d-TC. In contrast, peptide neurotoxins form an overlapping but more extended range of interactions with the principal and complementary faces of the binding site. This is clear upon comparison with the subtype-specific antagonist of nAChRs, α-conotoxin ImI. However, peptide toxins and smaller antagonists like d-TC and strychnine share a similar molecular mechanism of action: both classes of antagonists stabilize loop C in a similar extended conformation. Notably, for the ligands characterized in this study there is a significant correlation between C-loop closure observed in AChBP crystal structures, molecular dynamics equilibrium, and the frequency that characterizes AChBP oscillation. Thus, we propose that the conformation of loop C and the induced oscillation of the extracellular domain arise from residue interactions in the ligand-binding site. Combined, the effects on C-loop extension reflect the intrinsic properties of any given ligand and therefore predict well its type of action. We suggest that antagonist effects are transmitted through a thermodynamic stabilization of the extracellular domain and arise from a limited range of residue contacts as shown for strychnine and d-TC. These defined ligand-receptor interactions are found for homologous residues in different CLRs and likely explain the low selective antagonism of strychnine and d-TC.
Aplysia AChBP was expressed and purified from Sf9 insect cells as previously published [25]. Strychnine and d-TC were obtained from Sigma and co-crystallized at a concentration of 1–5 mM. Crystals for Ac-AChBP+ strychnine were grown in 200 mM sodium acetate, 100 mM bistrispropane at pH 8.5, 15.5% PEG3350. Crystallization conditions for Ac-AChBP+d-TC were 200 mM Na2SO4, 100 mM bistrispropane at pH 8.5, 15% PEG3350. Growth of crystals at 4 °C was essential to obtain good diffraction for both complexes. Cryoprotection was achieved by adding glycerol to the mother liquor in 5% increments to a final concentration of 30%. Crystals were flash-cooled by immersion in liquid nitrogen. Diffraction data processing was done with MOSFLM and the CCP4 program suite. The structure was solved by molecular replacement using MOLREP. Automated model building was carried out with ARP/wARP and refinement was done with REFMAC or PHENIX with TLS and NCS restraints. Manual building was done with COOT and validation of the final model was carried out with MOLPROBITY. All model figures were prepared with PYMOL.
Competitive binding assays with 3H-epibatidine were carried out as previously published with minor modifications (see Text S1). Electrophysiological assays were carried out using the two-electrode voltage clamp technique. The cDNA encoding human α1 GlyR was subcloned into pGEM-HE for oocyte expression with a PCR strategy and verified by sequencing. The cDNA was linearized with NheI and transcribed with the T7 mMessage mMachine kit from Ambion. The cDNA encoding the human α7 nAChR was cloned into pMXT and linearized with BamHI for transcription with the SP6 mMessage mMachine kit from Ambion. All mutants were engineered using a Quikchange method (Stratagene) and verified by sequencing. Recordings were obtained from oocytes 2–5 d after injection with 50 nl of ∼1 ng/nl RNA. For the determination of IC50-values varying concentrations of strychnine or d-TC were co-applied with EC50-concentrations of glycine or acetylcholine, respectively. Peak current responses in the presence of increasing concentrations of strychnine or d-TC were averaged and the mean ± s.e.m. analyzed by non-linear regression using a logistic equation (GraphPad Prism 5). Student's t test was used for statistical comparison of paired observations.
Sequence analysis was performed with the ClustalW2 algorithm. Homology models of the α1 GlyR and α7 nAChR were used to verify our mutagenesis strategy, which was based on predictions from sequence alignments. Modeller9v6 was used without any GUI software and protein structures with pdb code 2vl0 and 2bg9 as templates. Energy for all models was minimized with CHARMM27. Docking was performed in AUTODOCK4 with 32 flexible bonds in receptor selected from residues inside a sphere r = 5Å centered at mass center of the ligand in the corresponding X-ray structure. A full description of molecular dynamic simulation methods is given in Text S1. Molecular analysis and visualization was performed in UCSF Chimera and PyMol. For video editing AVS Video Editor was used.
Protein Data Bank accession codes for previously published X-ray crystal structures are: Ac-AChBP in complex with lobeline (2bys), epibatidine (2byq), imidacloprid (3c79), thiacloprid (3c84), HEPES (2br7), polyethyleneglycol (2byn), DMXBA (2wnj), 4OH-DMXBA (2wn9), cocaine (2pgz), anabaseine (2wnl), in silico compound 31 (2w8f), MLA (2byr), in silico compound 35 (2w8g), sulfates (3gua), α-conotoxin ImI (2c9t and 2byp), α-conotoxin PnIA variant (2br8), α-conotoxin TxIA (2uz6), apo state (2w8e), Y53C-MMTS (2xz5) with acetylcholine, Y53C-MTSET (2xz6). Bt-AChBP in complex with CAPS (2bj0) and Ls-AChBP in complex with nicotine (1uw6), clothianidin (2zjv), HEPES (1ux2), carbamylcholine (1uv6), imidacloprid (2zju), α-cobratoxin (1yi5). Monomeric extracellular domain from mouse α1 nAChR in complex with α-bungarotoxin (2qc1).
Structures of Ac-AChBP in complex with strychnine (2xys) and d-tubocurarine (2xyt) were obtained during this study.
PubChem coordinates: strychnine (441,071) and d-tubocurarine (6,000).
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10.1371/journal.pmed.1002558 | Maternal age and offspring developmental vulnerability at age five: A population-based cohort study of Australian children | In recent decades, there has been a shift to later childbearing in high-income countries. There is limited large-scale evidence of the relationship between maternal age and child outcomes beyond the perinatal period. The objective of this study is to quantify a child’s risk of developmental vulnerability at age five, according to their mother’s age at childbirth.
Linkage of population-level perinatal, hospital, and birth registration datasets to data from the Australian Early Development Census (AEDC) and school enrolments in Australia’s most populous state, New South Wales (NSW), enabled us to follow a cohort of 99,530 children from birth to their first year of school in 2009 or 2012. The study outcome was teacher-reported child development on five domains measured by the AEDC, including physical health and well-being, emotional maturity, social competence, language and cognitive skills, and communication skills and general knowledge. Developmental vulnerability was defined as domain scores below the 2009 AEDC 10th percentile cut point.
The mean maternal age at childbirth was 29.6 years (standard deviation [SD], 5.7), with 4,382 children (4.4%) born to mothers aged <20 years and 20,026 children (20.1%) born to mothers aged ≥35 years. The proportion vulnerable on ≥1 domains was 21% overall and followed a reverse J-shaped distribution according to maternal age: it was highest in children born to mothers aged ≤15 years, at 40% (95% CI, 32–49), and was lowest in children born to mothers aged between 30 years and ≤35 years, at 17%–18%. For maternal ages 36 years to ≥45 years, the proportion vulnerable on ≥1 domains increased to 17%–24%. Adjustment for sociodemographic characteristics significantly attenuated vulnerability risk in children born to younger mothers, while adjustment for potentially modifiable factors, such as antenatal visits, had little additional impact across all ages. Although the multi-agency linkage yielded a broad range of sociodemographic, perinatal, health, and developmental variables at the child’s birth and school entry, the study was necessarily limited to variables available in the source data, which were mostly recorded for administrative purposes.
Increasing maternal age was associated with a lesser risk of developmental vulnerability for children born to mothers aged 15 years to about 30 years. In contrast, increasing maternal age beyond 35 years was generally associated with increasing vulnerability, broadly equivalent to the risk for children born to mothers in their early twenties, which is highly relevant in the international context of later childbearing. That socioeconomic disadvantage explained approximately half of the increased risk of developmental vulnerability associated with younger motherhood suggests there may be scope to improve population-level child development through policies and programs that support disadvantaged mothers and children.
| There has been a shift towards later childbearing in recent decades; although the perinatal risks are well documented, the consequences on child development are less clear.
Several observational cohort studies have reported that increasing maternal age is associated with better child development outcomes, although the numbers of children born to older mothers were limited and broad maternal age categories have often been used.
Recent evidence suggests that the relationship between older maternal age at childbirth and child cognitive ability may have shifted over time; this is largely explained by shifts in the sociodemographic and perinatal characteristics related to maternal age at childbirth.
We used linked, population-level data to study the association between maternal age at childbirth and developmental vulnerability at age five in 99,530 children who started school in New South Wales, Australia, in 2009 or 2012.
Children born to the youngest mothers had the highest risk of developmental vulnerability on all domains at age five, and the risk declined with increasing maternal age through to 30 years.
Older motherhood was associated with a small increased risk of developmental vulnerability at age five, particularly in the physical health and well-being, social competence, and emotional maturity domains, equivalent to the risk for children born to mothers in their early twenties.
Socioeconomic disadvantage largely accounted for the increased risk of developmental vulnerability associated with younger motherhood.
To our knowledge, this study is the largest-scale evidence internationally on the relationship between maternal age at childbirth—across the whole distribution of maternal ages—and early childhood development.
There may be scope to improve child development at a population level via policies and programs that support disadvantaged mothers and children.
The small increased risk of developmental vulnerability in children born to older mothers is highly relevant in the international context of childbearing at increasingly older ages.
Further research is needed to understand the mechanisms that underlie the elevated risk of developmental vulnerability in children born to older mothers, as well as the early childhood factors that potentially offset the increased pregnancy/birth risks associated with older motherhood.
| In recent decades, there has been a shift towards later average age at childbearing in high-income countries, underpinned by an increasing proportion of women giving birth at older ages, combined with a reduction in teenage pregnancies [1]. Some of the primary drivers behind this trend have altered the age-related demographic profile of mothers over time. Whereas older mothers in previous generations often had higher parity and lower socioeconomic position, today it is common for mothers aged 35 years and older (henceforth ‘older mothers’) to be highly educated, professionally employed, and primigravida [2,3]. Because a woman’s childbearing age is related to biological, social, economic, and behavioural factors that may impact a child’s development from conception through childhood [2,4] and child development relates to later health and well-being [5,6], understanding the relationship between maternal age at childbirth and child development is important.
Although the increased perinatal risks of childbearing at both younger and older maternal ages are well documented [7–11] and the development of the offspring of younger mothers has received attention [12–16], few studies have examined child development across the full maternal age range; hence, the consequences of later childbearing on offspring development remain unclear [4]. Increasing maternal age has been associated with better cognitive ability [17], fewer social and emotional difficulties [18], and better language acquisition [18] in two studies of approximately 30,000 children, after accounting for differences in demographic and perinatal characteristics across the maternal age range. Several smaller cohort studies (<5,000 children) have also reported better development outcomes with increasing maternal age [19,20]; however, estimates were grouped into broad maternal age categories because of sample size constraints, masking potential variation in outcomes among children born to older mothers. In contrast, older maternal age (i.e., >35 years) was negatively associated with offspring cognitive ability measured in half a million men aged 17–20 years of age in Sweden [21]. The variation in conclusions between studies may be partially attributed to differences in sample size, study period, and the outcomes examined. For example, recent evidence suggests that cognitive development may have improved among children born to older mothers over time, largely accounted for by differences in socioeconomic and perinatal characteristics of mothers and infants between cohorts [3]. To provide a more accurate and policy-relevant picture of how offspring developmental outcomes vary across the maternal age range, including children born to mothers aged 35 years and older, larger sample sizes and contemporary estimates across a broad range of developmental domains are needed.
In this study, our objective was to quantify a child’s risk of developmental vulnerability on five domains—physical health and well-being, emotional maturity, social competence, language and cognitive skills, and communication skills and general knowledge—according to the mother’s age at childbirth, for the whole distribution of maternal ages in a contemporary cohort of 99,530 Australian children in their first year of school.
This retrospective cohort study using linked, cross-sectoral population datasets is reported as per the RECORD guidelines [22] (S1 RECORD Checklist).
This study used data from the Australian Early Development Census (AEDC), which is a triennial, nationwide census of child development conducted since 2009 among children enrolled in the first year of full-time school [23]. In Australia’s most populous state, New South Wales (NSW), 97% of children enrolled in the first year of school participated in the 2009 and 2012 AEDC. A third-party agency (the NSW Centre for Health Record Linkage) linked the AEDC data to other population datasets in NSW, including the following used in this study: the Perinatal Data Collection; the Register of Births, Deaths and Marriages birth registrations; the Admitted Patient Data Collection; and Public School Enrolment records. Detailed information about the data sources, data linkage, and the population-based cohort of children included in the linked data resource for the broader ‘Seeding Success’ study have been reported elsewhere [24,25]. Briefly, the Seeding Success data resource includes data for a population-based cohort of children who were in their first year of school and had an AEDC record in 2009 or 2012 and a linked perinatal record and/or birth registration in NSW (N = 166,278 children). Of these, 7,755 (4.6%) children were identified as ‘high need requiring special assistance due to chronic medical, physical, or intellectually disabling conditions (e.g., Autism, Cerebral Palsy, Down Syndrome)’, based on teacher report of a medical diagnosis on the AEDC [26]. The AEDC scores of children with special needs were not included in the central derivation of national cut points for developmental vulnerability because the instrument had not been validated in children with special needs at that time.
The study population for this analysis were selected from the 166,278 children in the Seeding Success data resource. For this analysis, we restricted the study population to (i) children enrolled in a NSW public school (N = 107,666), because parental education and occupation information are collected and available from public school enrolment records; (ii) singletons (N = 104,491), because of the greater risk of adverse perinatal and childhood outcomes in multiple gestation pregnancies and births; (iii) children with complete data for maternal age at childbirth and at least one outcome variable (N = 104,200); and (iv) children without special needs (N = 99,530), because the AEDC categorical outcome data were not available for children with special needs (S1 Fig).
Early childhood development outcomes were measured using the AEDC, which collects teacher-reported information about a child’s development on the following domains: (1) physical health and well-being, (2) social competence, (3) emotional maturity, (4) language and cognitive skills, and (5) communication skills and general knowledge [23]. In Australia, the school year commences in late January/early February and the AEDC was conducted between May and August in 2009 and 2012. Because AEDC domain scores are highly skewed, we used the categorical AEDC outcomes for each domain, dichotomised into developmentally vulnerable or not. As per national reporting, the categorical outcomes, which are adjusted for the child’s year of age, classify children as developmentally vulnerable on each domain if they score below the 2009 AEDC 10th percentile cut point. Children were also classified as being developmentally vulnerable on one or more of the five domains. Several studies indicate acceptable measures of validity and reliability for the AEDC and its predecessor, the Canadian Early Development Instrument [27–32].
The month and year of birth for the mother and child were obtained from the birth registration, or the Perinatal Data Collection if the birth registration was unavailable, and used to calculate maternal age at childbirth, in years.
We classified the following variables available in the source data as potential confounders: child’s age at the start of the school year, child’s sex, mother partnered/single parent at child’s birth, mother born in Australia or overseas, private health insurance/patient at child’s birth, number of previous pregnancies (i.e., parity), antenatal care before 20 weeks gestation, smoking during pregnancy, whether child speaks English as a second language, child’s Aboriginality (children were classified as Aboriginal and/or Torres Strait Islander if indicated for the child and/or either parent on the birth registration, perinatal, or hospital birth records or the AEDC [24]), AEDC year, mother’s highest level of school education, highest occupation level of either parent recorded on the child’s public school enrolment, geographical remoteness (defined by the Accessibility/Remoteness Index of Australia [ARIA+] [33]), and area-level socioeconomic disadvantage (defined by the Australian Bureau of Statistics’ Index of Relative Socio-economic Advantage and Disadvantage [34]). Area-level variables were assigned according to the mother’s statistical local area of residence at the child’s birth. A modifiable and potentially mediating variable available in the source data was participation in preschool/childcare in the year before school. Maternal age is a population risk indicator for the complex causal pathways associated with infant and childhood outcomes. We hypothesised that the following available variables may be on the causal pathway between maternal age at childbirth and child development at age five and, accordingly, did not adjust for these variables in the statistical models: gestational age, birth weight, preterm birth, small for gestational age, low 5-minute Apgar score, neonatal intensive care unit/special care nursery admission, resuscitation at birth, and additional developmental needs (e.g., hearing impairment).
For the 99,530 children included in the study population, the proportion of missing data for most covariates was <2%, with the exception of mother’s school education level (8.6%), the occupation level of either parent (6.9%), preschool/childcare (6.5%), mother single parent/partnered (3.1%), and antenatal care before 20 weeks gestation (2.3%) (S1 Table). In total, 20,833 children (20.9%) had missing data for one or more covariates. In response to peer review, imputation via chained equations [35] was used to generate five copies of the complete dataset with filled-in missing values, which we analysed in parallel, pooling estimates using standard rules [36] to optimise use of available data for the 99,530 children in the study population.
Children were followed from birth until their first year of full-time school in 2009 or 2012. The distribution of maternal age at childbirth for children in the study population is presented as a histogram to illustrate the proportion of children contributing to the analysis at each maternal age. We estimated the proportion of children who were developmentally vulnerable on each domain, and ≥1 domains, with 95% confidence intervals, for every year of maternal age at childbirth; children born to mothers at the extremes of the maternal age range were grouped into ≤15 years and ≥45 years due to small numbers.
Our prespecified analysis plan involved fitting nonlinear regression models with a quadratic term to allow for the nonlinear relationship between maternal age and the risk of developmental vulnerability that we observed in the raw data. Following peer review, we applied piecewise linear regression models and compared results to those from the nonlinear regression models with the quadratic term (S1 Text). Based on the Akaike Information Criterion (AIC), there was a small—but statistically significant—improvement in the model fit using the piecewise linear regression methods, compared with the quadratic models (S2 Table). Analysis of residuals indicated that the improvement to model fit was primarily at the younger extreme of maternal age (S2 Fig). Although the substantive findings were similar using both methods (S3 Fig), we applied piecewise linear regression methods in the revised manuscript because of improved model fit.
Because of the small numbers of children born to mothers aged <15 years (N = 31) and >45 years (N = 67), the piecewise linear regression models were restricted to the 99,432 children in the study population who were born to mothers aged ≥15 to ≤45 years. For each year of maternal age at childbirth from 15 to 45 years, we estimated the absolute risk of developmental vulnerability on each domain, and ≥1 domain. The risk estimates are based on logistic regression models. However, instead of presenting the odds ratio estimates, we recycled the fitted model parameters to derive estimates of absolute risk using an approach referred to as regression risk modelling [37] or marginal standardisation [38]. The estimated model parameters from the logistic regression models were used to repeatedly calculate the probability of developmental vulnerability for each individual at hypothetical maternal ages from 15 to 45 years, conditional on their other observed model covariates. Averaging the resulting predictions across the whole population for a given maternal age returns an estimate of the risk of developmental vulnerability for that maternal age, assuming a common distribution of model covariates at each age. Maternal age was parameterised as a piecewise linear function with three segments: 15–<30 years, 30–<35 years, and 35–<45 years. The effect of maternal age was constrained to be zero between ages 30–<35. The choice of cut points was based on the pattern of risk observed in the raw data, commonly used maternal age groups in the related literature, and peer review. We also explored a parameterisation with four segments, which further divided the younger maternal age range into 15–<20 years and 20–<30 years; however, the improvement in model fit when specifying four segments compared to three did not compensate for the inclusion of an additional parameter; that is, there was no further reduction in the AIC between the two models (S2 Table). Accordingly, we selected the simpler model with maternal age parameterised as a piecewise linear function with three segments.
We fitted a sequence of regression models adjusted for child’s age at school entry, sex, and AEDC year (Model 1); we further adjusted for potential confounders, including private health insurance/patient, mother partnered/single parent, mother’s parity, mother born in Australia, whether child speaks English as a second language, child’s Aboriginality, highest level of maternal school education, highest occupation level of either parent, area-level socioeconomic disadvantage, and geographic remoteness (Model 2). We further adjusted for potentially modifiable factors, including antenatal care prior to 20 weeks gestation, smoking during pregnancy, and participation in preschool and/or childcare in the year before school (Model 3). Each model was estimated repeatedly on each of the five imputed datasets. Adjusted absolute risk estimates were calculated using the adjrr postestimation procedure [39]. Cluster-adjusted standard errors were applied in all models to account for the grouping of similar children within schools. The resulting estimates were combined using Rubin’s rules [36]. Analysis was conducted using Stata 12.1 [40].
To assess the potential impact of excluding children from nongovernment schools, we compared the age- and sex-adjusted absolute risk estimates of developmental vulnerability on ≥1 domains in all children who had complete exposure data on the aggregate outcome available (N = 152,556) and the study population for this analysis, which was restricted to NSW public school children (N = 99,530). Because children with special needs lacked outcome data, and some of the conditions classified as special needs may be related to maternal age, we also conducted a sensitivity analysis that assumed a worst-case scenario, whereby all children with special needs were classed as developmentally vulnerable. To assess the difference between two approaches to addressing missing data, we compared the absolute risk estimates of developmental vulnerability on ≥1 domains from piecewise linear regression models applied to (i) the imputed data (N = 99,432) and (ii) the dataset restricted to children with complete covariate information (i.e., complete case data) (N = 78,293).
Ethical approval was obtained from the NSW Population Health Services and Research Ethics Committee (2014/04/523), the NSW Aboriginal Health and Medical Research Council Ethics Committee (1031/14), and the Australian National University Human Research Ethics Committee (2014/384), which included a waiver of consent to obtain the de-identified, population-level data for this record linkage study.
The mean age of mothers when the study children were born was 29.6 years (standard deviation [SD], 5.7) and the median age was 30 years (interquartile range [IQR], 26–34; range, 13–56). The distribution of maternal age at childbirth for children in this study population peaked between 28 and 33 years; >6% of all children were born in each year of maternal age between 28 and 33 years, which, combined, equated to 40% of all children (Fig 1).
Children born to younger mothers were more likely to have indicators of socioeconomic disadvantage, including single parenthood, lower levels of maternal education, and parental occupation, whereas older mothers were more likely to have indicators of socioeconomic advantage, such as private health insurance and living in major cities and more socioeconomically advantaged areas (Table 1). It was more common for young mothers not to attend antenatal care before 20 weeks gestation and to smoke during pregnancy, compared with older mothers. Children born to younger mothers were less likely to attend preschool/day care in the year before school, although >80% of children born to mothers aged <20 years attended preschool/day care.
Of the 99,530 children in the study population, outcome data were available for 99,437 children on the physical health and well-being domain, 99,380 children on the social competence domain, 98,935 children on the emotional maturity domain, 99,434 children on the language and cognitive skills domain, 99,428 children on the communication skills and general knowledge domain, and 99,015 children on the ‘vulnerable on ≥1 domain’ aggregate outcome (S1 Table). The proportion of children vulnerable on the five AEDC domains and vulnerable on ≥1 domain followed a reverse J-shaped distribution across the maternal age range (Fig 1). Children born to the youngest mothers had the highest proportion of developmentally vulnerable. Among children born to mothers aged ≤15 years at childbirth, 13%–19% were vulnerable on each of the five developmental domains and 40% (95% CI, 32%–49%) were vulnerable on ≥1 domains (Fig 1). The proportion vulnerable on each AEDC domain, and ≥1 domain, decreased with increasing maternal age up to 29–35 years. For children born to mothers aged 29–35 years of age, the proportion vulnerable for every year of maternal age was <5% on the language and cognitive domain, 4%–8% on the other four domains, and 17% (95% CI, 16%–18%) to 18% (95% CI, 17%–19%) on ≥1 domain. Among children born to mothers aged 36 years and older, the proportion vulnerable generally increased, ranging between 3% and 14% on the five AEDC domains and 17% (95% CI, 16%–19%) to 24% (95% CI, 18%–30%) vulnerable on ≥1 domain; however, the point estimates were variable and the confidence intervals wider because fewer children were born to mothers aged >35 years.
For the 99,432 children who were born to mothers aged ≥15–≤45 years, there was a significant negative association between maternal age and developmental vulnerability in the 15–<30-year maternal age range and a significant positive association in the ≥35–45 year maternal age range on the five AEDC domains, and ≥1 domain, in the models adjusted for age, sex, and year (Table 2; Model 1, p < 0.001 on all). Adjustment for potential confounders attenuated the association in the 15–<30-year maternal age range on all outcome measures, compared with Model 1, but had a smaller impact in the ≥35–45-year maternal age range (Table 2; Model 2). Further adjustment for potentially modifiable factors, such as antenatal care, smoking during pregnancy, and preschool/childcare, accounted for minimal additional differences in the association between maternal age and developmental vulnerability for all outcomes (Table 2, Model 3). The risk estimates presented in Figs 2 and 3 confirm this pattern. For example, the estimated risk of developmental vulnerability on ≥1 domains was 40.1% (95% CI, 38.5%–41.7%) for children born to mothers aged 15 years in Model 1 (adjusted for age, sex, and year), which was attenuated to 27.6% (95% CI, 26.2%–29.0%) after adjusting for sociodemographic and perinatal characteristics in Model 2, and 26.8% (95% CI, 25.4%–28.2%) after further adjusting for potentially modifiable factors in Model 3 (Fig 3). In contrast, the estimated risk was relatively stable across the three models in the ≥35–45-year maternal age range. For example, among children born to mothers aged 45 years, the adjusted risk of developmental vulnerability on ≥1 domain was 23.2% (95% CI, 21.3%–25.2%) in Model 1, 22.8% (95% CI, 21.0%–24.6%) in Model 2, and 23.0% (95% CI, 21.2%–24.8%) in Model 3 (Fig 3).
Sensitivity analyses revealed that the overall association between maternal age at childbirth and developmental vulnerability at age five was not materially different between the sample of children with complete exposure information and at least one outcome and the study population who attended NSW public schools (S4 Fig). Moreover, the pattern of association between maternal age and developmental vulnerability was similar to the main analysis when children with special needs were included in the developmentally vulnerable group (S5 Fig) and when analyses were conducted on imputed versus complete case datasets (S6 Fig).
In this large, population-based cohort study, we found that the risk of developmental vulnerability on five domains followed a reverse J-shaped association according to maternal age at childbirth. The risk of developmental vulnerability was highest in children born to the youngest mothers and decreased with every additional year of maternal age through to the late twenties/early thirties, depending on the developmental domain. The risk of developmental vulnerability was lowest in children born to mothers in their late twenties to mid-thirties, corresponding with the maternal age range with the highest birth rates. Among children born to mothers older than 35 years, the risk of developmental vulnerability generally increased, although the risk estimates were variable and the confidence intervals wider due to the decreasing number of births at the older extreme of maternal age. Adjustment for sociodemographic characteristics accounted for a substantial proportion of the increased risk of developmental vulnerability associated with younger motherhood. On the measures of language and cognition, and communication skills and general knowledge, adjustment for sociodemographic characteristics accounted for most of the small but significant increased risk of developmental vulnerability associated with older motherhood. Further adjustment for modifiable factors—including antenatal care attendance, smoking during pregnancy, and preschool/childcare—accounted for minimal additional absolute differences in developmental vulnerability across the maternal age range.
The main strengths of this study were the large sample size and broad range of developmental outcomes collected on a contemporary general population. Limitations of this study potentially include bias, such as selection and measurement biases. However, derivation of the study population from linked population-level datasets minimised the selection biases associated with nonresponse and attrition in traditional cohort studies, and recall and social desirability biases were also minimised through the use of data recorded by midwives and teachers instead of self- or parent report. The multi-agency linkage yielded a broad range of sociodemographic, perinatal, health, and developmental variables at the child’s birth and school entry; however, we were necessarily limited to available data sources and variables available in the source data, which were recorded for administrative purposes. For example, school enrolment data were available for children enrolled in NSW public schools but not for children who were enrolled in nongovernment schools in NSW. Because school enrolment data contain information on parental education and occupation, we restricted our study population to the 65% of children enrolled in NSW public schools for this study. Although this reduced our sample size, sensitivity analyses suggest the relationship between maternal age and child development was similar in children attending government and nongovernment schools. Another limitation was incomplete information for several covariates, which was more common among children born to younger mothers. To optimise the use of available data, we imputed missing values for the main analysis, which produced slightly lower estimates of the risk of vulnerability at the younger extreme of maternal age compared with analysis of children with complete covariate information. Although we were able to adjust for several indicators of family-level socioeconomic disadvantage in this study, we cannot rule out residual confounding relating to unmeasured characteristics. Furthermore, we were unable to explore certain potential mediating factors (e.g., parenting behaviours) or the use of assisted reproduction using the available data. Another limitation was the lack of outcome data for and subsequent exclusion of children with special needs; this is potentially problematic because maternal age may be associated with some of the special needs conditions, including chromosomal and congenital anomalies [41–43]. However, our ‘worst-case scenario’ sensitivity analysis, whereby all children with special needs were classified as developmentally vulnerable, suggests that the overall study conclusions were not affected by the exclusion of children with special needs. In addition to chromosomal and congenital anomalies, maternal age is associated with termination of pregnancy [44] and the risk of perinatal mortality [11], and thus our study only evaluated children who survived to school age. Although our study adds to the literature on advanced maternal age as a population risk marker for some adverse offspring outcomes, further investigation into the role of specific pregnancy and obstetric factors (e.g., preterm birth, maternal hypertension, or diabetes) that lie on the causal pathway between maternal age and developmental vulnerability at age five is warranted.
Our finding that the risk of developmental vulnerability on all domains decreased with every additional year of maternal age between 15 and 30 years is consistent with several previous studies of childhood development [3,17,18] as well as adverse perinatal outcomes [7,9,45], psychosocial and behavioural problems [19,46,47], academic outcomes [13], and adult cognitive ability [21]. Meanwhile, our finding that there was a small increase in the risk of developmental vulnerability of children born to older mothers, equivalent to the risk for children born to mothers in their early twenties, suggests there may be limits to the previously claimed benefits of increasing maternal age on offspring childhood development [17,18,20]. One of the important factors that may underlie the varying conclusions between studies is difference in the scale of the evidence. To our knowledge, our study had more than double the sample size of the largest and most comparable previous studies of childhood development [17,18], including more than 16,000 children born to mothers aged 35 years and older. Accordingly, we were able to observe outcomes by year of maternal age through to 45 years, offering the highest-resolution picture of early childhood development at the older extreme of the maternal age range to date. Of note, the pattern of risk for early childhood developmental vulnerability in our study is consistent with the previously observed relationships between older maternal age and offspring academic outcomes at age 16 years [48] and offspring adult cognitive ability [21] from studies that followed more than half a million individuals using linked register data in Sweden. Another factor relevant to the comparison of findings between studies is whether the unadjusted association between maternal age and childhood development was reported in addition to the adjusted association. Because the two largest and most comparable previous studies of childhood development did not report the unadjusted estimates from statistical models [17,18], we are unable to compare the distribution of development outcomes across every year of the maternal age range and how the adjustment for potential confounders impacted on the risk estimates in each cohort. For this reason, it is not clear whether adjustment for socioeconomic disadvantage attenuated the risk in children born to younger mothers in previous studies, as observed in our study. Other important factors that may at least in part have contributed to the variation in findings between studies are the time period [3] and the type of outcomes measured. Outcome measures that quantify the average level of development rather than developmental vulnerability may have a different pattern of association with maternal age—for example, if there is greater variation in such measures at the older extreme of maternal age.
Historically, there has been considerable focus on negative outcomes among offspring of young mothers, often defined as 20 years or less. Although these children experience the highest risk, our data illustrate there is a continuing decline in the risk of developmental vulnerability with increasing maternal age, from children born to the very youngest mothers through to mothers in their early thirties, and this is largely underpinned by disadvantage. Furthermore, few children are born to young mothers. In this context, policies and programs that target disadvantaged mothers and children rather than focusing on children born to young mothers alone are likely to reach more children at risk of poor development outcomes and have a greater impact on child development at a population level [12,49]. At the other end of the spectrum, it is well established that the offspring of older mothers have a greater risk of chromosomal abnormalities [10], congenital conditions [41], and adverse perinatal outcomes, including preterm birth and being small for gestational age [9], although prenatal screening has impacted the incidence of children born with chromosomal and congenital abnormalities [50]. Adverse perinatal outcomes are, in turn, associated with later child development [51,52]. It has been argued that, beyond the perinatal period, the socioeconomic and other potential advantages of older motherhood may offset the biological disadvantages during pregnancy and childbirth [53]. However, limitations in the scale of the evidence in previous studies may have oversimplified the pattern of risk of developmental vulnerability at the older extreme of maternal age. In the context of the later childbearing trend, even a small increase in the risk of developmental vulnerability among children born to older mothers may be of population-level importance in terms of later health and well-being [21].
In this, the largest and most comprehensive study of early childhood development outcomes and maternal age to our knowledge, to date, we have confirmed that children born to younger mothers have the highest risk of developmental vulnerability. In addition, we identified a small increased risk of developmental vulnerability in children born to older mothers, which is highly relevant in the international context of childbearing at increasingly older ages. That the increased risk of developmental vulnerability among children born to the very youngest through to average-aged mothers was largely explained by socioeconomic disadvantage suggests there may be scope to improve child development at a population level through policies and programs that support disadvantaged mothers and children. Future research to elucidate the mechanisms that underlie the elevated risk of developmental vulnerability in children born to older mothers, as well as the early childhood factors (such as parenting behaviours) that potentially offset the increased perinatal risks associated with older motherhood, may further inform policies and interventions to promote positive child development across the population.
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10.1371/journal.pgen.1002224 | Cholesterol Metabolism Is Required for Intracellular Hedgehog Signal Transduction In Vivo | We describe the rudolph mouse, a mutant with striking defects in both central nervous system and skeletal development. Rudolph is an allele of the cholesterol biosynthetic enzyme, hydroxysteroid (17-beta) dehydrogenase 7, which is an intriguing finding given the recent implication of oxysterols in mediating intracellular Hedgehog (Hh) signaling. We see an abnormal sterol profile and decreased Hh target gene induction in the rudolph mutant, both in vivo and in vitro. Reduced Hh signaling has been proposed to contribute to the phenotypes of congenital diseases of cholesterol metabolism. Recent in vitro and pharmacological data also indicate a requirement for intracellular cholesterol synthesis for proper regulation of Hh activity via Smoothened. The data presented here are the first in vivo genetic evidence supporting both of these hypotheses, revealing a role for embryonic cholesterol metabolism in both CNS development and normal Hh signaling.
| The molecules and signaling pathways that regulate growth and patterning of the developing embryo are still being elucidated, and one valuable experimental approach is the use of animal models, such as the mouse. We have identified a recessive mutation in the mouse, rudolph, that causes abnormal forebrain development and have determined that the mutated gene encodes hydroxysteroid (17-beta) dehydrogenase 7 gene, an enzyme necessary for cholesterol biosynthesis. Cholesterol is essential for proper signal transduction of the hedgehog family of proteins, key regulators of both developmental biology and tumor progression. We show that hedgehog signaling is diminished in our rudolph mutant. Our conclusions from studying this mouse mutant support two recent hypotheses in developmental biology. First, several human malformation syndromes are known to be caused by defects in cholesterol metabolism, but support linking the malformation to abnormal hedgehog signaling has not definitively been made. Second, while in vitro studies have shown that proper levels of metabolic by-products of cholesterol are necessary for proper hedgehog signaling, our studies offer the strongest genetic animal model evidence to support this idea.
| Hedgehog (Hh) ligands have numerous and fundamental roles in both embryonic development [1], [2] and tumor biology [3]. Mammalian hedgehog proteins bind to the transmembrane receptor Patched (Ptc) and thereby relieve its repression of Smoothened (Smo). Intracellular transduction of Smo activity requires processing of GLI proteins. The primary cilium has shown to be an essential structural component for proper Hh signaling in mammals with dynamic localization of Ptc in response to binding of Sonic hedgehog (SHH; [4], [5], [6], [7]). Functional SHH signaling requires removal of PTC from the cilium, translocation of SMO to the cilium, and activation of SMO by an as yet unknown mechanism [5], [6], [8], [9].
It has been well established that cholesterol is an essential component of Hh signal transduction. Processing of the Hh ligand in the producing cell includes the covalent modification of cholesterol to the carboxyl end of the immature protein. Although cholesterol in Hh proteins is thought to facilitate the proper dispersal of SHH through the target field, it is not necessary for signal transduction [7]. More recently, however, metabolites of cholesterol and cholesterol biosynthetic pathway intermediates have been shown to have an intracellular role in SHH signal transduction. For example, pharmacological inhibition of cholesterol biosynthesis leads to defective responses to SHH ligand, independent of SHH processing, both in vitro and in vivo, with the in vivo effect mimicking Shh loss of function phenotypes [10], [11], [12]. Moreover, not only can cholesterol-derived oxysterols activate the Hh pathway in vitro [13], but treatment of cells with oxysterols has been shown to cause translocation of PTC and SMO to the cilium in the absence of SHH ligand [8].
Mutations in enzymes required for cholesterol biosynthesis are associated with a number of human diseases [14]. The best known is Smith-Lemli-Opitz Syndrome, in which patients have central nervous system (CNS) malformations, including holoprosencephaly and microcephaly, and skeletal defects (most often postaxial polydactyly) caused by mutations in 7-dehydrocholesterol reductase (DHCR7) [15]. Other disorders of cholesterol biosynthesis include desmosterolosis, lathosterolosis, X-linked dominant chondrodysplasia punctata (CDPX2), CHILD syndrome (congenital hemidysplasia with icthyosiform erythroderma or nevus and limb defects) and Greenberg skeletal dysplasia. Overlapping features of these disorders include abnormalities in the CNS, facial dysmorphisms, and skeletal defects, often including polydactyly or other digit patterning defects. Mouse models also exist for many of these disorders and have similar defects. The frequent occurrence in these human syndromes and mouse mutants of defects in neurodevelopment, craniofacial morphogenesis and skeletal growth and patterning has led to the proposal that abnormal Hh signaling may be the root cause of these embryological defects. This speculation is based principally on genetic experiments that have shown a role for Hh signaling in the development of all of these tissues, and the known role of sterol metabolism in Hh signal transduction. Despite this, there is relatively little direct evidence from these mouse models for a defect in Hh signaling.
Here we describe the phenotype of the rudolph mouse mutant, an ethyl-nitrosourea (ENU)-induced mutation in hydroxysteroid (17-beta) dehydrogenase 7 (Hsd17b7), which was the last enzyme of the cholesterol biosynthetic pathway to be identified and one of four proteins of the sterol-4-demethylase complex [16]. Rudolph mutants have severe developmental abnormalities in several tissues including the brain and appendicular skeleton. We find that tissues from rudolph mutants have an abnormal sterol profile consistent with impaired activity of the sterol-4-demethylase complex. We further demonstrate that the rudolph mutant has deficient responses to Hh signaling, both in vivo and in vitro. These results support the recently proposed model that functional intracellular sterol metabolism is required for proper cilia-mediated activation of the Hh signaling pathway.
We recently recovered the rudolph mutation via an ENU mutagenesis screen designed to identify recessive mutations affecting development of the mammalian forebrain. Rudolph mutants were first ascertained by a blood spot on the end of the nose and their abnormally curved forelimbs (Figure 1B). The precursor to this nasal phenotype was sometimes evident at earlier stages as a blebbing of the craniofacial epithelium (Figure S1A). Examination of the embryonic skeleton revealed that all long bones of the appendicular skeleton were significantly shorter than those of wild-type littermates while the axial skeleton and ribs appeared normal. (Figure 1D, Figure S2, Table S1). Further analysis of the embryos revealed severe defects in CNS development. The telencephalic tissue was markedly reduced in size and highly disorganized in mutants at embryonic day (E) 16.5 (Figure 1F, 1H). Mutants had a smaller neurogenic ventricular zone and clumps of cells in the developing cortical plate. Similar defects were seen in the E16.5 retina and spinal cord (Figure 1J, Figure S3B). Initial cortical morphogenesis appeared largely normal (Figure S3D, S3F).
To further characterize the rudolph phenotype, we performed a molecular analysis of the cortical phenotype. We assessed cell proliferation at E14.5 by BrdU treatment of pregnant dams or immunostaining of embryos with Ki67 and found a marked decrease in proliferation in mutants (Figure 2B). Some of the mitoses we detected were seen as foci of BrdU-positive cells (inset in Figure 2B). We thus interpreted the clusters of cells seen histologically in the rud cortex to be neurogenic foci. To determine the cause of the reduced neuronal tissue, we assayed apoptosis at E14.5 using the TUNEL reaction and found increased levels of cell death in the mutant tissue, distributed throughout the cortex and enriched along the ventricular surface (Figure 2D). An increased level of cell death was not seen in non-neural tissue (data not shown). Decreased, disorganized neuronal proliferation and increased cell death were also evident at E12.5 (data not shown). Immunohistochemistry for TuJI to identify differentiated neurons at E14.5 showed a marked decrease in differentiation in mutants compared to wild-type (Figure 2F). In addition, foci of TuJI immunoreactivity appeared in regions of no differentiation, consistent with the disorganization of the cortex seen histologically. Disorganized proliferation was also evident in the developing rudolph retina, where we observed similarly abnormal neuronal differentiation, decreased cell proliferation and increased apoptosis, but at different stages of development. Whereas at E12.5 we saw no significant decrease in proliferation or apoptosis between wild-type and mutant (Figure 2H; data not shown), at E14.5 we found a decrease in BrdU incorporation in the mutant retina (Figure 2J) and an increase in apoptosis (Figure 2L). Furthermore, the pattern of neuronal disorganization we saw in the rud cortex was similarly evident in the rud retina at E14.5 (Figure 2N).
The phenotypes of the rudolph mutants exhibit a variability in severity that appears to be dependent on genetic background. For example, we noted increased blebbing on the developing head and limbs in embryos that came from a mixed background (Figure S1B–S1D). Embryos from our ENU screen have a mixed genetic background with contributions coming from both A/J (the mutagenized strain) and FVB mice (introduced as part of the outcross for mapping purposes). In this A/J; FVB background, embryos were recovered in approximately Mendelian ratios from E10.5–E18.5 (Table S2). No mutants have been recovered after birth, suggesting they are among the stillborn fetuses. Upon introducing the B6 background, the number of mutant embryos did not decrease significantly, but we then began to see a number of dead embryos from E11.5 and older (7.8%), an increase in the severity of the nasal blebbing (Figure S1A–S1C: 14.8%) and limb patterning defects (10.4%). Further introgression into the B6 background resulted in no significant decrease in recovery of mutant embryos but a large increase in the incidence of more severe blebbing (48.5% in pooled N1, N2, and N3B6 mice). Backcrossing to FVB rescued this defect, and blebbing is essentially absent in N3 FVB mice. The reduced fecundity of 129 mice limited our analysis of this genetic background, preventing any definitive comments on modifiers on the 129 background (Table S2).
We initially mapped the rudolph mutation to Chromosome 1 using a whole-genome 768-marker single nucleotide polymorphism (SNP) panel [17]. Examination of the 255 predicted and known genes in the region suggested Hsd17b7 as a candidate for further analysis because of its known function in cholesterol metabolism and its reported expression pattern in limb buds and the developing nervous system [16]. Sequencing of Hsd17b7 revealed a point mutation in the sixth intron, 27 base pairs upstream of the intron-exon boundary (Figure 3A). We analyzed transcripts by RT-PCR with primers spanning exon 7 and found the predominant PCR product in mutant tissue to be smaller than that of wild type (Figure 3B). Sequencing of this product revealed a precise excision of the seventh exon in the smaller PCR species. The loss of exon 7 was confirmed with primers in the sixth and seventh exons (Figure 3C). Interestingly, a small amount of this truncated transcript was present in wild-type cDNA, and, conversely, mutant tissues retained a very small fraction of the wild-type transcript (Figure 3B, 3C). We hypothesize that these RT-PCR products represent two naturally occurring forms of the Hsd17b7 transcript and that the rudolph ENU mutation affects the ratio of their abundances. The phenotypes we observe in the rudolph mutants appear to be somewhat tissue specific and have differing expressivity in different strains. However, the variation in the cDNA splicing pattern does not differ among different tissues examined (heart/lung, limbs, brain, or whole embryo) or depend on varying genetic backgrounds, suggesting that tissue specific transcription and variation in genetic background account for the variability in phenotype (Figure S1E).
Loss of the seventh exon of HSD17B7 is predicted to encode a protein with an in-frame deletion of 19 amino acids. To assess the effect of this splicing mutation, we tested HSD17B7 expression by Western immunoblot analysis and found only trace levels of protein in mutant tissue lysates (Figure 3D). Although the deleted seventh exon is part of a putative endoplasmic reticulum anchoring sequence, in vitro expression of a rud-GFP fusion protein results in deficient protein rather than mislocalization, suggesting that the rudolph deletion generates an unstable protein product (Figure S4). Embryos homozygous for a null allele of Hsd17b7 generated from a 129 genetic background do not survive past E10.5, suggesting the rudolph allele is likely a hypomorph [18], [19].
We analyzed the sterols present in liver and brain tissue from wild-type, heterozygous, and rudolph embryos at E12.5 by gas chromatography-mass spectrophotometry (Figure 4) and found marked differences between wild-type and mutant tissues. The identified abnormalities in methylsterol abundances are consistent with reduced function of the Hsd1b7 enzyme in rudolph brain tissues (Table 1). Sterol species upstream of Hsd17b7 activity were present in increased amounts, the most prominent of which were the HSD17B7 substrates zymosterone and 4methyl-zymosterone, and a third ketosterol tentatively identified as methylcholest-7-en-3-one. Various mono- and dimethylsterols that do not normally accumulate in wild-type tissues were also present in increased amounts, including 4α-methyl-5 α-cholest-8-en-3β-ol, 4 α-methyl-5 α-cholest-7-en-3 β-ol, 4 α-methyl-cholesta-8(9),24-dien-3 β-ol, and 4,4-dimethyl-5 α-cholest-8(9),24-dien-3 β-ol. Desmosterol, a compound downstream of Hsd17b7, was reduced. The predominance of zymosterone (5 α-cholesta-8,24-dien-3-one) and 4 α-methylzymosterone in the brain compared to liver is in keeping with the known decreased activity of desmosterol reductase in the brain, especially fetal brain, and the retention of the 24-unsturated bond in certain sterol species in the normal brain. In wild-type brain, desmosterol (5 α-cholesta-5,24-dien-3β-ol) is the most abundant 24-unsaturated sterol.
In view of the important role of Hh signaling in patterning of the limbs, face, and brain, and the genetic and cellular evidence for a role of cholesterol metabolism in this pathway, we hypothesized that Hh signaling is perturbed in the rudolph mutant. Furthermore, recent evidence specifically implicates intracellular sterols in the regulation of the subcellular localization of the Hh signaling components, Patched and Smoothened, supporting the possibility that an abnormal sterol profile in rudolph mutants could disrupt Hh signaling [5], [6], [8]. To assess this, we generated mice homozygous for the rudolph mutation that carried the Patched-lacZ gene, a transcriptional target of Gli2 and thus a reporter of Shh signaling activity. In these embryos, we found reduced levels of Ptc-lacZ in the developing brain at both E11.5 and E14.5 (Figure 5B, 5D; Figure S5; data not shown). We also noted decreased expression of another Shh target gene, Gli1, in the retina and brain of rudolph mutants at E14.5 (Figure 5F, Figure S5). Furthermore, analysis using quantitative RT-PCR demonstrated reduced Ptc mRNA in rudolph brain tissue compared to wild-type (63% of wild-type, p = 0.053, data not shown). All tissues with reduced SHH target gene expression have severe morphological abnormalities in the rudolph mutant and are known sites of Hh signaling. Because dorsal-ventral patterning of the neural tube also requires SHH signaling, we examined the dorsal-ventral character of the rudolph neural tube and found that, at both E10.5 (Figure S6A–S6N) and E12.5 (Figure S6O–S6BB), all immunohistochemical markers for cell fate we tested showed normal patterns of expression along the dorsal-ventral axis of the rudolph neural tube.
We also observed limb patterning defects in mice from a mixed A/J, FVB, B6 (Figure 6A, 6B, Table S2). As Hh signaling is important for proper patterning, we examined Hh signaling in the developing limb bud. Normal patterning of the limb results in elevated Shh signaling in the posterior portion of the developing limb bud as compared to the anterior. We observed Ptc expression in rud mutants (n = 3) from a mixed background using both Ptc-lacZ expression and whole mount in situ hybridization and found one embryo with reduced Ptc expression in the posterior limb bud (Figure 6D) as compared to littermate control (Figure 6C). The variable penetrance of the limb patterning phenotype is consistent with an incompletely penetrant reduction in Shh activity in the developing limb bud. Hh signaling is also involved in long bone growth (where the relevant ligand is Indian hedgehog). We also see reduced expression of Ptc in the developing limb of rud mutants using either the Ptc-lacZ allele (Figure 6F) or an in situ riboprobe for Ptc (Figure 6H).
To further determine if Hsd17b7 expression affects SHH signaling, we generated primary mouse embryonic fibroblasts (MEFs) from wild-type and rudolph embryos and assessed their response to added SHH protein. In wild-type MEFs, treatment with SHH protein resulted in increased cell proliferation and increased Ptc and Gli mRNA levels, which is consistent with the known role of SHH as a mitogen in several systems and with Ptc and Gli being direct targets of SHH signaling. In contrast, the effects of SHH treatment were blunted in mutant cells (Figure 7A–7C). We also generated MEFs from wild-type;Ptc-lacZ and rudolph;Ptc-lacZ embryos to measure SHH transcriptional activity via the accumulation of β-galactosidase. In this assay, wild-type;Ptc-lacZ MEFs responded to SHH treatment with increased β-galactosidase production, whereas rudolph;Ptc-lacZ MEFs did not (Figure 7D). Together these data suggest that rudolph mutant mice have reduced intracellular signal transduction distal to the binding of SHH ligand in the SHH signaling cascade.
In a parallel approach, we used Pzp53MED cells [20], which are SHH-responsive cells carrying the Ptc-lacZ allele, to assess the role of Hsd17b7 in SHH signal transduction. We used lentiviral infection followed by clonal selection with plasmids for RNAi knockdown of Hsd17b7 to create cell lines with reduced levels of Hsd17b7 (approx. 80% reduced, data not shown) and then treated the lines with recombinant SHH, as done with the MEFs. Two independent RNAi clones did not induce significant β-galactosidase production upon SHH treatment while the control cell line responded robustly (Figure 7E).
The decreased response to SHH protein in vitro shows that the signaling defect is downstream of ligand binding to cell surface receptors. Recent data have shown that treatment with oxysterols can cause a change in the subcellular localization of PTC and SMO protein [8], which is necessary but not sufficient for activation of the SMO protein [6], [21]. The Hsd17b7 phenotype may be caused by dysregulated sterol biosynthesis affecting the localization and/or active state of the Smo receptor. We therefore examined the localization of a SMO-GFP fusion protein in wild-type and rudolph MEFs upon treatment with SHH, but found no decreased mobility of the SMO-GFP to the cilia in the mutant MEFs (Figure 8). Wild-type MEFs without SHH treatment had SMO-GFP throughout the primary cilia in only a subset of cells examined (43.3%: n = 29/67 ciliated, transfected cells in two independent experiments). Upon addition of SHH, SMO-GFP was found throughout the cilium in the majority of cells examined (89.6%; 65/73 cells). Mutant MEFs behaved similarly, with untreated cells having 38.7% SMO-GFP positive cilia (24/62) and SHH treatment leading to 86.7% (72/83) of cells with SMO-GFP throughout the cilium. In addition to the increase in cells with SMO-GFP throughout the cilium upon SHH treatment, we also note that untreated cells often had SMO-GFP largely at the base of cilium. SHH treatment resulted in very few cells showing SMO-GFP localization to the base of the cilium, but rather throughout the length of the cilium. Taken together, these data suggest that the rudolph mutation does not affect the localization of SMO within the primary cilium in response to SHH treatment.
In this report, we describe the rudolph mouse mutant phenotype, which is caused by a mutation in the cholesterol biosynthetic enzyme Hsd17b7, the 3-ketosteroidreductase element of the sterol-4-demethylase complex. The rudolph phenotype includes marked abnormalities in the development of the nervous system and appendicular skeleton, which correlate with a decreased effectiveness of SHH signaling both in vivo and in vitro. Because sterols have been shown to affect SMO subcellular localization, we tested the movement of SMO to the primary cilium in response to SHH protein and found no defect in SMO trafficking. This suggests that the abnormality in rudolph affects the activation of SMO, resulting in a decreased response to SHH ligand binding.
Our study of the sterols present in brain tissues from the mutant mice is consistent with reduced Hsd17b7 enzymatic function, as we observe an increase in compounds of the cholesterol biosynthetic pathway upstream of the Hsd17b7 enzyme. While no patient has yet been identified with a defect in Hsd17b7, increased levels of mono and dimethyl sterols have been reported in plasma and/or skin of patients with mutations in two other genes of the sterol-4-demethylase complex, SC4MOL (sterol C4-methyloxidase like) and NSDHL (NAD(P)H steroid dehydrogenase-like protein) [22], [23].
To explain the phenotype of the rudolph mouse we considered several possible metabolic effects, including 1) a decrease in the cellular level of cholesterol, 2) a decreased level of another product of Hsd17b7 enzymatic function, and 3) teratogenic effects of high levels of the cholesterol precursors detected in our study. Several lines of evidence suggest the last to be the major cause of the phenotype we observe. If simply a deficiency of the end-product, cholesterol, caused the rudolph phenotype, one might expect mice carrying mutations in the cholesterol biosynthetic pathway to resemble each other. This is not the case, since, despite some phenotypic overlap in adjacent disorders in the pathway, overall there are significant phenotypic differences across the spectrum of mouse models of human cholesterol biosynthetic disorders [24]. The best-characterized disorder of cholesterol biosynthesis is Smith-Lemli-Opitz syndrome [25], [26], caused by mutations in DHCR7, which encodes the 7-dehydrocholesterol reductase that converts 7-dehydrocholesterol to cholesterol [15], [27], [28]. Two mouse models with null alleles of Dhcr7 have abnormal phenotypes including cleft palate, but lack the striking brain phenotypes we found in the rudolph mutant [29], [30]. The Dhcr7 mutant mice have decreased cholesterol and increased 7-dehydrocholesterol levels in serum and tissues [29]. The enzyme immediately preceding DHCR7 is SC5DL (sterol C5-desaturase-like) which is deficient in human patients with lathosterolosis [31]. A null Scd5 allele in the mouse is a neonatal lethal with craniofacial and limb defects and decreased cholesterol similar to those of the Dhcr7-deficient mouse, but with increased levels of lathosterol (the substrate of Sc5d) in all tissues [32]. Given that the Dhcr7 and Scd5 mouse models have a significant decrease in cholesterol levels, which is not apparent in the rudolph mutant, and that their phenotypes do not resemble the rudolph phenotype, we conclude that cholesterol deficiency does not cause the distinctive embryological abnormalities of the rudolph mouse.
Nsdhl, another element of the sterol-4-demethylase complex, is the enzyme immediately preceding Hsd17b7 in the canonical cholesterol biosynthetic pathway [33]. NSDHL mutations in humans cause CHILD syndrome (congenital hemidysplasia with ichthyosiform erythroderma and limb defects), a rare X-linked dominant disorder with presumed lethality for CHILD causing alleles [34] and, with hypomorphic NSDHL mutations, CK syndrome, a form of X-linked mental retardation [22]. Mutations in Nsdhl are found in the Bare patches (Bpa) and Striated (Str) mice [35]. Analysis of tissue samples from Bpa/Str females showed an accumulation of 4-methyl and 4,4′-dimethyl sterol intermediates. Human mutations in EBP cause X-linked dominant chondrodysplasia punctata (CDPX2, Conradi-Hunermann syndrome [36]). Patients with CDPX2 usually have normal plasma total cholesterol levels but increased levels of other sterols, including 8-dehydrocholesterol and cholesta-8(9)-en-3β-ol [36], [37], [38], [39]. Tattered (Td) carries a missense mutation in the Ebp gene and phenotypically resembles the rudolph mutation most among all the known cholesterol biosynthetis mouse mutants. Male Td embryos die between E12.5 and birth and have defects in the skeleton and brain similar to those we describe here [40]. The sterol profile of heterozygous female mice includes elevated 8-dehydrocholestrol and choles-8(9)-en-3β-ol. All of these findings combined with our data suggest a model in which the accumulation of specific sterols leads to the defects we observe. One of the effects these inhibitory sterols may be to dampen the intracellular response to Shh signaling.
Although rudolph mutants lack some of the classic features of the Shh null mice, such as holoprosencephaly, other more specific ablations of SHH signaling have some features resembling the rudolph phenotype. In particular, the skeletal defects we observe in the rudolph mutant are similar to those described in the Indian hedgehog (Ihh) and dispatched-1 (Disp1) loss of function mice and the conditional ablation of Smo from the developing skeleton [41], [42], [43]. As Ihh is the most active Hh ligand in development of long bones, we suggest that the similarities between rud and the HH-signaling mutants reflect the conservation of intracellular signaling transduction mechanisms between the different Hh ligands, and that these defects are due to an insufficient response to secreted IHH in the cartilage. In addition, the disorganized retina in rud mutants also resembles that seen in embryos with an ablation of Shh using a Thy1-Cre [44]. The role of Shh and Ihh as mitogens in retinal neuroblast proliferation has been established [45], and Shh, Ihh and Gli1 are expressed in retina at E12 and E13 [44], [46]. The difference in timing between the cortical and retinal defects (rudolph retinal molecular defects are seen at E14.5, but not E12.5, Figure S4) is consistent with the later expression pattern of Shh signaling components in the retina as compared to forebrain tissue.
The developing rudolph forebrain phenotype has both similarities to and differences from the known effects of Shh loss of function. The decreased cell proliferation and increased apoptosis we find are completely consistent with a role for Shh as a mitogen for the developing neural tissue and with the observations that blocking SHH function can lead to cell death [47], and that conditional ablation of Smo throughout the cortex by E9 using the Foxg1-Cre leads to increased cell death [48]. Emx1-Cre ablations of Shh and Smo in the dorsal telencephalon by E10 cause a smaller telencephalon featuring reduced proliferation and neuronal differentiation with increased cell death [49]. However, the striking disorganization of the cortex in rudolph mutants resembles more a loss of polarity phenotype, such as the cortical ablation of numb and numb-like [50]. Because Shh loss of function has not been demonstrated to directly affect polarity, we suggest that abnormalities of cortical signaling mechanisms other than Shh must be disrupted in the rudolph cortex to explain some of the developmental abnormalities of the CNS we observe. Because cortical dysplasia is not characteristic of any of the known human disorders of cholesterol biosynthesis, the extremely high CNS level of zymosterone, normally only a trace sterol in the brain, suggests that zymosterone or other 3-ketosterols in the rud brain could have a direct toxic effect or could otherwise impair neuronal differentiation.
Two reports have recently described the phenotypes of Hsd17b7 null allele mice [18], [19]. These embryos have major morphological abnormalities by E10.5, precluding direct comparison to the phenotypes studied here. The forebrain did appear smaller in the Hsd17b7 homozygous null embryos, and development does not proceed past E9.5. A sterol analysis performed in these mutants demonstrated increased Hsd17b7 enzyme substrates and unchanged cholesterol levels, similar to the results we report [19]. Maternal supply of cholesterol was also suggested to account for the normal embryonic cholesterol levels. Expression of Shh and Ptc was examined in the Hsd17b7 homozygous null embryos at E8.5, but the domain and levels of expression did not differ from wild-type [19].
The phenotypic differences between the rudolph and Shh mutants may be due to the hypomorphic nature of the rudolph allele. However, we also note that the rudolph phenotype in the CNS begins to emerge around E12.5, which is the time when the blood-brain barrier forms and synthesis of cholesterol within the CNS becomes separated from non-neural cholesterol synthesis [51], [52], [53], [54]. The developmental consequences of reduced sterol concentrations in the early embryo could be mitigated by the maternal circulation in view of evidence that, in rodents, substantial amounts of maternal cholesterol can be transported to the fetus through the placental-fetal interface [55], thus possibly compensating for a lack of early Hsd17b7 function in the initial patterning stages of embryonic development. We therefore propose that formation of the blood-brain barrier creates a neurodevelopmental compartment absolutely requiring endogenous Hsd17b7 function, which, when absent, results in the severe phenotypes we describe here.
Perhaps the most intriguing aspect of this study is that it is the first in vivo validation of several recent studies suggesting an intracellular role for sterols in SHH signaling [8], [11], [12]. These cholesterol intermediates have been demonstrated to have a role in the transduction of HH ligand signaling as well as the subcellular localization of HH signaling components. The fact that the mutant MEFs demonstrate normal Smoothened localization to the cilium, but a compromised response to SHH ligand, suggests that normal sterol concentrations are required for proper activation of Smoothened [6]. Alternatively, an inhibitory sterol may be present at increased levels, preventing the activation of the pathway.
Treatment with statins is a well-accepted method for lowering cholesterol levels in human patients by inhibiting HMG-CoA reductase (Hmgcr), the rate-limiting step in cholesterol biosynthesis. Statin treatment could also have significant effects on local concentrations of oxysterols generated from intermediates in the cholesterol biosynthetic pathway further downstream. Mouse Hmgcr mutants, which should genetically mimic a total block in the pathway at the site of action of statins, are not viable past implantation [56] and are therefore not informative in this regard. It will be important to study further the role of cholesterol intermediates and metabolites in various physiological settings and signaling paradigms. In doing so, we may find that statin treatments may be having unintended consequences in human health in sites of adult Hh activity, including adult neurogenesis.
Rudolph mice were originally generated by ENU mutagenesis of A/J mice and then outcrossed to FVB/J mice (both obtained from Jackson Labs, Bar Harbor, ME). Initial mapping was done with a whole genome SNP panel similar to one we previously described [17], and the mutation mapped to a 19.6 Mb interval on chromosome 1. Exon directed sequencing (including some flanking intronic sequence to identify mutations potentially affecting splicing) identified the rudolph mutation. Genotyping is done with either D1Mit454 or D1Mit524 microsatellite markers depending on strains involved. We maintained the colony with a combination of intercrossing and outcrossing to FVB. The C57BL/6J Ptc1-lacZ mouse was obtained from the Jackson laboratory and intercrossed with rud heterozygous mice;Ptc1-lacZ genotyping was done with standard lacZ primers. We have also performed backcrosses of the rud allele to mice on C57BL/6J and 129X1/SvJ backgrounds. All animals were housed in accordance with the Harvard Medical School ARCM regulations. Timed matings were checked for signs of copulation in the morning; vaginal plugs were noted and noon of that day was established as embryonic day (E) 0.5.
Embryos used for histological analysis were fixed with Bouin's fixative for at least forty-eight hours and processed for paraffin embedding using a Leica TP1020 automated tissue processor. Sections were cut at a thickness of 14 µm and stained with hematoxylin and eosin using standard techniques. Microscopy was done with a Leica DC500 or Zeiss AxioImager with ApoTome. TUNEL assay was performed with the In Situ Cell Detection Kit, TMR Red, following the manufacturer's instructions (Roche). BrdU labeling was done with a BrdU Labeling and Injection Kit (Roche). The TuJI antibody (SIGMA) was used at 1∶500 for 2 hours at room temperature on paraffin sections with citrate buffer antigen retrieval. Neural tube immunohistochemistry was performed using standard methods with antibodies from the Developmental Studies Hybridoma Bank.
To measure the size of the skeletal elements, embryos were stained for cartilage and bone using standard methods [57] and photographed. The length of each element was calculated using NIH Image J software, and units were converted to mm using standards.
Mouse embryonic fibroblasts were generated using standard methods and plated at a density of 20,000 cells/cm2 in the presence or absence of 200 ng/mL SHH amino terminal peptide (R&D Systems). Cell number was determined with the CyQuant Cell Proliferation Assay (Invitrogen), and β-galactosidase production was measured with the Galacto-Light Plus System (Applied Biosystems). Assays were performed 48 hours after plating. Cell growth experiments were done with an initial culture of 6,000 cells in a 96-well plate.
Lentiviral particles were made via transfection of 293T cells with plasmids including a plKO.1 control and a validated RNAi construct against mouse Hsd17b7 (Open Biosystems, Huntsville, AL; clone TRCN0000041646). PzP53Med cells [20] were infected with 293T supernatant containing lentivirus. After puromycin selection, resistant cells were plated at clonal density and individual clones were isolated, maintained and analyzed with qRT-PCR for Hsd17b7 levels. Control and knock-down clones were treated with SHH protein as described above.
Total RNA from either brains or MEF cultures was prepared with TRIZOL (Invitrogen) and cDNA was made with qScript cDNA synthesis kit (Quanta) or the SuperScript RTIII system (Invitrogen). Hsd17b7 transcripts were analyzed with both random hexamer primed cDNA and gene specific primed cDNA synthesis (primer: TTTTGGTACCTCAGCTCGGGTGATCCGATTTCTG). Hsd17b7 transcripts were analyzed with primers amplifying exons 6–8 (F: TCTGTATTCCAGTGTGATGTGC; R: CTTTTGGCCCGTGACGTAAT; 259 bp) or exons 6–7 (F: TCTGTATTCCAGTGTGATGTGC; R: CCACATTATGGGTAGGAGCAA ; 100 bp). Quantitative RT-PCR was done on a BioRad iCycler using either total RNA with Taqman probes (Applied BioSystems) or cDNA with Perfecta SYBR Green SuperMix (Quanta). SYBR-GREEN probes used were: Ptc-F (CCTGCAAACCATGTTCCAGTT ), Ptc-R (TCGTAGCCCCTGAAGTGTTCA) Gli1-F (CCAAGCCAACTTTATGTCAGGG), Gli1-R (AGCCCGCTTCTTTGTTAATTTGA), Gapdh-F (ACTCCACTCACGGCAAATTC), and Gapdh-R (TCTCCATGGTGGTGAAGACA).
Section mount in situ hybridization was done as previously described [58] with hybridization at 60 degrees and with BM Purple (Roche) for visualization of riboprobes. Probes used are published: Gli1 [59] and Ptc [60]. Embryos were stained with lacZ using standard protocols [57] and then processed for paraffin histology as described above. Older embryos were fixed in 4% paraformaldehyde, cryoembedded in OCT, sectioned at 20 µm and stained on slides.
Embryos were homogenized in 1% SDS Lysis Buffer and protein extracts were run on a 10% polyacrylamide gel. A rabbit polyclonal antibody was used for Hsd17b7 (1∶1000, overnight at 4 degrees C), and a mouse monoclonal anti-actin antibody (1∶5000, SIGMA, 60 minutes at room temperature) was used as a loading control.
Full-length mouse Hsd17b7 from wild-type and mutant tissue was initially cloned into a pENTR/D/TOPO vector (Invitrogen) and then into the pcDNA-DEST47 vector (Invitrogen). DNA for either Hsd17b7-GFP or rud-GFP was co-transfected with Sec61β-mCherry (gift of T. Kirchhausen) into NIH3T3 cells using Fugene (Roche) following manufacturer's instructions. 48 hours after transfection, cells were fixed with 4% paraformaldehyde and counter-stained with DAPI. SMO localization within ciliated MEFs was observed by transfection of 20–30% confluent MEFs plated on 0.4% gelatin or poly-lysine coated coverslips in 24-well plates with the pBabePuro-A1∶Smo∶GFP plasmid ([9], gift of A. McMahon). Confluent cells were treated overnight with SHH peptide 48 hours after transfection. Immunocytochemistry was done by fixing with 4% paraformaldehyde/0.2 TritonX-100 for 20 minutes and blocking with 1% BSA for 60 minutes. Antibodies used were acetylated α-tubulin (SIGMA) at 1∶2000 for 60 minutes at room temperature and Goat anti-mouse Alexa Flour 594 (Invitrogen) at 1∶500 for 30 minutes at room temperature. Cells were mounted with VectaShield (Vector Laboratories) and imaged on a Zeiss AxioImager.
Sterols were extracted from brain tissue as previously described with the addition of sterol-specific ions for the compounds of interest to this study [61]. Sterol levels are reported as a fraction of total sterols.
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10.1371/journal.pntd.0003638 | Sensitivity and Specificity of a Novel Classifier for the Early Diagnosis of Dengue | Dengue is the commonest arboviral disease of humans. An early and accurate diagnosis of dengue can support clinical management, surveillance and disease control and is central to achieving the World Health Organisation target of a 50% reduction in dengue case mortality by 2020.
5729 children with fever of <72hrs duration were enrolled into this multicenter prospective study in southern Vietnam between 2010-2012. A composite of gold standard diagnostic tests identified 1692 dengue cases. Using statistical methods, a novel Early Dengue Classifier (EDC) was developed that used patient age, white blood cell count and platelet count to discriminate dengue cases from non-dengue cases.
The EDC had a sensitivity of 74.8% (95%CI: 73.0-76.8%) and specificity of 76.3% (95%CI: 75.2-77.6%) for the diagnosis of dengue. As an adjunctive test alongside NS1 rapid testing, sensitivity of the composite test was 91.6% (95%CI: 90.4-92.9%).
We demonstrate that the early diagnosis of dengue can be enhanced beyond the current standard of care using a simple evidence-based algorithm. The results should support patient management and clinical trials of specific therapies.
| Dengue is a very common acute infectious disease in the tropical world. Health care professionals are able to better care for dengue patients if they can make an early diagnosis and make a plan for case management. This current study investigated fever in 5729 children in Vietnam with 3 days or less of fever and identified 1692 dengue cases using advanced, gold standard methods. We systematically collected a range of medical and laboratory findings on each patient when they entered the study and used statistical tools to determine if these medical and laboratory findings could enable early diagnosis, independent of sophisticated, gold-standard laboratory tests. Our results, called the Early Dengue Classifier, had performance characteristics suggesting it could improve the diagnostic proficiency of health care professionals. However the performance of the Early Dengue Classifier is not perfect and likely will not change the practice of experienced doctors in dengue endemic settings. Our study highlights the need for 2nd generation, easy-to-use rapid diagnostic tests that can accurately diagnose dengue in the first few days of fever.
| Dengue is an acute, systemic viral infection and a public health problem in the tropical world [1]. The etiological agents of dengue are any of the four dengue viruses (DENV-1-4). In endemic countries it is common for all four DENV serotypes to co-circulate. Late-stage trials of a dengue vaccine with intermediate efficacy have recently been reported, offering hope of a public health intervention [2, 3].
The World Health Organisation (WHO) has a stated goal of reducing global dengue mortality by 50% by 2020 [1]. Improvements in case diagnosis and management will be central to achieving this aim. Significant loss of intravascular plasma volume leading to hypovolemic shock (dengue shock syndrome (DSS)), usually between the 4th-6th day of illness, is the commonest life-threatening complication of dengue [1, 4]. It’s widely held that the case-incidence of DSS can be reduced via careful monitoring and the judicious use of parenteral fluids to maintain an adequate intravascular volume [1]. Ideally, this case management approach is enabled because the attending physician had made an early diagnosis and thus alerted clinicians, nurses and family caregivers to the signs and symptoms suggestive of clinical worsening. Additional benefits of an early diagnosis include support to community level public health interventions and improvements in the sensitivity of case surveillance systems and disease burden estimates. Furthermore, it is likely that the therapeutic window of opportunity for a dengue antiviral drug lies in the first 48–72 hours of illness [5]. Thus, programmatic use of therapeutic interventions in the future will likely go hand in hand with strategies for early diagnosis.
Yet there are numerous challenges for busy primary care clinicians in making a diagnosis of dengue in the first few days of illness. Rapid lateral flow tests, based on the detection of the viral NS1 antigen, are available in some settings and can provide a confirmatory diagnosis [6–8]. The diagnostic performance of the WHO dengue case definition, which relies on non-specific signs and symptoms that overlap with other infectious diseases, is unknown in the first few days of illness [1]. Potts et al concluded that more prospective studies were needed to construct a valid and generalizable algorithm to guide the differential diagnosis of dengue in endemic countries [9]. To this end, several prospective studies have described the creation of classifiers for the diagnosis of dengue [10–12]. However none of these studies have exclusively focused on pediatric fever cases presenting to primary care facilities with short illness histories, a very common scenario in dengue endemic settings. Against this backdrop, the purpose of the current study was to prospectively derive a dengue diagnostic algorithm from routinely collected clinical and laboratory findings in pediatric patients with <72 hours of illness history and compare this approach against the diagnostic performance of a leading NS1 rapid test (BioRad NS1 Ag STRIP) in the same patients. The results provide pragmatic methods to enhance the early diagnosis of dengue in primary care settings.
The study protocol was approved by the Hospital for Tropical Diseases scientific and ethical committee and the Oxford University Tropical Research Ethical Committee (OXTREC 35–10). The accompanying parent/guardian of each child provided written informed consent.
Recruitment occurred in the public sector outpatient departments of Children’s Hospital No. 1 (HCMC), Children’s Hospital No. 2 (HCMC), The Hospital for Tropical Diseases (HCMC), Tien Giang Provincial Hospital, Dong Nai Children’s Hospital, Binh Duong Provincial Hospital and Long An Provincial Hospital. These outpatient departments function as primary care providers to their local communities. A patient presenting to one of the study sites was eligible for enrolment if they met the following inclusion criteria—a) fever at presentation (or history of fever) and less than 72 hours of symptom history, b) in the attending physicians opinion dengue was a possible diagnosis, c) 1–15 years of age inclusive, d) accompanying family member or guardian had a mobile phone and e) written informed consent for the child to participate was provided by the parent/guardian. Patients were excluded if- a) the attending physician believed they were unlikely to be able to attend follow-up or b) the attending physician believed another (non-dengue) diagnosis was more likely. Patient enrolment occurred consecutively during normal clinical hours on weekdays without restriction. All patients were enrolled into the study before the attending physician received the results of any routine laboratory tests.
At the time of enrolment, information on the patient’s age, sex, illness history, presenting signs and symptoms were recorded in a case report form. The definitions used to support standardized data capture are shown in S1 Table. Blood samples were drawn for routine hematology, biochemistry and NS1 rapid test. All NS1 rapid tests (NS1 Ag STRIP, BioRad) were performed on the same day of patient enrolment by one of two trained laboratory technicians at the Hospital for Tropical Diseases. Routine hematology results, but not biochemistry or NS1 rapid test results, were made available to the attending physician, who decided on the management approach, i.e. hospitalization or ambulatory follow-up.
A 2nd blood sample for the purposes of serology was collected around the time of defervescence from all patients that were hospitalized anytime during their acute illness. If the patient was managed solely on an ambulatory basis for the duration of their illness, then a 2nd early convalescence blood sample for the purposes of serology was collected only from a randomly selected 10% of this patient population. The randomization code to select ambulatory cases for follow-up was generated by software. All clinical and laboratory data were stored in an ICH-GCP compliant, clinical data management platform called “CLIRES”. Demographic and clinical data were double entered. Electronic data files containing hematological results were uploaded directly to CLIRES. Independent study monitoring was performed by the Clinical Trials Unit of the Oxford University Clinical Research Unit which examined adherence to the trial procedures, data collection and recording and compliance with ICH-GCP.
The gold standard diagnostic result was a composite derived from three tests; RT-PCR, IgM serology and NS1 detection by ELISA. First, all enrolment plasma samples were tested with a validated, quantitative RT-PCR assay to detect DENV RNA [13]. Next, any enrolment plasma samples that were negative in the RT-PCR assay were tested using the Platelia Dengue NS1 Ag ELISA assay (BioRad) and scored according to the manufacturer's instructions. Samples with equivalent results were repeated and if still equivocal they were scored as negative. Next, IgM ELISA serology (Panbio, Brisbane, Australia) was performed according to the manufacturer's instructions for patients who had paired plasma samples (enrolment and early convalescence) and who were negative in both the DENV RT-PCR assay and Platelia Dengue NS1 ELISA. Any patient who was—a) DENV RT-PCR positive, b) NS1 ELISA positive, or c) had DENV IgM seroconversion in paired plasma samples, was classified as a laboratory-confirmed dengue case. IgM seroconversion was defined as a change in the MAC ELISA test result from negative to positive in paired plasma samples with the 2nd sample collected 6 or more days after illness onset and >2 days after the 1st sample.
Any patient who was DENV RT-PCR negative, NS1 ELISA negative and did not IgM seroconvert in paired plasma samples was classified as “not dengue”. Any patient who was DENV RT-PCR negative and NS1 ELISA negative at the time of enrolment, but did not have paired samples available for serology, was classified as a “presumptive not-dengue” case. For analysis, data from “not dengue” and “presumptive not-dengue” cases were pooled.
Plasma samples were enriched for proteins with molecular weight >100kDa using Amicon filtration units (Millipore). Briefly, 200μl of plasma was concentrated to ~30μl and then tested in the Platelia NS1 ELISA. All concentrated samples were tested in parallel with an aliquot of the original plasma samples and the filtrate (containing proteins with molecular weight <100kDa).
Logistic regression was used for the development of the diagnostic algorithm. A detailed assessment of the model assumptions of linearity and additivity was performed (S1 Text). All pre-defined candidate predictors listed in S1 Table and significant interaction terms were included in the full model. The model was then simplified using step-wise backwards selection using Akaike’s Information Criterion (AIC) and stability selection [14]. Alternative statistical models such as classification and regression trees (CART) and random forests (RF) were also investigated in order to find an optimal diagnostic algorithm [15, 16]. The performance of the model was assessed with respect to discrimination (receiver operating characteristic curves (ROCs) and area under the ROC curve (AUC)), calibration (calibration plots and calibration intercepts and slopes), and standard accuracy criteria of binary diagnostic tests (sensitivity, specificity, negative and positive predictive values). We selected the cut-off point to classify a patient as dengue positive at a predicted risk of dengue of ≥33.3%, corresponding to assuming that the “cost” of missing a true dengue patient is twice as large as the cost of a false-positive [17]. To avoid over-optimistic estimates of model accuracy and performance due to model derivation and evaluation on the same dataset, all accuracy measures were corrected for optimism by validation. Validation was performed for the whole model development process including variable selection. Two validation schemes were employed to mimic external validation:
The final logistic model was also presented as a nomogram for direct clinical use. All statistical analyses were performed using the statistical software R v3.1.1 (R foundation for statistical computing, Vienna, Austria) and its companion packages c060 version 0.2–3 (for stability selection), randomForest version 4.6–7 (for random forest) and rpart version 4.1–8 (for CART).
5729 children with fever of less than 72 hours were enrolled at one of the seven clinical study sites in southern Vietnam between October 2010 and December 2012. A summary of the patient screening, enrolment and diagnostic outcomes is shown in S1 Fig A total of 5707 patients were included in the analyses. 1692 (29.6%) participants had laboratory-confirmed dengue. The baseline characteristics of the dengue and non-dengue cases are shown in Table 1. Notably, dengue cases were older than non-dengue cases. All four DENV serotypes were detected; DENV-1 was the commonest serotype, followed by DENV-4, -2 and -3.
Enrolment plasma samples (n = 5707) were tested for the presence of NS1 by NS1 Ag Strip test in a blinded, real-time fashion. Against the composite gold-standard reference diagnostic result, the NS1 Ag Strip test had a sensitivity of 70.4% (95%CI: 68.2–72.6%), specificity of 99.2% (95%CI: 98.9–99.5%), positive predictive value (PPV) of 97.4% (95%CI: 96.3–98.2%), and negative predictive value (NPV) of 88.9% (95%CI: 87.9–89.8%) for the diagnosis of dengue (Table 2). There was a striking difference in diagnostic performance by serotype, with NS1 detection being less sensitive in DENV-2 infections irrespective of the serological response (primary vs secondary)(S2 Table). The detection of NS1 was strongly associated with the concentration of DENV RNA in the same plasma sample; the odds of NS1 detection increased by 1.8 (95%CI: 1.6–1.9) for each 10-fold higher DENV RNA concentration (Table 2). These data define the strengths and weaknesses of NS1 rapid testing; it is highly specific but is compromised by suboptimal sensitivity, especially for DENV-2 cases.
Volume enrichment of the plasma molecular weight fraction containing multimeric NS1 (>100,000kDa) was performed on plasma samples from 21 viremic dengue cases enrolled in this study. However, despite 5-10-fold concentration of plasma, this processing failed to materially improve the diagnostic yield, with only 1 of 11 samples changing their status from negative (original sample) to positive (concentrated sample) in the Platelia NS1 ELISA (S3 Table).
Multivariate logistic regression analyses of clinical, demographic and laboratory data from 5707 patients were performed to generate a practical dengue diagnostic classifier that could replace or augment NS1-based diagnosis in the first 72 hours of illness. The most parsimonious model, derived from stability selection, used the patient’s age, white cell count and platelet count at the time of enrolment to classify dengue from non-dengue cases (Table 3). Alternative approaches to feature selection yielded models with only slightly higher performance but relied on many more (more than ten) variables (S4 Table). The most parsimonious model, herein called the Early Dengue Classifier (EDC), had a sensitivity of 74.8% (95%CI: 73.0–76.8%), specificity of 76.3% (95%CI: 75.2–77.6%), positive predictive value of 57.1% (95%CI: 56.2–59.0%), and negative predictive value of 87.8% (95%CI: 86.8–88.5%) for correctly classifying dengue cases in the entire dataset at the pre-defined cut-off of 33.3%. Of note, this pre-defined cut-off reflecting clinical priorities was very close to the cut-off corresponding to the point on the ROC curve closest to the upper left corner (perfect model), which was 34.2% (Fig 1A). The area under the ROC curve (AUC) was 0.829 (Fig 1B) and the predicted risk of dengue agreed well with the observed risk (Fig 1C). The EDC had sensitivity of 72.9% (95% CI: 69.6–76.6%) for DENV1, 74.7% (95%CI: 71.0–79.7%) for DENV2, 68.4% (95%CI: 59.2–74.5%) for DENV3 and 78.2% (95%CI: 75.5–83.3%) for DENV4 infection. The overall performance characteristics of the EDC under temporal, leave-one-site-out validation or seasonality (rainy versus dry season), are summarized in S5 Table. These results suggest that, in settings where NS1 rapid tests are not routinely available, the EDC could assist primary care physicians in dengue diagnosis.
In settings where NS1 rapid tests are routinely used, the EDC can be combined with the NS1 rapid test as a composite test (classified as positive when either NS1 rapid test or EDC are positive, and classified as negative when both NS1 rapid test and EDC are negative). This composite test had sensitivity of 91.6% (95%CI: 90.4–92.9%) while the specificity was 75.7% (95%CI: 74.5–77.0%). Corresponding positive and negative predictive values were 61.7% (95%CI: 60.6–63.1%) and 95.5% (95%CI: 94.9–96.1%). If a higher specificity was desired, a higher cut-off value of the EDC could be used for the combined test instead, e.g. a cut-off of 50% would lead to a sensitivity of 86.0% (95%CI: 84.5–87.6%) and specificity of 89.6% (95%CI: 88.7–90.5%). These results imply that the EDC is useful in settings with and without NS1 rapid testing.
Fig 2 presents a nomogram of the EDC. The nomogram assigns points to all risk factors and translates the total point score to a predicted risk for dengue. For example, a 9-year-old patient with platelet count 100x103/mm3, and white blood cell count 5x103/mm3 has a total points score of 15+32+84 = 131, and the corresponding risk of dengue is about 70%. The predicted risk of dengue is larger than 33.3% so the patient would be classified as dengue positive. Alternatively, the EDC could be implemented as a smartphone app. The exact formula for the estimated risk of dengue (p) is given by the following logistic equation: logit(p) = 1.236 + 0.139*age (in years)– 0.254*white blood cell (in 103/mm3)– 0.006 *platelet (in 103/mm3).
The early and accurate diagnosis of dengue on the grounds of clinical signs and symptoms alone is problematic [9]. Physicians need better tools to assist in early diagnosis if the WHO ambition of a 50% reduction in global dengue mortality is to be achieved by 2020. This study characterized the performance of three diagnostic approaches; the NS1 rapid test, a stand-alone diagnostic classifier and the combination of NS1 rapid test and diagnostic classifier together. Our results highlight the utility of NS1 rapid tests for an early specific diagnosis, yet also remind that 2nd generation tests are needed with improved sensitivity. The diagnostic classifier described here could help guide diagnosis in endemic settings, or be used as an adjunct to help exclude dengue in patients returning a negative NS1 rapid test result.
There is a body of literature describing the performance of NS1 rapid tests for the diagnosis of dengue [6–8, 19–22]. This current study extends that literature in several ways. First, by virtue of the large sample size we demonstrate with high precision the differential sensitivity of the NS1 Ag STRIP for different DENV serotypes. This test was sensitive (between 75–85%) for DENV-1, -3 and -4 infections, but poorly sensitive in DENV-2 infections (46.4%). Lower sensitivity was partially attributable to the great majority of DENV-2 infections being associated with secondary serological responses, although we note sensitivity was also low in primary DENV-2 infections. This suggests that there are particular virological (e.g. lower viral burdens in vivo) or intrinsic aspects of the NS1 test, that limit DENV-2 NS1 detection. [23–26]. Second, we make the novel observation that 5–10 fold enrichment of proteins with molecular weight >100kDa in plasma specimens (the NS1 hexamer has predicted molecular weight of 310kDa [27]) did not lead to improved NS1 detection rates. These data suggest that dengue patients who return negative NS1 rapid test results in the first 3 days of illness have free plasma NS1 concentrations substantially below the limit of sensitivity of existing assays and that 2nd generation tests might need to be at least an order of magnitude more sensitive. Nonetheless, better NS1 rapid diagnostic tests are needed if they are going to be widely adopted by clinical services in primary care settings. In malaria, HRP2 rapid diagnostic tests for Plasmodium falciparum infection are an example of how improvements to assay performance can lead to recognition as a diagnostic standard of care [28]. Finally, although serum NS1 concentrations have been proposed to have prognostic value in a small study, this is yet to be independently validated and is likely to be difficult given that blood NS1 concentrations vary widely according to the infecting DENV serotype, serological response and day of illness [8, 24, 29, 30].
Previous studies have described clinical and/or routine laboratory findings that distinguish patients with dengue from those with other febrile illnesses [12, 31–37]. What is striking in the literature is that only three prospective studies have considered dengue diagnostic algorithms exclusively in children and of these the largest contained 1227 patients, of who 614 had dengue [11, 38, 39]. More generally, most diagnostic studies failed to report positive and negative predictive values for their diagnostic algorithms, thus making it difficult to assess their utility in routine practice. Against this backdrop, a strength of the current study is the large sample size, the presence of all four DENV serotypes, robust statistical validation techniques and transparent performance characteristics. The clinical signs and symptoms that make up the WHO case definition for dengue were not used in the final, parsimonious diagnostic EDC classifier. Instead, we found that only three variables—patient age, white blood cell count and platelet count, provided similar discriminatory information as alternative models that relied upon a much larger set of clinical data.
The purpose of this study was to explore whether it was possible to develop any kind of simple, evidence-based algorithm for early diagnosis—the results demonstrate this feasibility, albeit the performance characteristics of the end-result algorithm are not so outstanding that they will result in widespread adoption or change the practice of experienced clinicians. We concur with Potts et al in the belief that diagnostic rules for dengue are not a replacement for good clinical acumen and management [9]. Nonetheless, the EDC described here offers an evidence-based guide that can likely improve the prevailing diagnostic accuracy of most Vietnamese physicians working in primary care who do not possess extensive experience in dengue diagnosis and management. In particular, in settings where NS1 rapid tests are not routinely available or affordable, or where DENV-2 is the most prevalent virus in circulation, the EDC could help guide clinicians in making their differential diagnosis. An early diagnosis of dengue can assist in patient triage and management by directing clinical/caregiver attention to clinical warning signs and/or the appearance of capillary permeability, for which supportive oral and/or parenteral fluid therapy is recommended in order to prevent circulatory compromise. Additionally, in the first days of illness many dengue cases are infectious to Aedes aegypti mosquitoes and hence an early diagnosis could support measures to prevent further transmission, e.g. by use of topical repellents and local mosquito control [40].
Our study has several design features and limitations that might preclude its wider generalizability. The EDC relies on routine hematology findings that are commonly accessible in primary care settings in Vietnam but might not be available everywhere. By design, our study focused on patients with <72 hours of illness and hence our results might not be applicable to patients who present to medical care at later time-points. By using the age of the patient as a component of the EDC, it’s likely that the EDC would not perform well in settings where the burden of dengue falls on age-groups different from that in southern Vietnam. Nonetheless, this study has delivered the largest population-based and quantitative framework to guide early diagnosis of pediatric dengue. Further prospective validation in Vietnam and other endemic countries with similar epidemiology will be needed to establish the clinical utility of the EDC.
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10.1371/journal.ppat.1004974 | A Comprehensive Analysis of Replicating Merkel Cell Polyomavirus Genomes Delineates the Viral Transcription Program and Suggests a Role for mcv-miR-M1 in Episomal Persistence | Merkel cell polyomavirus (MCPyV) is considered the etiological agent of Merkel cell carcinoma and persists asymptomatically in the majority of its healthy hosts. Largely due to the lack of appropriate model systems, the mechanisms of viral replication and MCPyV persistence remain poorly understood. Using a semi-permissive replication system, we here report a comprehensive analysis of the role of the MCPyV-encoded microRNA (miRNA) mcv-miR-M1 during short and long-term replication of authentic MCPyV episomes. We demonstrate that cells harboring intact episomes express high levels of the viral miRNA, and that expression of mcv-miR-M1 limits DNA replication. Furthermore, we present RACE, RNA-seq and ChIP-seq studies which allow insight in the viral transcription program and mechanisms of miRNA expression. While our data suggest that mcv-miR-M1 can be expressed from canonical late strand transcripts, we also present evidence for the existence of an independent miRNA promoter that is embedded within early strand coding sequences. We also report that MCPyV genomes can establish episomal persistence in a small number of cells for several months, a time period during which viral DNA as well as LT-Ag and viral miRNA expression can be detected via western blotting, FISH, qPCR and southern blot analyses. Strikingly, despite enhanced replication in short term DNA replication assays, a mutant unable to express the viral miRNA was severely limited in its ability to establish long-term persistence. Our data suggest that MCPyV may have evolved strategies to enter a non- or low level vegetative stage of infection which could aid the virus in establishing and maintaining a lifelong persistence.
| MCPyV is a recently discovered human polyomavirus that is likely to cause the majority of cases of Merkel cell carcinoma (MCC), a rare but highly aggressive skin cancer. While much research has been focused on understanding transforming functions of MCPyV gene products, owing to the lack of fully permissive replication systems, the natural lifecycle of the virus is poorly understood. Using high-throughput analyses, here we have interrogated a semi-permissive replication system to study the viral transcription program and elucidate the functions of the viral microRNA (miRNA) mcv-miR-M1. We find that, similar to other polyomavirus miRNAs, mcv-miR-M1 has the ability to negatively regulate expression of viral gene products required for viral DNA replication. Unexpectedly, however, we also observe that mcv-miR-M1 augments long-term episomal persistence of MCPyV genomes. Given that MCPyV establishes persistent infections in the majority of healthy human adults, our observations shed new light on the mechanisms that may be employed by this tumor virus to mount a lifelong chronic infection of its host.
| Merkel cell polyomavirus (MCPyV), first identified in 2008 in tissue from Merkel cell carcinoma (MCC) [1], is the only human polyomavirus considered to be the etiological agent of tumors arising in its natural host. Several lines of evidence, including frequent detection of monoclonally integrated sequences bearing hallmark mutations and constitutive expression of T antigens in tumor tissues suggest that the virus is causally linked to MCC pathogenesis (reviewed in [2]). Epidemiological studies suggest that MCPyV infection occurs in childhood and persists for life in the majority of the adult healthy population [3–7]. Hence, the occurrence of MCC is an extremely rare complication of MCPyV infection.
Like all polyomaviruses, MCPyV encodes the early large and small T antigens (LT- and sT-Ag), as well as the late structural antigens VP1 and VP2 [1, 8]. Whether MCPyV also expresses a functional VP3 antigen remains a matter of debate [9]. Polyomavirus T antigens are produced via alternative splicing from a single gene cassette that is transcribed early during infection. In MCPyV, alternative splicing of early transcripts additionally produces a 57K T antigen of hitherto unknown function [8]. A recent study furthermore revealed the existence of an alternative open reading frame (ALTO) which can be produced by leaky scanning of T-Ag encoding transcripts [10]. Although ALTO shares certain sequence features with the middle T antigens (mT-Ag) of other polyomaviruses, its precise functions remain unknown. Experimental evidence suggests that presence or absence of ALTO does not affect viral DNA replication [10].
In addition to above protein products, MCPyV has been found to encode a single microRNA (miRNA) precursor which can produce two mature miRNAs, termed mcv-miR-M1-5p and -3p [11]. miRNAs are small (~22 nt.), non-coding RNAs that can be produced from primary transcripts via sequential processing by the nucleases Dicer and Drosha [12]. After incorporation into the RNA-induced silencing complex (RISC), mature miRNA can negatively regulate the expression of transcripts that are recognized via sequence complementarity. In animals, target site recognition is primarily guided by perfect Watson-Crick pairing of the so-called seed sequence (nucleotides 2–8) of the miRNA, whereas the distal sequences typically only exhibit poor sequence complementarity [13]. This partial pairing leads to translational inhibition of the mRNA (although frequently a modest reduction in overall transcript levels can also be observed). Although rarely seen for animal miRNAs, plant miRNAs as well as miRNAs encoded by some animal viruses can also bind to their targets with perfect complementarity, resulting in RISC-mediated endonucleolytic cleavage of the mRNA.
Recent studies have shown that a number of human and animal polyomaviruses encode miRNAs [11, 14–19]. Although their precise genomic location varies, all known polyomavirus miRNA are expressed from sequences that are located in antisense orientation to the early T antigen encoding transcripts. Consequently, mature miRNA species expressed from these loci exhibit perfect complementarity to early transcripts, and a number of studies suggest that all hitherto identified polyomavirus miRNAs share the ability to negatively regulate expression of early gene products [11, 15–17, 19, 20]. For most of the known PyV miRNAs (including MCPyV), experimental evidence for the above is limited to ectopic heterologous reporter systems. However, miRNA-knockout viruses have been generated for SV40, murine PyV and BKPyV, and in vitro studies using such viruses demonstrated that the viral miRNAs are indeed able to efficiently limit LT-Ag expression as well as DNA replication in the context of authentic episomes [15, 16, 20]. So far, experimental in vivo infections with miRNA-deficient viruses have only been performed for SV40 and murine PyV [15, 21]. Indeed, miRNA-deficient SV40 mutants produce consistently higher viral DNA loads in both liver and kidney of infected syrian golden hamsters when compared to wt viruses. However, both wt and mutant viruses were able to establish persistent infections, and thus far only limited evidence for increased clearance of miRNA-mutants has been observed [21]. In the case of murine PyV, the kinetics of both infection establishment as well as subsequent viral clearance in experimentally inoculated mice were comparable between wt and mutant viruses, indicating that (at least under the experimental conditions used) murine PyV miRNA expression is not essential for the infection of mice [15]. The above therefore suggests that the role of PyV miRNAs during natural infection may involve aspects of acquisition, spread or persistence which are not properly recapitulated by the experimental in vivo systems used. Hence, while evolutionary conservation suggests important function for miRNA-mediated autoregulation of LT-Ag expression and DNA replication, the precise selectional advantage conferred by this regulatory mechanism remains unclear [22–25].
The molecular mechanisms that lead to polyomavirus miRNA expression thus far have not been studied in much detail. Circumstantial evidence, however, suggests that at least in some polyomaviruses transcriptional read-through beyond weak late strand polyadenylation signals can generate primary RNA molecules that traverse the miRNA precursor sequences [15–17]. In such a model, miRNA expression is coupled to expression of coding transcripts that originate from the late promoter in the non-coding control region (NCCR). Indeed, a recent study of BK polyomavirus (BKPyV) has demonstrated that NCCR rearrangements which naturally arise in patients suffering from BKPyV-associated disease result in decreased late strand transcription and miRNA expression [20]. In contrast, archetype viruses express robust levels of the viral miRNA, which in turn dampens T antigen expression and viral replication. As the archetype virus is thought to be responsible for establishment of persistent urinary tract infections, these findings suggest that, similar to herpesviruses, polyomaviruses may employ miRNAs to facilitate chronic infection of their host [20, 26]. Whether similar mechanisms as the above may dictate viral miRNA expression in MCPyV, a virus that is only distantly related to BKPyV, has thus far not been elucidated.
Given its association with human tumors, experimental research on MCPyV thus far has been largely focused on growth promoting and transforming functions of early viral gene products. In contrast, there is a profound lack of knowledge regarding the natural life cycle of the virus. In large part, this is due to the fact that all currently available in vitro systems produce only very low titers of viral progeny [27–30]. Although recent evidence suggests that MCPyV may persist in the hematopoietic compartment [31–33], it is unknown which type of cell may support viral replication and/or serve as a reservoir for persistent infection in vivo. It is therefore also unclear whether the low transmissibility observed in vitro reflects an inherent property of the virus (e.g., similar to what is observed for archetype BKPyV) or simply results from the lack of appropriate cell culture systems.
In addition to (and partially as a result of) the above deficits, there is only very limited knowledge regarding the MCPyV transcription program. Thus far, experimental studies addressing this subject have mainly employed subgenomic MCPyV fragments under the control of heterologous promoters to study expression and processing of the viral miRNA, or to explore the structure and coding potential of early region transcripts [8, 11]. Additionally, endogenous expression of early gene products and the viral miRNA has been investigated in MCC-derived cell lines (MCCL) or MCC tissues [11, 19, 34, 35]. These studies have shown that the defective viral genomes integrated in MCC constitutively express proteins encoded by the early region, but only produce the viral miRNA at low levels. Thus, it remains unknown whether intact episomal MCPyV genomes express the miRNA at levels which permit efficient autoregulation of LT-Ag expression and viral DNA replication.
We have previously established a semi-permissive replication system which is based on synthetic MCPyV genomes (MCVSyn) that are 100% identical to prototypical field strain sequences [27]. After transfection, viral genomes undergo active DNA replication, express early and late antigens, and produce infectious progeny (albeit at very low titers). Here, we have used the above system to study the viral transcription program and elucidate expression mechanisms and functions of the viral miRNA during short and long term culture of cells harboring actively replicating episomes.
Ectopic expression of a select number of computationally predicted pre-miRNA candidates previously identified a single pre-miRNA hairpin (termed mcv-miR-M1) encoded by MCPyV [11], located in an antisense orientation to the early coding region at genomic coordinates 1168 to 1251 (Fig 1A). While low-level expression of mature miRNAs from this genomic locus has been confirmed in primary MCC tissues [34], an unbiased investigation of small RNAs produced from intact and replicating viral episomes had previously not been performed. We therefore sought to determine i) whether the previously identified miRNA mcv-miR-M1 is expressed at significant levels by replicating episomes, ii) whether mcv-miR-M1 is the only miRNA expressed by authentic MCPyV genomes and iii) whether mature mcv-miR-M1 moieties may undergo differential processing in MCC-derived cell lines (MCCL). The latter point was of particular interest given that a recent study reported MCC-derived mature 5p miRNAs that differed from those described by Seo et al. in a 2 nt. shift, resulting in an altered seed sequence and a therefore a differential set of predicted cellular target transcripts [11, 34].
To investigate above questions, we transfected the neuroectodermal tumor cell line PFSK-1 cells with MCVSyn, a viral genome that is 100% identical to prototypical field strain sequences [27]. Small RNA moieties were harvested after 4 days of transfection and subjected to high throughput sequencing using the NEBNext library preparation protocol. As shown in Fig 1A, the great majority (99.5%) of all MCVSyn-derived small RNAs mapped to the previously identified mcv-miR-M1 locus. The remaining reads were randomly scattered across the viral genome (not visible at the scale shown in Fig 1A; see S1 Dataset for complete coverage data), suggesting they represent random mRNA breakdown products. Hence, the previously identified mcv-miR-M1 is the only miRNA expressed by actively replicating MCPyV genomes.
Comparison of viral and human miRNA read counts suggests that mcv-miR-M1-derived miRNAs are highly expressed in MCVSyn-transfected PFSK-1 cells, accounting for approximately 3% of the total of 19.3 million mature miRNA reads (Table 1). Mature miRNAs derived from the 5'-arm of the pre-miRNA hairpin were approximately twofold more abundant than those derived from the 3'-arm (mcv-miRs-M1-5p and -3p, respectively, in Table 1). Even though transfection efficiencies achieved with re-circularized genomes were generally below 5%, mcv-miRs-M1-5p and -3p were the 7th and 14th most highly expressed miRNAs, respectively, amongst all mature miRNAs detected in PFSK-1:MCVSyn cultures (Table 2 and S2 Dataset).
As shown in Fig 1B, in perfect accord with the original findings by Seo et al. we find that the majority of mcv-miR-M1-5p reads are derived from nucleotides 16–37 of the pre-miRNA hairpin. The seed sequence (GGAAGAA) in this miRNA extends from nucleotide 17–23 of the pre-miRNA (underlined in Fig 1B). Overall, mature miRNAs with this seed (referred to as 5p17-23 species in the following) accounted for greater than 94% of all 5p reads (left panel in Fig 1C, green bars, and S3 Dataset). We additionally detected alternatively processed mature 5p species (isomiRs) of lower abundance. Although the mature miRNA species identified by Lee and colleagues (seed sequence CUGGAAG, termed 5p15-21 in the following) in MCC tissues was the most prominent among these, its relative abundance accounted for only 5.5% of all 5p reads. To investigate whether these miRNA species may be more abundant in MCC-derived cells, we performed additional small RNA sequencing from the MCPyV-positive MCCLs WaGa and MKL-1. In both cell lines, the frequency of mcv-miR-M1-derived miRNAs was more than 3 orders of magnitude lower than in MCVSyn-transfected PFSK-1 cells (approx. 0.001% of all mature miRNA reads, see Table 1). However, the relative distribution of seed sequences was very similar to that seen in PFSK-1 MCVSyn cells, with the seed sequence observed by Lee being only marginally abundant at 10 to 14% (red and blue bars in the left panel of Fig 1C).
We hypothesized that a potential bias during library preparation might have been responsible for the discrepancies between our or Seo et al.'s results and those observed by Lee and colleagues. It is well documented that biases especially during the ligation step can result in a gross underrepresentation of individual miRNA species between different library preparation methods [36–40]. To formally investigate this possibility, we re-sequenced the same small RNA material using the standard Illumina TruSeq small RNA library preparation kit. Indeed, as shown in the right panel of Fig 1C this analysis primarily recovered mature miRNAs of type 5p15-21. Importantly, however, as in the first set of experiments, the observed seed distribution was similar between PFSK-1 MCVSyn, WaGa and MKL-1 cells, demonstrating that MCPyV miRNAs do not undergo differential processing in MCC-derived cell lines. Generally, normalized read counts for 5p15-21 miRNAs were comparable between the two library preparation methods while those for 5p17-23 species were about 100fold less abundant in the TruSeq experiments (S3 Dataset), suggesting that the observed differences in relative seed distributions were likely due to failure to retrieve 5p17-23 species during TruSeq library preparation.
As for mature 5p miRNAs, 3p miRNA species also showed a differential seed sequence distribution depending on whether libraries were prepared with NEBnext or TruSeq protocols (Fig 1D). Again, however, we observed no major differences between miRNA processing in MCCL or PFSK-1 MCVSyn cells. The most abundant 3p species in the NEB dataset mapped to nts. 51–72 of the mvc-mir-M1 hairpin (seed sequence 52–58: UGCUGGA, see Fig 1B and 1D), whereas in the TruSeq data, reads were more evenly distributed between this miRNA species and an isomiR offset by -1 nucleotide.
Regardless of their exact seed sequence, all mature miRNA species are perfectly complementary to transcripts originating from the opposite strand of the MCPyV genome. Consequently, provided they are efficiently incorporated into RISC, all species should be able to negatively regulate early transcripts. Indeed, a number of previous studies have suggested that the ability to autoregulate LT-Ag expression may represent an evolutionary conserved function of polyomavirus miRNAs [11, 15–17, 19, 20, 22–25]. In support of this notion, Seo and colleagues have formally demonstrated that ectopically expressed mcv-miR-M1 can negatively regulate luciferase expression of a chimeric reporter construct containing the mcv-mir-M1 complementary region and flanking sequences [11]. To determine whether mcv-mir-M1 also can suppress LT-Ag expression in the context of transcription from intact episomes, we generated a mutant MCPyV genome unable to express the viral miRNA. To this end, we introduced a total of 14 mutations designed to disrupt the mcv-mir-M1 pre-miRNA hairpin structure (Fig 2A) which is required for the processing of pre- and mature miRNAs by Drosha and Dicer, respectively. All nucleotide substitutions were designed such that the coding capacity of LT-Ag encoded on the opposite strand remained unaltered (S1 Fig). The resulting viral genome (referred to as MCVSyn-hpko in the following) or the parental MCVSyn genome was transfected into PFSK-1 cells. As shown in Fig 2B, parental MCVSyn genomes expressed viral miRNA moieties that were readily detectable by northern blotting at 4 days post infection. As expected, no mcv-mir-M1 pre- or mature miRNAs were produced in cells transfected with MCVSyn-hpko mutants. As shown in Fig 2C, the absence of viral miRNA expression resulted in substantially higher expression of LT-Ag on the protein level. To investigate whether elevated LT-Ag expression also affected the efficacy of viral DNA replication, we performed a DpnI resistance assay on HIRT extracts. As shown in the Southern Blots of Fig 2D, MCVSyn-hpko genomes indeed replicated to appreciably higher levels when compared to the wt genome. Collectively, the above data thus suggest that i) complementary target sites in full length LT-Ag transcripts are accessible to mcv-mir-M1 binding, ii) levels of mcv-mir-M1 expressed by replicating MCPyV genomes are sufficient to induce substantial downregulation of LT-Ag expression and iii) the extent of LT-Ag downregulation mediated by mcv-mir-M1 is sufficient to limit the replication of transfected MCPyV genomes.
The use of our semi-permissive replication system extended the possibility to perform an in depth analysis of transcripts expressed by intact viral episomes. In addition to providing valuable information about the structure of coding transcripts, we expected that such analyses would also provide clues with regard to the mechanisms that control viral miRNA expression. As MCPyV transcripts have thus far only been evaluated by Northern Blotting in cells transfected with early region expression cassettes driven by a heterologous CMV promoter [8], the location of transcriptional initiation and polyadenylation sites remains unknown. Since such sites are often difficult to capture in standard RNA-seq protocols due to the usually poor coverage of accurate 5'- and 3' transcript ends, we performed 5'- and 3'-RACE on RNA isolated from MCVSyn transfected PFSK-1 cells after 4 days of transfection.
Polyadenylation sites were determined with a conventional 3’RACE protocol, using gene specific 5'-primers for the distal coding regions of early and late transcripts together with anchored oligo dT 3'-primers (Fig 3A). Amplification products were subcloned in bulk, and between 16 and 26 (for early and late transcripts, respectively) randomly picked clones were subjected to Sanger sequencing. As shown in Fig 3A and 3B, 100% (16 of 16) clones derived from early transcripts terminated at position 3094, 14 nucleotides downstream of a canonical polyadenylation signal (AAUAAA) which overlaps with the T-Ag stop codon, and 9 nucleotides upstream of a GU-rich element (Fig 3B). The 3’-RACE products from late transcripts were more diverse: As shown in Fig 3A and 3C, 14 (53%) of the 26 clones terminated at position 2842 (pA site L1), 317 nucleotides downstream of the VP1 stop codon. Another 8 clones (31%) terminated in a distance of 451 from the VP1 stop codon at position 2708 (pA site L2). Canonical polyadenylation signals were observed immediately upstream of both cleavage sites (Fig 3C). Although U-rich regions are present 13 or 31 nucleotides downstream of pA sites L1 and L2, respectively, neither site exhibits a clearly discernible GU-rich element. The four remaining clones from late transcripts were predominantly found at A-rich regions of the viral genome, suggesting they had resulted from mispriming of the oligo dT primers to internal regions of viral transcripts extending into the early region. Together, these results suggested highly efficient termination of early transcripts, but relatively weak late polyadenylation signals that allow at least some transcriptional read-through.
For the determination of transcriptional initiation sites we employed Cap-dependent 5'-RACE, a protocol which greatly decreases the rate of false positives that result from degradation products and/or premature termination of reverse transcription. Gene-specific RT-PCR anchor primer sites for late transcripts were situated approximately 400 nucleotides downstream of the VP2 start codon (Fig 4A). To allow the detection of putative transcripts that may initiate near the recently identified ALTO reading frame [10], primers for early transcripts were designed to bind to a region in the second exon of the LT-Ag. As we expected that initiation sites may be more heterogeneous than polyadenylation sites, amplification products were analyzed by high throughput sequencing (HTS) instead of Sanger sequencing. After mapping of reads to the MCPyV genome, we counted the number of reads that initiated at a given nucleotide position. Only nucleotides which received at least 1% of the total reads were considered as potential transcriptional initiation sites.
As shown in Fig 4B and 4C, the great majority (93%) of the ~55.000 analyzed reads from early transcripts initiated between nucleotides 147–150 (TI-E1 in Fig 4B and 4C) with a marked peak at position 149 (56.7% of all reads). As shown in Fig 4C, a canonical TATA Box is present 26 nucleotides upstream of the major initiation site. A second, much weaker accumulation of reads (4%) was observed between nucleotides 112 and 120, with a peak at nucleotide position 115 (marked with an asterisk in Fig 4C), suggesting that a minority of early transcripts initiates upstream of the TATA box. Of note, approximately 10% of the corresponding reads exhibited a splice junction which fused nucleotide 141 to the previously identified splice acceptor of the second LT-Ag exon. This splice event generates a transcript in which the first AUG triplet is the start codon of the ALTO open reading frame, 49 nucleotides downstream of the transcript's 5'-end. While we have formally confirmed the existence of the junction by RT-PCR primers (S2 Fig, lane 3), whether or not such rare transcripts contribute to the production of ALTO remains to be established. The remainder of reads was randomly scattered across the viral genome, indicating they were derived from breakdown or premature RT termination products.
Consistent with the fact that the region between the origin of replication and VP2 lacks a canonical TATA box, we observed that late transcripts were derived from a broader initiation zone (termed TI-L1 in the following) located between nucleotides 5264 and 5222, with a total of 15 nucleotide positions accumulating at least 1% of the ~159.000 total reads (Fig 4B and upper panel in Fig 4D, S4 Dataset). The bulk of initiation sites (~72%) mapped to a C/T-rich region between nucleotides 5241 and 5250, with the major initiation site (35% of all reads) being located at position 5245, 127 basepairs upstream of the VP2 start codon. Interestingly, another 4.452 reads (2.8%) mapped to nucleotide position 1367, well outside of the NCCR (TI-L2 in Fig 4B and lower panel in Fig 4D). The observed initiation site is located 116 nucleotides upstream of the mcv-miR-M1 locus, suggesting the existence of miRNA-encoding transcripts that originate outside of the NCCR.
Given the observation of transcripts initiating upstream of the viral miRNA we sought to investigate whether mcv-miR-M1 could be expressed independently of NCCR-initiated transcription. For this purpose, we sub-cloned the entire early T-Ag coding region in either sense or antisense orientation downstream of an heterologous CMV promoter (pCMV:ER-S and –AS, respectively; see Fig 5A). As expected, forced transcription of the early region antisense strand gave rise to readily detectable pre- and mature miRNA moieties of mcv-miR-M1 (Fig 5B, left panel). However, similar levels of miRNA expression were observed when the CMV-promoter initiated transcription traversed the early region in the sense (i.e. T-Ag coding) orientation (Fig 5B, center panel). Indeed, a promoterless construct harboring the entire early region (pER) was likewise able to express the viral miRNA (Fig 5B, right panel), albeit at considerably (approx. 10 fold) lower levels than either CMV promoter-driven construct (see GAPDH-normalized stem-loop RT-qPCR data in Fig 5C; note that the Northern Blot in the right panel Fig 5B was exposed for longer time period than those shown for the pCMV constructs). While we presently cannot explain the seemingly disparate observation that strong miRNA expression was observed independent of the CMV promoter’s orientation relative to mcv-miR-M1, we suspect that CMV-promoter driven transcription through the locus may activate an intrinsic promoter. The fact that we had observed a transcriptional initiation site approx. 100 nt. upstream of the miRNA using a 5’-CAP dependent RACE protocol suggested that such transcripts are likely produced by RNA polymerase II. To investigate this assumption, we treated pER-transfected PFSK-1 cells with α-amanitin, a potent inhibitor of RNA-polymerase (RNA-pol) II, and investigated mcv-miR-M1 expression 24 hours later. As controls for RNA pol II and III transcribed RNAs, we additionally measured levels of GAPDH mRNA and tRNA-meth, respectively. As shown in Fig 5D, α-amanitin treatment strongly reduced expression of GAPDH and mcv-miR-M1, but not that of tRNA-meth. Hence, an intrinsic promoter activity within in the early region of MCPyV can lead to RNA pol II-dependent transcription of mcv-miR-M1.
If the region upstream of the mcv-miR-M1 locus exhibits promoter activity, then intact episome should exhibit an open chromatin conformation that permits transcriptional initiation at this position. To investigate this notion, we performed ChIP-seq experiments to evaluate patterns of the postranslational histone modification H3K4me3, a mark which is strongly enriched at transcriptional start sites. Additionally, we performed ChIP-seq to elucidate binding patterns of LT-Ag binding across the viral episome. For this purpose, we transfected PFSK-1 cells with MCVSyn and, 48 hours later, performed chromatin immunoprecipitation with antibodies specific for H3K4me3, or with the LT-Ag antibody CM2B4. An immunoprecipitation with IgG served as a negative control. The complete coverage data is given in S5 Dataset. As shown in the top panel of Fig 6B, the CM2B4 antibody produced a marked peak centered at the core origin of replication, consistent with the previously observed binding of LT-Ag to an array of GRGCC pentamers located in this region [41, 42]. No additional peaks were observed, indicating that, at least under the conditions used here, LT-Ag appears not to stringently bind to other loci on the viral episome. As expected, the NCCR of MCPyV was also highly enriched in the activation-associated histone mark H3K4me3 (Fig 6B, center panel). The H3K4me3 profile in this region presented as a broad peak which extended from the late to the early transcription initiation sites mapped during our 5’-RACE analysis. Indeed, consistent with our previous experiments that had suggested promoter activity of the region upstream of the viral miRNA locus, a second prominent H3K4me3-enriched zone was located within the T-Ag coding region. The summit of this peak mapped precisely to the transcriptional start site upstream of the viral miRNA locus identified during our 5’-RACE analysis. We next sought to determine whether mutation of upstream sequences would negatively affect miRNA expression. Given that the H3K4me3 enriched region is located in the early coding region, deletion of the putative promoter would also disrupt LT-Ag expression and thus abrogate the replication ability of MCVSyn episomes. However, we hypothesized that introduction of synonymous triplet mutations which preserve the LT-Ag coding capacity may be sufficient to ablate or reduce promoter activity. Accordingly, we generated a MCVSyn mutant (termed MCVSyn-pmt in the following) with a total of 68 triplet mutations in a ~200 bp region located 28 nt upstream of the miRNA locus (Fig 6C). Introduction of the same set of mutations in the context of early region-only construct pER (see Fig 5A) and subsequent transfection of the resulting plasmid pER-pmt into PFSK-1 cells confirmed that the mutations led to a significant reduction of NCCR-independent miRNA expression (S3 Fig).
To investigate the effect of the mutations on miRNA expression by full length genomes, MCVSyn-pmt or the parental MCVSyn construct were transfected into PFSK-1 cells, and levels of mature mcv-miR-M1-5p were evaluated 48h later by quantitative stem-loop PCR. The hairpin knockout mutant MCVSyn-hpko served as a negative control. As shown in Fig 6D, MCVSyn-pmt indeed expressed mcv-miR-M1-5p at significantly lower levels compared to the wildtype genome, albeit the residual expression levels (approx. 60%) were higher than those observed with the early region construct pER-pmt (approx. 25%; see S3 Fig).
To investigate the effect of the introduced mutations on the chromatin level, we additionally performed ChIP-seq analysis of PFSK-1 cells transfected with MCVSyn-pmt. As expected, neither LT-Ag binding to the viral origin nor H3K4me3 accumulation at the NCCR was affected in MCVSyn-pmt. However, in accord with the observed decrease in mcv-miR-M1-5p expression levels, the mutations resulted in the almost complete elimination of the H3K4me3 peak upstream of the miRNA locus. Collectively, the above data thus suggest that the genomic region upstream of mcv-miR-M1 exhibits promoter activity and can contribute to NCCR-independent expression of the viral miRNA in the context of replicating episomes. However, given that elimination of the H3K4m3 peak had reduced but not abrogated miRNA expression, we suspected that a considerable fraction of the viral miRNA may also be generated from late strand transcripts that originate from the NCCR and ignore the apparently weak late strand polyadenylation sites. To investigate this possibility, and to furthermore evaluate the overall structure of viral transcripts and the influence of the viral miRNA on transcript abundance, we proceeded to perform strand-specific mRNA-seq experiments.
To evaluate viral transcription patterns we transfected PFSK-1 cells with MCVSyn or the miRNA knockout MCVSyn-hpko and harvested mRNA after 4 days of transfection. Two independent rounds of transfection and sequencing were carried out for each the parental and mutant genomes. In Fig 7B and 7C, we present coverage plots which represent the accumulated/mean data of the replicates. S4 Fig shows the individual data plots for each of the replicates and demonstrates that both experiments produced near-identical results. Full coverage data are provided in S6 Dataset.
Consistent with the observation of a highly efficient early polyadenylation site we observed that the coverage of early transcripts exhibited a sharp decline towards the 3'-end of T-Ag coding sequences. As shown in the upper plot in Fig 7B and Table 3, in MCVSyn transfected cells greater than 99% of all reads from the early strand mapped to the region flanked by the transcriptional initiation and polyadenylation sites identified in our RACE analysis. In contrast, the coverage data for transcripts originating from the late strand were indicative of profound read-through beyond polyadenylation sites pA-L1 and-L2. While approx. 60% of late strand reads mapped to the region delineated by the initiation site TS-L1 and the late polyadenylation sites, the remaining 40% were derived from the antisense strand of the early region and the NCCR. As shown in Fig 7C, the overall late strand coverage profiles in MCVSyn-hpko transfected profiles were near identical to those observed in cells transfected with the parental genome. However, consistent with the observation that mcv-miR-M1 negatively regulates LT-Ag expression, the relative fraction of reads derived from early transcripts was considerably increased in MCVSyn-hpko transfected cells (90% in MCVSyn-hpko vs. 61% in MCVSyn transfected cells; see Table 3). Moreover, early strand coverage profiles in PFSK-1 MCVSyn-hpko cells showed increased coverage immediately downstream of the region antisense to mcv-miR-M1. This is consistent with the fact that the RNA-seq protocol captures polyadenylated RNAs and therefore selects for 3’-fragments of miRNA-cleaved transcripts. In contrast, 5’-cleavage products lack a polyA tail and thus are lost prior to library preparation, resulting in a relative decrease in read counts upstream of the cleavage site. Together, the above data thus suggest that viral MCPyV miRNAs negatively regulate T-Ag expression via cleavage of early strand transcripts.
Thus far, elucidation of viral splice patterns has been limited to evaluation of the ectopically expressed early region [8]. To investigate splice patterns of early and late transcripts expressed from full-length genomes, we analyzed the structure and frequency of spliced reads from our RNA-seq experiments. We considered such reads as evidence of an authentic splice event if i) the event was supported by at least two independent observations among the individual experiments, ii) the junction was consistently observed in both replicates of MCVSyn or MCVSyn-hpko transfections and iii) the splice sites exhibited the sequence features commonly observed at donor and acceptor sites. For each of the identified sites, we additionally calculated the number of unspliced reads to evaluate the efficiency with which the given site underwent splicing. In Table 4, we present the identified donor sites and junctions along with the read numbers and splice frequencies as calculated from the accumulated data from both datasets of MCVSyn or MCVSyn-hpko transfected cells. The structures of known or novel early and late transcripts are shown in Fig 7D and 7E, respectively. For transcripts mapping to the major early and late transcription cassettes, an estimation of relative abundance is shown after each transcript. S5 Fig shows the sequence context of known and the novel splice sites observed in this study.
The great majority of splice events among early transcript mapped to the junctions previously identified by Shuda and colleagues [8]. Together, these transcripts (T1 to T4 in Fig 7D) are estimated to account for approximately 95% of all early strand mRNAs. Additional splice junctions were detected only at low frequency. The corresponding putative mRNAs include transcripts (tentatively named T’5 through T’7), which are predicted to encode T antigens with estimated molecular weights of 9, 21 or 64 kDa. All of these protein products contain the first 93 amino acids shared by LT- and sT-Ag, but entirely or partially lack the sequences encoded by the second exon of LT-Ag. Similar low early region transcripts of low abundance have been previously observed in other polyomaviruses, but in most cases it is not clear whether their protein products are of biological significance [43–49].
While splice events that originated outside of the major early transcription cassette were infrequent, the majority of such events consisted of the junction already observed during our 5’-RACE analysis in the putative ALTO-encoding transcript (d141-a861 in Table 4, transcript T’8 in Fig 7E). Additionally, we observed a very rare splice event, which uses the same acceptor at position 861, but a donor upstream of the origin (d5355). Such transcripts may either be produced by upstream initiation events which were too infrequent to be picked up by our RACE analysis, or by occasional read through beyond the early region polyadenylation site. RT-PCR formally confirmed the existence of this rare splice event (S2 Fig, lane 1).
Among the late strand transcripts we detected a total of 5 splice junctions involving 2 donor and 3 acceptor sites (Table 4 and Fig 7E). Abundance estimation predicts that the majority of late messages are unspliced transcripts which encode VP2 (L1 in Fig 7E). Splicing from a donor at position 5145 to an acceptor 503 nt downstream (a5119) in approximately 10–15% of late strand transcripts generates a message in which the first AUG codon initiates the VP1 ORF (transcript L2). The same donor is joined to an alternative acceptor at position 5119 in another 2–4% of late transcripts (L3 in Fig 7E). This event leads to removal of an immediately upstream of the VP2 start codon, and the resulting L3 transcripts are thus predicted to code for VP2. We did not detect transcripts which splice to the start codon of the predicted VP3 ORF.
Interestingly, 4–5% of all splice events observed for the donor at position 5145 (Table 4) connect to an acceptor (a5308) which is located upstream of the late transcriptional start sites identified in our 5’-RACE analysis (LL in Fig 7E). This splice event consequently requires primary transcripts which traverse the entire episome, similar to the leader-to-leader splice observed in other polyomaviruses [50–55]. RT-PCR analysis with junction spanning primers indicates that multiple copies of the leader can be present at the 5’-end of late transcripts (S2 Fig, lane 7), indicating that the RNA polymerase can complete several rounds of transcription along the viral episome. As the leader sequence does not contain putative AUG start codons, its presence is not expected to alter the coding capacity of transcripts L1, L2 or L3.
We additionally detected another splice event which extended over the NCCR, joining a donor at downstream of the viral miRNA (d1142) to the acceptor at position 5308. While the existence of this splice was confirmed by RT-PCR (S2 Fig, lane 5), only ~5–13% of all reads traversing the donor are spliced (Table 4), and transcripts containing this splice (tentatively named L’4 in Fig 7E) are therefore expected to be rare. As such transcripts may originate from transcriptional read-through beyond late polyadenylation sites or from transcripts which are initiated upstream of the viral miRNA, their coding potential remains unknown.
The results described thus far demonstrate that mcv-miR-M1 efficiently suppresses early gene expression and viral DNA replication between two and four days post transfection. These time points were chosen because they guarantee robust genome amplification and allow readily detectable expression of viral genes. Interestingly, however, in an independent set of experiments we had repeatedly observed that, after transfection in a number of cell lines, MCPyV genomes remained detectable for several weeks or even months by Southern Blotting and qPCR. To confirm these findings in PFSK-1 cells, and to furthermore investigate influence of the viral miRNA expression on long term persistence of MCPyV genomes, we transfected PFSK-1 cells with MCVSyn or MCVSyn-hpko and monitored the resulting cultures for a period of at least 3 months. At regular intervals, we collected total DNA, small RNA and protein to evaluate relative MCPyV genome copy numbers as well as expression of mcv-miR-M1 and LT-Ag. Fig 8A shows relative genome copy numbers and viral miRNA expression of the wt MCVSyn episome as determined by qPCR or quantitative stem-loop RT-PCR, respectively. All values were normalized for genomic GAPDH locus copy numbers and are shown relative to the earliest sampled time point at d2 post transfection (set to 1). Consistent with the previous results from our short-term DNA replication assays, MCVSyn genomes exhibited an initial increase of relative copy numbers within the first ~10 days, which was followed by a steep decline over more than two orders of magnitude in the following two weeks. After this loss phase, however, MCVSyn copy numbers did not further decline, suggesting that viral genomes had entered a state of near-stable long term maintenance. Furthermore, temporal changes of viral miRNA expression levels closely mirrored changes in relative genome copy numbers, indicating the per-genome expression levels of mcv-miR-M1 remained stable over the course of the experiment. Interestingly, and contrary to what might have been expected based on increased DNA replication in short term assays (Fig 2D), MCVSyn-hpko genomes were unable to reach a state of stable maintenance. Whereas copy numbers of parental genomes remained stable even beyond a 6 month time point, the hairpin mutant was progressively lost from the cultures such that it became undetectable by day 105 (blue and red symbols, respectively, in Fig 8B). The qPCR results were furthermore confirmed by Southern Blotting of DpnI-resistant DNA (Fig 8C; note that owing to the lower sensitivity of these assays MCVSyn-hpko genomes become already undetectable at day 70).
Western Blot analysis of the bulk cultures confirmed the absence of LT antigen in MCVSyn-hpko transfected cells after the loss of genomic DNA (Fig 9A, lane 12). In contrast, LT antigen expression could be readily observed in MCVSyn transfected cultures even after more than 160 days (lane 11). To also analyze LT-Ag expression on the single cell level, we performed immunofluorescence analyses using the CM2B4 antibody. Fig 9B shows representative images from an early (4d) and several late time points of MCVSyn transfected cells. LT-Ag staining presented as distinct, strictly nuclear dots that are likely to represent foci of viral DNA replication. At 4 days post-transfection, we estimated the percentage of LT-Ag positive cells in both MCVSyn and MCVSyn-hpko transfected cultures to be approximately 2–3%. In accord with our qPCR and southern blot experiments, LT-Ag positive cells became approximately 100fold less frequent, but cells with multiple foci (albeit smaller than those observed after 4 days) remained clearly detectable for several months in MCVSyn cultures. Apart from the fact that LT-Ag positive cells were absent from late time points of MCVSyn-hpko-transfected cultures, we did not detect fundamental differences in the LT-Ag staining patterns between MCVSyn or MCVSyn-hpko transfected cells.
In Fig 8D and 8E, we show two independent repeats of our long term maintenance assays. Although parental MCVSyn genomes were less efficiently maintained in these experiments, the miRNA-deficient mutant consistently demonstrated an accelerated rate of loss and became undetectable at least four weeks earlier than the wt genome.
Although PFSK-1 cells produce only very low levels of infectious virus particles and do not allow efficient serial transmission [27], we considered it formally possible that the decreased long term persistence of MCVSyn-hpko genomes may reflect alterations in particle production. To directly investigate this scenario, we inspected viral genome copy numbers in total genomic DNA and DNaseI treated supernatants from freshly transfected PFSK1 cells (4d p.t.). In accord with our previous results, total copy numbers of viral genomes were higher in MCVSyn-hpko transfected cells (S6A Fig). However, there was no appreciable difference between DNaseI-resistant genome copy numbers in the supernatants of MCVSyn or MCVSyn-hpko transfected cells, indicating comparable levels of virion production. We additionally used freeze-thaw lysates from such cultures to inoculate fresh PFSK-1 cultures and measure the amount of nuclear viral DNA recovered after 4 or 8 days post-inoculation (S6B Fig). As expected, the overall amount of DNA recovered from infected cells was strongly reduced compared to levels observed in transfected input cultures. Again, however, we did not detect significant differences between cultures inoculated with lysates from MCVSyn or MCVSyn-hpko-transfected cells, suggesting that the differences observed in long-term maintenance assays are likely to be independent of potentially altered particle production levels.
Given the very low level of infectious viral particles produced in PFSK-1 cells, we hypothesized that long term persistence of MCVSyn genomes could either result from efficient episomal maintenance, or (similar to MCC-derived cell lines) reflect stable transmission of integrated genomes. To investigate these possibilities, we first established a FISH assay for MCPyV. As a control for the sensitivity and specificity of the assay, we analyzed the two MCPyV positive MCC cell lines MKL-1 and WaGa cells. As shown in Fig 10A, we detected a single distinct signal per cell in MKL-1 and two foci in WaGa cells, indicative of one or two integration events, respectively. In contrast, FISH analysis of the long-term PFSK:MCVSyn cultures shown in Figs 8A–8C and 9 detected a considerably larger number of foci per cell nucleus (Fig 10B; see S7 Fig for exemplary images taken at a lower magnification). In accord with our LT-Ag immunofluorescence assays, while approximately 2.5% of all cells were positive for MCVSyn at 4 days post transfection, the number of positive cells dropped over time and reached a steady state of ~0.01% at late time points. In agreement with the qPCR and Southern Blotting results, we were able to detect MCVSyn positive cells by FISH for more than 160 days (lower panel in Fig 10B).
While the observation of multiple nuclear foci of viral DNA argues against rare integration events being responsible for long-term maintenance, FISH analysis cannot provide definite proof of episomal persistence. To more directly address this issue, we therefore performed rolling circle amplification (RCA) for MCPyV DNA, a protocol which selectively amplifies circular templates and produces large concatameric DNA molecules [56]. In Fig 11, we present an RCA analysis of DNA isolated from PFSK-1: MCVSyn cultures (the same cultures as shown in Figs 8A–8C, 9 and 10B) at 4 or 136 days post transfection (lanes 3–4 and 9–10, respectively). Genomic DNA from the MCC-derived cell lines WaGa and MKL-1 (lanes 5–6 and 7–8, respectively) served as a control for cells harboring integrated viral genomes. Indeed, while no RCA products were observed in mock-transfected PFSK-1 cells or the two MCCL cultures, the material from early and late PFSK-1:MCVSyn cultures yielded efficiently amplified viral DNA.
Collectively, the data presented in Figs 8–11 thus suggest that MCPyV genomes are able to persist as extrachromosomal episomes for several months after transfection into PFSK-1 cells, and furthermore that a miRNA knockout mutant is considerably impaired in its ability to establish long term episomal maintenance.
In this study, we report an in-depth analysis of the MCPyV-encoded miRNA miR-M1 and its functions during short and long-term replication of intact viral episomes. Besides of confirming prior studies which had suggested that mcv-miR-M1 autoregulates T-Ag expression, we demonstrate that mcv-miR-M1 can be expressed independently of NCCR-initiated transcription and uncover an unexpected role for the viral miRNA in episomal persistence.
Our small RNA sequencing data suggest that replicating MCPyV genomes express the viral miRNA to very high levels. In our analysis of MCVSyn-transfected PFSK-1 bulk cultures, mature mcv-miR-M1 species ranked among the top 15 of all miRNAs. Owing to generally low transfection efficiencies achieved with re-circularized genomes, only ~2–5% of cells in such cultures carry the viral genome. Hence, it is likely that mcv-miR-M1 species dominate the spectrum of expressed miRNAs in MCVSyn-positive cells. In contrast, we find that mcv-miR-M1 is expressed at only very low levels in MCC-derived cell lines. The observed frequencies of viral miRNA reads (~0.001%) are in very good accord with those calculated from a recent metastudy of mcv-miR-M1 expression in primary tumor material (0.002%) [19]. Overall, when taking into account transfection efficiencies, mcv-miR-M1 expression levels are estimated to be more than four orders of magnitude higher in cells harboring replicating episomes. Our cross-comparison of mcv-miR-M1 expression levels in PFSK1:MCVSyn and MCCL thus strongly supports the previous notion that the viral miRNA is unlikely to contribute to the progression of MCC via the continuous downregulation of cellular target transcripts [19].
We also find no evidence for the hypothesis that MCC cells may preferentially express an isomiR variant that could target host immune response genes [34]. Our side-by-side comparison of two different library preparation methods rather shows that the relative distribution of miRNA seeds is very similar between MCCL and PFSK1:MCVSyn cells. While the isomiR identified by Lee et al. was indeed the most prominent variant when using one of the two investigated library preparation methods, absolute and relative read counts (Table 1 and S3 Dataset) strongly suggest that the seeming dominance of this isomiR was due to failure to capture the 5p17-23 species. The observed discrepancies likely resulted from biases during small RNA library preparation, most notably the influence of small RNA and adapter sequence combinations on the efficiency of 3’ adapter ligation (27–31). Given the difficulties in determining the accurate seed sequences of viral miRNAs, our results thus once more underline the notion that identification of potential host targetomes must be based on unbiased experimental screens with authentic precursors rather than computational prediction alone.
Non-withstanding above considerations, our data show that mcv-miR-M1 expression negatively regulates expression of early messages transcribed from the opposite strand. Similar to SV40, murine PyV and BKPyV [15, 16, 20], a miRNA knockout mutant exhibited appreciably higher levels of LT-Ag expression and DNA replication, but did not produce significantly altered levels of viral progeny (S6 Fig). However, given that the currently available MCPyV replication system generally produces only very low titers of infectious progeny, it remains possible that mcv-miR-M1 may behave differently in a fully permissive system.
Comparison of relative transcript abundance between MCVSyn or MCVSyn-hpko transfected cells suggests that the majority of early transcripts are negatively regulated by miR-M1, as they become more abundant in cells harboring the miRNA knockout (Fig 7). A notable exception is transcript T’5, which is approximately 30fold more abundant in MCVSyn-transfected cells. This observation is consistent with the fact that the d420-a2778 splice removes the sequences complementary to mcv-miR-M1. As the miRNA knockout is expected to selectively destabilize those transcripts which contain target sites, it is to be expected that T’5 accounts for a lower relative fraction of early viral transcripts in MCVSyn-hpko cells. It is also interesting to note that, among the remaining transcripts, those that encode LT-Ag and 57K-Ag (T1 and T4, respectively) appear to be most strongly upregulated in PFSK-1:MCVSyn-hpko cells. This may suggest that they are more efficiently targeted by mcv-miR-M1, e.g. due to secondary structures that facilitate binding of mature miRNAs to their target sites. Certainly, however, further investigation will be required to establish whether this is indeed the case.
Our RNA-seq analysis also identified early strand splice events which originate outside of the major transcriptional cassette defined by 5’- and 3’-RACE analyses. These products are of interest as they may produce dedicated ALTO-encoding messages. While we have formally confirmed the existence of these junctions, RNA-seq coverage also indicates that they are of very low abundance. It is thus unlikely that such transcripts significantly contribute to ALTO production in our system, considering that the protein can be efficiently produced from canonical early region transcripts via leaky scanning [10]. However, it remains possible that increased usage of alternative upstream initiation sites (e.g. that observed at position 115) could elevate transcript abundance. Previous studies from other polyomaviruses such as mouse PyV, SV40 and JCPyV indicate that upstream initiation sites are increasingly used during late stages of the infection cycle [57–59]. Once a fully permissive MCPyV replication system becomes available, it thus will be interesting to study whether similar mechanisms could lead to increased expression of dedicated ALTO-messages during late stages of a productive infection.
We detected two splice junctions that map within the major late strand transcription cassette. While the resulting transcripts are predicted to produce VP1 and VP2, we do not observe splice events which remove the VP2 start codon to produce a dedicated transcript for the putative minor capsid protein VP3. As the VP2 start codon is in a strong Kozak context [9], translation of VP3 from above transcripts via leaky scanning is predicted to be inefficient. Our data are thus in support of a recent study that has concluded MCPyV is unlikely to express a functional VP3 [9].
Interestingly, we find that MCPyV also expresses late strand transcripts with multiple copies of 5’-structures that are reminiscent of the leader-to-leader splice events observed in mouse polyomavirus [50–55]. Similar to the early stages of mouse PyV infection [60], we find that the major transcriptional start site is located downstream of the leader-to-leader splice acceptor. As productive mouse PyV infection proceeds, transcriptional initiation occurs with increasing frequency at alternatives sites which are located upstream of the leader-to-leader splice acceptor [60]. As for early messages, it is thus possible that a similar shift in transcriptional initiation site usage may occur in a fully permissive infection system. Production of leader-to-leader transcripts from primary transcripts which ignore late strand polyadenylation sites has been shown to contribute to the accumulation of late strand transcripts during productive mouse polyomavirus infection [51, 53, 55]. It was suggested that the presence of nuclear antisense RNAs produced from the intron of the late leader-to-leader splice leads to abundant A-to-I editing and subsequent destabilization of early transcripts. Since the processed late mRNA does not contain the complementary intron sequences, it is not subject to editing and thus the ratio of late versus early messages increases [54]. To investigate whether similar mechanisms may occur in our system we scrutinized our RNA-seq data for evidence of A-to-I editing events. Even when allowing 20 mismatches during the alignment step, the rate of A-to-I transitions was below 0.1%. Hence, at least under the conditions and in the semi-permissive system used here, negative regulation of early genes by late strand transcription products appears to proceed predominantly via expression of the viral miRNA. Notably, this does not rule out a role for leader-to-leader splicing, as these events would allow processing of the miRNA from intronic sequences while still preserving the integrity of late strand coding mRNAs. Given that mouse PyV encodes a miRNA at a similar genomic location [15] it appears possible that intron-derived miRNAs could also contribute to accumulation of late mouse PyV mRNAs.
In addition to miRNA expression from canonical late strand transcripts, we also provide evidence for NCCR-independent expression of mcv-miR-M1. Evidence for this conclusion includes (i) identification of a transcriptional initiation site (TI-L2) upstream of the genomic miRNA locus which (ii) exhibits profound enrichment of the histone modification H3K4me3, (iii) autonomous expression of the viral miRNA from subgenomic fragments containing the early coding region and (iv) an approximately 2fold reduction in miRNA expression concomitant with (v) elimination of H3K4me3 peaks upon the introduction of triplet mutations upstream of the genomic mcv-miR-M1 locus. At first, the 2fold reduction in miRNA expression by MCVSyn-pmt may appear unexpected when considering the relatively low frequency of 5’-RACE products observed at TI-L2 (approx. 3%). However, as exonic miRNA processing destroys the precursor transcript, the 5’-RACE protocol can only capture those transcripts which have escaped processing by Drosha. The primary rate of transcriptional initiation events at this site may thus be higher. It should also be noted that, while our data suggest that transcription occurs via RNA polymerase II, we were unable to drive expression of luciferase via the early strand coding fragments upstream of the viral miRNA locus. Hence, it is possible that sequences downstream of the initiation site may be required for efficient expression, perhaps similar to the as of yet undefined genomic pre-miRNA sequence features that can mediate autonomous transcription of some human miRNA loci [61]. The fact that distally initiated transcription through the mcv-miR-M1 locus seems (Fig 5) seems to increase intrinsic promoter activity is also of interest, as such a mechanism could potentially provide a negative feedback for early gene expression. Interestingly, expression of a viral miRNA from an internal promoter has been previously reported for bandicoot papillomatosis carcinomatosis virus type 1 and 2 (BPCV1 and -2, respectively), two viruses which shares distinct features of both the polyomavirus and papillomavirus families [62–64]. In BPCV1 and -2, the non-coding region 2 (NCR2) contains the genomic template as well as promoter for miRNA expression [64]. It was noted that the NCR2 region also contains a predicted LT-Ag consensus binding site (GRGGC), yet whether BPCV LT-Ag indeed binds to this site remains unknown [62–64]. Indeed, the region flanking initiation site TI-L2 contains a cluster of 6 GRGGC pentamers, including two overlapping sites in a head-to-head orientation just 6 nucleotides upstream of the transcriptional start position (S8 Fig). All sites are furthermore perfectly conserved in Gorilla gorilla gorilla polyomavirus 1 (GggPyV1) and Pan troglodytes verus polyomavirus 2 (PtvPyV2a), two close relatives of MCPyV that have been shown to encode orthologues of mcv-miR-M1 [64, 65]. While it is intriguing that, aside from the viral origin of replication, no other locus in the MCPyV, GggPyV1 or PtvPyV2a genomes exhibits a similar accumulation of GRGGC pentamers, we clearly did not observe LT-Ag peaks upstream of the viral miRNA in our ChIP-seq experiments (Fig 6). Hence, if LT-Ag indeed binds to these sites under the conditions used here, it must do so transiently or with significantly reduced affinity compared to the viral origin of replication. Considering all of the above, additional experiments will undoubtedly be required to fully characterize factors and features which regulate NCCR-independent expression of mcv-miR-M1.
If mcv-miR-M1 expression can occur independent of late gene expression, then why do integrated genomes in MCC fail to efficiently express the viral miRNA? While we presently can only speculate, given that high level early antigen expression is required for survival of MCC cells [66] we would predict that tumor progression selects for silencing of the viral miRNA promoter. We are currently evaluating the chromatin status of integrated genomes to investigate this scenario.
Perhaps the most intriguing finding of our study is that, in the absence of any selection pressure, wild type MCPyV genomes can persist in continuously growing cultures for more than 6 months after the initial transfection. In contrast, the mcv-miR-M1 knockout mutant was consistently lost at an accelerated rate. The fact that MCVSyn genome copy numbers reached a stable plateau phase in only one out of the three independently performed experiments, however, also suggests that long-term maintenance is affected by as of yet unknown stochastic events. While the results presented in Fig 11 clearly argue against chromosomal integration constituting such an event, another possibility would be the accumulation of adaptive mutations or genomic rearrangements in long term cultures. The fact that long term maintenance did not require selection would seem to argue against such a possibility. More importantly, however, we have subjected RCA amplification products from long-term cultures to high-throughput sequencing and found their sequences to be 100% identical to those of the input genomes. It is interesting to note that the observations made here bear some resemblance to the latency establishment phase of the gammaherpesviruses EBV and KSHV. While incoming genomes rapidly adopt latent gene expression profiles, EBV as well as KSHV episomes exhibit an accelerated loss rate during the first few weeks of infection until rare epigenetic events of hitherto unknown nature lead to stabilization of episomes and subsequent long term maintenance [67–69]. Considering the transient increase in copy numbers observed at day 84 of the experiment shown in Fig 8A–8C, it is tempting to speculate that a stochastic event occurring at this time point may have allowed subsequent stabilization of MCPyV episomes. Unfortunately, due to the very low frequency of MCPyV-positive cells at this or later time points we were unable to investigate the chromatin state of stable viral episomes in these cultures. We are currently striving to enrich cells harboring stable MCPyV episomes to allow a detailed investigation of their epigenetic landscape and gene expression profile. It should be pointed out that we currently cannot exclude the possibility that MCPyV maintenance may also involve continuous shedding of infectious virions. Thus, long term maintenance may result from latency-like episomal persistence, continuous low level infection of initially untransfected cells (or cells having lost the viral genome), or a combination of such processes. However, in our view the fact that PFSK-1 cells do not allow efficient particle production and serial transmission together with the observation that mcv-miR-M1 deficiency does not affect the levels of viral progeny (S6 Fig) argues against continuous virion shedding as the primary maintenance mechanism. We are currently performing long term studies with additional MCPyV mutants to investigate whether persistence requires virion production.
Why is the miRNA-knockout mutant MCVSyn-hpko impaired in long-term persistence? Several possibilities come to mind. Firstly, given that small RNAs can induce local chromatin changes via hitherto poorly understood mechanisms [70–73], viral miRNA expression may negatively affect a potentially stabilizing event as discussed above. While formally possible, we do not consider this possibility to be very likely. We instead favor a second and much simpler possibility: That continued mcv-miR-M1 expression prevents accumulation of LT-Ag protein to levels which impair cell growth. It has previously been shown that LT-Ag can have growth promoting activities, but via carboxyterminal sequences may also inhibit proliferation if expressed at high levels [74]. We have formally confirmed that PFSK-1 cells respond to LT-Ag expression with a dose dependent decrease in proliferation rates (S9 Fig). Hence, we suspect that lack of the viral miRNA results in aberrantly high LT-Ag expression, leading to accelerated loss of MCPyV-positive cells and a decreased probability that genome stabilization may occur. This model provides a convenient explanation for the fact that MCVSyn-hpko genomes exhibit increased DNA replication at early time points, but nevertheless exhibit accelerated loss in long-term cultures. Lastly, a third (and not mutually exclusive) possibility is that, similar to some herpesvirus miRNAs [22, 23, 75–77], mcv-miR-M1 may downregulate host transcripts to create a cellular environment that is supportive of long-term episomal maintenance. Although attractive, experimental identification of candidate host targets (preferably via unbiased screens) will be required to substantiate such a scenario.
What is the biological significance of the observed ability of MCPyV episomes to persist over extended periods of time? It is tantalizing to speculate that MCPyV may have evolved similar mechanisms as papillomaviruses to persist in a non-vegetative state of infection. However, it must be pointed out that it is currently unclear to what extend the cell system used here adequately reflects the behavior of MCPyV-infected cells in vivo. While PFSK-1 cells (like all other cell lines or-types tested thus far) do not support lytic growth of MCPyV, the precise cells types in which the virus establishes productive and/or persistent infections in vivo remain unknown. Since, obviously, long term persistence would not provide a significant benefit in fully permissive infected cells (which would likely die within a few days after initial infection), one would thus have to postulate that there may be semi-permissive host cell types (or differentiation stages) in which the virus establishes latent or quasi-latent infections. Although we find this to be a very intriguing possibility, verification of such models will ultimately have to await identification of the authentic in vivo target cells in healthy carriers.
Finally, are the findings reported here of any relevance for the pathogenesis of MCC? At present, the limited amount of available data does not allow us to draw such a conclusion. Within the limits of the caveats discussed above, we propose that, similar to BKPyV [20], the physiological function of the MCPyV miRNA may be to augment viral persistence which, given the existence of an autonomous promoter, may proceed in a non-vegetative state of infection. If so, one may also speculate that prolonged episomal persistence could increase the overall chance of integration events that are expected to be extremely rare during natural infection, but which are a hallmark of all MCPyV-positive MCC. Thus, while spurious expression of the viral miRNA is likely to be inconsequential once integration has occurred, in the above scenario mcv-miR-M1 may very well make an indirect contribution by supporting long-term persistence of viral episomes. Of note, LT-Ag has been demonstrated to directly interact with the bromodomain protein BRD4, a factor which is targeted by several papillomavirus E2 to mediate episomal tethering during persistent infection [78–83]. Although highly speculative, given recent findings suggesting a role for the E2/BRD4 interaction during papillomavirus integration [84], one may envision that the interaction between BRD4 and LT-Ag could also more directly contribute to MCPyV integration events. Indeed, our own preliminary studies indicate that LT-Ag binds to a large number of host chromosome loci in a non-random manner. We are currently investigating whether such loci may also represent preferred sites of chromosomal integration in MCC. In the meantime, the findings reported here open new lines of investigation that are expected to significantly improve our understanding of the MCPyV lifecycle.
PFSK-1 cells (ATCC, CRL-2060) and MCC cell lines (MKL-1 [85] and WaGa [8]) were maintained in RPMI medium supplemented with 10% fetal calf serum (FCS) and 5% penicillin/streptomycin. HEK293 cells [86] were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% FCS and 5% penicillin/streptomycin. All cells were grown in a 5% CO2 humidified atmosphere at 37°C.
Construction of the synthetic consensus clone pMK-MCVSyn used for production of re-circularized MCPyV genomes has been described previously [27]. To generate the hairpin mutant MCVSyn-hpko, a 384 bp fragment spanning nucleotides 1118–1501 of the MCVSyn genome and containing 14 nucleotide substitutions in the region encoding mcv-miR-M1 (S1 Fig) was synthetically generated (GeneART, Regensburg). A fragment spanning the same genomic region, but containing 68 nucleotide exchanges (S1 Fig) in the suspected promoter region upstream of the mcv-miR-M1 locus was synthesized to generate the mutant genome MCVSyn-pmt. Both fragments were inserted into pMK-MCVSyn using BamHI and SanDI restriction sites.
Plasmid pER was produced by amplification of the MCPyV early coding region from MCVSyn using primers MCPyV EcoRV F/MCPyV XhoI R and subsequent cloning into the pCR2.1 plasmid (life technologies). To generate plasmid pER-pmt, the region upstream of the viral miRNA locus in pER was substituted by the mutated region from MCVSyn-pmt. Plasmids pCMV:ER-AS and pCMV:ER-S were generated by excision of the MCPyV early coding region from plasmid pER and directional cloning into pCDNA3.1.
All primer- and oligonucleotide-sequences used in this study are listed in S1 Table.
Re-circularization and transfection were carried out as described previously [27]. Briefly, the bacterial backbone of pMK-MCVSyn, pMK-MCVSyn-hpko or pMK-MCVSyn-pmt plasmids was excised by SacI digestion (FastDigest, Thermo Scientific). After gel purification of the linearized viral genomes, intramolecular ligation was performed using T4 DNA Ligase (Thermo Scientific), followed by spin column purification of the recircularized DNA. 3x105 PFSK-1 cells were seeded in 6-well dishes one day prior to transfection. 200 ng of re-circularized viral DNA together with 500 ng carrier DNA (pUC18) were transfected using X-tremeGene HP DNA Transfection Reagent (Roche) following the manufacturer’s instructions. Mock treated cells were transfected with equivalent amounts of carrier DNA only. Cells were harvested for analysis at the indicated time points.
DpnI resistance/replication assays were performed after HIRT extraction of low molecular weight DNA (HIRT DNA) as previously described [87]. Extracted DNA was subsequently digested with EcoRI and DpnI (FastDigest, Thermo Scientific) for 60 min at 37°C. 1 μg of the resulting DNA was separated on a 0.8% agarose gel and transferred to a nylon membrane (Zeta-Probe GT Membrane, Bio-Rad). For detection of MCVSyn DNA, a genomic fragment was amplified from the early viral region using primers MCPyVLT fw and MCPyV LT rev and labeled with 32P dCTP (Rediprime II DNA Labeling System, GE Healthcare). Blots were hybridized with the labeled probe for 16h at 42°C in ULTRAhyb buffer (Ambion). Blots were washed 2x 20 min with 1% SSC, 0.1% SDS and 2x20 min with 0.1x SSC, 0.1% SDS at 50°C. After at least 24 h of exposure, blots were scanned with the Fuji phosphorimager FLA7000 and analyzed with Multigauge software.
For the determination of MCPyV genome copy numbers, genomic DNA (gDNA) was extracted from isolated nuclei of transfected cells. Nuclei were prepared by adding 2x lysis buffer (0.65 M Sucrose, 20 mM Tris-HCl pH 7.8, 10 mM MgCl2, 2% Triton-X 100) to a final concentration of 1x to trypsinized cells resuspended in PBS. After 5 min incubation on ice, nuclei were centrifuged and resuspended in 50 μL PBS. 300 μL gDNA lysis buffer (100 mM NaCl, 10 mM Tris-HCl pH 8, 25 mM EDTA pH 8, 0.5% SDS) supplemented with 200 μg proteinase K (Peqlab) were added, followed by incubation at 54°C for 16 h. The DNA was purified by phenol/chloroform extraction and isopropanol precipitation. After treatment with RNase A (Peqlab) for 30 min at 37°C gDNA was digested with EcoRI and DpnI (FastDigest, Thermo Scientific) for 60 min at 37°C.
25 ng gDNA were used as input for quantitative PCR. Viral genomes were quantified with primers binding to the late region (MCPyV VP1 fw and MCPyV VP1 rev), spanning three DpnI restriction sites. SacI digested, linear MCVSyn DNA was used to generate a standard curve with a defined number of viral genomes. The number of viral genomes was normalized to GAPDH locus copy numbers (primers: GAPDH DNA fw and GAPDH rev), which were determined using a standard curve with a defined number of GAPDH locus copies.
Rolling circle amplification was performed with the TempliPhi 100 amplification kit (Amersham Biosciences) according to the manufacturer's instructions with additional 450 μM dNTP as described in Rector et al. 2004 [88]. RCA products were digested with restriction enzymes, separated on a 0.8% agarose gel and analyzed by ethidium bromide staining. Libraries of RCA products for HTS analysis were generated with the NEBNext Ultra DNA Library Prep Kit for Illumina and sequenced on an Illumina HiSeq2500 instrument. Reads were subjected to de novo assembly using the Trinity package (v r2013-02-25) [89] and resulting full-length MCPyV genomes (average coverage >200) were compared to input sequences by blast analysis (BLAST Plus Package v 2.2.28).
For detection of small RNAs by northern blotting, total RNA was harvested using RNABee (AMS Biotechnology) according to the manufacturer's instructions. 14 μg of total RNA were separated on a denaturing 15% polyacrylamide urea gel and transferred to Zeta-Probe GT membranes (Bio-Rad) by electro blotting. Blots were hybridized to a 32P dATP labeled antisense oligonucleotide probe (mcv-miR-M1 probe, S1 Table) in ExpressHyb (BD Biosciences Clontech) hybridization buffer for 16h at 37°C. Membranes were washed twice in 2x SSC, 0.1% SDS at room temperature and subsequently subjected to autoradiography. Blots were scanned on the BAS-Reader and analyzed with AIDA Software.
For quantitative stem-loop RT-PCR, reverse transcription (RT) was performed as described by Varkonyi-Gasic et al. [90] using 1 μg of total RNA as input for each sample and a mcv-miR-M1 specific stem-loop primer (SL mcv-miR-M1). For normalization, a reverse primer for GAPDH (GAPDH rev) was included in the RT reaction. 1.5 μL cDNA per sample were analyzed by real-time PCR on the Rotor-Gene Q (Qiagen) using the Rotor-Gene Multiplex PCR Mastermix (Qiagen) and the following primer pairs: mcv-miR-M1: mcv-miR-M1 fw/universal rev; GAPDH: GAPDH BSP fw/GAPDH rev. Differently labeled TaqMan probes (Taqman probe mcv-miR-M1, Taqman probe GAPDH) were used to quantify the expression of mcv-miR-M1 and GAPDH in the same real-time PCR reaction. Analysis of real-time PCR experiments was carried out with Rotor-Gene Q software (Qiagen).
Chromatin Immunoprecipitation (ChIP) was performed as previously described [91, 92]. In brief, 4d post transfection with MCVSyn wt or MCVSyn mutants, chromatin of 1×106 cells was crosslinked by incubation with 1% formaldehyde. The reaction was stopped by the addition of glycine. Chromatin was extracted from isolated nuclei and fragmented by sonication (Bioruptor, Diagenode) to an average length of 200–500 bp. A fraction of the total chromatin sample was set aside for the preparation of input control. The remaining material was pre-cleared with BSA blocked protein-G sepharose beads (GE Healthcare) to reduce non-specific background binding. For immunoprecipitation, 2 μg of antibodies specific for the histone modification H3K4-me3 (Millipore, #04–745) or for MCPyV LT-Ag (CM2B4, Santa Cruz Biotechnology, sc-136172) or IgG anti-rabbit (Millipore, #12–370) antibody were added to the chromatin and incubated for 16 h at 4°C. Chromatin-immunocomplexes were precipitated by the addition of protein-G sepharose beads, washed with increasing salt concentrations, eluted and de-crosslinked for 16 h at 65°C. DNA was purified by phenol-chloroform extraction and ethanol precipitation. For HTS analysis, libraries were prepared from ChIP samples using the NextFlex ChIP-Seq kit (Bioo Scientific) and sequenced on the Illumina HiSeq2500. Reads were mapped to MCVSyn or MCVSyn-pmt genome sequences using Bowtie (v 0.12.9).
For Western Blotting, MCVSyn transfected cells were harvested at the indicated time points and resuspended in lysis buffer (50 mM Tris pH 8.0, 150 mM NaCl, 1% NP40, 0.5% Na-Deoxycholat, 5 mM EDTA, 0.1% SDS, proteinase inhibitor cocktail, Roche). 25 μg of protein were separated by SDS-PAGE (10% gels) and electroblotted on a PVDF-membrane. Blots were incubated with MCPyV LT-Ag antibody CM2B4 (Santa Cruz, sc-136172).
For immunofluorescence analyses, PFSK-1 cells grown on coverslips were fixed with methanol at room temperature for 30 min and rinsed in PBS for 10 min. Fixed cells were blocked with 4% BSA in PBS for 30 min and then incubated with a 1:50 dilution of the LT-Ag antibody CM2B4 in 4% BSA in PBS/0.05% Tween-20 for 2 h. Coverslips were washed three times in PBS for 10 min each, followed by incubation with a 1:1000 dilution of goat anti-mouse Alexa Fluor 555-conjugated secondary antibody (Life Technologies, A 21422) in 4% BSA in PBS/0.05% Tween-20 for 2 h. Coverslips were washed three times in PBS for 10 min each, counterstained and mounted with vectashield mounting medium with DAPI (Vector, H-1200). Images were acquired with a confocal laser-scanning microscope (Nikon C2+).
5x104 cells were cytospun for 5 min at 900 rpm on Superfrost/Plus slides (Fisher) and fixed in methanol, followed by digestion (0.01% pepsin, 0.01 N HCl) for 5 min at 37°C and RNase A incubation (100 μg/ml) in 2x SSC for 1 h at 37°C. After washing in PBS and refixation (3% formaldehyde/PBS, 50mM MgCl2), slides were passed through a dehydration series of 70%, 85%, and 100% ethanol for 5 min each and air dried. For denaturation, slides were incubated in 70% formamide in 2x SSC for 5 min at 73°C and then promptly placed in ice-cold 70% ethanol for 5 min, and dehydrated again as described above.
1 μg of MCVSyn DNA was labeled with Dig-Nick Translation Mix (Roche, 11745816910) according to the manufacturer’s instructions. Labeled DNA was ethanol precipitated in the presence of excess sonicated salmon sperm DNA (Life Technologies). The final product was resuspended in hybridization buffer (50% formamide and 10% dextran sulfate in 2x SSC) to a final concentration of 10 ng/μl and stored at -20°C. 5 μl (50ng) of the probe were heat-denatured for 5 min at 73°C and placed under a coverslip on the appropriate area of the slide. The coverslip was fixed with fixogum (Marabu, 290110000). Slides were hybridized in a humid chamber overnight at 37°C.
After hybridization, slides were washed three times (2x SSC, 0.2% Tween) for 2 min each, twice at 20°C and in between at 70°C, followed by blocking with 4% BSA/PBS for 30 min at 37°C and incubation with sheep-anti-Digoxigenin-FITC-antibody (Roche, 11207741910), diluted 1:50 in 4% BSA/PBS with 0.2% Tween, for 2hr at 37°C in the dark. Slides were washed with PBS/0.2% Tween three times for 10 min each at 20°C in the dark, counterstained and mounted with vectashield mounting Medium with DAPI (Vector, H-1200). Images were acquired with a confocal laser-scanning microscope (Nikon C2+).
3’-RACE analysis was performed according to the protocol of Scotto-Lavino et al. [93] with minor modifications. In brief, 5 μg of total RNA of MCVSyn transfected PFSK-1 cells 4d post transfection were subjected to cDNA synthesis using Superscript III (Invitrogen) and an anchored oligo-dT primer (QT). The input RNA was digested by addition of 1.5 U RNAse H (NEB) and incubation at 37°C for 20 min. For the first round of amplification, a gene specific primer (LTo/VP1o) and a primer specific for the sequence of the QT primer were used. A second round of amplification with nested gene specific reverse primers (LTi/VP1i) and the forward primer Qi was used to increase specificity and add restriction sites to the ends of the PCR product. After digestion with the respective restriction enzymes (Fast Digest, Thermo scientific), RACE products were cloned into pCR2.1 plasmid. After transfection into bacteria, individual clones were subjected to Sanger sequencing.
5’ RACE analysis of MCVSyn transfected PFSK-1 cells was performed with the GeneRacer Kit protocol (Invitrogen) according to the manufacturer’s instructions. Briefly, 5 μg of total RNA were dephosphorylated, decapped and then ligated to a 5’ RACE RNA adapter. cDNA was synthesized with gene specific primers (early/late region rev, S1 Table) using Superscript III according to the manufacturer’s instructions. After touchdown PCR with gene specific primers, nested PCR was performed by which restriction sites were added at both ends of the amplification products (primers: early/late region rev nested). All RACE PCR amplifications were performed with Pfu Ultra II (Agilent technologies) according to the manufacturer’s protocol. 5’ RACE products were analyzed by HTS after library preparation with the NEBNext Ultra DNA Library prep Kit for Illumina. Reads were mapped to the MCVSyn genome with TopHat2 [94].
For sequencing of small RNA moieties, RNA from MCVSyn transfected cells and MCC cell lines was subjected to library preparation using the TruSeq Small RNA Sample Preparation Kit (Illumina) or the NEBNext Small RNA Library Prep Set for Illumina. Small RNA libraries were sequenced on the Illumina HiSeq platform. After adapter trimming, mapping of reads to the MCVSyn genome and quantification of mature miRNA deposited in the miRNA registry (miRBase) release 21 [95] were performed using CLC Genomics Workbench v7.5.1 (Quiagen), allowing an offset of 5 nucleotides of mature miRNAs along the precursor to ensure detection of isomiRs.
Library preparation for strand specific RNA sequencing was carried out using the NEXTflex Directional RNA-Seq Kit (Bioo Scientific) according to the manufacturer’s instructions. Libraries were sequenced on the Illumina HiSeq 2500 platform. To allow detection of splice events that extend over the origin, reads were mapped to two concatenated copies of the MCVSyn or MCVSyn-hpko genomes using TopHat2 v 2.0.13 [94]. The positions of mapped reads and junctions were subsequently collapsed back on unit-length genomes. From the resulting SAM files, we counted the number of unspliced reads that extended over splice sites of junctions detected by TopHat to determine splice site efficacy and frequency of individual junctions. To estimate transcript abundance, for each combination of splice junctions that mapped within either the major early or late transcription cassettes we calculated a relative strand-specific combinatorial frequency value by multiplying observed frequency values for individual donor sites. The relative ratio of late to early transcripts was subsequently estimated by calculation of normalized RPKM (reads per kilobase per million mapped reads) for each of the transcripts.
Four days after transfection with re-ligated MCVSyn, cell culture supernatants were collected and sterile filtered. 1 ml of supernatant was supplemented with 10x DNaseI reaction buffer and DNA was digested with 10 μl DNase I (amplification grade, Invitrogen) for 1 h at 25°C. DNase I was heat inactivated for 10 min at 65°C in the presence of 2.5 mM EDTA. Proteins were degraded by addition of 5 μl Proteinase K and 1% SDS at 50°C for 16h. DNA was retrieved by phenol-chloroform extraction and precipitation.
For re-infection assays, PFSK-1 cells were transfected with MCVSyn or MCVSyn-hpko. 8 days post transfection; cells were lysed by three freeze-thaw-cycles. Cell debris was removed by centrifugation and lysates were passed through a 0.22 μm filter. Lysates prepared from a 10 cm dish were used to inoculate one 6-well of freshly seeded PFSK-1 cells. 24h post infection, medium was changed and cells were incubated for additional 3–7 days as indicated prior to DNA isolation.
PFSK-1 cells in 10 cm dishes were transfected with the indicated amounts of a pCDNA3.1 LT-antigen expression plasmid added up to 10 μg of total plasmid DNA with an empty pCDNA3.1 plasmid.
At 24h post transfection, cells were seeded in 96-well plates and grown for another 24h. For measurement of proliferation, cells were incubated with 10 μl MTT reagent (Chemicon) per well for 4h. Afterwards, cell culture medium was removed and the formazan crystals were resuspended in 200 μL DMSO. Absorbance was measured at 540 nm with a reference wavelength of 690 nm.
All MCPyV sequences and genome coordinates in this study refer to the MCVSyn genome, which is 100% identical to the prototypical MCPyV field strain R17b (genbank accession numbers JN707599 and NC_010277, respectively). The accession numbers for Gorilla gorilla polyomavirus 1 (GggPyV1) and Pan troglodytes verus polyomavirus 2 (PtvPyV2a) sequences as shown in S8 Fig are HQ385752.1 and HQ385748.1, respectively. Primary read and mapping data of all small RNA-seq, RACE-seq, ChIP-seq and mRNA-seq experiments performed in this study are publicly available at the European Nucleotide Archive (ENA, http://www.ebi.ac.uk/ena) under accession numbers PRJEB9667 (small RNA-seq data), PRJEB9666 (RACE-seq data), PRJEB9670 (ChIP-seq data) and PRJEB9669 (mRNA-seq data).
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10.1371/journal.pcbi.1002149 | Efficacy of Synaptic Inhibition Depends on Multiple, Dynamically Interacting Mechanisms Implicated in Chloride Homeostasis | Chloride homeostasis is a critical determinant of the strength and robustness of inhibition mediated by GABAA receptors (GABAARs). The impact of changes in steady state Cl− gradient is relatively straightforward to understand, but how dynamic interplay between Cl− influx, diffusion, extrusion and interaction with other ion species affects synaptic signaling remains uncertain. Here we used electrodiffusion modeling to investigate the nonlinear interactions between these processes. Results demonstrate that diffusion is crucial for redistributing intracellular Cl− load on a fast time scale, whereas Cl−extrusion controls steady state levels. Interaction between diffusion and extrusion can result in a somato-dendritic Cl− gradient even when KCC2 is distributed uniformly across the cell. Reducing KCC2 activity led to decreased efficacy of GABAAR-mediated inhibition, but increasing GABAAR input failed to fully compensate for this form of disinhibition because of activity-dependent accumulation of Cl−. Furthermore, if spiking persisted despite the presence of GABAAR input, Cl− accumulation became accelerated because of the large Cl− driving force that occurs during spikes. The resulting positive feedback loop caused catastrophic failure of inhibition. Simulations also revealed other feedback loops, such as competition between Cl− and pH regulation. Several model predictions were tested and confirmed by [Cl−]i imaging experiments. Our study has thus uncovered how Cl− regulation depends on a multiplicity of dynamically interacting mechanisms. Furthermore, the model revealed that enhancing KCC2 activity beyond normal levels did not negatively impact firing frequency or cause overt extracellular K− accumulation, demonstrating that enhancing KCC2 activity is a valid strategy for therapeutic intervention.
| Fast synaptic inhibition relies on chloride current to hyperpolarize the neuron or to prevent depolarization caused by concurrent excitatory input. Both scenarios necessarily involve chloride flux into the cell and, thus, a change in intracellular chloride concentration. The vast majority of models neglect changes in ion concentration despite experimental evidence that such changes occur and are not inconsequential. The importance of considering chloride homeostasis mechanisms is heightened by evidence that several neurological diseases are associated with deficient chloride extrusion capacity. Steady state chloride levels are altered in those disease states. Fast chloride dynamics are also likely affected, but those changes have yet to be explored. To this end, we built an electrodiffusion model that tracks changes in the concentration of chloride plus multiple other ion species. Simulations in this model revealed a multitude of complex, nonlinear interactions that have important consequences for the efficacy of synaptic inhibition. Several predictions from the model were tested and confirmed with chloride imaging experiments.
| In the central nervous system, fast inhibition is mediated by GABAA and glycine receptor-gated Cl− channels (GABAAR and GlyR). Influx of Cl− through these channels produces outward currents that cause hyperpolarization or prevent depolarization caused by concurrent excitatory input (i.e. shunting) [1], [2]. Hyperpolarization and shunting both typically reduce neuronal spiking. However, Cl− influx through GABAAR necessarily increases [Cl−]i, which in turn causes depolarizing shifts in the Cl− reversal potential (ECl) [3], [4]. As the Cl− gradient is depleted and ECl rises, the efficacy of GABAAR-mediated control of spiking is compromised [5]. Therefore, mechanisms that restore the transmembrane Cl− gradient are crucial for maintaining the efficacy of GABAAR-mediated inhibition.
Cation-chloride cotransporters (CCCs) play a key role in maintaining the Cl− gradient across the membrane [6], [7]. Most relevant to neurons are the Na+-K+-2Cl− cotransporter (NKCC1), which normally mediates uptake of Cl− [8], and the K+-Cl− cotransporter, isoform 2, (KCC2), which normally extrudes Cl−. Interestingly, a reduction in KCC2 expression and/or function is involved in the pathogenesis of several neurological disorders, including epilepsy and neuropathic pain [9]–[15]. Motivated by the clinical relevance of hyperexcitability caused by changes in KCC2 activity, conductance-based compartmental models have been used to study how changes in ECl influence inhibitory control of neuronal spiking [5].
ECl can change as a result of altered KCC2 expression or activity [7], [16], [17]. ECl can also change dynamically, on a fast time scale, as a result of Cl− flux through GABAA receptors, particularly in small structures like distal dendrites [2], . If ECl changed only slowly, it could be reasonably approximated as static relative to other neuronal processes occurring on a faster time scale; however, since ECl changes rapidly, it may interact in potentially complex ways with important neuronal processes like synaptic integration. To investigate those interactions, one must treat [Cl−] as a dynamical quantity evolving in space and time.
The spatio-temporal dynamics of [Cl−]i depend on several factors, including GABAAR-mediated Cl− flux, longitudinal diffusion within dendrites and the soma, and CCC activity. Furthermore, Cl− dynamics involve complex non-linear interactions with other ion species, which have been overlooked by previous models [19]. To understand how these dynamical processes interact with each other, we built an electrodiffusion model that monitors intra- and extracellular concentrations of several ion species (Cl−, Na+, K+, Ca2+, HCO3−, H+, HPO42−, H2PO4−) across neuronal compartments (see Fig. 1A–C). Our model revealed several consequences of impaired Cl− extrusion on neuronal function, including a positive feedback loop between intracellular Cl− accumulation and excitatory activity or spiking that can lead to catastrophic failure of inhibition. Several predictions of the model were confirmed by direct measurement of [Cl−]i, by fluorescence lifetime imaging microscopy (FLIM).
Past experiments have established that Cl− extrusion via KCC2 plays a crucial role in maintaining the values of ECl and EGABA below the resting membrane potential [20], but they have not established how KCC2 activity relates quantitatively to ECl and EGABA, in particular under conditions of ongoing, distributed synaptic input. Therefore, as a first step, we varied KCC2 activity and measured the impact on ECl and EGABA (measured at the soma) in a model neuron receiving a fixed level of background excitatory and inhibitory synaptic input (Fig. 1D). Values of ECl and EGABA in middle and distal dendrites are described by similar curves shifted to slightly more depolarized values (data not shown) consistent with the somato-dendritic gradient described below. This is important since neurons in vivo are bombarded by synaptic activity [21], but it remains unclear how this may affect ECl and, in turn, be affected by ECl. Consistent with qualitative experimental findings [9], [22], [23], both reversal potentials underwent depolarizing shifts as KCC2 activity was reduced, with ECl approaching the mean membrane potential (Fig. 1D). Notably, EGABA was less negative than ECl,, especially at high values of KCC2 activity, consistent with EGABA depending jointly on [Cl−]i and [HCO3−]i [24]. However, unlike the large depolarizing shift in ECl caused by reducing KCC2 activity, increasing KCC2 activity beyond its normal value caused only a marginal hyperpolarizing shift in ECl, which approached the K+ reversal potential (EK) near -90 mV. This is consistent with KCC2 normally operating near equilibrium. Hence, while reduction in KCC2 activity can cause strong reduction of inhibition, excess KCC2 activity has a limited influence on the strength of inhibition, insofar as we assume that strength of GABAAR-mediated inhibition is a function of the value of EGABA.
Thus, in addition to validating our model, this first set of simulations revealed an interesting nonlinear relationship between KCC2 activity and ECl. However, we expected that ECl should depend not only on KCC2, but also on factors like GABAAR input – this was the main motivation for developing an electrodiffusion model. As a preliminary test, we varied the rate of inhibitory synaptic input together with KCC2 activity. Results show that ECl underwent a depolarizing shift, the magnitude of which depended on KCC2 activity, as the rate of inhibitory input increased (Fig. 1E). At a normal KCC2 level, increasing the activation rate of GABAAR synapses from 0.2 to 5 Hz drove ECl up by only 7 mV, whereas the same change in activation rate drove ECl up by 24 mV when KCC2 activity was decreased to 33% of its normal value. Thus, KCC2 activity not only controls baseline ECl, it also determines how stably ECl is maintained when the Cl− load is increased by synaptic input. Tonic inhibition due to activation of extrasynaptic GABAA receptors by ambient GABA can also contribute to intracellular Cl− accumulation and depolarize ECl. To test the impact of tonic inhibition, we performed simulations with and without this form of inhibition. Results obtained with and without tonic inhibition were qualitatively the same (Fig. 1E).
To test experimentally the impact of the level of KCC2 activity on intracellular Cl− accumulation, we loaded neurons in primary cultures (>21 days in vitro; DIV) with MQAE and measured changes in [Cl−]i using FLIM. FLIM measurements have the advantage of being unbiased by the amount of indicator from cell to cell (Fig. 2A, B), minimizing the variability between measurements as well as shielding the measurements from changes in cell volumes [25]. We first bath applied the GABAAR agonist muscimol to trigger Cl− influx through GABAAR channels. We then applied various concentrations of furosemide or VU 0240551 for 20 minutes to block KCC2 activity. In the presence of Cl− load through activated GABAA channels, application of furosemide or VU 0240551 led to dose-dependent Cl− accumulation (Fig. 2C, D), in agreement with the predictions of simulations (cf. Fig. 1D). At high doses, furosemide can antagonize both KCC2 and NKCC1; however, at > 21 DIV, hippocampal neurons in culture are generally thought to fully express KCC2 but to no longer express NKCC1 [6]. To test this, we used bumetanide at a concentration (50 µM) where it selectively blocks NKCC1. Administration of bumetanide to cells exposed to muscimol cause no change in [Cl−]i (Fig. 2D). The presence of significant Cl− export through KCC2 may however mask any NKCC1-mediated Cl− import. To test for this, we blocked KCC2 with the recently developed selective blocker VU 0240551 [25]. Further addition of bumetanide after KCC2 blockade had no effect on [Cl−]i, confirming absence of significant NKCC1-mediated transport in these neurons (Fig. 2D). These results indicate a) significant KCC2 co-transport in > 21 DIV hippocampal neurons in culture, maintaining [Cl−]i at a low level, and b) that both furosemide and VU 0240551 could be used under these conditions to selectively block KCC2-mediated transport.
With the importance of nonlinear interaction between GABAAR activity and KCC2 activity for intracellular Cl− regulation thus established, we moved onto more detailed analysis of how Cl− flux impacts the efficacy of synaptic inhibition.
Spatial variation in ECl (or EGABA) between cellular compartments has been observed in several experiments [20], [26]–[29] but it is not typically accounted for in conventional neuron models. While a longitudinal, axo-somato-dendritic [Cl−]i gradient could be due to differentially distributed cotransporter activity, it could also arise from intense focal GABAAR-mediated input. To test the latter scenario, we simulated high frequency GABAAR-mediated input to a single dendritic synapse and measured [Cl−]i at different distances from the synapse at different times after the onset of input (Fig. 3A). Under the conditions tested, a GABAAR synapse activated at 50 Hz produced a longitudinal [Cl−]i gradient of 50 µM/µm, which extended as far as 60 µm and could yield changes in EGABA on the order of 5 mV within 200 ms (Fig. 3A). There were only subtle differences between centripetal and centrifugal diffusion (i.e. toward or away from soma, respectively; Fig. 3B). According to these data, if a GABAA synapse receives sustained high frequency input, [Cl−]i will increase near that synapse, influencing EGABA at the original synapse as well at nearby synapses. This was further investigated by placing a “test” GABAA synapse (activated at 5 Hz) at varying distances from the original GABAA synapse (activated at 50 Hz). Both synapses were activated simultaneously. As predicted, EGABA at the test synapse was affected by other GABAAR-mediated input on the same dendrite as far away as 50 µm (Fig. 3C top), or even farther when KCC2 activity was reduced. However, interactions also depended on synapse position relative to the neuron topology; for instance, synapses in relatively close proximity but located on different primary dendrites exhibited little if any interaction (Fig. 3C bottom), consistent with the soma acting as a sink that clamps [Cl−]i.
Under in vivo conditions, neurons are known to be constantly bombarded by synaptic input [30]. We therefore tested whether this synaptic noise affects [Cl−]i differently depending on the cellular compartment. We performed simulations in the presence or absence of KCC2 activity and in the presence or absence of synaptic noise. Simulations of distributed ongoing synaptic input with KCC2 distributed uniformly across the cell compartments yielded a clear somato-dendritic [Cl−]i gradient (Fig. 4A black). In contrast, in the absence of simulated synaptic noise, there was no significant somato-dendritic [Cl−]i gradient despite the presence of KCC2 (Fig. 4A green). Lack of a significant somato-dendritic [Cl−]i gradient was also observed in the reverse scenario, i.e. in the presence of synaptic noise but without KCC2 (Fig. 4A red). Thus, a significant somato-dendritic [Cl−]i gradient can exist when there is ongoing Cl− influx, redistribution of that Cl− load via diffusion, and Cl− extrusion by KCC2. This clearly demonstrates that differential extrusion, i.e. inhomogeneous KCC2 density (see below), is not necessary for inhomogeneous transmembrane Cl− gradients.
To test the predictions made by the model, we used FLIM to measure [Cl−]i in MQAE-loaded neurons in culture (Fig. 4B). To mimic distributed Cl− influx across the dendritic tree, we exposed the cultures to 100 µM muscimol. FLIM measurements indicated a significant [Cl−]i gradient along dendrites (Fig. 4B top) which was either reduced by bicuculline (Fig. 4B middle and 4C) or blocked by the addition of furosemide or the recently developed more specific KCC2 inhibitor VU 0240551 [25] (Fig. 4B bottom and 4C), consistent with predictions from simulations (cf. Fig. 4A). The small remaining gradient in the presence of furosemide may indicate the presence of another chloride transport mechanism not accounted for in the model.
Our simulations were based on the assumption of even distribution of KCC2 along the dendrites and this configuration appears to be sufficient to explain the somato-dendritic gradient observed. However, this does not rule out the possibility of a gradient of KCC2 along the dendrites. To test for the presence or absence of such gradient, we sought to perform quantitative fluorescence immunocytochemical analysis of the distribution of KCC2 along dendrites. Measuring KCC2 immunolabeling may not be sufficient, however, to obtain an estimate of the distribution of functional KCC2 because it has recently been suggested that the oligomeric form of KCC2 is the functional one [31], [32]. To specifically measure the density of KCC2 dimers along the dendrites we took advantage of a technique we recently developed, entitled Spatial Intensity Distribution Analysis (SpIDA) which allows quantitative measurement of the density and oligomerization of proteins from conventional laser scanning confocal microscopy analysis of immunocytochemical labeling [33], [34]. We thus applied SpIDA to analysis of of KCC2 immunostaining of dendrites of the neurons used in the pharmacological experiments described above (Fig. 4). The monomeric quantal brightness was estimated using immunolabeling of KCC2 in neurons that have been in culture for only 5 days, because, at that stage of development, KCC2 has been shown to be essentially monomeric [31]. The monomeric quantal brightness was estimated to be 3.9×106±0.2 (mean ± SEM) intensity units or 3.9±0.2 Miu, and was constant along the dendrite of 5-DIV neurons (52 regions from 11 neurons). Using automated intensity binary masks [35], the dendrites of the mature neurons (> 21 DIV) were carefully detected and intensity histograms were generated for each analyzed region and a two-population (monomers and dimers) mixture model was assumed. For each analyzed region, SpIDA was performed on the image of the z-stack (0.5 µm between images) that had the brightest mean intensity in the chosen region. To estimate the true membrane density of KCC2, the final value for each region was averaged over the two adjacent images of the z-stack. A neuron with example regions and their corresponding histogram and SpIDA fit values are presented in Figure 5A,B. The results indicate that the membrane density of KCC2 is constant along the dendrites, at least as far as 200 µm from the center of the cell body (Fig. 5C).
While our experimental results indicate homogeneous distribution along the dendrite length, this does not necessarily apply to all conditions and, in particular, our analysis did not focus on local inhomogeneities, e.g. microdomains. We therefore also sought to determine if longitudinal intracellular Cl− gradients could also arise from inhomogenous CCC activity at small length scales. For instance, non-uniform distribution of KCC2 at the subcompartent-level might produce local gradients comparable to those observed with synaptic inputs (see Fig. 3). Indeed, clustering of KCC2 has been observed near some synapses [36], but KCC2 near excitatory synapses has been shown to serve a role in scaffolding rather than as a co-transporter [37]. Nevertheless, to test whether subcellular distribution of KCC2 can yield local gradients, we simulated high frequency synapses at 20 µm intervals, between each firing synapses Cl− extrusion through KCC2 was localized at a single point that was placed at different distances from the synapses (Fig. 6A). In all cases, the location of KCC2 had an impact on ECl of <2 mV. Thus, our simulations showed that subcompartemental distribution of KCC2 (i.e. inhomogeneities on the spatial scale of 0-10 µm) has little impact on the perisynaptic value of ECl.
The results above do not rule out the possibility of inhomogeneities in CCC expression underlying gradients in other cells types, as well as inhomogeneities in the axon initial segment and soma with respect to dendrites. For instance, absence of KCC2 in the axon initial segment (AIS) [9], [38], selective expression of the inward Cl− transporter NKCC1 in the AIS [28], or the combination of both expression patterns would be expected to cause ECl to be less negative in the AIS. To test ECl in the AIS and how it impacts neighboring compartment, we simulated different levels of NKCC1 in the AIS in combination with different levels of KCC2 in the soma and dendrites with or without background synaptic input (Fig. 6B–C). NKCC1 expression in the AIS can produce an axo-somatic [Cl−]i gradient, but this gradient does not extend far, if at all, into the dendrites (Fig. 6B). As expected, combining NKCC1 expression in the AIS with synaptic noise (like in Fig. 4A) resulted in a “double gradient” (Fig. 6C right panel).
Thus, simulations in our electrodiffusion model demonstrated that subcellular distribution of GABAAR input and CCC activity can produce spatial inhomogeneities in ECl, which should translate into inhibitory input having differing efficacy depending on the location of the synapse. This is true even if KCC2 activity is uniformly distributed in the presence of background GABAAR input. Moreover, focal Cl− influx through one synapse (or a cluster of synapses) can affect the efficacy of neighbouring synapses, although this depends on subcellular localization of those interacting synapses, e.g. proximity to the soma. In contrast, subcompartmental inhomogeneity in KCC2 activity is not sufficient to cause local [Cl−]i gradients.
Figures 4 and 6 emphasized how spatial variations in [Cl−]i can arise from ongoing GABAAR input. To extend these results to include temporal changes in ECl, we considered how [Cl−]i evolves during stimulus transients. This was motivated by experimental observations that EGABA can rapidly collapse during bursts of GABAAR synaptic events [20], [28], [39], [40]. Activity-dependent changes in EGABA depend on the location of the input: somatic input has less impact on EGABA than dendritic input [4], [28]. Simulations in our electrodiffusion model replicated those experimental data (Fig. 7A) as well as results from simpler models [19]. A train of synaptic inputs to the soma produced a small depolarizing shift in EGABA, which translated into a small reduction in GABAAR-mediated current. The depolarizing shift in EGABA was greater and occurred increasingly faster for input to progressively more distal dendrites. This was despite the presence of KCC2 (red). Removing KCC2 (black) increased the amplitude and speed of the collapse in Cl− gradient during high frequency input to distal dendrites, but had virtually no impact for input to the soma. The finding that amplitude of the initial synaptic event in each of the compartments was unaffected by removing KCC2 appears to contradict the observation that the standing [Cl−]i gradient depends on KCC2 activity (see Fig. 4). We hypothesized that this was due to the absence of ongoing Cl− load caused by the lack of background synaptic activity. We therefore repeated simulations shown in Figure 7A but with background synaptic input (Fig. 7B). As predicted, the initial IPSC amplitude was affected by the KCC2 activity level when background synaptic input was present (compare Fig. 7B and A). These results suggest that the rate of local intracellular Cl− accumulation depends principally on diffusion (which redistributes the intracellular Cl− load), whereas the extent of accumulation depends on KCC2 activity (which reduces intracellular Cl− load via extrusion).
To investigate these processes more thoroughly, we systematically varied the intraburst frequency, location of the “test” synapse and KCC2 activity, and we measured the mean IPSC amplitude at the “test” synapse throughout the burst. During high frequency input to distal dendrites, the net mean current through GABAAR synapses switched from outward to inward whereas the same rate of input to the soma continued to produce strong outward currents (Fig. 7C). Thus, while increasing intraburst frequency can effectively enhance hyperpolarization in the soma, it rapidly becomes ineffective in dendrites and can even become depolarizing in distal dendrites. For a fixed intraburst frequency, ECl converged to different steady-state levels (Fig. 7D) with different rates (Fig. 7E) depending on the location of the test synapse and the level of KCC2 activity. In other words, the steady-state value of [Cl−]i increased with distance from the soma (reminiscent of the standing gradient reported in Fig. 4A and C) and it decreased when KCC2 activity was increased. On the other hand, Cl− accumulation converged to a steady state more rapidly with increased KCC2 activity as well as with distance from the soma. The two convergence processes are due to different phenomena: Enhanced KCC2 activity allows the dendrite to restrict the extent of Cl− accumulation (see above), while Cl− accumulates faster in distal dendrites simply because the effective volume is smaller and diffusion is restricted. In summary, under dynamic conditions, restricted diffusion in distal dendrites causes a rapid collapse of EGABA, but the extent of this collapse is limited by KCC2, consistent with experimental measurements [8], [9], [28].
The above results led us to predict that, for equivalent total synaptic input, many broadly distributed GABAAR synapses activated at low frequency would produce greater hyperpolarization than a few clustered synapses (or just one synapse) activated at higher frequency, especially for synapses located on distal dendrites. We tested this by comparing the outward current produced by one synapse activated at an intraburst frequency of 50 Hz with the total hyperpolarizing current produced by ten distributed synapses activated at 5 Hz; this was repeated for dendritically and somatically positioned synapses (Fig. 8A). In the soma, ten synapses activated at 5 Hz produced more outward current than one synapse activated at 50 Hz (Fig. 8A middle). This is due to the fact that the total synaptic conductance does not scale linearly with frequency because of saturation. Even more important is the fact that distributed dendritic input is capable of producing a strong outward current despite Cl− accumulation, whereas clustered dendritic input was totally inefficient in producing an outward current. These results suggest that dendritic inhibition is most effective when spatially distributed, consistent with data in Figs. 3 and 6. Maintaining spatially distributed GABAA synapses in dendrites is also important because the rapid dynamic collapse of distal hyperpolarizing GABAAR currents will limit their effectiveness at controlling somatic signals because membrane potential changes extend farther than changes in conductance [8], [41]. Given that shunting remains even when ECl collapses, we submitted the neuron to distributed excitatory input and measured the mean firing frequency of the model neuron to verify that loss of hyperpolarizing current translates into effective disinhibition (Fig. 8A right). We found that firing rate reduction mirrored the change in charge carried (cf. Fig. 8A right and middle panels).
In addition to synapse location, the rate and duration of synaptic inputs would be expected to interact with dynamic changes in EGABA to alter the efficacy of inhibition. Although increasing the rate or duration of GABAAR inputs may initially increase IPSC amplitude, such changes would also accelerate depletion of the Cl− gradient and thereby eventually reduce IPSC amplitude, at least when Cl− influx overwhelms local diffusion mechanisms and Cl− extrusion capacity. Using our model, we studied the influence of KCC2 activity level, synaptic frequency and time constant of GABAAR-mediated events (τIPSC) on the mean current through a dendritic GABAAR synapse. Simulations indicated that increasing KCC2 activity always led to larger mean outward current. In contrast, increasing synaptic input frequency (Fig. 8B left) or τIPSC (Fig. 8B right) did not necessarily increase the mean current; in both cases, the mean current was largest at intermediate values of those parameters. Similarly, mean firing rate was reduced most at intermediate values of those parameters (Fig. 8C). To establish the generality and robustness of the result, we repeated simulations for neurons endowed with different ion channels affecting spike generation. We added non-inactivating Ca2+-activated K+ channels known to decrease firing rate or persistent Na+ channels known to increase firing rate, and we also performed simulations in which dendritic Hodgkin-Huxley (HH) channels were concentrated at branch points. Although these modifications to the model changed the overall firing rate, our qualitative finding remained unchanged; that is, firing rate increased if GABAAR input was augmented beyond a certain level (Fig. 8C right).
The above results indicate that more or longer GABAAR inputs may not always produce more inhibition, i.e. stronger outward current. We therefore asked what GABAAR input conditions produce the strongest inhibition? This question was addressed by measuring which parameter combinations produced the largest outward current. We found that the GABAAR input frequency yielding the largest outward current increased with KCC2 activity and decreased with τIPSC (Fig. 8D). This optimal frequency was as low as 6 Hz when KCC2 activity was depleted to 10% of its normal value and τIPSC was set to 50 ms; in other words, GABAAR-mediated synaptic events occurring either at lower or at higher frequencies than 6 Hz produced less outward current. The optimal GABAAR input frequency climbed to 28 Hz when KCC2 activity was set to baseline and τIPSC was set to 10 ms. Thus, the optimal GABAAR input frequency may vary quite widely depending on other factors, but the key observation is that beyond some point (determined by the robustness of Cl− homeostasis), more GABAAR input does not necessarily produce more inhibition. Increasing the frequency of GABAAR input showed a similar inverted bell-shaped curve when estimating effective inhibition with either total charge carried or firing rate reduction (Fig. 8B and C).
Results of simulations presented in Figure 7 showed that the current through GABAAR could reverse polarity if there was sufficient accumulation of intracellular Cl−. However, as the Cl− gradient collapses, one would expect Cl− flux to stop, but not to change its direction; likewise, the IPSCs would be expected to become smaller but not to invert. Indeed, if the GABAAR is modeled as passing only Cl− ions, the IPSC decreases in size as Cl− accumulates intracellularly, but it does not reverse direction (Fig. 9A) thus showing that bicarbonate flux must be accounted for in order to explain IPSC inversion [42], [43]. An important and novel feature of our model is that HCO3− is not assumed to be constant. Even if the relative stability of [HCO3−]i has been shown to result from complex interaction between HCO3− efflux, carbonic anhydrase-mediated reaction and proton extrusion mechanisms, most models choose to consider it constant de facto. However, simulating the various mechanisms involved in [HCO3−]i management proved a useful tool for investigating the legitimacy of assuming [HCO3−]i is constant and for studying potential interactions between Cl− and HCO3− dynamics. Bicarbonate efflux produces an inward current, but that current is (normally) masked by the larger outward current produced by Cl− influx, since the permeability ratio between Cl− and HCO3− anions is approximately 4∶1 [2], [43]. But as the Cl−-mediated outward current becomes smaller, the HCO3−-mediated inward current becomes relatively larger, eventually causing the net current through GABAAR to become inward. Unlike the Cl− gradient, the HCO3− gradient tends not to collapse (Fig. 9B) because intracellular HCO3− is replenished by carbonic anhydrase-catalyzed conversion of CO2, which can readily diffuse across the membrane [44], [45].
But although the reactants of the carbonic anhydrase-catalyzed reaction (i.e. CO2 and H2O) are not depleted, the forward reaction produces H+ in addition to HCO3−. By removing HCO3−, GABAAR activity would be expected to reduce the intracellular pH, which has been observed experimentally [24]. Since accumulation of intracellular H+ shifts the equilibrium point of the reaction, intracellular HCO3− slowly decreases, with a time constant in the order of several seconds, which explains the small hyperpolarizing shift in EHCO3 seen in Figure 9B over long time scales. By ECl and EHCO3 shifting in opposite directions, EGABA tends toward the membrane potential. We therefore predicted that reducing changes in EHCO3 would lead to greater changes in ECl and, vice versa, that reducing changes in ECl would lead to greater changes in EHCO3. To test the first prediction, [HCO3−]i was held constant (thus maintaining HCO3− efflux), which enhanced the depolarizing shift in ECl; on the other hand, increasing intracellular HCO3− depletion by reducing proton extrusion via the Na−-H+ exchanger (thus reducing HCO3− efflux) mitigated the depolarizing shift in ECl (Fig. 9C). To test the second prediction, [Cl−]i was held artificially constant, which enhanced the hyperpolarizing shift in EHCO3; conversely, increasing intracellular Cl− accumulation by reducing Cl− extrusion via KCC2 mitigated the hyperpolarizing shift in EHCO3 (Fig. 9D). These results demonstrate a trade-off between stability of [Cl−]i and stability of intracellular pH based on their common reliance on [HCO3−]i. It remains an open question whether [Cl−]i or intracellular pH is more strongly regulated under normal conditions, but one can reasonably extrapolate when KCC2 activity is reduced, that the primary depolarizing shift in ECl will conspire with a smaller secondary hyperpolarizing shift in EHCO3 to produce a large depolarizing shift in EGABA. This is particularly relevant to steady state conditions because, on the time scale of individual synaptic events, pH buffering mechanisms are not saturated, while on longer time scales, the rate limiting components of HCO3− homeostasis are the slower kinetics of the HCO3− and H+ membrane transporters.
The Cl−/HCO3− exchanger can also play a role in pH management and Cl− homeostasis regulation. To gain some insight into the impact of this exchanger, we repeated simulations of Figure 9C–D adding different levels of Cl−/HCO3− exchanger activity to the model. As is the case for such ion exchangers, the Cl−/HCO3− exchanger will drive ECl and EHCO3 towards one another, namely depolarizing ECl and hyperpolarizing EHCO3 (Fig. 9E). This result may seem counterintuitive since the exchanger would be expected to play a helpful role in pH management. However, in the instance of another source of acidification, EHCO3 can undergo a hyperpolarizing shift, and the resultant change in HCO3− gradient can reverse Cl−/HCO3− transport, driving Cl− out and HCO3− in, thus preventing overt acidification (Fig. 9F).
These results predict that ECl can become more hyperpolarized during episodes of acidification. To test this, we modeled H+ influx occurring over 5 seconds and monitored the time course of ECl during and after acidification in simulations with and without the Cl−/HCO3− exchanger. In such simulations, proton influx triggers a reaction with HCO3− thus leading to a decrease in [HCO3−]i. In turn, this leads to hyperpolarization of EHCO3 which will eventually become more hyperpolarized than ECl, effectively inverting the exchanger and leading to hyperpolarization of ECl (Fig. 9F). As the influx of H+ is stopped, H+ extrusion through the Na+/H+ exchange restores pH and the carbonic anhydrase mediated reaction is able to replenish intracellular HCO3−. As this slow change in [HCO3−]i translates into a change in the activity of the Cl−/HCO3− exchanger, the value of ECl slowly becomes more depolarized until it returns to its resting value (Fig. 9F). As expected, these changes in ECl cannot be observed when simulations are conducted without the Cl−/HCO3− exchanger (Fig. 9F). Thus, the Cl−/HCO3− exchanger may be seen as a failsafe mechanism preventing overt acidification, at least when this acidification is not caused by HCO3− efflux through GABAA channels.
To extrude Cl− from the cell, KCC2 must pass an equal number of K+ ions since the net process is electroneutral. Therefore, K+ efflux through KCC2 could reduce the transmembrane K+ gradient and produce a depolarizing shift in EK, which would, in turn, reduce Cl− extrusion via KCC2 because of the reduction in KCC2 driving force. To investigate this putative negative feedback mechanism, we varied KCC2 activity and measured the impact on EK (measured at the soma) in a model neuron receiving a fixed level of background excitatory and inhibitory synaptic input. Simulations showed that under conditions of distributed GABAAR input at in vivo-like background frequencies, KCC2 activity actually had little impact on EK unlike its large impact on ECl (Fig. 10A, compare left and right panels). We investigated this further by monitoring intra- and extracellular concentrations of K+ (Fig. 10B). Although large in absolute terms, changes [K+]i were small in relative terms, yielding much smaller shifts in EK than those observed with ECl. Furthermore, KCC2 activity had only a small influence on [K+]o, which is controlled principally by the balance of K+ leak conductance, active pumping by the Na+-K+-ATPase, and extracellular diffusion.
The insignificant effect of KCC2 activity on [K+]o is apparently inconsistent with experimental observations [46], but those experiments involved applying a heavy Cl− load, which is not comparable to the physiological conditions tested in Figure 10A and B. To test whether a larger Cl− load could provoke a KCC2-mediated increase in [K+]o, we simulated a constant 5 nS, 500 ms-long GABAAR conductance on a dendrite. Under those conditions, [K+]o was significantly altered by KCC2 activity, as shown by the positive correlation between the maximal value of [K+]o and KCC2 level (Fig. 10C). Repeating those simulations with reduced extracellular K+ clearance confirmed that extracellular diffusion did not dramatically alter [K+]o under these “heavy load” conditions (Fig. 10C). Regardless of whether KCC2 activity does or does not influence extracellular K+ accumulation, extracellular K+ accumulation is nonetheless expected to reduce the efficacy of KCC2 by reducing its driving force. To test this, we repeated the simulations shown in Figure 1D with different fixed values of [K+]o and observed that the KCC2 efficacy is indeed reduced by the extracellular K+ accumulation and stops passing ions when [K+]o = 10 mM (Fig. 10D).
It is important to understand that changes in [K+]o have a much larger effect on EK than equivalent absolute changes in [K+]i. Hence, although KCC2 activity is not expected itself to change EK under normal physiological conditions (see above), changes in EK caused by other factors (e.g. high firing rates, reduced Na+-K+-ATPase activity, etc.) reduce KCC2 activity. In other words, there is no closed negative feedback loop directly linking KCC2 and EK, but extrinsic factors can modulate Cl− extrusion by affecting extracellular K+ accumulation. Indeed, it is significant that Cl− extrusion could be reduced (and inhibition thereby rendered ineffective) under conditions where excessive spiking (perhaps the result of disinhibition) causes extracellular K+ accumulation – this would constitute a multi-step positive feedback loop (see also below).
As shown in previous sections, GABAAR input and KCC2 activity are prominent determinants of ECl. However, since Cl− influx depends on the Cl− driving force (i.e. V – ECl), variation in membrane potential will influence intracellular Cl− accumulation, as shown in voltage clamp experiments [20]. Therefore, we predicted that increased depolarization caused by increased synaptic excitation would exacerbate intracellular Cl− accumulation. To test this, the frequency of inhibitory synaptic events, finh, was fixed at 0.4 Hz/synapse while the frequency of excitatory synapses, fexc, was varied (0.4 Hz was chosen for inhibitory events so that when fexc/finh = 2, fexc was still within its normal physiological range [24], ). As predicted, the depolarizing shift in ECl scaled with fexc (Fig. 11A). Moreover, given that spike generation makes membrane potential a highly nonlinear function of synaptic activity, we further predicted that the presence or absence of spiking would have a profound influence on [Cl−]i because each spike represents a large, albeit short, increase in Cl− driving force; in other words, if GABAAR channels are open during a spike, those spikes are expected to dramatically accelerate intracellular Cl− accumulation. To test this, we measured Cl− accumulation in a model with and without spikes (i.e. with and without HH channels, respectively). Results confirmed that Cl− accumulation was indeed increased by spiking (Fig. 11B). The time series in Figure 11C shows the biphasic Cl− accumulation associated with this phenomenon: When inhibition was first “turned on”, it successfully prevented spiking but, over time, [Cl−]i increased asymptotically toward some steady-state value. If the associated steady-state EGABA was above spiking threshold (as in Fig. 11C), the membrane potential could increase beyond threshold and the neuron began spiking, at which point intracellular Cl− began a second phase of accumulation. This second phase of Cl− accumulation was paralleled by acceleration of the spike rate – clear evidence of the predicted positive feedback loop between spiking and Cl− accumulation, which leads to catastrophic failure of inhibition.
To verify experimentally the model prediction that excitatory activity exacerbates intracellular Cl− accumulation, especially when KCC2 activity is depleted, we performed [Cl−]i measurements in primary cultured neurons exposed to muscimol, followed by addition of furosemide and kainate. The latter was to cause tonic activation of AMPA subtype glutamate receptors. As predicted by the model, addition of furosemide caused Cl− accumulation in the cell, and subsequent application of kainate led to further accumulation (Fig. 11D).
The fact that ECl collapses as a result of GABAAR activity itself (Figs. 1, 3, 9) as well as excitatory input (Fig. 11A and D) and spiking (Fig. 11B and C) highlights the importance of treating ECl as a dynamic variable. To assess the importance of those dynamics on GABAAR modulation of the firing rate, we compared the relationship between firing rate and synaptic input in conditions where both inhibitory and excitatory input change in a proportional manner (i.e., finh α fexc). We performed simulations in which ECl was treated as a static value (as in conventional cable models) or as a dynamic variable (as in our electrodiffusion model). In the former case, EGABA was fixed at -65 mV, while in the latter case, KCC2 activity was reduced to 33% of its normal level. With weak excitatory and inhibitory input, spiking was higher in the model with static ECl (Fig. 11E). However, as the frequencies of excitatory and inhibitory inputs were increased, all the mechanisms that contribute to a collapse of ECl (examined above) combined to drive fout nonlinearly beyond the value predicted by fixed ECl (Fig. 11E). In short, these results show that ECl cannot be approximated by a single, static value when considering a range of stimulus conditions because of the rich dynamics governing ECl under natural conditions. Those dynamics can only be fully understood by accounting for numerous, interdependent biophysical processes.
In this study, we built a neuron model that incorporates multiple processes controlling ion flux in order to investigate how interactions between those processes influence GABAAR-mediated inhibition. This was prompted by the recognition that conventional neuron models make oversimplifying assumptions (e.g. reversal potentials are temporally invariant and spatially uniform or consider changes in only one ion specie) that are likely to be particularly consequential for GABAAR-mediated inhibition. For instance, experiments have shown that EGABA can shift during the course of sustained GABAAR input [2], [42], that EGABA is not uniform across different regions of the same neuron (our results and [26]–[28], [46]) and that EK has an important impact on Cl− dynamics. Computational simulations are an ideal tool for investigating questions related to electrodiffusion and interaction between multiple ion species as well as for making predictions to guide subsequent experiments, but the accuracy of those simulations depends on the accuracy of the starting model. With that in mind, we built a neuron model that tracked [Cl−] changes as well as other ions that interact with [Cl−] homeostasis. Our model accurately reproduced activity-dependent decrease of IPSC amplitude, including differential decrease depending on the site of synaptic input and the compartment geometry [1], [47]. Our model also reproduced spatial variations in EGABA and its dependence on the interplay between strength of cotransporter activity and spatial distribution of GABAAR input. Having thus validated the model, we explored several other questions.
Upregulation of KCC2 has been linked with the hyperpolarizing shift in EGABA observed during early development [7], [20], [45], [48]. Likewise, downregulation of KCC2 has been linked with the depolarizing shift in EGABA seen in various disease states [16], [49], [50]. However, the relationship between KCC2 and EGABA has not heretofore been quantitatively explored. Simulations in our electrodiffusion model showed that that relationship is highly nonlinear: Reducing KCC2 activity caused a dramatic depolarizing shift in EGABA, whereas increasing KCC2 activity above normal levels had only a small effect on EGABA. The reason is that KCC2 already operates near its equilibrium point under normal conditions [51]. These observations suggest that therapies aiming to restore depleted KCC2 levels should not cause excessively strong GABAAR-mediated inhibition if KCC2 overshoots its normal level. Moreover, the importance of investigating KCC2 regulation as a therapeutic target is emphasized by the observation that increasing the frequency or duration of GABAAR input cannot effectively compensate for disinhibition caused by KCC2 depletion since activity-dependent accumulation of intracellular Cl− is increased under those conditions. In fact, our simulations illustrate how the optimal rate and time course of GABAAR input mutually influence each other and also depend on the level of KCC2 activity. Those observations help to explain why drugs that act by increasing GABAAR input have variable effects on the treatment of pathological conditions involving disrupted Cl−homeostasis, e.g. in neuropathic pain or epilepsy. While administration of benzodiazepines has some efficacy at reversing tactile allodynia in neuropathic pain models, beyond a certain dose, they become counterproductive and enhance allodynia [52], [53]. This bell shaped response to benzodiazepines on neuropathic pain follows directly the predictions from our model (Fig. 8).
Beyond helping understand pathological conditions, our model also provides insight into synaptic inhibition under normal conditions. The importance of interactions between Cl− diffusion and transmembrane Cl− flux became apparent when we considered the temporal dynamics of [Cl−]. Simulations revealed that Cl− accumulation near a highly active synapse is rapidly redistributed by intracellular diffusion, whereas Cl− extrusion via KCC2 tends to act more slowly. The large volume of the soma keeps somatic [Cl−]i relatively stable, in contrast to dendrites where diffusion is limited by the small diameter of the compartment. Thus, on short time scales, the soma acts as a Cl− sink. It follows that the extent of Cl− accumulation in dendrites does not only depend on the diameter of the dendrite, but also on the distance of the synapse from the soma. Since the dendrite diameter tends to decrease with the distance from the soma, the effects on diffusion are cumulative. As a result, diffusion is responsible for redistributing (and thus mitigating) transient, local changes in Cl− load, while KCC2 level controls the steady-state balance of Cl− influx and efflux. Thus, the faster dynamical collapse of EGABA that occurs upon repetitive GABAAR input to distal dendrites results from limited diffusion rather than from inefficiency of Cl− extrusion.
xThe functional impact of this result is that distributed synaptic input is more effective than clustered input, especially on distal dendrites where longitudinal Cl− diffusion is particularly restricted. The more labile Cl− gradient in distal dendrites causes a rapid collapse of GABAAR-mediated hyperpolarization upon repetitive input, which limits its ability to influence somatic integration especially because, although remote current sources can hyperpolarize the soma, remote conductances do not cause shunting in the soma [1]. This implies that multiple GABAergic connections originating from the same presynaptic cell will be more effective if those synapses are distributed on different dendritic branches. It is interesting to note that this corresponds to the morphological arrangement observed in several systems [54]. This broad distribution contrasts the clustering of axo-axonic synapses that necessarily occurs when a presynaptic cell forms multiple synapses on a postsynaptic neuron's soma and AIS [54], [55]. In the latter case, dynamical collapse of EGABA does not occur because the soma acts as a Cl− sink.
The functional impact of the standing [Cl−]i gradient along the somato-dendritic axis resulting from the interplay between background GABAAR input and cotransporter activity may lead, under certain conditions, to differential impact of distal dendritic vs. somatic GABAergic synaptic input such as, for example, concurrent dendritic GABAA-mediated excitation and somatic inhibition [1].
In addition to Cl− dynamics, one must keep in mind that Cl− flux does not occur independently from other ion species. For example, Cl− influx through GABAAR is coupled with HCO3− efflux. The relationship between Cl− flux and HCO3− flux is crucial for explaining how the net current through GABAAR can invert as Cl− accumulates intracellularly [2], [44]. Beyond causing a given shift in EGABA, the HCO3− efflux has consequences on the dynamics of the system. Without HCO3− efflux, Cl− influx would rapidly stabilize when membrane potential reached EGABA because EGABA would equal ECl. However, due to HCO3− efflux, and given that EGABA is less negative than ECl, intracellular Cl− continues to accumulate when the membrane potential initially reaches EGABA. In the absence of other extrinsic factors and during sustained GABAAR input, intracellular Cl− accumulation and membrane potential drift would progress until ECl = EGABA = EHCO3. This progression may, however, be prevented by the influence of other intrinsic currents. In any case, HCO3− efflux effectively delays stabilization of the system until a more depolarized membrane potential is reached, which can make a crucial difference for whether or not membrane potential increases above spike threshold (see below). Consistent with these observations, a recent study showed that blocking carbonic anhydrase (and thereby presumably reducing HCO3− efflux through GABAAR) can mitigate some of the behavioral manifestations of neuropathic pain thought to arise from KCC2 downregulation [52]. Moreover, based on their common reliance on HCO3−, regulation of [Cl−]i competes with regulation of intracellular pH on long time scales (tens of seconds to minutes) consistent with experimental observations [3], [24], [56]. One functional consequence of this is that intracellular Cl− accumulation (and, by extension, possibly the loss of KCC2 expression in pathological conditions) may act as a protective mechanism to prevent an excessive drop in intracellular pH during sustained GABAAR input.
The relationship between pH and Cl− homeostasis may also be relevant to recent controversies regarding the necessity of ketone bodies for maintenance of EGABA in the developing nervous system [57]–. Given the HCO3− dependence of the beta-hydroxybutyrate effect on EGABA in these experiments, it has been proposed that the explanation may reside in the fact that beta-hydroxybutyrate, lactate or pyruvate act as weak organic acids, thus acidifying the neuronal cytoplasm and reversing Cl−/HCO3− exchange; this counteracts the drop in [HCO3−]i due to acidification but, by the same token, it lowers [Cl−]i and drives EGABA to a more negative value [59], [61]. Our simulations are consistent with this explanation.
Given the coupled efflux of Cl− and K+ through KCC2, Cl− extrusion happens at the expense of extracellular K+ accumulation. This may appear counter-productive as extracellular K+ accumulation counteracts inhibition and plays a role in the onset of epilepsy [62], [63]. However, we found that under physiological conditions, K+ efflux through KCC2 is offset by the fact that KCC2 activity enhances inhibition, thus decreasing firing rate and reducing K+ efflux via transmembrane channels. The net effect is a reduction of excitability because K+ efflux via transmembrane channels is larger than via KCC2. We found that this negative feedback stabilizes [K+]o over a wide range of KCC2 activity. Disrupting this homeostasis requires sustained input from extrinsic factors. For example, intense GABAergic activity, which can maintain a continuous Cl− load leading to a large and sustained K+ efflux through KCC2, has been observed during giant depolarizing potentials [46]. Likewise, excessive spiking yields continuous extracellular K+ accumulation, which renders KCC2 inefficient, causing a collapse of inhibition due to intracellular Cl− accumulation.
Another interesting observation was that membrane depolarization tends to encourage intracellular Cl− accumulation because Cl− influx through GABAAR depends on Cl− driving force, which is increased by depolarization. The consequences are profound: If sustained GABAAR input fails to prevent depolarization caused by concurrent excitatory input, the resulting depolarization will accelerate Cl− influx, which in turn further reduces the GABAAR-mediated outward current, thus supporting a positive-feedback cycle of failing inhibition. If the membrane potential reaches the spike threshold under these conditions, spike generation compounds the positive feedback process leading to an absolute failure of inhibition having potentially catastrophic consequences with respect to the neuron's response to stimulation. The only way for a neuron to avoid entering this vicious cycle is to regulate [Cl−]i, through Cl− extrusion via KCC2.
In summary, we built a neuron model that incorporates multiple processes controlling the flux of different ion species in order to investigate how interactions between those processes influence inhibition mediated by GABAAR. Many of those processes cooperate or compete with one another, thus producing nonlinearities. The most dramatic of those is arguably the catastrophic failure of inhibition that can develop when depolarization and spiking conspire with Cl− accumulation to form a positive feedback loop. As demonstrated in this study, such details may be critical for understanding important aspects of synaptic inhibition, in particular, for understanding why and how inhibition fails under certain pathological conditions.
We built a conductance-based model of a whole neuron using the NEURON simulation environment [64] (model code will be made available at ModelDB). The model is composed of 30 dendritic compartments unless otherwise indicated, one somatic compartment, one compartment for the axon initial segment (AIS), and 10 myelinated axonal compartments separated by nodes of Ranvier. Details of the geometry are summarized in Figure 1A. Ionic currents flowing through channels, pumps and cotransporters were computed at each time step in order to update the membrane potential according to where C is membrane capacitance of the neuron compartment and the sum is taken over synaptic currents, current through voltage gated channels and electrogenic Na-K ATPase. Transmembrane ion flux due to those currents was also calculated. Moreover, longitudinal and radial diffusion were incorporated into the model in order to account for intracellular ion gradients. Likewise, extracellular ionic diffusion was taken into account as well as chemical reactions that produce the various ion types (see below). Transmembrane ion flux, ion movement through diffusion, and ion generation through chemical reactions (Fig. 1B) were all taken into account when updating the concentration of ion specie x in each compartment at each time step according to the differential equation , where F is the Faraday constant, z is the ion valence, Vol is the compartment volume, Reacx is a term accounting for chemical reactions involving ion species x and Diffx is a term modeling the electrodiffusion of ion x [65], [66]. Synaptic events are expressed in currents, but the membrane potential was not clamped, consistent with realistic conditions. This is of importance since invasive cell manipulations have been shown to alter the nature (inhibitory or excitatory) of GABAA mediated input [67].
Ion currents obey the equation where Ex denotes the reversal potential for ion x and gx is the channel conductance with respect to ion x. Reversal potentials were continuously updated during the simulation using the Nernst equation where R is the perfect gas constant and T is absolute temperature, which was taken to be 310°K (37°C). Because GABAA receptors pass both Cl− and HCO3− anions in a 4∶1 ratio [24], EGABA was calculated using the Goldman-Hodgkin-Katz equation
Each of these ionic currents was taken into account for computing change in concentration of their respective ion species and their sum yielded the net current used to update the membrane potential.
Synaptic input was modeled as a Poisson process. Each inhibitory synapse was activated at a mean frequency of 0–10 Hz and each excitatory synapse was activated at a mean frequency of 0–2 Hz. Unless otherwise stated, the maximal conductance of inhibitory synapses was 1±0.3 nS (mean ± standard deviation) and kinetics were modeled as instantaneous rise and exponential decay with τIPSC of 30 ms [30], [68]–[70]. GABAAR synaptic density was 60 synapses per 100 µm2 in the AIS, 40 synapses per 100 µm2 in the soma and 12 synapses per 100 µm2 in the dendrites. Density of excitatory synapses was 60 synapses per 100 µm2 in dendrites and no excitatory synapses were present elsewhere [21], [30]. Unless otherwise stated, the maximal conductance of excitatory synapses was taken to be 0.5±0.2 nS (mean ± standard deviation) and the kinetics were modeled as an instantaneous rise and exponential decay with τEPSC of 10 ms.
Hodgkin-Huxley (HH) channels were modeled using parameter values reported by [12]. The voltage-dependant Na+ current was given by
Where VT = −58 mV and VS = −10 mV. The voltage gated K+ channels were described by
The density of HH channels was 12 mS/cm2 in the AIS and 1.2 mS/cm2 in soma and dendrites [21], [30]. The model also included K+ and Na+ leak channels with respective densities of 0.02 mS/cm2 and 0.004 mS/cm2 in soma, 0.03 mS/cm2 and 0.006 mS/cm2 in proximal dendrites, 0.1 mS/cm2 and 0.02 mS/cm2 in distal dendrites, 0.02 µS/cm2 and 0.004 µS/cm2 in axon internodes, and 15 mS/cm2 and 3 mS/cm2 in axon nodes as described in [21], [30].
For some simulations, we added other types of conductances to account for the many possible types of spike generating mechanisms. Namely, we added non-inactivating Ca2+-activated K+ channels and persistent Na+ channels to test spike reducing and spike enhancing mechanisms, respectively. The Ca2+-activated K+ channels obey the following sets of equations
Where the auxiliary functions are defined by
With the constants defined as cvm = 28.9 mV, ckm = 6.2 mV, ctm = 0.000505 s, cvtm1 = 86.4 mV, cktm1 = -10.1 mV, cvtm2 = -33.3 mV, cktm2 = 10 mV. τz = 1 s, ch = 0.085, cvh = 32 mV, ckh = -5.8 mV, cth = 0.0019 s, cvth1 = 48.5 mV, ckth1 = -54.2 mV, cvth2 = -54.2 mV, ckth2 = 12.9 mV.
The persistent Na+ channels were described by the following set of equations:
Where the auxiliary functions are defined by
Where the constants are given by vsm = −2 mV and vsh = −5 mV.
Finally, to account for non-synaptic, tonically activated Cl− conductance, in some simulations we added GABAA leak channels with the same ratio of Cl−:HCO3− permittivity as the synaptic channels, the density of such channels was 0.003 mS/cm2 in soma, 0.0045 mS/cm2 in proximal dendrites, 0.015 mS/cm2, 0.003 µS/cm2 in axon internodes and 2.3 mS/cm2 in axon nodes.
The Na+-K+-ATPase pump uses the energy from hydrolysis of one ATP molecule to pump three Na+ ions out of the neuron and two K+ ions inside. The activity of this pump is dependent on [K+]o and [Na+]i as well as on the membrane potential as observed in [71]. The Na+ current through the pump is given by the following equations[72], [73]. With Km,Ko = 1.5 mM, Km,Nai = 10 mM and NaHalf = 20 mM. The outgoing Cl− flux though KCC2 was modeled according to [2], [4] by
The K+ current through KCC2 was assumed to be equal in absolute value but opposite in polarity to the Cl− current so that net current through KCC2 was equal to zero. The maximal Cl− current going through KCC2 was taken to be ICl,max = 0.3 mA/cm2 for the normal activity level. This value was chosen to give ECl = −80 mV when mean synaptic input frequencies were 3.2 Hz and 1.6 Hz for inhibition and excitation, respectively. This value also turned out to yield maximal Cl− clearance rates near 10 mM/s, consistent with experimental data [4]. The value of the driving force (ECl-EK) at which the Cl− current through KCC2 reaches its half maximal value (Vhalf) was set to 40 mV. This corresponds to [Cl−]i = 15 mM under the assumption that [Cl−]o = 120 mM and EK = -95 mV.
In some simulations, we also modeled NKCC1 activity in the AIS. The Cl− influx through NKCC1 was modeled by
Where ENKCC1 is the value of ECl at which Cl− flow through NKCC1 reverses direction and is given by ENKCC1 = (EK+ENa)/2. The maximal Cl− current going through NKCC1 was taken to be ICl,max = 0.3 mA/cm2 for the normal activity level which was taken to equal the value obtained for maximal current through KCC2. Na+ and K+ currents through NKCC1 were each half of ICl,NKCC1 so that net current through NKCC1 was equal to zero.
Finally, in some simulations we also modeled the Cl−/HCO3− exchanger which was assumed to be ubiquitous in the neuron and uniformly distributed on the membrane. Kinetics of the exchanger were described by the following simplified equation.where ICl,max = 0.1 mA/cm2 and Vhalf was set to 50 mV. The HCO3- and Cl- currents through the exchanger were taken to be equal in amplitude but opposite in direction so that the exchange process is electroneutral.
Since HCO3− ions also flow through GABAAR channels (see above), it was important to model the ionic fluxes and reactions regulating [HCO3−]. Intracellular HCO3− loss due to outgoing flux via GABAAR is compensated by the carbonic anhydrase-catalyzed reaction [3], [44], [56], [74]. Since the diffusion of CO2 gas through the membrane is faster than ionic fluxes through channels, we treated pCO2 as constant. The equilibrium constant of the reaction was 10−6.35 M and the rate constant for CO2 hydration was 106 sec−1 [75]. Since the above reaction produces a drop in pH [42], [44], [56] by causing intracellular H+ accumulation, we also modeled the reaction between H+ and the main buffering ion H2PO4− such that H+ buffering occurred through the reaction . Although other reactions play important roles in pH buffering, we kept the model as simple as possible while preserving the global value of pH buffering capacity, estimated to be 25–30 mMol/pHU [76], [77]. Buffering reactions are responsible for maintaining the pH value constant at short time scales (∼100 ms), but proton extrusion via exchangers plays an important role on longer time scales (>10s). For the sake of simplicity, we limited ourselves to modeling the Na+-H+ exchanger such that the proton flux was given bywhich is a generic scheme for transporters. We used IH,max = 0.03 mA/cm2 and Vhalf = 10 mV. These values were chosen to make the model consistent with the global proton extrusion rate in healthy neurons that has been measured to be 0.04 pHU/s [78]–[80].
An important and novel feature of the model is that the intra- and extracellular concentrations of K+, Na+, Ca2+, Cl−, HCO3− as well as intracellular concentrations of H+, HPO42− and H2PO4− were treated as dynamical variables updated in each compartment at each time step. Each compartment was divided in four concentric annulus-shaped subcompartments to account for radial diffusion. Diffusion coefficients were assumed to be the same as in water (in 10−5 cm2/s): 2.03 for Cl−, 1.33 for Na+, 1.96 for K+, and 9.33 for H+ [81]. Longitudinal electrodiffusion is described by the equation where y stands for the longitudinal axis, Dx for the diffusion coefficient with respect to ion specie x, Vol for section volume and Surf for the surface of the cross section [66]. The first term is due to pure diffusion while the second term accounts for the electrical force acting on the ions. The second term was used only to compute electrodiffusion between outer annuli of dendritic sections and was set to 0 for inner annuli, consistent with the fact that electrical field extends only to a thin region near the membrane. This is because the membrane act as a capacitor and electric field is known to decrease rapidly [82]. Radial diffusion was computed in a similar way but with y representing the radial axis.
Extracellular space was represented as a thin shell (i.e. Frankenhaeuser-Hodgkin or FH space) with equivalent volume equal to one fourth the intracellular volume of the corresponding cell compartment. The inner surface of the FH space communicated with the adjacent intracellular compartment while the outer surface was linked to an infinite reservoir where ion concentrations were assumed to be constant. This modeling takes into account changes in [K+]o due only to our cell, and thus does not address changes in [K+]o due to network activity. The study of such network related effects is beyond the scope of the current study. The equation used to update extracellular ion concentration is given by where z is the ion valence, kbath is the concentration of ion x in the infinite reservoir and τFH is the time constant taken to be 100 ms [79], [83].
The differential equations were integrated using a forward Euler method with a time step of 0.05 ms. Several preliminary simulations showed this time step to be both sufficiently small for accurate equation solving, while sufficiently large for reasonably fast computing. Initial intracellular concentrations were (in mM): [Cl−]i = 6, [K+]i = 140, [Na+]i = 10, [HCO3−]i = 15 [H2PO4−]i = 30 and [HPO42−]i = 30. Initial extracellular concentrations were in (mM) [Cl−]o = 120, [K+]o = 3, [Na+]o = 45 and [HCO3−]o = 25 [81]. Preliminary simulations were conducted to determine initial concentrations such that they were stable under normal conditions (in the absence of high frequency synaptic input). For simulations in which the value of maximal Cl− current through KCC2 (ICl,KCC2) was different than the normal one stated above (ICl,max = 0.3 mA/cm2), different initial values of [Cl−]i were used in order to start the simulation near steady state, as determined by preliminary testing. Unless stated otherwise, simulations were run for 200 s of simulated time, short enough to allow manageable simulations and long enough to allow collection of sufficiently large data sample to insure relevance of mean values.
Dissociated hippocampal neurons from Sprague-Dawley rats were prepared as previously described [84] plated at P0 to P2 at a density of approximately 500–600 cells/mm2 and imaged after 21–30 days in vitro (DIV). Glial proliferation was stopped at 5 DIV with Ara-C.
Cells were loaded in a 5 mM solution of the Cl− indicator MQAE (N-6-methoxyquinolinium acetoethylester; Molecular Probes) for 30 min at 37 °C [85]. Prior to observation, cells were transferred to a perfusion chamber and bathed in bicarbonate-buffered saline containing: 100 NaCl, 2.5 KCl, 1 NaH2PO4, 26 NaHCO3, 1 MgCl2 and 1.2 CaCl2. Muscimol (100 µM, Tocris), furosemide (50-200 µM, Sigma), kainic acid (50 nM, Tocris), VU 0240551 (25-50 µM, Tocris) and bicuculine (100 µM, Sigma) were selectively added as described in the result section. Upon addition of drugs, cells were allowed to adjust for 10–20 minutes before a steady-state image of their Cl− contents was taken.
Fluorescence lifetime images of MQAE were acquired using a Becker & Hickl SPC-830 module coupled to a Zeiss LSM 510 microscope. MQAE was excited using a femtosecond pulsed Ti-Sapphire laser tuned at 760 nm (Chameleon Ultra, Coherent), through a 40X water-immersion objective (Zeiss, 0.8 NA). Fluorescence lifetime data was collected through the non-descanned port of the microscope using a band-pass filter (469/35 nm, Semrock) coupled to a laser block (short-pass 750 nm; Semrock). Photon emission was detected using a PMC-100-1 photosensor (Hamamatsu). Lifetime in each cell compartment was calculated and extracted using SPCImage software (Becker & Hickl). Lifetime in the cell body was averaged over the total cell body area excluding the nucleus region, whereas in the dendrites it was averaged in segments of 4 µm over 120 µm of dendrite length. Fluorescence lifetime measurements were used because they are not sensitive to dye concentration (peak intensity) in the range we are using [85], [86]. The lifetime measurements are thus not affected by differences in dye loading from cell to cell or by volume changes that could occur in different cell compartments (Fig. 2A). The Cl− dependence of MQAE lifetime is described by the Stern-Volmer relation (τ0/τ = 1 + Ksv [Cl−]i), where τ0 is the fluorescence lifetime in 0 mM Cl−, and Ksv, the Stern-Volmer constant, is a measure of the Cl− sensitivity of MQAE (Fig. 2B).
For calibration of absolute Cl− concentrations, the fluorescence lifetime of MQAE-loaded cells was measured in the presence of different known extracellular [Cl−] (8, 15 or 20 mM) in the bath. To dissipate the Cl− gradient across the membranes, 20 µM tributyltin (Cl−-OH exchanger) was used and 20 µM nigericin (K+-H+ exchanger) was added to clamp the intracellular pH using high K+ driving force while Cl− changes. Calibration solutions contained (in mM) KCl and KNO3 (140 K+ total with desired amount Cl−), 10 D-glucose, 10 HEPES, 1.2 CaCl2, 1 MgCl2, pH adjusted to 7.2 using KOH.
Primary hippocampal cultures (5 and 28 DIV) were fixed for 10 min with 4% paraformaldehyde and then permeabilized for 45 min with 0.2% triton in 10% normal goat serum (NGS). Primary antibody incubations were performed overnight at 4 °C using a polyclonal marker of KCC2 (Rabbit anti-KCC2 1∶500, Upstate) in the presence of 5% NGS. Alexa 546 conjugated secondary antibodies (1∶750; Invitrogen, Eugene, OR) were applied for 2 hrs at room temperature. Images were obtained on the Zeiss LSM 510 microscope using a 63X/1.4NA oil objective (Zeiss).
SpIDA is a recently developed analysis method that can resolve concentration of mixtures of different monomeric and oligomeric labels in single fluorescence images by fitting its intensity histogram. Precise details of the technique and detector calibration are presented in [33]. Briefly, the intensity histogram fitting function for a system with density of N particles is:
Where with .I(r) is the illumination intensity profile of the excitation laser, ε represents the quantal brightness of a single fluorescent particle, and k is the probability of observing an intensity of light (assumed to be proportional the number of photons emitted). H is normalized over all intensity values so the integral over k gives one. A constant factor, A, is introduced, which is the number of pixels in an analyzed region of the image where the fluorescent particles are distributed. This allows for the fit of an image intensity histogram to be performed. Three parameters are fit: the number of pixels (A), the fluorescent particle density (N particles per laser beam effective focal volume) and the quantal brightness (ε intensity units, iu, per unit of pixel integration time). In confocal laser scanning microscopes, the fluorescence intensity is measured using photon multiplier tubes (PMTs), and the number of collected photoelectrons is a function of the polarization voltage.
If dimers are present in the sample, they will yield quantal brightness of 2ε. When the monomer and dimer populations are mixed within the same region in space, the total histogram becomes the convolution of the two individual distributions:
To obtain accurate results, noise characteristics of the detector also has to be studied and taken into account in the analysis, See [33] for complete analysis.
For each sample, an optimal setting of the laser power and PMT voltage was chosen to minimize pixel saturation and photobleaching. The CLSM settings were kept constant for all samples and controls (Laser power, filters, dichroic mirrors, polarization voltage, scan speed). Acquisition parameters were always set within the linear range of the detector which was determined by calibration [33]. All the images were 1024×1024 pixels with pixel size of 0.115 µm and 9.1 µs pixel dwell time. The z-stacks were taken by optical sectioning with a z step of 0.5 µm per image.
All experiments were performed in accordance with regulations of the Canadian Council on Animal Care.
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10.1371/journal.pgen.1000500 | The Role of Geography in Human Adaptation | Various observations argue for a role of adaptation in recent human evolution, including results from genome-wide studies and analyses of selection signals at candidate genes. Here, we use genome-wide SNP data from the HapMap and CEPH-Human Genome Diversity Panel samples to study the geographic distributions of putatively selected alleles at a range of geographic scales. We find that the average allele frequency divergence is highly predictive of the most extreme FST values across the whole genome. On a broad scale, the geographic distribution of putatively selected alleles almost invariably conforms to population clusters identified using randomly chosen genetic markers. Given this structure, there are surprisingly few fixed or nearly fixed differences between human populations. Among the nearly fixed differences that do exist, nearly all are due to fixation events that occurred outside of Africa, and most appear in East Asia. These patterns suggest that selection is often weak enough that neutral processes—especially population history, migration, and drift—exert powerful influences over the fate and geographic distribution of selected alleles.
| Since the beginning of the study of evolution, people have been fascinated by recent human evolution and adaptation. Despite great progress in our understanding of human history, we still know relatively little about the selection pressures and historical factors that have been important over the past 100,000 years. In that time human populations have spread around the world and adapted in a wide variety of ways to the new environments they have encountered. Here, we investigate the genomic signal of these adaptations using a large set of geographically diverse human populations typed at thousands of genetic markers across the genome. We find that patterns at selected loci are predictable from the patterns found at all markers genome-wide. On the basis of this, we argue that selection has been strongly constrained by the historical relationships and gene flow between populations.
| One of the central problems in evolutionary biology is to understand the genetic and ecological mechanisms that drive adaptation. With the advent of large-scale SNP and DNA sequence data it is now possible to study selection and adaptation at a genome-wide scale. In recent years there has been considerable progress in identifying potential signals of selection in a wide variety of species [1]–[4].
In this study, we focus on recent adaptation in human populations. In particular, we examine the role of geography and population history in the spread of selectively favored alleles. The methods that we use provide information about adaptive events that have occurred since the divergence of African and non-African populations—i.e., over the last 50–100 KY [5]–[8]. During this time period the environment and ecology of humans have changed profoundly. Humans have spread out of Africa to colonize almost all of the world's land mass, and in the process have experienced a vast range of new climates, diets and ecosystems [6],[9]. Humans have also encountered new pathogens as they moved around the globe and moved into close proximity with domesticated animals, and as human population densities increased.
These changes in human ecology suggest that there has been ample scope for the action of natural selection in recent human evolution. Moreover, most species, including humans, probably face various additional selection pressures on a persistent basis: e.g., due to sexual competition, viability selection and resistance to evolving pathogens. Hence, it seems reasonable that our genomes would show evidence for recent selection, and there is great interest in understanding what types of environmental pressures and biological processes show the strongest signals of adaptation [1],[10],[11].
Some of the strongest evidence for recent adaptation comes from candidate genes where there is both a strong biological hypothesis for selection as well as evidence for selection from unusual haplotype patterns, homozygosity, or extreme values of FST [1]. Examples include genes involved in malaria resistance such as G6PD and the Duffy antigen gene [12]–[14]; genes involved in lighter skin pigmentation in non-Africans (e.g., SLC24A5, SLC45A2 and KITLG) [15]–[21]; and a pair of genes involved in dietary adaptations (lactase and salivary amylase) [22]–[25].
Recent studies have also cast a wider net to identify signals of selection using genome-wide SNP data [16], [17], [26]–[31], or large-scale resequencing data [32],[33]. Most of these studies report many candidate signals of positive selection. However, for most of the signals detected in this way, we do not yet know how the variation affects phenotypes or the nature of the selective pressures; indeed even the target genes are often uncertain. It is difficult to assess what fraction of the candidate signals are genuinely due to selection, rather than being extreme outliers in the neutral distribution [34]; however, simulations generally show that extreme values of various test statistics are more abundant in the real data than would be expected under neutral models [16],[17],[28],[35]. Some studies have also reported enrichment of selection signals in and around genes, as might be expected if selection is concentrated near genes [16],[31],[36], and a recent study has provided robust genome-wide evidence of selection shaping patterns of diversity [37].
While most recent papers on selection in humans have focused on identifying genes and phenotypes involved in selection, our paper aims to learn more generally about the nature and prevalence of positive selection in humans. We also highlight some of the conceptual and methodological challenges in studies of selection. A separate companion paper focuses more closely on individual selection signals of particular interest [21], and a genome browser of our results is available (http://hgdp.uchicago.edu/).
We analyzed genome-wide SNP data from two primary sources, namely, the Human Genome Diversity Panel CEPH (HGDP), and the Phase II HapMap. Together, these two data sets provide the best available combination of dense geographic sampling (HGDP) and dense SNP data (Phase II HapMap) and hence provide complementary information for our analysis.
The HGDP data reported by Li et al. [38] consist of 640,000 autosomal SNPs genotyped in 938 unrelated individuals. These individuals include samples from 53 different human populations. They represent much of the span of human genetic diversity [39],[40], albeit with notable sampling gaps in Africa and elsewhere [41],[42]. Using these samples, Rosenberg et al. [40] identified five major genetic clusters corresponding to native populations from sub-Saharan Africa, west Eurasia, east Asia, Oceania and the Americas. There is also an overall relationship between genetic differentiation and geographic distance [43],[44] suggesting that human population history is likely a complex mixture of population splits and gene flow [45].
The HapMap data consist of over 3 million SNPs genotyped in 210 unrelated individuals [26],[36]. These individuals include 60 Yoruba from Ibadan, Nigeria (YRI), 60 individuals of northwest European ancestry from Utah (CEU) and 90 individuals from east Asia (from Beijing and Tokyo) that we analyzed as a single “analysis panel”(here denoted ASN). For those analyses in which uniform SNP ascertainment is most important, we used a subset of the HapMap data consisting of 900,000 SNPs identified by Perlegen Sciences [46]. These SNPs were detected using array-based resequencing in a multiethnic panel, and subsequently genotyped in the HapMap. This screen should have good power to detect high- FST SNPs since both alleles of a high- FST SNP are likely to be present in a multiethnic sample (see Methods for further details). Throughout this paper we consider only the autosomes since the smaller effective population size and the smaller sample sizes in the X chromosome data make it inappropriate to merge the X and autosomal data.
As noted above, we now know of several genes in which recent selection appears to have been very strong, driving new alleles to high frequencies in particular populations or groups of populations [48]–[50]. Some genome-wide studies have estimated that strong selection, with selection coefficients above 1%, is widespread in the genome (e.g., [16],[47]). Similarly, studies of other organisms have identified cases in which selection has created large allele frequency differences between populations, even in the presence of high rates of gene flow [48],[49],[50]. Together, these studies suggest that selection in humans might be a strong force that allows for local adaptation via large allele frequency shifts at individual loci.
If this were the case, then we might expect to find SNPs whose frequency distributions in the HGDP differ dramatically from neutral patterns. For example, some SNPs might show extreme allele frequency differences between closely related populations due to divergent selective pressures [51]. More broadly, we might expect to find alleles whose geographic distributions differ dramatically from the expectations of neutral population structure, if their frequencies are driven by factors such as diet or climate [24],[52]. However, neutral forces including migration and admixture would tend to work against selection, reducing frequency differences between geographically close populations [53],[54]. Hence it is unclear whether selection pressures in humans are strong enough, and sufficiently divergent over short geographic scales, to produce large frequency differences at individual loci.
In this paper, we begin to answer some of these questions by examining the distributions of potentially selected SNPs at a variety of geographic scales. Our approach combines the complementary strengths of the HGDP and HapMap data sets: we use the HGDP to study the geographic distributions of putatively selected alleles at fine scales, and the much denser HapMap data to study differences between continental populations. We aim to learn whether selection in humans is strong enough to generate highly divergent allele frequencies between closely related populations, and geographic distributions that diverge strongly from neutral patterns. At the largest geographic scales, we ask: How effective has selection been at driving allele frequency differentiation between continental groups?
At its most basic level, natural selection acts to change allele frequencies in populations. Hence, geographically localized selection will lead to allele frequency differences between populations, both at a selected locus and at other closely linked loci. Throughout this paper, we use extreme allele frequency differences between populations as a tool for identifying candidate signals of selection [55].
A major hurdle for any population genetic study of positive selection is to show that the measures used do in fact detect signals of selection rather than just the outliers of a neutral distribution. To test whether the extremes of allele frequency differentiation between populations are enriched for signals of selection, we examined whether large frequency differences between populations are more likely to occur in or near genes (“genic SNPs”) than in non-genic regions. The premise is that genic SNPs are more likely to be functional and so are more likely to be targets of selection. A similar analysis of the HapMap data by Barreiro et al. [31] revealed that the tails of the FST distribution are enriched for genic variants, and nonsynonymous variants in particular. We extended their analysis to examine the enrichment of genic SNPs in the extremes of frequency differentiation between each pair of HapMap population groups, and included information about the derived allele. To avoid the confounding effects of SNP ascertainment, we used only the subset of SNPs ascertained by resequencing in a multi-ethnic panel (the Perlegen “Type A” SNPs). Figure 1 shows that there is a strong enrichment of genic SNPs in both tails of derived allele frequency differences between all pairs of HapMap populations. There is a similar, perhaps even stronger, enrichment at nonsynonymous sites although, together, nonsynonymous SNPs contribute only a small part of the total genic enrichment (Supplementary Figure 2 in Text S1) [31].
The overall genic enrichment is present in all three population comparisons, and each tail seems to be similarly enriched for high- FST genic SNPs. However, the number of derived alleles in each tail does differ substantially (see Supplementary Table 1 in Text S1) and is biased towards derived alleles outside Africa and especially in east Asia. Thus, the statistical evidence for enrichment of events inside Africa is weaker than for the other two populations (we return to this point later).
Simulations show that this type of enrichment is expected under models with positive selection and is difficult to explain by other mechanisms (Supplementary Figure 3 in Text S1). One might worry that subtle biases in the Perlegen ascertainment could lead to better detection of high- FST SNPs in genic regions, but this does not seem to be the case (see Methods). Another reasonable concern is whether models with weakly deleterious mutations could produce this effect either through drift [36] or allelic surfing [56]. However, simulations suggest that models of bottlenecks with weak purifying selection do not inflate FST in genes (Supplementary Figure 3 in Text S1). Finally, background selection could increase drift in genic regions, thereby increasing the abundance of high- FST SNPs [57, Supplementary Figure 4 in Text S1]. Theoretical considerations suggest that background selection in humans may be weak [58]; however, direct empirical estimates of the size of this effect are yet to be made, and there is a need for more work on this issue. Thus, in summary, Figure 1 and our simulations strongly suggest that positive selection and associated hitch-hiking are the cause of many of the extreme frequency differences between populations. In light of these results, we will use extremely high- FST SNPs between these populations as candidate selection signals, while noting that some fraction of these high- FST SNPs are likely to be drawn from the extreme tail of the neutral distribution.
Given that a substantial fraction of SNPs with high FST between the HapMap groups may be targets of selection, we next examined the geographic distributions of high- FST SNPs across the HGDP. For signals of local adaptation, we searched for examples of SNPs that have highly diverged allele frequencies in pairs of populations that are closely related according to mean FST (Figure 2). Note that mean FST between a pair of population is a reasonable proxy for the geographic distance separating the pair [43],[44]. Of course, a possible caveat of studying FST in the HGDP data is that the Illumina tag SNP panel contains only a subset of all SNPs, and the selected sites might not be included. However, sweeps should usually be detectable because they would change the allele frequencies at nearby tag SNPs; tag SNPs tend to transfer well among the HGDP populations [59]. (Sweeps on standing variation–i.e., existing polymorphisms–are likely to be less-well tagged than sweeps that start from new mutations [60].)
In fact, the data show no examples of SNPs with very extreme allele frequency differences between closely related populations (i.e., population pairs with low mean FST). Moreover, the mean pairwise FST is highly predictive of the very extreme tail of allele frequency differentiation. If local adaptation were a strong force, we might have expected to find at least some SNPs with extreme frequency differences between closely related populations, or some population pairs with large numbers of high- FST SNPs. This would be true especially if strong antagonistic selection were widespread: i.e., where different alleles were strongly favored in different locations. Instead, the observation that the extremes of allele frequency differences are so well-predicted by mean FST might seem consistent with the expectations of an entirely neutral model [61].
However, several observations argue against a fully neutral model for these data. First, simulations show that the tails of differentiation observed here are more extreme than expected under neutral models (see Supplementary Figure 7 in Text S1). Second, as shown in Figure 1, the extremes of allele frequency differences in the HapMap are enriched for genic SNPs, as might be expected if many of these SNPs are selectively favored. This result is also observed at finer geographic scales in the HGDP data (Supplementary Figure 8 in Text S1), although it is unclear whether this result is robust to the Illumina SNP ascertainment scheme. Finally, many of the most extreme SNPs (across a range of mean FST) fall close to strong candidate genes for selection, including skin pigmentation genes, lactase, and Toll-like receptor 6 [21],[22],[62, Supplementary Figure 9 in Text S1]. Although such SNPs with large allele frequency differences are especially strong candidates for being targets of selection, they are not strong outliers from the curves seen in Figure 2, suggesting that they, too, are governed by the predictive relationship between mean FST and extreme allele frequency differences.
To further investigate the geographic patterns of putatively selected loci, we next focused on the global distributions of SNPs that show extreme differentiation between particular pairs of populations. In the following discussion, we focus on SNPs with extreme pairwise FST between three HGDP populations: the Yoruba, French and Han Chinese. These three populations were chosen because they are geographically far apart and because there is evidence that selection is responsible for many of the extreme FST values between each of these groups (Figure 1). Results for additional comparisons are shown in Supplementary Figures 10 and 11 in Text S1.
Under strong selection, the geographic distributions of selected alleles detected in pairwise comparisons might differ greatly from one locus to another. For example, a selected allele that strongly differentiates the French from both the Yoruba and Han could be strongly clinal across Europe, or at high frequency in Europe and absent elsewhere, or follow any other distribution according to the geographic nature of the selective pressure.
However, we see that the global geographic distributions of these putatively selected alleles are largely determined simply by their frequencies in Yoruba, French and Han (Figure 3). The global distributions fall into three major geographic patterns that we interpret as non-African sweeps, west Eurasian sweeps and East Asian sweeps, respectively. The boundaries of these three patterns are highly concordant with neutral population structure inferred from random microsatellites or SNPs [38],[40]. This is the case even for loci such as KITLG, SLC24A5 and EDAR where there is a strong biological case for the genes being targets of selection. Moreover, these patterns are robust to the choice of populations used to identify high- FST SNPs: for example, very similar results are obtained for SNPs with high FST between Mandenka, Balochi and Yakut (Supplementary Figure 14 in Text S1).
The first pattern, the “non-African sweep”, is exemplified by a sweep near the KIT ligand gene (KITLG) (Figure 4A, B). It has been reported previously that HapMap Europeans and East Asians have undergone a selective sweep in the KITLG region on a variant that leads to lighter skin pigmentation [20]. Haplotype patterns in the HGDP indicate that a single haplotype has swept almost to fixation in nearly all non-African populations (Figure 4A). More generally, at SNPs that strongly differentiate the HGDP Yoruba from both the Han and French (Figure 3A, B), we observe that typically one allele is rare or absent in all the HGDP Africans, and at uniformly high frequency across Eurasia, the Americas, and usually Oceania. This pattern could be consistent either with sweeps across all the HGDP African populations, or with non-African sweeps that pre-date the colonization of the Americas some 15 KYA [6]. As outlined below, it seems that in fact most of these signals are, like KITLG, due to non-African sweeps.
The second pattern, the “west Eurasian sweep” is illustrated by a nonsynonymous SNP in the SLC24A5 gene (Figure 4C, D). The derived allele at this SNP is also strongly associated with lighter skin color [15],[63] and has clear signals of selection in the HapMap Europeans [15],[17],[35], and in the Middle East and south Asia (Figure 4C). The derived allele is also at high frequency in US-sampled Indian populations [64], supporting the idea that the sampled Indian populations may be similar to the western eurasian HGDP populations at selected as well as neutral SNPs [65]. The derived allele is near fixation in most of the HGDP Eurasian populations west of the Himalayas, and at low frequency elsewhere in the world. More generally, alleles that strongly differentiate the French from both the Han and Yoruba (Figure 3D) are typically present at high frequency across all of Europe, the Middle East and South Asia (an area defined here as “west Eurasia”), and at low frequency elsewhere. This pattern of sharing across the west Eurasian populations is highly consistent with observations from random markers showing that the populations in west Eurasia form a single cluster in some analyses of worldwide population structure [40]. Allele frequencies at high- FST SNPs in two central Asian populations, the Uygur and Hazara, tend to be intermediate between west Eurasia and east Asia, consistent with observations that these populations have recent mixed ancestry between west Eurasia and east Asia [38],[40],[66].
Finally, the “east Asian sweep” pattern is defined by SNPs that differentiate the Han from French and Yoruba (Figure 3E, F). One example is provided by a nonsynonymous SNP in the MC1R gene [67], for which the derived allele is at high frequency in the east Asian and American populations, and virtually absent elsewhere (Figure 4E, F). MC1R plays an important role in skin and hair coloration, although the functional impact of this variant in MC1R–if any–is unknown [68]. A nonsynonymous SNP in the EDAR gene that affects hair morphology shows a very similar geographic pattern [35]. It is interesting that although west Eurasians and east Asians have both evolved towards lighter skin pigmentation, they have done so via largely independent sets of genes [18]. This suggests that favored mutations have not spread freely between the two regions.
It should be noted that rare examples of strong frequency clines within geographic regions do exist, in contrast to the sharp steps seen in Figure 3. For example, SNPs in the lactase [22],[69] and Toll-like receptor 6 [62] gene regions are among the most differentiated SNPs between the French and Palestinian populations (Supplementary Figure 10 in Text S1), and are strongly clinal across Europe. However, these clinal alleles do not appear in Figure 3 because the values for these SNPs between the Yoruba, French and Han are less extreme than for the SNPs in Figure 3. We suggest that these alleles may represent relatively recent selection events that have not yet generated extremely large frequency differences between continental groups or had time to disperse more evenly across a broad geographic region.
In summary, we find that the geographic distributions of SNPs with extreme values are highly regular, and agree with population clusters identified using randomly chosen markers. While selected alleles that spread rapidly between geographic locations would not be detectable by [70], such shared sweeps would be visible from haplotype based signals of selection. Patterns of sharing of haplotype-based signals of selection in the HGDP based on the “integrated haplotype score” (iHS) [16], while somewhat more noisy, support the observation that there is relatively little sharing of partial sweep signals between east Asia, west Eurasia and Africa, but many shared signals within west Eurasia (Supplementary Figure 15 in Text S1; [21]). Thus, the overall distribution of selected alleles is strongly determined by the historical relationships among populations, and suggests again that very local selection pressures (e.g., divergent selection pressures within continental regions) have not given rise to very high- FST SNPs.
Since the allele frequencies of high- FST SNPs in the Yoruba, French and Han are highly predictive of their frequencies throughout the HGDP, we next turned to the HapMap data–which have much higher SNP density–to further investigate these candidate sweeps. For this analysis, we used Perlegen Type A SNPs that were genotyped in the HapMap [36]. These 900,000 SNPs were identified by screening ∼10% of the genome in a uniform multiethnic panel (see Methods). Figure 5 plots the derived allele frequencies for SNPs with extreme allele frequency differences between each pair of HapMap populations. Results from the full HapMap data are similar (Supplementary Table 3 and Figures 17–20 and in Text S1).
Several interesting points emerge from Figure 5. First, more than 80% of the high- FST SNPs occur in the Yoruba–east Asia comparison. After clustering together sets of high- FST SNPs that are tightly linked we again reach a similar result: there are 76 genomic regions with at least one SNP having an allele frequency difference >90% between YRI and ASN, 33 such regions between YRI and CEU, and 6 such regions between CEU and ASN (see Methods for details on the clustering).
Second, the derived allele is almost always at higher frequency in Europeans or east Asians than in Yoruba [36]. This implies that in most cases the sweeps are occurring in the non-African populations. The derived allele is most common in Yoruba at fewer than 10% of the high- FST SNPs. Even among these few possible examples of sweeps in Yoruba, many seem to be due to hitchhiking of ancestral alleles in non-African sweeps (Supplementary Figure 21 in Text S1). Moreover, simulations show that even if most selection in the Yoruba acted on standing variation, we would still have power to detect about half of all strong YRI sweeps (Supplementary Figure 16 in Text S1). The east Asian bias is unlikely to be due to stronger drift of neutral alleles in the east Asians [71] since the enrichment of genic SNPs is at least as strong in the east Asians as in the other populations (Figure 1).
Third, among the derived alleles that are at low frequency in Yoruba and at high frequency in east Asians, we find that essentially all of these alleles are at intermediate frequency in Europeans (Figure 5A, Supplementary Figure 11 in Text S1). We also observed that for most of these SNPs, the allele frequencies in the Americas are similar to Han frequencies, suggesting that in most cases these alleles were already at high frequency prior to colonization of the Americas some 15,000 years ago (Supplementary Figure 11 in Text S1). Together, the latter observations suggest that perhaps the east Asian sweeps tend to be relatively old. To examine this idea further, we looked at whether the high-frequency high- FST SNPs in east Asia are surrounded by regions of strongly reduced diversity, as would be expected for recent completed sweeps. Using the XP-EHH measure (cross-population extended haplotype homozygosity) [35], we find that high- FST SNPs tend to lie in regions of lower variability than random control SNPs. However, the shift in XP-EHH is relatively small, and is far less than for simulated data in which new mutations sweep up with selection coefficients of 1% (see Methods and Supplementary Figures 22 and 23 in Text S1). (But note that strong selection on standing variation would also generate relatively modest XP-EHH signals [60]).
Finally, it is striking just how few SNPs in the genome have extreme allele frequency differences between populations. For example, in the entire Phase II HapMap there are only 13 non-synonymous SNPs with a frequency difference >90% between Yoruba and east Asians (Supplementary Table 5 in Text S1). There are especially few fixation events in the Yoruba: the derived allele is at high frequency in the Yoruba at just one of these 13 nonsynonymous SNPs. These numbers likely represent a substantial fraction of all non-synonymous SNPs in the genome with such extreme frequency differences.
Different analyses of genetic data provide conflicting evidence on the strength and abundance of recent adaptation in humans. An important signal of selection in genome-wide data is that genic (and especially nonsynonymous) SNPs are more likely than nongenic SNPs to have high FST values between pairs of HapMap populations ([31],[36], Figure 1). This supports the role of positive selection in generating a substantial fraction of the very high- FST signals. Further support for the action of selection comes from the recent work of [37], and comparisons of genome-wide selection scans with neutral simulations [16],[17],[28],[35]. But in other respects, the data seem to argue that neutral processes–especially population history, migration, and drift–exert powerful influences over the fate and geographic distribution of selected alleles.
We propose below that even if positive selection is common in the genome, strong selection that drives new mutations rapidly to fixation appears to be rare. Our results also argue against a strong form of adaptation in local populations by very large allele frequency shifts at individual loci. However, our data do not preclude a weaker level of adaptive tuning: i.e., modest frequency changes may often occur in response to local conditions [23],[24],[52]. Indeed, it is still possible that small frequency shifts at multiple loci could allow populations to effectively adapt to local conditions even in the absence of large frequency changes at individual loci.
Recent studies of humans and other species have shown that populations may adapt to local selection pressures by large frequency changes at relatively few loci [20],[22],[49]. When selection is antagonistic–i.e., different alleles are favored in different environments, as seen for skin pigmentation–then strong selection should generate large allele frequency differences between populations. However, our data show that the geographic distributions of even the highest- FST SNPs follow patterns that are predictable from neutral variation. Across the entire HGDP data set there are no examples of SNPs with very extreme allele frequency differences between closely related populations, and the distribution of the largest values of allele frequency differentiation between population pairs is accurately predicted by mean FST (Figure 2). Similarly, at a global scale, the geographic distributions of alleles with high FST between Yoruba, French and Han, or between Mandenka, Balochi and Yakut, fall into predictable patterns based just on their frequencies in those three populations.
Why is this? First, it is likely that environmental pressures often vary smoothly with geographic distance, and so closely related populations would usually experience similar pressures. Nonetheless, there should be cases in which pairs of closely related populations do face sharply divergent selective pressures due to differences in diet, climate, pathogens or other factors [23],[24],[52]. Similarly, although there should be sets of populations that share particular selective pressures despite not being closely related, the data do not provide obvious examples of this. For example, recall that within Eurasia, the geographic distribution of the skin pigmentation locus SLC24A5 agrees with population structure estimated from neutral markers, rather than with latitude or climate (Figure 3B).
Our results therefore suggest that local adaptation is tightly constrained by the ancestral relationships and migration rates among populations. It seems likely that selection in humans is generally not divergent enough to generate large frequency differences at individual loci between population pairs that are either recently separated, or regularly exchange migrants [53],[54]. Furthermore, populations may be too mobile, or their identities too fluid, to experience very localized pressures consistently over the several thousand years that may be required for large allele frequency changes.
However in contrast, it seems that selected alleles may not spread effectively between broad geographic regions (see Figure 3, Supplementary Figure 15 in Text S1 and [21]). Perhaps this is because populations usually adapt to similar selection pressures by parallel mutation [18],[23],[25] rather than by the spread of migrants between regions [72],[73].
In summary, we propose that the strongest determinants of the geographic distribution of favored variants may be the times at which they first spread to intermediate frequencies and the subsequent history of population movements and range expansions, population splitting and exchange of migrants. We suggest that variants that are broadly distributed across the non-African populations (such as the KITLG mutation) typically reached intermediate frequencies shortly after the out-of-Africa migration, and subsequently spread around the globe as populations expanded. At the other extreme, we suggest that local, strongly clinal patterns (as seen in Europe at lactase and Toll-like receptor 6 [62]) may usually indicate that these alleles have spread to intermediate frequency comparatively recently. These hypotheses will need to be tested by future studies.
We next turn to our results on SNPs that have high FST between continental groups (Figures 5 and 6). Most notably, we observed that the total number of nearly fixed differences is surprisingly low, especially at nonsynonymous sites; that there is a strong fixation bias towards non-Africans, and east Asians in particular; and that high-frequency, high- FST SNPs in east Asians generally appear to be old. However, the enrichment of genic SNPs among those SNPs with the highest FST argues against a mostly-neutral model.
A key issue for interpreting these data is the long-term rate of gene flow among continental populations. Recent population genetic studies have disagreed on whether there has been measurable gene flow between African and non-African populations [71],[74]. In principle, high rates of gene flow could prevent favored alleles from achieving high FST , and indeed, asymmetric gene flow of beneficial alleles from Africa towards east Asia could help generate the bias that we saw towards high- FST SNPs in east Asia (Figure 5). However, some aspects of the data suggest that selected alleles have generally not been able to spread freely between continental groups, and especially between Africa and east Asia (Figure 3, Supplementary Figure 15 in Text S1 and [21]). This does not rule out the possibility that selected alleles may be introduced at low frequencies by migration between broad geographic regions. A potential example of this is the light-skin allele at SLC24A5, which is at very low frequency in sub-Saharan Africa and east Asia (Figure 4B). However, the fact that most of the HGDP SNPs in Figure 3 are tags rather than the actual selected alleles prevents us from knowing how common it is for selected alleles to spread to low frequencies in other continents. Moreover, even if migration levels have been nontrivial, both the Asian XP-EHH results (Figure 6) and the similarity between Eurasians and all the American populations (Figures 3A, 3B) argue that there have been very few rapid, recent fixations in Eurasia.
We interpret these results to imply that it is rare for strong selection to drive new mutations rapidly to near fixation. The genomic regions around high- FST SNPs in east Asians show only a modest increase in haplotype homozygosity compared to random SNPs (Figure 6). Moreover, the overall dearth of high- FST SNPs shows that strong selection has rarely acted to create nearly fixed differences between populations. The Yoruba have especially low rates of completed sweeps: for example, the HapMap data include just one nonsynonymous SNP for which the derived allele is at high frequency in Yoruba and has a frequency difference from east Asians that exceeds 90%. Figure 7 shows that the separation times between populations would have allowed ample time for strongly selected variants to fix within populations. For example, new variants with a 1% advantage could have fixed since the European-east Asian split, and variants with a 0.5% advantage could have fixed since the split of Africans and non-Africans.
Taken together, these results suggest that it is rare for variants to experience selection that is both strong enough and sustained consistently over the 10–50 KY required to drive a new mutation to fixation. Additionally, we suggest that some or all of the following factors may help to explain the data: non-African populations may have experienced more novel selection pressures than Africans; bottlenecks inflated the number of weakly selected alleles that have reached high frequency in non-Africans; and most selected traits are multigenic, and that this leads to a systematic weakening of selection on individual variants as these variants increase in frequency. We now discuss each of these factors in turn.
We observed more high-frequency high- FST SNPs in the HapMap Europeans and east Asians than in the Yoruba, consistent with a recent genome-wide scan for full sweeps that found few compelling signals in the Yoruba [35]. A plausible explanation is that humans experienced many novel selective pressures as they spread out of Africa into new habitats and cooler climates [75],[76]. Hence, there may simply have been more sustained selective pressures on non-Africans for novel phenotypes. The selective sweeps at skin pigmentation loci are likely examples of this.
While novel selection pressures outside Africa may be an important factor, this is likely not the entire story. In particular, this does not easily explain the excess of high-frequency high- FST alleles in east Asians compared to Europeans. (Greater drift of neutral alleles in east Asia is also unlikely to explain this pattern since the enrichment of genic SNPs among high- FST SNPs is similar in both populations (Figure 1A,B)). It is not obvious why there would be more sustained strong selection in east Asia than in Europe, and besides, our results suggest that most of these alleles were already at intermediate frequency prior to the European-east Asian divergence. A higher rate of gene flow of selected alleles between Europe and Africa than East Asia and Africa could potentially generate this result, although we currently have little evidence for widespread migration of selected alleles between the African and non-African populations (Supplementary Figure 15 in Text S1 and [21]).
It is also worth noting that this explanation does not imply an absence of positive selection in the Yoruba. Indeed, two studies of partial sweeps have actually reported more signals in YRI [16],[47]. African populations have presumably also experienced a variety of new selection pressures during the same time-period, due to the appearance of new pathogens, changes in diet, etc. While these pressures may have been less numerous or sustained than in non-Africans, there may also be reasons why we might have lower power to detect them. Given that African populations harbor more genetic variation than non-Africans, it is possible that there have been more sweeps on standing variation, which we are more likely to miss. Similarly, the response to selection pressures within Africa might also have been more polygenic (see below), resulting in smaller changes in allele frequencies at larger numbers of loci.
Another important part of the explanation may be the impact of genetic drift on weakly selected variants. If strong selection is rare, then perhaps adaptation is more often due to selection on alleles with smaller fitness advantages. For selection coefficients of about 0.3% or less, the average time to fixation of a new favored allele is considerably longer than the ∼70,000 years since the split of the African and non-African HapMap populations (Figure 7). Therefore, such mutations would usually not generate extreme frequency differences between modern populations. However, since the frequency trajectory taken by a favored allele as it goes to fixation is stochastic–due to genetic drift–there will be some alleles that increase in frequency faster than expected. Given that the magnitude of drift since the HapMap populations diverged has been greatest in the east Asians, and least in the Yoruba, this model predicts a larger fraction of high- FST high-frequency derived alleles in the east Asians and Europeans than in the Yoruba (Figure 8 and Supplementary Figure 24 in Text S1). This greater fixation rate comes at the expense of these populations also having lost many favored alleles during bottlenecks.
While our simulations do show an east Asian fixation bias, the magnitude of the bias is smaller in the simulations than in the real data (Supplementary Figure 24 in Text S1). Hence it is possible that the effect of increased drift combines with geographic differences in selection pressures (e.g., between African and non-African environments) to generate the observed bias. Additionally, inaccuracies in the assumed demographic model might lead us to underestimate the importance of drift in east Asians. For example, it has been proposed that drift is especially active at the front of range expansions [56], [77]–[79], which might model human history better than the bottleneck model used here.
Additionally, properties of selection pressures themselves may contribute to the observed low rate of rapid fixation events (and small number of high- FST signals). First, it is likely that selection pressures fluctuate through time [80], and also that human cultural change modifies selection pressures through time. Thus, mutations may be driven to intermediate frequency by strong selection, but subsequently drift to loss or fixation when the selective pressure weakens.
Second, the genetic architecture of selected phenotypes has fundamental implications for the action of selection. While the genetic basis of some selected phenotypes may be monogenic (e.g. lactase within Europe), it is likely that most selected phenotypes are influenced by mutations at multiple genes (as seen for skin pigmentation, for example). If favored mutations increase in frequency at several genes simultaneously, then this can shift the phenotype of typical individuals of a quantitative trait towards an adaptive optimum, thus reducing the overall strength of selection on each favored mutation [81],[82]. This is a form of epistasis on fitness. Consequently, even a strongly selected phenotype may not lead to rapid fixation of favored mutations. Instead, favored mutations may increase in frequency rapidly at first, and then start to drift as the strength of selection becomes weaker.
Similarly, the “soft sweep” model in which multiple equivalent mutations sweep up simultaneously at a single locus also does not lead to full sweeps. The population adapts to a new selection pressure, but none of the favored mutations sweeps up to very high frequency [83].
We have argued here that strong, sustained selection that drives alleles from low frequency to near fixation has been relatively rare during the past ∼70 KY of human evolution. Is this conclusion compatible with recent work on haplotype-based signals reporting an abundance of partial sweeps with selection coefficients of ≥1% [16],[29],[35],[47]? One possible explanation for the apparent discrepancy is that there might be many more partial sweeps than completed sweeps. This could occur if selection pressures tend to be highly variable so that favored alleles often rise to intermediate frequency and then start to drift as a result of fluctuating selection pressures or polygenic adaptation.
Alternatively, it is possible that recent studies have substantially overestimated the number and strength of partial sweeps. Perhaps the most important current challenge in selection studies is to obtain better estimates of the fraction of true positive selection signals in different types of analyses. This is especially pressing since we have shown that even extreme signals of the data have patterns that are predictable from neutral loci.
Moreover, one important unknown is the extent and strength of background selection. If background selection is concentrated in and around genes, thereby increasing the rate of drift in genic regions, it could well contribute to the observed enrichment of high- FST SNPs in genic regions [57, Supplementary Figure 4 in Text S1]. The impact of background selection for plausible biological parameters requires further investigation; see [37] for discussion of selected sweeps and background selection. If background selection is an important factor, then the role of positive selection in generating nearly fixed differences may be yet smaller than we have estimated here.
To some extent, our understanding of these issues has been hampered by the limitations and caveats of analyzing SNP data. Hopefully the next generation of genome sequence data will allow major progress on these issues. Additionally, the increasing number of genotype-phenotype associations offer the possibility of linking more selection signals to phenotypes; this may strengthen the evidence that individual signals are real and give us deeper insight into the overall impact of selection.
Finally, since high- FST SNPs are rare in the human genome, our study raises the question of whether human populations can effectively adapt to new environments or new selective pressures over time-scales of, say, ten thousand years or so. Our results seem to suggest that rapid adaptation generally does not occur by (nearly) complete sweeps at single loci. If human populations can adapt quickly to new environments, then we propose that this might instead occur by partial sweeps simultaneously at many loci.
The HGDP consists of 1048 individuals, some of whom were previously found to be related [84]. For the analysis in this paper we used the set of 938 “unrelated” individuals genotyped previously on Illumina's “HumanHap650Y” platform [38]. The SNPs genotyped by this platform were selected to provide effective genome-wide SNP tagging in all of the HapMap populations [85].
Data cleaning and manipulation of the HGDP data was performed in PLINK [86]. We excluded 74 SNPs that were monomorphic across the entire HGDP panel, and 177 SNPs that were missing more than 5% of genotypes. To test for violations of Hardy-Weinberg Equilibrium (HWE) we constructed three large groups of individuals from three sets of populations (East Asia, Europe, Bantu Africa) that have relatively little population structure, and performed a test for HWE for each SNP within each large group [86],[87]. 1808 SNPs were removed for failing the HWE test at cutoff in at least two of the three groups (and have minor allele count greater than five in each group failing). We excluded 2055 SNPs in total. We note that none of the HWE-violating SNPs excluded showed pairwise population frequency differences extreme enough to contribute to Figure 2 or 3. We analyzed a total of 640,698 autosomal SNPs.
Throughout the paper we make use of the Type A SNPs reported in Hinds et al. [46]. While these SNPs represent just a subset of the SNPs in HapMap Phase II, they offer two important advantages:
The ascertainment was based on 20–50 haploid anonymous genomes isolated from the NIH Polymorphism Discovery Resource [88]. That resource is 27% European-, 27% east Asian-, 27% African-, 13% Mexican- and 13% native American [88]. The median coverage depth was 14 chromosomes per base resequenced [46]. The depth of resequencing at discovered SNPs was essentially the same for genic and non-genic SNPs. The median number of chromosomes assayed was 17 for both genic and non-genic SNPs; the mean number was 15.84 for genic and 16.17 for non-genic SNPs (personal communication, D. Hinds). This confirms that the ascertainment is indeed relatively uniform across genic and non-genic regions, suggesting that while it is an incomplete representation of all SNPs, the discovery process for Type A SNPs does not differ substantially between genic and non-genic regions due to ascertainment.
Hinds et al. [46] reported that they screened 964 MB to identify 1.62 M SNPs; they designed successful genotyping assays for 1,263,750 Type A SNPs. 896,758 of these “Type A” SNPs were genotyped in all three of the HapMap samples and have unambiguous dbSNP entries. There are a number of reasons why certain Type A SNPs were not included in the Phase 2 HapMap: the bulk of the excluded SNPs were SNPs in which it was difficult to design a genotyping assay; other criteria for exclusion included a minor allele frequency in a previous study or that SNP which is a perfect proxy () had already been typed in the HapMap [36]. None of these criteria suggest a bias in favour of preferentially including high FST SNPs in genes. Further none of the criteria should have reduced our ability to detect high FST SNPs, or bias detection towards particular HapMap populations. The MAF cutoff should not have excluded high FST Perlegen type A SNPs as they would have a global MAF well above 0.05 in [46]. While not typing perfect proxies could have excluded Perlegen SNPs from the Hapmap, a perfect proxy would still be in HapMap.
The approximate expected number of SNPs from sequencing L base pairs in 14 chromosomes would be , where is the population scaled mutation rate per base pair (∼0.0008 in humans). This suggests that the ∼900,000 Perlegen Type A SNPs typed in HapMap represent a screen of around 345 Mb, or ∼10% of the genome (taking the genome length = 3300 Mb). We analyzed frequencies in the HapMap data, rather than in the Perlegen data, since the HapMap sample sizes are larger and Perlegen used African-Americans, who have substantial European ancestry. We used allele frequencies calculated from the HapMap phased data, with the small amount of missing data filled in by imputation. To confirm that the anonymous chromosomes in Hinds et al. [46] resequencing panel contained representatives of all three continental groups we examined the HapMap “type A” dataset for alleles present in only one of the populations and found ∼93,000 YRI-, ∼24,000 CEU-, and ∼12,000 ASN-specific alleles, suggesting that all three populations had close representatives in the anonymous resequencing panel, and so fixed differences between these populations would have been detected by the resequencing. We excluded 24 SNPs that have high FST in HapMap, but where the high FST appears to be due to allele labeling problems (allele-flips) since the reported allele frequencies in the corresponding HapMap and Perlegen samples differed by >50%.
The genotyped SNPs were identified from a variety of sources [26],[36]. Phase II includes nearly all SNPs in dbSNP release 122 that could be genotyped on the Perlegen platform [36].
To identify all non-synonymous SNPs with high levels of differentiation between HapMap populations, we used the March 2008 ‘all’ dataset from hapmap.org, consisting of 3.9 M SNPs in ASN and 3.8 M in CEU and YRI. This set contains SNPs that may have only been successfully typed in one or two populations. The list of non-synonymous SNPs with >90% frequency difference was checked by hand for potential allele calling flips using the dbSNP database and HGDP data (when the SNP was typed on this panel). A list of these non-synonymous SNPs is given in Supplementary Table 5 in Text S1.
The XP-EHH statistic was calculated on the HapMap “consensus” phased data released in July 2006 from hapmap.org, which contains all SNPs successfully genotyped in all three populations. After removing monomorphic SNPs, these data consist of 3,106,757 SNPs.
We checked the highly differentiated SNPs found in consensus HapMap data for allele flips (these data are used in the main paper to identify regions for the XP-EHH analysis and in the Text S1 for XP-EHH and versions of Figure 5). We downloaded the HapMap “2007-3 redundant genotype frequencies” data, which contains information about SNPs typed by multiple centers. SNPs that had been typed by multiple centers were discarded if the centers disagreed by more than 50% in the estimate of the allele frequency in any of the three populations.
Gene annotation information was obtained from the RefSeq database [89]. This information was primarily used for obtaining the gene start and gene end coordinates. Where required, genome coordinates were converted from NCBI build 36 (hg18) to build 35 (hg17) using the Batch Coordinate Conversion tool available at UCSC web browser [90]. A SNP was defined as nongenic if it is more than 2 kb from an annotated gene transcript; otherwise it was considered genic. Ancestral states for all SNPs were estimated using whole genome human-chimpanzee alignments from the UCSC database [90]. Based on the physical position of the SNP in the human genome (Build hg17), the allele at the corresponding position in the chimp genome (Build pantro2) was obtained. If the human SNP position aligned to missing data in the chimpanzee genome, or if the chimpanzee allele did not match either human allele, then the corresponding SNP was excluded from further analysis.
FST was calculated using the Weir and Cockerham estimator [91]. This estimator is unbiased by sample size; however, extreme values of the distribution still depend on sample size. Accordingly, we excluded low sample size populations from Figure 2.
Hitchhiking results in clustering of highly differentiated SNPs, reducing the number of independent signals in the data. When we needed to ensure that independent genomic regions underlie our results or count the number of signals, we assigned strongly differentiated SNPs within 100 kb of another strongly differentiated SNP to the same cluster, such that different clusters do not contain any SNPs within 100 kb of another cluster.
To produce Figure 3, for each particular pair of comparisions (e.g. Yoruba-Han Chinese, Yoruba-French) we found all SNPs that fall in the 99.8% tail of FST for both comparisons. We then clustered these SNPs as described in ‘Clustering of SNPs with extreme frequency differences’. For each cluster we then plotted the HGDP allele frequencies for the “top” SNP for each cluster; where the top SNP was chosen by ranking SNPs in a cluster by the product of their empirical p-values in the two pairwise FST comparisons. For the HGDP Yoruba-French, Yoruba-Han comparison (Figure 3A, B) the minimum frequency difference between the pairs was 80% and 86% respectively. For the Yoruba-French, French-Han comparison (Figure 3C, D) the minimum frequency difference between the pairs was 73% and 63% respectively. For the Yoruba-Han, French-Han comparison (Figure 3E, F) the minimum frequency difference between the pairs was 79% and 63% respectively. In Supplementary Figures 10–14 in Text S1 we give versions of the plot for smaller numbers of SNPs and single pairwise comparisons. The pie chart maps were generated using the program of Wessel et al. [92].
The HGDP data were phased using fastPHASE; see Text S1 for details. To visualize the haplotypes in each genomic region shown in Figure 4, we used an algorithm similar to that presented in Conrad et al. [59]. This algorithm starts by identifying the eight most common haplotypes spanning a genomic region. These eight haplotypes are called the ‘template’ haplotypes. Each template is assigned a distinct color. Next, it colors each observed haplotype as a mosaic of the eight templates, requiring exact matches between the observed haplotype and the template that is being copied. Roughly speaking, the coloring minimizes the number of switches between templates (see Text S1 for more details). Rare alleles not found on any template were dropped from the analysis in the version shown in Figure 4. The populations shown in Figure 4 are, from left to right and top to bottom: Mandenka, Russian, French, Mongola, Pima, Bantu Kenya, Druze, Balochi, Han, Maya, Biaka Pygmy, Palestinian, Makrani, Cambodian, and Papuan. For each population, 20 chromosomes were sampled without replacement for plotting.
XP-EHH was calculated as in Sabeti et al. [35]. Briefly, XP-EHH is defined relative to a given SNP in two populations, and . In each population, the expected haplotype homogygosity (EHH) [14] was integrated with respect to genetic distance in both directions from . The log of the ratio of these integrals is the unnormalized XP-EHH. We chose the limit of the integration to be where the EHH in the pooled population sample dropped below 0.05. The final XP-EHH was normalized with respect to the genome as a whole by subtracting out the mean and dividing by the standard deviation. For the analyses presented in the main text, the genetic map used was estimated by the method presented in Voight et al. [16] in the YRI population only; for the detection of selection in the ASN populations, this approach gave us the most reliable results in simulations (data not shown).
In Figure 6, XP-EHH is plotted for SNPs with a greater than 90% frequency difference between YRI and ASN. To ensure that independent signals were plotted, we clustered all SNPs with >90% frequency difference between YRI and ASN (as described in ‘Clustering of SNPs with extreme frequency differences’) and plotted the XP-EHH value for the SNP with the largest frequency difference in a cluster (choosing at random amongst tied SNPs). A version of this figure including only SNPs typed by multiple centers (to further reduce the potential for allele flips) is given in Supplementary Figure 22 in Text S1.
We used simulations that are based, with slight modifications, on a historical population genetic model, “cosi” [7], as this model is one of the few that incorporates both the Africa–non-Africa and Europe–east Asia population splits. This model provides a close fit to various aspects of the genetic data (Supplementary Table 4 and Supplementary Figure 25 in Text S1), although there is still considerable uncertainty about key parameters of this model, including the population split times and the amount of subsequent gene flow–if any–among them.
Simulations of haplotypes for the calculation of XP-EHH were done using a hybrid coalescent/forward-time scheme following the cosi model of human demography [7]. In the coalescent step, the portion of the demographic history before the split of the three populations was simulated using cosi. After this initialization of the population, the haplotypes were simulated forwards in time using a Wright-Fisher model. To increase efficiency, parameters were scaled by a factor of five, following Hoggart et al. [93]. That is, all population sizes and generation times were decreased by a factor of five, while all other parameters were increased by a factor of five.
As these simulations were compared to the HapMap, we had to match ascertainment and SNP density. Since the ascertainment of SNPs in the HapMap is variable and largely irreproducible, we used rejection sampling to match the joint allele frequency of the simulation SNPs and the real data [16]. We first estimated the joint allele frequency distribution of the HapMap and that of the simulations on a 12×12×12 grid of allele frequencies across the three populations. We used rejection sampling to roughly match the simulated distribution to the HapMap distribution: for each SNP in a simulation, it was accepted if a uniform(0,1) random variable was , where is the density in the simulations, is the density in the HapMap and is a normalizing constant. Note that is a vector of three allele frequencies. In order to perfectly match the HapMap distribution, should be the maximum of the ratio between the two densities, and . However, perfect matching to the HapMap distribution resulted in inefficient simulations; we found that a value of produced satisfactory results while maintaining efficiency.
Simulations of single sites (i.e. independent sites) were designed to simulate a constant rate of new mutations, per individual per generation, with a selection coefficient . This constant rate per individual assumes that evolution is mutation limited, such that the rate of adaptation scales roughly linearly with the population size. To increase efficiency of our simulations, we modified the cosi demographic model [7], removing the very low levels of migration between the populations and the weak pre-out-of-Africa population expansion (both of these aspects are present in the haplotype simulations). In this model, then, there are five branches of the tree on which a new mutation can arise: the branch before the split between African and non-African populations, the branch before the split between Europe and Asia, and the three population-specific branches. For each simulation, a mutation is chosen to have arisen on a given branch with probability ; conditional on this it arises uniformly at random on this branch. The allele frequency is then simulated using a Wright-Fisher model forward in time until the present day. Alleles which are lost from the populations are discarded.
For a branch , the probability that a selected allele arises on this branch, , is proportional to the number of selected alleles that arise on the branch. This quantity is the time length of the branch () weighted by population size () along that branch:The exception is branch 1 that represents the ancestral population before the out-of-Africa split, which in our modified cosi model represents the population at equilibrium. To avoid having to simulate the process from far enough in the past to ensure equilibrium, we sampled the process directly from the equilibrium stationary distribution. The number of selected alleles we introduced on this branch (), is the expectation of the number of derived selected alleles segregating at equilibrium, namely(1)whereand , with [94]. If the selected mutation is chosen to have arisen on the branch before the out-of-Africa split, we draw its allele frequency, , from the stationary distribution (we discretize this distribution into units of ).
We used R to perform many of the analyses and to produce most of the figures [95].
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10.1371/journal.pgen.1006897 | Altered paracellular cation permeability due to a rare CLDN10B variant causes anhidrosis and kidney damage | Claudins constitute the major component of tight junctions and regulate paracellular permeability of epithelia. Claudin-10 occurs in two major isoforms that form paracellular channels with ion selectivity. We report on two families segregating an autosomal recessive disorder characterized by generalized anhidrosis, severe heat intolerance and mild kidney failure. All affected individuals carry a rare homozygous missense mutation c.144C>G, p.(N48K) specific for the claudin-10b isoform. Immunostaining of sweat glands from patients suggested that the disease is associated with reduced levels of claudin-10b in the plasma membranes and in canaliculi of the secretory portion. Expression of claudin-10b N48K in a 3D cell model of sweat secretion indicated perturbed paracellular Na+ transport. Analysis of paracellular permeability revealed that claudin-10b N48K maintained cation over anion selectivity but with a reduced general ion conductance. Furthermore, freeze fracture electron microscopy showed that claudin-10b N48K was associated with impaired tight junction strand formation and altered cis-oligomer formation. These data suggest that claudin-10b N48K causes anhidrosis and our findings are consistent with a combined effect from perturbed TJ function and increased degradation of claudin-10b N48K in the sweat glands. Furthermore, affected individuals present with Mg2+ retention, secondary hyperparathyroidism and mild kidney failure that suggest a disturbed reabsorption of cations in the kidneys. These renal-derived features recapitulate several phenotypic aspects detected in mice with kidney specific loss of both claudin-10 isoforms. Our study adds to the spectrum of phenotypes caused by tight junction proteins and demonstrates a pivotal role for claudin-10b in maintaining paracellular Na+ permeability for sweat production and kidney function.
| Claudins are tight junction proteins forming paracellular barriers that are critical for normal development and homeostasis. The tissue specific paracellular barrier properties are determined by the protein composition of tight junctions that regulates the permeability of solutes and water between different compartments of the body. We show, for the first time, that a mutation in claudin-10b, forming paracellular cation channels in different tissues, causes perturbed Na+ selectivity through altered tight junction formation and function as well as increased degradation of the protein. The mutation is associated with the inability to sweat (anhidrosis) and heat intolerance as well as abnormal cation reabsorption, hypermagnesemia and kidney damage. Our combined findings show that the claudin-10b-mediated paracellular Na+ transport is required for normal sweat production and for the regulation of cation homeostasis in the kidneys.
| Epithelial and endothelial cells constitute sheets that divide organs into functional compartments. Homeostasis of different organ and body compartments are dependent on epithelial cells and their paracellular barrier that prevents solutes and water from leaking between the cells. The tissue specific paracellular barrier properties are determined by the protein composition of tight junctions (TJs) [1]. Some TJs form truly impermeable barriers whereas others contain paracellular channels for selective exchange of small ions between compartments. The selectivity is largely determined by the expression of specific members of the claudin protein family [2, 3]. In mammals, the claudin protein family comprises 27 members consisting of small, highly conserved transmembrane proteins with four transmembrane helices and two extracellular loops [2]. The first extracellular loop contributes to ion selectivity in channel-forming claudins [4] and the second extracellular loop is of importance for claudin-claudin interactions [5, 6]. The critical roles for claudin proteins in development and homeostasis are documented by mouse models as well as by some rare human diseases. Early lethality or specific phenotypes have been identified in mice targeted for the genes encoding claudin 1, 2, 4, 5, 7, 10, 11, 15, 16 18, and 19, respectively [7]. In humans, variants in the CLDN16 and CLDN19 genes are associated with hypomagnesemia, hypercalciuria and nephrocalcinosis (OMIM #248250 and OMIM #248190), CLDN14 mutations causes a form of autosomal recessive deafness (OMIM #614035) and CLDN1 mutations have been described in rare patients with ichthyosis, leukocyte vacuoles, alopecia, and sclerosing cholangitis (ILVASC; OMIM#607626) [8–12].
CLDN10 encodes two distinct isoforms that differ in their amino terminal transmembrane helix and the first extracellular loop [13]. Expression of isoform 10a is restricted to the kidney and uterus while isoform 10b is ubiquitously expressed. Claudin-10a forms an anion-selective channel whereas claudin-10b forms a water-impermeable cation-selective channel with preference for Na+ [13, 14]. In mice, loss of claudin-10b in the distal segments of the nephron has been shown to cause impaired Na+ permeability as well as increased Ca2+ and Mg2+ resorption that lead to hypermagnesemia and nephrocalcinosis [15]. However, the effect of impaired claudin-10 function in humans has remained unknown.
Here, we report on a homozygous CLDN10b variant c.144C>G, p.(N48K), in 13 individuals from two kindreds presenting with anhidrosis, alacrima (inability to produce tears), xerostomia (dry mouth) and kidney failure associated with hypermagnesemia. We demonstrate that the claudin-10b N48K variant has pathogenic consequences, since it alters paracellular Na+ transport in a model for sweat secretion, claudin oligomerization tight junction formation at cell-cell contacts, electrophysiological properties of epithelial monolayers and the amount of claudin-10b in the cell membrane. Together, our data reveal mechanisms caused by impaired claudin-10b function and its phenotypic consequences.
Two distantly related Pakistani kindreds segregating heat intolerance and generalized anhidrosis from birth were identified. Altogether 13 affected individuals were ascertained and several loops of consanguinity suggested an autosomal recessive mode of inheritance (Fig 1A). The anhidrosis was associated with inability to produce tears (alacrima) and dry mouth (xerostomia) in all 13 individuals. In addition, several affected members suffered from recurrent kidney pain due to nephrolithiasis with onset in adolescence (individuals 4, 7, 12 and 14). Heat intolerance was assessed by exposure to heat during 20 minutes (ind. 4 and ind. 11) and resulted in a rapidly increased body temperature from 37°C to 39.6°C when compared to gender- and age-matched control individuals (Fig 1B). The increased skin temperature was accompanied with an increase in heart rate from 106 bpm to 170 bpm. Perspiration was sparse or absent in patients when using the starch-iod test applied on different body parts consistent with generalized anhidrosis (S1 Fig).
Analysis of serum electrolytes revealed increased Mg2+ levels but no other overt abnormalities (Table 1). An abnormal renal reabsorption of cations was reflected in urine spot samples that showed low concentrations of Mg2+ as well as Ca2+ in six affected individuals. Parathyroid hormone (PTH) was analyzed in two affected individuals and revealed a two- and three-fold increase, respectively, when compared to normal levels. The same two individuals had reduced 25-hydroxy vitamin D levels. In combination, these observations suggested secondary hyperparathyroidism and kidney damage and further supported by an eGFR in the lower normal range (Table 1). In contrast, creatinine, urea and bicarbonate in serum were normal (n = 4) as well as the 24h urine production (n = 2; 1400ml and 1450ml, respectively). Computer tomography (CT) scans of kidneys (n = 2) were normal. Pancreatic function was assessed by analysis of amylase and lipase levels that turned out normal (n = 2). Lung functions were investigated by spirometry and turned out normal (n = 2; S1 Table).
Autozygosity mapping of the family identified a homozygous region of 235 consecutive SNPs spanning a 2 Mb region on chromosome 13q32. Fine mapping using microsatellite markers confirmed homozygosity and linkage analysis resulted in a maximum two-point logarithm of odds (LOD) score of 4.25 (Fig 1A). We enriched genomic DNA spanning the candidate region on chromosome 13q32 (average fold enrichment x386) from one affected family member. Variant detection using v2.1 of the LifeScope Software (Life Technologies) revealed only two homozygous missense variants in the linked homozygous region: A c.144C>G, p.(N48K) in CLDN10b (NM_006984.4) and a c.982T>G, p.(S328A) in UGGT2 (NM_020121.3), respectively. The c.982T>G variant in UGGT2 was annotated as a SNP in dbSNP132 (rs816142, mean allele frequency 0.14; ExAC) and without predicted severe impact on function. The CLDN10b gene variant c.144C>G results in the change of an uncharged asparagine into a positively charged lysine at amino acid position 48 in the first extracellular loop of the protein (Fig 1C). The N48 residue is part of the conserved claudin consensus motif (W-G/NLW-C-C) and the substitution was predicted to have a severe impact on claudin-10b function. The c.144C>G transition was found in a homozygous state in all affected family members and in a heterozygous state in unaffected parents. Furthermore, the variant was excluded on 600 control chromosomes from Pakistan and it was not present in the ExAC database (http://exac.broadinstitute.org/) suggesting this variant to be very rare [16].
The finding of a homozygous missense variant in the CLDN10b gene suggested altered paracellular ion permeability as a mechanism of disease. Anhidrosis was an early and prominent symptom of the disease and hence we performed histological investigations of sweat glands in forearm punch biopsies of two affected individuals. The morphology and number of sweat glands appeared normal and immunohistochemical analysis of claudin-10b, the single claudin-10 isoform expressed in sweat glands, revealed strong signals in cells of the secretory portions without visible differences between patients and healthy controls. Immunofluorescence staining of sweat glands revealed that claudin-10b is localized in the periphery of cells from the secretory portions (corresponding to the clear cells), most likely in the basal membrane infoldings, as well as in the plasma membranes lining the canaliculi and, to a lesser extent, the lumen. Claudin-10b staining co-localized with that of the “sealing” claudin-1 and claudin-3 (S2A and S2B Fig). Furthermore, co-staining of claudin-10b and the TJ protein occludin confirmed an overlap in membranes lining the canaliculi and the lumen. In addition, occludin co-localized with the TJ protein ZO1 in both canalicular and luminal membranes (S2C and S2D Fig). Compared to healthy subjects, the claudin-10b staining in sweat glands of two affected individuals (ind. 4 and ind. 15) showed staining in membranes facing the lumen, but was otherwise mainly intracellular and dramatically reduced in the canaliculi (Fig 1D and 1E). The pattern suggested impaired association of claudin-10b to TJ and an intracellular accumulation, possibly as degraded products, in vesicles. In contrast, immunofluorescence staining of the TJ protein occludin did not reveal any differences in distribution when comparing sweat glands from patient to those of healthy controls (Fig 1D and 1E).
To mimic sweat secretion in a model system we cultured MDCK-C7 cells in Matrigel. Under these conditions, MDCK-C7 cells form three-dimensional cysts (apical side towards the lumen). As demonstrated by Bagnat et al. 2007, lumen formation and expansion in MDCK cells depends on transcellular Cl- secretion that is accompanied by paracellular Na+ movement. The resulting osmotic gradient drives water into the cyst lumen. Cyst lumen diameters were shown to increase, when cells were transfected with the cation channel-forming zebrafish claudin-15 [17]. We therefore hypothesized that the presence of the cation channel forming claudin-10b should similarly enhance fluid secretion. As shown in Fig 2A–2C, this is indeed the case: claudin-10b expression in MDCK-C7 cells resulted in an increase in the mean lumen diameter (untransfected control, 33.5 ± 2.6 μm, n = 10 different z-stacks [total of 151 cysts]; claudin-10b WT clone #3, 70.7 ± 4.7 μm, n = 9 [140], p < 0.01; claudin-10b WT clone #39, 70.6 ± 5.6 μm, n = 11 [187], p < 0.01, student’s t-test with Bonferroni-Holm correction, mean ± SEM). Claudin-10b N48K transfected MDCK-C7 cells on the other hand, formed cysts that showed considerably less lumen expansion (claudin-10b N48K clone #21, 52.68 ± 1.7 μm, n = 7[113], p < 0.01 vs control, p < 0.01 vs WT claudin-10b #39; claudin-10b N48K clone #5, 48.5 ± 4.4 μm, n = 9[167], p < 0.05 vs control, p < 0.01 vs WT claudin-10b #3). Both WT and N48K claudin-10b resided in the TJ in 2D as well as 3D cultures (Fig 2D and 2E), however, the mutated variant showed a distribution that suggested an increased intracellular accumulation (Fig 2D).
Claudin-10b acts as a paracellular cation channel that is expressed in multiple tissues. To determine a possible effect of the claudin-10b N48K variant on ion permeability we generated MDCK-C7 cells stably expressing the mutated and the WT proteins. Starting 21 days after transfection we measured dilution potentials at weekly intervals. Initially, the dilution potentials, and thus the ratio for Na+ and Cl- permeabilities (PNa/PCl), were similar for cell layers expressing claudin-10b WT and claudin-10b N48K, but considerably increased when compared to control cell layers containing an empty vector. However, whereas dilution potentials of claudin-10b WT transfected cell layers remained stable over several passages, dilution potentials of cell layers expressing claudin-10b N48K progressively decreased (Fig 3A), signifying a time-dependent reduction in permeability ratio PNa/PCl. Transepithelial resistance (TER) was considerably reduced in claudin-10b WT expressing cell layers, as expected for a channel-containing tight junction (Fig 3B). In contrast, the TER reduction in cell layers expressing claudin-10b N48K was less pronounced and similar to cell layers with a weak expression of claudin-10b WT. Relative permeabilities for the monovalent cations Li, Na, K, Rb and Cs in claudin-10b N48K-expressing cell layers followed Eisenman sequences, resembling cell layers with weak expression of claudin-10b WT (Fig 3C) [14]. These observations strongly suggest a reduced selectivity for Na+ over other cations mediated by the p.N48K variant, or a reduced amount of claudin-10b within the tight junction.
To clarify if increased degradation of claudin-10b N48K may contribute to the reduced Na+ selectivity, we analyzed the turn-over of the protein in HEK293 cells stably expressing claudin-10b fused to CFP or YFP. For claudin-10b N48K, a higher proportion of cells showed cytosolic CFP/YFP fluorescence, when compared to that of claudin-10b WT (S3A and S3B Fig). This indicates that presence of p.N48K increases cleavage of the CFP/YFP moiety from the fusion protein. Furthermore, Western blot analyses of cell lysates revealed more cleaved products for claudin-10b N48K when compared to claudin-10b WT (S3C and S3D Fig). These data suggest that p.N48K enhances the degradation of claudin-10b in addition to the effect on Na+ selectivity.
Since claudin-10b is a TJ protein we sought to further investigate the effect of the p.N48K mutation on claudin-10b-mediated formation of TJs despite apparently normal immunofluorescence staining of occludin in sweat glands of patients. To this end, we expressed claudin-10b in HEK293 cells without endogenous TJs [5]. Formation and reconstitution of TJs after transfection with either YFP-claudin-10b WT or N48K, respectively, were analyzed by freeze fracture electron microscopy (EM). Stable expression of YFP-claudin-10b WT resulted in the formation of typical epithelial TJs with complex meshwork of continuous and branched TJ strands (Fig 4A). Smooth strands with continuity of intramembranous particles were detected on the protoplasmic fracture face (P-face) of the plasma membrane. In contrast, stable expression of YFP-claudin-10b N48K resulted in very few TJ strands and meshwork with a much lower complexity (Fig 4B). In addition, the strands consisted of separated intramembranous particles and partly two-dimensional particle arrays were observed. After transient expression, similar results were obtained as for stable expression: Transfection of YFP-claudin-10b WT resulted in extended meshwork of continuous TJ strands (Fig 4C), whereas expression of YFP-claudin-10b N48K gave rise to a sparser TJ meshwork with discontinuous and beaded intramembranous particles (particle-type strands) on the P-face (Fig 4D). These data suggest that p.N48K inhibits the formation of continuous-type TJ strands by claudin-10b.
To corroborate our findings, we analyzed the capability of claudin-10b for homophilic trans-interaction. We performed a cellular contact enrichment assay in which trans-interaction of claudins is measured from the selective enrichment of the construct of interest at contacts between two claudin-expressing cells [5]. We observed that both YFP-claudin-10b WT and N48K localized primarily to the plasma membrane of HEK293 cells (Fig 4E and 4F). Cells transiently expressing YFP-claudin-10b WT showed a strong contact enrichment indicating trans-interactions whereas cells expressing YFP-claudin-10b N48K showed no contact enrichment (Fig 4G). Similar results were obtained after stable expression of the two fusion proteins (Fig 4H). However, detection of the contact enrichment was more intricate in cells with stable expression when compared to the transient expression due to fragmented enrichments of the claudins at cell contacts. Hence, we used co-cultures of HEK293 cells expressing either YFP- or CFP-fusion proteins [18]. Accordingly, we quantified the enrichment of YFP and CFP that co-localized at the mixed cell contacts to discriminate between trans-interacting claudins and other potential local claudin enrichments in the plasma membrane (S4 Fig). The enrichment at contacts with co-localization of CFP and YFP was significantly lower for claudin-10b N48K than for WT (Fig 4H). Furthermore, similar differences between claudin-10b WT and claudin-10b N48K were obtained for transiently expressed fusion proteins with C-terminal GFP-tag (S5 Fig). Together, the data suggest that the p.N48K mutation does not prevent targeting of full length claudin-10b to the plasma membrane but does inhibit claudin-10b trans-interaction.
Claudins assemble both in trans (between opposing membranes) and in cis (within one membrane) to form paracellular ion channels or barriers. To test whether the p.N48K mutation affects cis-oligomerization of claudin-10b we employed a fluorescence resonance energy transfer (FRET) assay on HEK293 and MDCK-C7 cells expressing CFP- and YFP-tagged claudin-10b [5, 19]. In contrast to HEK293, MDCK-C7 cells contain endogenous claudins and form TJs. In both cell types the maximum FRET efficiency was significantly higher for YFP-claudin-10b N48K/CFP-claudin-10b N48K than for YFP-claudin-10b WT/CFP-claudin-10b WT. In addition, after co-transfection in HEK293 cells, maximum FRET efficiency for both YFP- claudin-10b N48K/CFP-claudin-10b N48K and YFP-claudin-10b WT/CFP-claudin-10b WT were significantly higher than that for YFP- claudin-10b N48K /CFP-claudin-10b WT (Fig 5). Hence, the p.N48K mutation affects, but does not prevent, cis-oligomerization of claudin-10b. Together, the microscopic analysis supports that p.N48K reduces formation of claudin-10b based tight junction strands by affecting cis-interaction and by inhibition of trans-interaction.
We generated a 3D homology model of claudin-10b using the crystal structure of murine claudin-15 (PBD ID: 47P9; 52% amino acid sequence identity to human claudin-10b) as template. The N48 residue is part of the consensus motif of claudins (W-G/NLW-C-C), where only claudin-15 and claudin-10b contain an asparagine (N) instead of a glycine (G). The claudin-10b model (Fig 6A) shows a fold that is very similar to the fold of the claudin-15 structure with a left-handed, four transmembrane helix bundles and a β-sheet connecting the extracellular loops (ECL) one and two [6]. Strikingly, residue N47 in the claudin-15 structure and the corresponding residue N48 in the claudin-10b model, seem to form hydrogen bonds bridging the backbone of consensus motif residues (L49 and W50 of claudin-10b) with the backbone (T27 of claudin-10b) at the transition of transmembrane helix one and ECL1 (Fig 6B and 6C). In the model, these conserved bridging interactions are disrupted by the replacement of asparagine for lysine (Fig 6D). Furthermore, a potential electrostatic interaction between residues D28 and K51 within claudin-10b could be disturbed in claudin-10b by the replacement of the uncharged for the positively charged side chain at position 48 (Fig 6B and 6D).
Together, the 3D modeling suggests that the p.N48K mutation alters the claudin-10b structure at the membrane-ECL1 transition around the claudin consensus motif. It is plausible that these intra-molecular alterations have an indirect effect on claudin-10b oligomerization. Such indirect effects of p.N48K on oligomerization are further supported by the fact that the corresponding N47 residue of claudin-15 is not part of an intermolecular interface in a polymer model reported previously [20].
Our work demonstrates that a homozygous missense mutation in the CLDN10b gene, encoding the TJ protein claudin-10b, is responsible for a phenotype characterized by generalized anhidrosis, xerostomia, alacrima and kidney damage. The missense mutation p.N48K is located in the first extracellular loop (ECL1) that distinguishes the ubiquitously expressed isoform 10b from the kidney specific isoform 10a. The loop determines the opposing electrophysiological properties of the two proteins: Isoform 10b is selective for cations and isoform 10a for anions. Notably, the N48 residue in claudin-10b is included in the consensus motif shared by all mammalian claudins.
Evidence for the pathogenic nature of the claudin-10b p.N48K was obtained by a combination of in vitro experiments. To model the effect of claudin-10b N48K on sweat production we cultured MDCK-C7 cell in matrigel to produce cysts. The expansion of cysts is driven by an osmotic gradient caused by transcellular Cl- secretion and paracellular Na+ transport that, in a similar way, drive sweat production. However, expression of claudin-10b N48K caused a reduced lumen expansion when compared to cysts expressing claudin-10b WT. This observation suggests that the mutation reduces overall Na+ conductance of the cysts that may be brought about by a reduction in single cell conductance or by a reduction of the number of paracellular channels. A reduction in single channel conductance cannot be excluded although dilution and bionic potential measurements indicate that claudin-10b N48K is still able to form charge and size-selective channels. However, claudin-10b N48K showed a tendency for intracellular accumulation in MDCK cells and in sweat glands of our patients. Thus, our observations suggest that the anhidrotic phenotype is not exclusively caused by the partial loss of function mediated by junctional claudin-10b N48K but also to its reduced incorporation into TJs.
Since claudin-10b forms TJ strands and paracellular ion-channel we sought to investigate the capability of the mutated protein for TJ strand formation by ultrastructural analysis. The p.N48K substitution of claudin-10b was associated with fewer TJ strands arranged in a less complex meshwork and in contrast to claudin-10b WT, the strands formed by claudin-10b N48K consisted of separated intra-membranous particle arrays similar to those found for claudin-2, claudin-5 and claudin-3/claudin-5 and claudin-10a/claudin-10b chimeric mutants [21–23]. It has been suggested that these ultrastructural changes are related to altered claudin subtype-specific oligomerization properties [22]. The claudins interact in trans as well as in cis and we show an aberrant assembly mediated by the p.N48K substitution in cis accompanied by a marked decrease in trans-interactions at cell-cell contacts. Thus, the combined data strongly suggest that p.N48K alters both trans- and cis-interactions resulting in perturbed TJ formation. However, TJ formation is not fully prevented by the N48K mutation. Furthermore, in a 3D model based on the crystal structure of the highly homologous murine claudin-15, the N48 residue is predicted to form hydrogen bonds bridging the NLW-motif in the ECL1 with the transition between trans-membrane helix one and the ECL1. These interactions seem to be disrupted by replacing the polar asparagine for the longer and positively charged lysine. The modeling and the fact that the corresponding N47 residue of claudin-15 is not part of an intermolecular interface of a previously reported claudin-15 polymer model suggests that p.N48K indirectly affects oligomerization of claudin-10b by altering intramolecular interactions [20].
In the kidneys, an important proportion of Na+ reabsorption takes place within the ascending limb (TAL) of Henle’s loop through both transcellular and paracellular transport. The concerted action of apical and basolateral ion transporters generates a transepithelial voltage that drives the reabsorption of both Ca2+ and Mg2+. Within the kidney, claudin-10a is expressed exclusively in cortical proximal tubulus segments of the nephron, whereas claudin-10b is highly expressed in the medulla where 50% of the reabsorbed Na+ takes the paracellular route. Interestingly, ablation of the claudin-10b isoform in mouse kidney results in hypermagnesemia due to an increase in renal Mg2+ reabsorption. The absence of claudin-10b in the TAL results in elevated transepithelial resistance, an increased transepithelial voltage and consequently, in an increased driving force for the paracellular reabsorption of cations [15]. Additionally, absence of claudin-10b in TAL tubules resulted in increased paracellular permeability for divalent cations, possibly due to increased expression of claudin-16 and claudin-19. However, absence of claudin-10b or presence of claudin-10b N48K should not affect the claudin 16/19 pore directly since claudin-10b does not physically interact with either claudin-16 or claudin-19 [24]. The mouse model further revealed that the reduced paracellular reabsorption of Na+ in the TAL did not result in sodium loss. Given the observations in the mouse model and the elevated levels of Mg2+ in serum of our patients we hypothesized that claudin-10b p.N48K disturbs the cation permeability and in particular the paracellular Na+ transport. Indeed, we observed that the p.N48K mutation was associated with a reduced selectivity for Na+ over Cl- and with a preserved transepithelial resistance in MDCK-C7 cell layers. Still, claudin-10b N48K retained the ability to interact with the hydration shell of monovalent cations Li, Na, K, Rb and Cs as judged from the higher Eisenman sequence. Furthermore, the reduction in PNa/PCl for the mutated protein was time dependent in culture and similar to that for clones with a weak expression of claudin-10b WT. These observations suggest that the paracellular channels formed by claudin-10b p.N48K have a subnormal Na+ permeability and that the number of channels is greatly reduced. In addition, the observed increased proteolytic cleavage of mutated YFP- and CFP- claudin-10b and the staining of sweat glands in our patients suggest that the N48K mutation leads to increased degradation of claudin-10b that contributes to the reduced formation of Na+ channels. The active transcellular transport of Cl- in the sweat glands as well as in the TAL generates a transepitheleal voltage and a driving force for paracellular cation transport. In the kidney, the severely reduced Na+ conductivity caused by the N48K mutation is thus a likely contributing mechanism for the increased Mg2+ and Ca2+ reabsorption that results in kidney damage in our family as shown by reduced eGFR values, reduced levels of 25-hydroxy vitamin D and increased PTH levels. Furthermore, the increased reabsorption of Mg2+ that is associated with claudin-10b p.N48K is consistent with the kidney specific lack of claudin-10 in mice showing decreased Na+ permeability in the TAL accompanied by increased reabsorption of Mg2+. Interestingly, the increased Mg2+ mediated by claudin-10b p.N48K contrasts with the Mg2+ wasting observed in patients who carry claudin-16 or claudin-19 mutations. In combination, these findings highlight the complex renal mechanisms mediated by claudins to maintain cation homeostasis.
Taken together, our data support that claudin-10b N48K causes a disturbed relative overall permeability for cations that results in increased reabsorption of Mg2+ and hypermagnesemia.
In contrast to the kidney specific isoform claudin-10a, the claudin-10b isoform is ubiquitously expressed and presumably of importance for paracellular Na+ transport in multiple organs. In our family, affected members presented with heat intolerance due to anhidrosis, and alacrima as first symptoms in early childhood. Sweat production is mediated by IP3 acting as an intracellular messenger and the release of Ca2+ that opens Cl- channels to the glandular lumen and thus activates transcellular Cl- secretion energized by the basolateral Na+K+2Cl- symporter [25]. The resulting electrochemical gradient drives paracellular Na+ flux that, together with AQP5 mediated water flux leads to a net secretion of a largely isotonic NaCl solution into the secretory portions of the glandular lumen [26, 27]. Our IHC analyses show claudin-10b staining in cells of the secretory portion of normal sweat glands with intense staining lining the canaliculi. This is consistent with a role for claudin-10b in the paracellular Na+ flux into the lumen. Compared to healthy individuals, the immunohistochemical staining of sweat glands in affected individuals showed a pronounced reduction of claudin-10b N48K in the cell peripheries and canaliculi. In sum, our data suggests that the likely mechanism behind abolished sweat production in our patients is a reduced incorporation of claudin-10b N48K into TJs mediated by altered trans- and cis-interaction properties accompanied by increased degradation of the mutated protein. Accordingly, the plausible explanation for alacrima and xerostomia is a reduced paracellular Na+ transport in the lacrimal and salivary glands, respectively, leading to a perturbed secretion of NaCl and water into the lumen of secretory portions [28].
In conclusion, we show that a mutation in the claudin-10b isoform results in abolished or reduced sweat production as well as a relative shift in cation resorption in the kidneys that leads to kidney damage. The affected organs contain epithelia in which transepithelial transport of NaCl is paralleled by paracellular transport of Na+ that is impaired by claudin-10b N48K. The combined findings expand our knowledge on the role of claudin-10b and the complex functional networks of claudins that may be useful in identifying the genetic basis for additional phenotypes caused by altered paracellular ion transport.
The kindred examined in this study were referred to the Health Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan, because of severe heat intolerance and anhidrosis. The patients were exposed to heat (45°C, 45% humidity) and perspiration was measured using starch-iodine together with healthy control individuals. It became evident that several family members also suffered from renal insufficiency. Blood and urine samples were obtained from available family members and punch skin biopsies were taken from two affected individuals. Consanguinity was ascertained over several generations and the affected individuals were related through five loops suggesting autosomal recessive inheritance (Fig 1A). The study was carried out under a protocol approved by the ethical committee of the National Institute of Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan, and the Regional Ethical Committee of Uppsala, Sweden. Informed consent was obtained from all study participants or their legal guardians.
SNP genotyping was performed on DNA samples from three affected family members (using the GeneChip Mapping 250K array (Affymetrix) according to the manufacturer’s protocol. Homozygosity mapping and sorting of genomic regions were performed as described previously with the dedicated software AutoSNPa [29]. Two point LOD scores were calculated for microsatellite markers using the MLINK program of LINKAGE computer package [30]. A custom enrichment design covering 6M base pairs (hg19 chr13:93278935–99228090, NimbleGen Sequence Capture Microarrays, Roche) was used to enrich for the linked region on chromosome 13. Sequencing of the enriched region was performed using the Illumina HiSeq system and variant detection was performed using v2.1 of the LifeScope Software (Life Technologies). Prediction of possible impact on protein function was performed using PolyPhen-2 analysis [31]. Variant allele frequencies were assessed using the Exome Aggregation Consortium (ExAC) database (Cambridge, MA (URL: http://exac.broadinstitute.org) accessed June 2016). Exon 1 of isoform b of the CLDN10 gene was analyzed for the identified variant by bi-directional sequencing of genomic DNA from all available family-members using the primers: ATC AAG GAA GGA GGG CTG AG (sense) and: AGA CGC CCG TGG AGT CGG TA (antisense).
Histological analysis of skin biopsies was performed after hematoxylin and eosin (H&E) staining. Immunofluorescence staining of claudin-1, claudin-3, claudin-10, occludin and ZO-1 α (Invitrogen, San Francisco, California rb-α-claudin-1, rb-α-claudin-3, m-α-occludin, rb-α-ZO-1, m-α-claudin-10) were added in blocking solution at a dilution of 1:100 and sections were incubated over night at 4°C. Secondary antibodies (Jackson ImmunoResearch, Newmarket, UK, Cy2 Fab gt-α-m-IgG, Cy5 Fab gt-α-rb-IgG) were applied at a dilution of 1:600 (at least 30 min at room temperature). Fluorescence images were obtained with a LSM (Zeiss LSM780, Jena, Germany).
HEK293 cells were transiently or stably transfected and MDCK-C7 cells were stably transfected with WT or mutated CLDN10b vectors containing a puromycin or neomycin resistance using PEI (Polyethylenimine, Sigma-Aldrich). Transiently transfected HEK293 cells were used for confocal laser microscopy after 24 or 48 hours. For stable transfection, MDCK-C7 cells were treated with puromycin (10 μg/ml, Sigma-Aldrich) or G418 (1000 μg/ml, Biochrom, Berlin, Germany), respectively. After 2 weeks, G418 resistant clones were picked with cloning-cylinders and the cells were further cultured with 600 μg/ml G418. Puromycin-resistant MDCK-C7 cells were pooled after 7 days, grown for further 10 days, seeded onto Millicell cell culture inserts (pore size 0.45 μm, effective area 0.6 cm2; Millicell-HA) and grown for further 5 days before they were mounted in an Ussing chamber. In contrast, HEK293 cells were treated with 600 U/ml G418 (Biochrom, Berlin, Germany) for 4 weeks and the resistant cells were further cultured in the presence of 150 U/ml G418.
Cells were trypsinized to stimulate proliferation. On the following day, cells were trypsinated again and 104 cells were seeded into 100 μl BD Matrigel (BD Biosciences, Heidelberg, Germany) per Lab Tek well (Lab Tek II Chambered Coverglass; Nunc). After 5 days, the cysts were fixed with 4% PFA in PBS (1 hour shaking at room temperature). Subsequently, the cysts were treated with a permeabilization solution (0.5% Triton X-100, 0.25% Saponin and 25 mM Glycine, pH 8.0, in PBS; 6 hours shaking at room temperature). For immunostaining of claudin-10, the cysts were incubated with rabbit anti-Cldn 10 (1:150, Assay bio Tech, Sunnyvale, CA, USA) overnight at 4°C. Cysts staining for diameter determination was achieved by incubation with Alexa Fluor 594 Phalloidin (1:450, Invitrogen) and DAPI (1:500, Roche) overnight at 4°C. All cysts were washed with permeabilization solution (6 hours shaking at room temperature) and the solution was changed every half hour. Cysts intended for diameter determination were subsequently covered with ProTaqs Mount Fluor (Biocyc, Luckenwalde, Germany). Dyed Cldn10-cysts were further incubated with Phalloidin-Dy-647P1 (1:1000, Dyomics GmbH, Jena, Germany), anti-rabbit IgG Alexa Fluor 488 (1:400, Molecular Probes), and DAPI (1:500) overnight at 4°C. Dyed Cldn10-cysts were washed with permeabilization solution (6 hours shaking at room temperature; solution changed every half hour). The cysts were subsequently covered with ProTaqs Mount Fluor. Fluorescence images were obtained with a LSM (Zeiss LSM780, Jena, Germany).
Z-stack images of cysts stained with DAPI and phalloidin were recorded by confocal laser scanning microscopy (Zeiss LSM780, x20). Within each stack, the layer with the largest diameter of each individual cyst was identified, the diameter marked with a straight line and the length of each line determined, using the Zeiss ZEN software. Mean diameters of each stack were calculated and averaged to obtain mean ± SEM for each cell clone. For size distribution histograms, individual cyst diameters were sorted into size intervals (width 15 μm), and the relative frequency was calculated by the ratio of cyst number per interval divided by total number of cysts of each cell clone. For visualization of the shift in the distribution, normal distributions were calculated for each histogram.
For freeze fracturing, HEK293 cells were washed twice with PBS with MgCl2 and CaCl2 (Sigma-Aldrich), fixed with phosphate-buffered 2.5% glutaraldehyde (Sigma-Aldrich) for 2 hours at room temperature. Cells were washed with PBS and stored in 0.1% glutaraldehyde in PBS at 4°C. Electron microscopy was performed similar as described before [32].
For claudin trans-interaction analysis, HEK293 cells, a cell line devoid of TJs, was used. As a measure for claudin-10b trans-interaction, enrichment of the transfected YFP-/CFP-claudin-10b constructs at contacts between two claudin-expressing cells was analyzed similar as described previously [5, 22]. Briefly, one or two days after transient transfection or replating stable lines, cells were transferred to Hanks' Balanced Salt Solution (HBSS) pH 7.4 with Ca2+, Mg2+, glucose, sodium bicarbonate, without phenol red (Thermo Scientific) and examined with a LSM 780 system (Carl Zeiss, Jena, Germany). Randomly chosen cells were analyzed using the ZEN software (Carl Zeiss, Jena, Germany) and YFP intensity profiles of confocal images. The enrichment factor (EF) was calculated as the intensity of the YFP signal at contact between two claudin-expressing cells divided by the sum of the intensities at contact between these two claudin-expressing cells and neighboring non-expressing cells. EF >1 indicates enrichment [5].
For analysis of the cis-interaction between claudin-10b constructs along the plasma membrane of one cell, FRET analysis was performed. HEK293 were co-transfected with two plasmids encoding a CFP-claudin-10b (mutated or WT) and an YFP-claudin-10b (mutated or WT) fusion protein, respectively and analyzed at cell-cell contacts as described previously [19]. Before and after acceptor bleaching, CFP and YFP intensity was detected. Since the FRET efficiency EF depends on the acceptor/donor ratio, EF was plotted as a function of YFP/CFP for each acceptor/donor pair [33, 34]. Signals were calibrated using an YFP-CFP tandem protein, so that equal YFP and CFP intensities denote equal amounts of these proteins. Curve fitting and data analysis to obtain the average FRET efficiency were carried out as described previously [19].
HEK293 cells were washed with ice cold PBS (with Ca2+ and Mg2+), lysed on ice with 1% (v/v) Trition X-100 in PBS containing EDTA-free protease inhibitor cocktail (Roche, Mannheim, Germany) and centrifuged at 10.000 x g (5 min, 4°C). Supernatants were mixed with Laemmli buffer, boiled and loaded on 10% SDS gels, transferred on PVDF membranes. The CFP/YFP-fusion proteins were detected using mouse anti-GFP (JL-8, Takara Clontech, Saint-Germain-en-Laye, France) and HRP-coupled (Jackson Immunoresearch) anti-mouse antibodies.
The protomer model of human claudin-10b was generated using the crystal structure of murine claudin-15 (PDB ID: 4P79) as template and Swissmodel (http://swissmodel.expasy.org) [6, 20, 35, 36]. Model quality was estimated employing the QMEAN server (http://swissmodel.expasy.org/qmean) [37]. In addition, Modeller was used and resulted in a similar model [38]. Images of the structures and models were generated using PyMOL (version 1.5.0.4 Schrödinger, LLC).
Cell culture inserts were mounted into Ussing chambers and the water-jacketed gas lifts on both sides were filled with 10 ml of a bath solution containing 119 mM NaCl, 21 mM NaHCO3, 5.4 mM KCl, 1.2 mM CaCl2, 1 mM MgSO4, 3 mM HEPES, and 10 mM D(+)-glucose. The solution was constantly bubbled with 95% O2 and 5% CO2, to ensure a pH value of 7.4 at 37°C. After equilibration, 5 ml of the apical or basolateral solution were replaced by a bath solution which, instead of 119 mM NaCl, contained 238 mM mannitol. The resulting voltage step (`dilution potential') was converted into the permeability ratio, PNa/PCl, as described previously [14]. Eisenman sequences were determined analogously by using solutions containing 119 mM XCl (LiCl, KCl, RbCl, or CsCl, respectively), instead of NaCl. PX/PNa was calculated from the resulting ‘bi-ionic potentials’ and the PNa/PCl obtaind from the dilution potential measurements. Complete equations are described by Günzel et al [14].
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10.1371/journal.pbio.2002612 | A multiscale cerebral neurochemical connectome of the rat brain | Understanding the rat neurochemical connectome is fundamental for exploring neuronal information processing. By using advanced data mining, supervised machine learning, and network analysis, this study integrates over 5 decades of neuroanatomical investigations into a multiscale, multilayer neurochemical connectome of the rat brain. This neurochemical connectivity database (ChemNetDB) is supported by comprehensive systematically-determined receptor distribution maps. The rat connectome has an onion-type structural organization and shares a number of structural features with mesoscale connectomes of mouse and macaque. Furthermore, we demonstrate that extremal values of graph theoretical measures (e.g., degree and betweenness) are associated with evolutionary-conserved deep brain structures such as amygdala, bed nucleus of the stria terminalis, dorsal raphe, and lateral hypothalamus, which regulate primitive, yet fundamental functions, such as circadian rhythms, reward, aggression, anxiety, and fear. The ChemNetDB is a freely available resource for systems analysis of motor, sensory, emotional, and cognitive information processing.
| The mammalian brain consists of a network of chemically diverse, multiscale, and multilayer neuronal wiring patterns that form the physical infrastructure underlying the processing of motoric, sensory, emotional, and cognitive information. Decades of histological track-tracing studies have generated a set of highly valuable yet disorganized data, which is very hard to retrieve in a hypothesis-free manner. We present an open-access database (ChemNetDB) that organizes over 50 years of neuroanatomical track-tracing and neurochemical measurements from 36,464 rats. This neurochemical connectome is terminologically consistent and shares several network invariants with mouse and macaque cortical networks, suggesting that the mammalian brain exhibits universal structural features. Furthermore, several network measures reflect and confirm the key functional roles of deep subcortical brain areas, which are known to be responsible for primitive, yet essential and evolutionary conserved functions related to survival. ChemNetDB provides the first whole-brain, multiscale, and consistently collated rat connectome database. ChemNetDB also includes neurochemical specificity and represents a powerful tool for in vitro, in vivo, and in silico investigations of brain function and disorders.
| Automatic keyword-based search (1,750 keywords) and manual grey search on electronic databases revealed 124,694 abstracts, titles, or both identified as original publications. Out of these, 4,517 studies were relevant for data mining and data from 1,560 original research articles with 36,464 rats were selected for the connectome identification based on the inclusion criteria. A flow diagram of the study selection process is represented in supplementary information (S1 Fig).
According to our stringent inclusion and exclusion criteria, these selected studies represent the relevant published outcome of 55 years of neuroanatomical research. All selected studies were performed in outbred rats with no specific genotype or phenotype. Furthermore, animals did not receive any pharmacological treatment or behavioral training. On average, 86.08% ± 0.02% of the cases were male animals. The strain of the animals was inverse Gaussian distributed (μ = 0.25;λ = 1.24). Thereby, 54.03% ± 0.01% and 24.41% ± 0.01% of the animals were Sprague-Dawley and Wistar rats, respectively.
In total, the systematic literature search identified 281 interconnected nuclei (4,585 axonal projections) of cerebral regions, cerebellum, and components of medulla oblongata. The connectivity matrices associated with this raw database are presented in the supplementary information (S7, S8 and S9 Tables) along with Bregma levels from rostral to caudal. The nomenclature-consistent, brain-wide (cerebral) core of the chemical connectivity database (ChemNetDB) comprises by 188 cortical and subcortical morphologically distinct regions with 3,712 connections. The database references are provided within S1 Text.
This database considerably differs from the existing macroscopic connectomes [17,18] of the rat central nervous system. In contrast to ChemNetDB, which is a multiscale connectome database, these databases are often restricted to a single spatial scale. Furthermore, with the exception of a few brain regions such as amygdala, the bed nucleus of stria terminalis (BNST), and cerebral cortex, they are only sparsely connected, and do not provide edge-complete connectivity. Databases lacking the edge-completeness property include brain regions that are either not connected to any other region, or receive only afferents from other regions, or send only efferents to other brain areas. None of these cases are biologically or physically feasible within the central nervous system. Thus, these databases effectively present cerebral subnetworks of the full connectome. In addition, concerns regarding nomenclature and data integration (such as combining data from neonatal and adult animals) and other inconsistencies make these platforms much more difficult to use. ChemNetDB overcomes these issues and shows significant advantages over the existing databases in that (1) it is currently the most comprehensive multiscale database that contains previous databases as subsystems and it is nearly edge-complete; (2) it integrates neurochemical information in a consistent and validated manner; and (3) it is consistent with respect to age of the animals and nomenclature (see S2 Text for more detail).
Divisive hierarchical algorithms with edge-completeness constraints further reduced the dimension of the network and integrated the extracted cerebral core into a multilayer cerebral neurochemical connectome of the rat brain. Neurochemical connections within and between brain regions were mapped into a 3-dimensional space using a standardized platform to generate a comprehensive and quantitative database of inter-areal and cell-type−specific projections. The connectome consists of 125 scale-consistent cerebral nuclei and 2,931 multiscale, multichemical connections (Fig 1a). Thereby, 632 bidirectional, 1,642 unidirectional connections, and 25 loops utilize 25 different neurochemical compounds, such as amino acids, monoamines, cannabinoids,- opioids, and several other neurotransmitters. Despite such diversity, the chemical coverage of the network is only 25.08% and the majority of the connections were treated as binary links. Gamma-aminobutyric acid-ergic (GABAergic) neuronal connections (Fig 1b) constitute the dominant chemical components at the short-range (18.36% of the chemically denoted connections). In contrast, dopamine (15.37%; Fig 1c), serotonin (13.47%; Fig 1d), glutamate (10.75%; Fig 1e), and enkephalin (7.76%; Fig 1f) represent the majority of chemical colocalizations of long-range axonal projections.
A comprehensive systematic review on receptor distribution according to their mRNA expression levels and receptor binding was used to improve the robustness of these observations by providing spatial colocalization patterns of the transmitter systems within the connectome (S10–S13 Tables). For this purpose data on glutamatergic (mGluR1-7, GluK4-5, GluA1-4, GluN1-2(A-D)), monoaminergic (D1-D5, 5-HT1-7(A-C), β1–2, α1-2(A-D)), cholinergic (M1-M5, nAChRα(2–6)β(2–3)), GABAergic (GABAAα(1–6)β(2–3)γ(1–3)δ, GABAB1(a,b,p), GABAB2), opioid (μ, δ, κ, ORL1), and cannabinoid receptors from 103 (out of 1,843) selected original research articles are qualitatively integrated into brain-wide receptor distribution density maps (S2–S12 Figs).
In the following, we provide examples of network analyses of the ChemNetDB.
First, we investigated the network properties of the whole-brain connectome at 2 levels of resolution, namely as a coarser 19-node network (see Methods) described by the G19×19 adjacency matrix [12] and then at full resolution, as a 125-node network (G125×125).
The G19×19 has a total of 236 directed links and thus a graph density of ρ19=23619×18=0.69, similar to the interareal networks in the macaque and the mouse [19,20]. The 125-node network G125×125 has 2,906 directed links and a graph density of ρ125=2906125×124=0.19, significantly lower than the coarse version.
Both graphs are relatively small and the degree distributions are noisy (Fig 2a and 2b), hence it is difficult to identify these distributions by simple fitting. However, below we present a model that generates predictions for these distributions (black lines in Fig 2a and 2b). Plotting the degree sequence in Fig 2c and 2d (rank ordering the nodes by their in- and out-degrees), we observe the existence of a small number of high degree nodes (hubs) receiving and/or sending many connections to the rest of the network. The BNST comprises 21 distinct brain areas, which are responsible for integration of limbic information and valence monitoring, processing threat reaction, fear, anxiety, and many other functions, collect 178 in-links, and project 365 out-links to the rest of the connectome. The dorsal raphe nucleus (DRN) has the largest number of outgoing projections (83) and the fourth largest of incoming links (53). This correlates with the fact that the DRN is the largest provider of serotonin innervation to the rest of the brain. Similarly, infra- and prelimbic cortices that comprise prefrontal cortex have high out-going projections (each 61) and a relatively high total degree of 105. This corresponds with the key role of prefrontal cortex in regulating cognitive functions. Moreover, another hub is the lateral hypothalamic area (with 52 in-degree and 60 out-degree), which is responsible for a significant array of functions, such as feeding behavior, wakefulness, thermoregulation, gastrointestinal functions, energy homeostasis, and visceral functions.
Supplementary tables show the top list of degrees (S1 and S2 Tables) and betweenness centralities (b.c.s; see Methods) for nodes (S3 and S5 Tables) and edges (S4 and S6 Tables) at both resolution levels, and Fig 2e–2h show the distributions of the betweenness values. According to these lists, DRN has the largest node betweenness, and that, expectedly, correlates with its high in- and out- degrees, a property that holds in general, although with some exceptions, such as the central nucleus of the amygdala (CeA). CeA, with the second highest b.c., has the largest in-degree, but it is only 19th in the out-degree list. The fact that it plays a key role in information transmission and processing resonates with the observation that it is a principal area for controlling emotional reactions.
The distributions of both node (Fig 2c and 2d) and edge betweenness (Fig 2e–2h) show a concentration at large values, suggesting a heterogeneous network structure; in particular, there are 8 edges with exceptionally high b.c. Given that for both macaque and mouse the interareal network is heterogeneous and shows a strong core-periphery structure [19–21], we have analyzed whether the same holds in the rat brain as well. By using a stochastic block modeling [22,23] method (see Methods section), we determined the probabilities of the individual nodes to belong either to the core or periphery (Fig 3a–3c). In Fig 3 the core nodes are colored red while the periphery nodes are blue. It shows a clear separation of the two classes in a similar fashion to the macaque and mouse. For G19×19 the core contains 12 nodes with an internal density of ρcc = 0.87, a periphery of 7 nodes of internal density ρpp = 0.24, and the subgraph of edges running between core and periphery nodes of density ρcp = 0.66. For G125×125 the core has 69 nodes with a high internal density of ρcc = 0.41, a periphery of 56 nodes of internal density ρpp = 0.03 and the subgraph of edges running between core and periphery nodes of density ρcp = 0.12. The list of core areas and the corresponding regions (in the 19-node segmentation) they belong to is shown in S13 Fig. Fig 3e shows the location of core nodes within G125×125, using a force-based layout representation. Note that while this analysis clearly indicates the core-periphery organization of the connectome, it does not reveal its internal structure, in particular, the internal network communities, if any. To explore this aspect, we performed a hierarchical decomposition of the network using the Girvan-Newman algorithm (see Methods), which outputs a hierarchical community dendrogram [24]. The results are shown in Fig 3b and 3d for both resolution levels. They indicate that in contrast with social networks, which have a clustering of communities over several levels, the rat connectome (at either resolution) has an onion-type structural organization [25], in which layers of areas are added on top of the previous ones. On the x axis, we colored the areas according to their core-periphery membership, which correlates well with depth in the dendrogram, with core nodes concentrating towards the inside of the onion structure. While this overall onion structure is a direct consequence of high-network density, the membership of the levels from the innermost to the outer layers is highly specific to the network.
Although the densities of G125×125 and its core are not drastically different (0.19 versus 0.41), at this resolution such difference implies significant specificity. Specificity can be estimated by computing an upper bound to the probability that an Erdos-Renyi random graph on 125 nodes of density p = 0.19 will have a core on 69 nodes of internal density ρcc = 0.41. The computation is shown in the Methods section, giving a very low probability of approximately 10−228, showing that the rat connectome core-periphery structure is, indeed, highly specific.
The raphe nuclei, DRN and medial raphe nucleus (MRN), constitute the deepest core of the dendrogram or the heart of the onion structure. These serotonergic systems play an important and generalized role in regulation of sleep-wake states and behavioral arousal. While MRN is mostly involved in stress- and anxiety-related processes [26], the DRN is critically involved in the neuronal regulation of circadian rhythms and sleep [27], which may support the hypothesis that DRN acts as a pacemaker of the network.
A prerequisite of controlling a complex biological system is its structural controllability [28–30]. As another example analysis, here we identify the minimal set of driver nodes that could be used to control the system, through searching for a maximum matching in the graph (see definitions in Methods section). A realization of maximum matching is shown in Fig 4a with four driver nodes: subthalamic nucleus (STh), subiculum, CA1, and medial nucleus of amygdala. The interesting finding here is the role of STh in the structural controllability of the rat brain. Numerous deep brain stimulation studies have already shown the remarkable effects of activation of neurons within STh on global circuit dynamics [31] and in the treatment of Parkinson disease and other disorders [32]. This observation suggests an agreement between structural and functional controllability of neuronal networks and attracts our attention towards hippocampal and amygdaloid regions as potential targets for deep brain and other stimulation techniques.
While the number of driver nodes (4) is small compared to the total number of 125 nodes of the network, it is still significantly larger than in random networks with similar density. To show this we performed 500 randomizations of the network preserving the degree sequence and obtained an average of 1.23 driver nodes (SD: 0.45). Sequentially removing the longest (weakest) edge from the network, the number of driver nodes increases, as expected, exponentially (Fig 4b). However, the number of driver nodes for the original network, is consistently larger than for its degree preserved randomized versions. Thus, the original network structure is such as to allow for more points of control in the network, that is for a larger control diversity, than in a similar random graph with the same degree sequence.
Consistent, cortex-wide retrograde tracer injections in several species (macaque [20,33]), mouse and microcebus [19] have shown that the distribution of the lengths of white matter (WM) axons follow an exponential decay, called exponential distance rule (EDR), with a species-dependent decay rate (λ). Naturally, 2 questions arise: Is the rat large-scale connectome also described well by the EDR network model? And (2) Does the agreement with the EDR model break with increasing resolution? The second question could not be answered for the other species, as there is no (nearly) edge-complete database available at higher resolutions for them, and thus ChemNetDB is especially valuable in this regard. By using ChemNetDB, we next attempt to answer both questions by modeling the connectome with the EDR network model at the G19×19 and G125×125 levels, respectively. Network comparison is based on parametric property matching described briefly in the Methods section and in detail in refs [19,20]. Fig 5 shows the results for the G19×19 using a set of commonly used graph measures: number of uni- and bidirectional edges (Fig 5a), the root-mean-square (RMS) of deviations for the 3-motif counts between model and data (Fig 5b, 5f and 5g), the RMS of clique-count deviations between model and data (Fig 5c and 5h), the RMS of the deviation of the eigenvalues of the co-occurrence matrix AAT between model and data networks (Fig 5d and 5i) and finally the clustering coefficients (Fig 5e). These results show that all models generate the same value for the decay rate λ in the range 0.60 − 0.65mm−1 consistently, indicating that the EDR network model is a good model for the rat connectome at the large-scale level. Fits generated by and EDR with λ = 0.6mm−1 are shown in Fig 2a and 2c for the degree distributions and the degree sequence, respectively.
Moving to the higher resolution connectome G125×125, the same procedure yields the comparisons shown in Fig 6. The figures now show a different picture: the best λ values determined from parameter matching are varying, from measure to measure, indicating that a single λ-parameter EDR model cannot describe the whole data network at this resolution. The reason lies with the fact that once we subdivide brain regions (as it was done in going from 19 areas to 125), there will be an increasing number of area pairs that are connected by gray matter (GM; nonmyelinated) connections, instead of WM connections. Note that at the G19×19 level, the connections are WM connections, just as for the cortical interareal networks in the mouse and macaque, where the EDR descriptions work well. As shown by experiments presented in Horvát et al. [19], the decay rate is sensitive to the nature of the medium in which the connections are running (WM versus GM). In particular, while local (GM) connections obey an EDR with almost the same decay rate (4.6 ÷ 4.9mm−1) in macaque, mouse and rat (see Fig 11B in [19]), for WM connections, the EDR decay rate decreases with increasing brain size (0.19mm−1 for macaque, 0.8mm−1 for mouse, and 0.6mm−1 for rat—this paper). The larger the brain, the smaller the decay rate for nonlocal (WM) connections. The EDR network model is defined via a single decay rate and it works well for all those brain networks for which the connections obey a single parameter EDR. Once the network contains both types of connections (GM and WM), as the rat brain at 125-area resolution, a single decay rate model will not work as shown in Fig 6. To develop a two-parameter EDR model, however, we also need to include information about the location of the GM connections with respect to the WM ones, resulting in a more involved model, which will be the subject of a forthcoming paper.
Most studies in neurobiology rely on a precise understanding of the neuronal connectivity and its neurochemical actors. Yet, investigators commonly face massive data that require enormous resources to be processed for their demands. By utilizing advanced neuroinformatics, our study resolves this problem and integrates over 50 years of neuroanatomy research on rat brains into a consistent multiscale, multilayer neurochemical cerebral connectome. Supervised machine learning was applied to resolve nomenclature issues resulting in an extensive standardized database, which combines the state-of-the-art knowledge of connectomics and neurochemistry. Establishing the neurochemical connectivity database (ChemNetDB) is a novel approach towards topological mapping of the brain that takes the neuroconnectomics well beyond the binary constructs and paves the way for more advanced and accurate investigations of healthy and disease states of rat brains.
We have investigated, as examples of analytic studies that could be done on the database, several network measures and their relationships with similar analyses in macaque and the mouse. Analysis of the structural properties of the resulting network reflect and confirm the key functional roles of deep subcortical brain areas such as lateral hypothalamus, BNST, and DRN. These regions are known to be responsible for primitive yet essential and evolutionary conserved functions, such as regulation of sleep and circadian rhythms, reward, anxiety, aggression, and fear. The importance of these brain areas for survival is also associated with an early formation in the developmental stages as reflected in the connectome by their extremal values of various graph connectivity measures such as b.c. and/or node degree.
Previous theoretical analysis demonstrated that the optimal core-periphery structures in networks that are stable against both random and targeted failures/attacks (targeting hubs) are onion-like structures [34], however, they have not been observed in real-world networks [25]. Our analysis shows that the onion structure occurs in large-scale, dense brain neuronal networks, where robustness against information transmission failures (of all types) is a critical requirement. The observed onion structure does not contradict the well-known hierarchically modular structure of brain networks. At this large-scale resolution, applying the Girvan-Newman methodology [24], the paths-based network modularity is not revealed due to the high density of the network. Instead, the onion organization is dominant and the community structure appears as core-periphery at this scale.
As another example of an analytical study on ChemNetDB, we tested whether the EDR property and the associated network model are consistent with the rat connectome structure, at different levels of resolution. The EDR, expressing economy of wiring has been found to hold in both macaque and mouse for both mesoscale WM connections and local GM connections. While direct measurements of the WM axon length distribution in the rat are currently lacking, the EDR network is highly consistent with the large-scale ChemNetDB connectome, suggesting that the whole-brain WM connectivity is also strongly determined by the EDR.
By using maximum matching algorithms, we have identified 4 nontrivial driver nodes of the network. In particular, STh appears as a feasible candidate to be involved in control mechanisms of the brain. However, the actual biological interpretation of driver nodes is not yet clear and the role of the identified brain regions in controlling brain activity requires further investigations and experimental validations.
Note that the outcomes of the example network analyses presented here might change with the addition of new data; however, the main observations, such as the strong core-periphery structure and network measures expressed as fractions (including density, the fraction of 3-motifs, etc.), are expected to be robust against changes due to the high density of the networks.
ChemNetDB provides the first, whole-brain, large-scale and consistently collated rat connectome database that also includes neurochemical specificity. This will provide researchers with a tool to gain insights into the fundamental relationships between connectome architecture, information processing, and brain function, with potential for advancing preclinical research and clinical applications such as those related to substance abuse and depression.
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10.1371/journal.ppat.0030030 | Female Inheritance of Malarial lap Genes Is Essential for Mosquito Transmission | Members of the LCCL/lectin adhesive-like protein (LAP) family, a family of six putative secreted proteins with predicted adhesive extracellular domains, have all been detected in the sexual and sporogonic stages of Plasmodium and have previously been predicted to play a role in parasite–mosquito interactions and/or immunomodulation. In this study we have investigated the function of PbLAP1, 2, 4, and 6. Through phenotypic analysis of Plasmodium berghei loss-of-function mutants, we have demonstrated that PbLAP2, 4, and 6, as previously shown for PbLAP1, are critical for oocyst maturation and sporozoite formation, and essential for transmission from mosquitoes to mice. Sporozoite formation was rescued by a genetic cross with wild-type parasites, which results in the production of heterokaryotic polyploid ookinetes and oocysts, and ultimately infective Δpblap sporozoites, but not if the individual Δpblap parasite lines were crossed amongst each other. Genetic crosses with female-deficient (Δpbs47) and male-deficient (Δpbs48/45) parasites show that the lethal phenotype is only rescued when the wild-type pblap gene is inherited from a female gametocyte, thus explaining the failure to rescue in the crosses between different Δpblap parasite lines. We conclude that the functions of PbLAPs1, 2, 4, and 6 are critical prior to the expression of the male-derived gene after microgametogenesis, fertilization, and meiosis, possibly in the gametocyte-to-ookinete period of differentiation. The phenotypes detectable by cytological methods in the oocyst some 10 d after the critical period of activity suggests key roles of the LAPs or LAP-dependent processes in the regulation of the cell cycle, possibly in the regulation of cytoplasm-to-nuclear ratio, and, importantly, in the events of cytokinesis at sporozoite formation. This phenotype is not seen in the other dividing forms of the mutant parasite lines in the liver and blood stages.
| Malaria parasites are transmitted between human hosts by female mosquitoes. Following fertilization between male and female gametes in the blood meal, zygotes develop into motile ookinetes that, 24 hours later, cross the mosquito midgut epithelium and encyst on the midgut wall. During this development, parasite numbers fall dramatically and as such, this may be an ideal point at which to disrupt transmission, but first essential parasite targets need to be identified. A protein family implicated in the interactions between parasites and mosquitoes is the LCCL/lectin adhesive-like protein (LAP) family. LAPs are highly expressed in the sexual and ookinete stages, yet when we removed genes encoding each of four LAPs from the genome of a rodent model malaria parasite, a developmental defect was only observed in the oocyst some ten days after the protein was first expressed. These “knockout” parasites did not undergo normal replication and consequently could not be transmitted to mice. Through genetic crosses with parasite mutants producing exclusively either female or male gametes, we demonstrate that parasites can only complete their development successfully if a wild-type lap gene is inherited through the female cell. These data throw new light on the regulation of parasite development in the mosquito, suggesting that initial development is maternally controlled, and that the LAPs may be candidates for intervention.
| Transmission of the malarial parasite Plasmodium from the vertebrate host to the mosquito vector requires rapid sexual development within the mosquito midgut, which is triggered upon ingestion of male and female gametocytes by the mosquito during a blood meal. Gametocyte activation and gametogenesis occur within 15 min, and fertilization between two haploid gametes results in formation of a diploid zygote, usually in the first hour. Zygotes immediately undergo meiosis and differentiate within 24 h into motile, invasive ookinetes. The ookinetes cross the mosquito midgut epithelium and differentiate beneath the basal lamina into oocysts, where circa 11 rounds of endomitosis give rise to up to circa 8,000 haploid nuclei. Sporozoites that finally bud from the oocyst invade the mosquito salivary glands to be transmitted back to a vertebrate host.
Sexual development and midgut invasion represent a major natural population bottleneck in the Plasmodium life cycle [1], during which the parasite is critically dependent on intercellular interactions, both between parasite cells (e.g., at fertilization) and between parasite and host. A protein family implicated in these interactions, based on its expression profile and the presence of signal peptides and predicted adhesive extracellular domains, is the Limulus clotting factor C, Coch-5b2, and Lgl1 (LCCL)/lectin adhesive-like protein (LAP) family (also referred to as the CCp family; see Table S1).
Six lap genes were identified in the Plasmodium genome, with lap2/lap4 and lap3/lap5 representing putative paralogues [2–8]. LAP1 is conserved across the Apicomplexa and contains a unique mosaic of scavenger receptor cysteine rich (SRCR), polycystine-1, lipoxygenase, alpha toxin/lipoxygenase homology 2 (PLAT/LH2), pentraxin/concanavalin A/glucanase, and LCCL domains. LAP2 and LAP4 contain an LCCL and a predicted lectin domain derived from the fusion of ricin B–like and galactose-binding domains. LAP6 has an LCCL domain and a C-terminal module with homologies to ConA-like lectin/glucanase-, laminin-G-like, and pentraxin domains [4]. The presence of SRCR domains and complex lectin domains in the predicted structures of these proteins has led to the hypotheses that LAP1 may function as an immune modulator [2,6], and that LAP1, 2, 4, and 6 may bind complex polysaccharides that are possibly of mosquito origin [4].
In Plasmodium berghei (pb), LAP1 has been detected in all life stages analyzed (including asexual blood, sexual, and all mosquito stages), LAP2 and LAP4 in gametocytes, ookinetes, and oocysts, and LAP6 in gametocytes, ookinetes, oocysts, and sporozoites ([2–4]; Figure S3; R. Stanway, J. Johnson, J. Yates III, and R. Sinden, unpublished data). The failure to detect PfLAP1, PfLAP2, and PfLAP4 in mosquito stages (ookinetes, oocysts, and sporozoites) by indirect fluorescence antibody assays is particularly intriguing given that PbLAP1 is essential for sporozoite formation in P. berghei [2], and PfLAP1 and PfLAP4 are essential for sporozoite infectivity to the salivary glands in Plasmodium falciparum (pf) [8]. However, as previously noted for the protein MAEBL, negative immunofluorescence data may be indicative only of the absence of a specific epitope, for example, due to conformational changes, proteolytic processing, or interactions with other proteins, and not necessarily the absence of the protein per se (discussed in [4]). Interestingly, in a proteomic analysis of separated male and female gametocytes of P. berghei, three members of the family, PbLAP1, 2, and 3, were exclusively detected in female, but not male gametocytes, an expression pattern confirmed in reporter studies [9].
In P. falciparum gametocytes, PfLAP1, 2, and 4 have been detected on the parasite surface, in the parasitophorous vacuole, in vesicles secreted from the parasite into the parasitophorous vacuole, and in the parasite cytoplasm [6,8]. In P. berghei, the localization of PbLAP1 is perinuclear in both asexual and sexual blood stages until gametogenesis, after which the protein appears to be relocated to the parasite surface [4]. These observations are all consistent with targeting of the LAPs through the endoplasmic reticulum into vesicles and their subsequent release onto the parasite surface or into the parasitophorous vacuole. Pradel et al. have subsequently demonstrated that surface expression of PfLAP1, 2, and 4 is interdependent, suggesting that the proteins interact functionally [10].
In this study, we have further investigated the functions of PbLAP1, 2, 4, and 6 through phenotypic analysis of P. berghei loss-of-function mutants. We demonstrate that these proteins are critical for oocyst maturation and sporozoite formation. Despite their similarity, the four members of the LAP family characterised in this study do not have mutually redundant functions and are all essential for parasite transmission through the mosquito. Using genetic crosses, we reveal that for sporogony to occur normally, the wild-type (wt) pblap genes have to be inherited from the female gametocyte. This leads us to suggest that the observable mutant phenotype in the late oocyst is a functional consequence of the absence of protein function early in parasite development in the mosquito, i.e., at a time when only the female-derived pblap genes are being expressed.
To investigate the function of PbLAP2, 4, and 6, pblap2, pblap4, and pblap6 were independently disrupted via double cross-over homologous recombination and integration of a modified Toxoplasma gondii dihydrofolate reductase/thymidylate synthase (dhfr/ts) gene cassette (which confers resistance to pyrimethamine) to create parasites Δpblap2, Δpblap4, and Δpblap6. The Δpblap2, Δpblap4, and Δpblap6 mutants were verified by diagnostic PCR and Southern blot (Δpblap2 and Δpblap4) or pulsed field gel electrophoresis (Δpblap6) analysis (Figure S1). Independent clones were generated and analyzed for each of these parasite lines. A similar approach was previously described to disrupt pblap1 to create the parasite denoted here as Δpblap1 [2]. Successful gene deletion was further confirmed by reverse transcriptase (RT)–PCR analysis on Δpblap ookinete cDNA, which failed to detect the respective pblap mRNA in the corresponding Δpblap line, whereas expression of all other lap genes was not affected (Figure S1).
Following inoculation of mice with infected blood, the morphologies and production rates of asexual and sexual (male and female) blood stages of Δpblap1, Δpblap2, Δpblap4, and Δpblap6 parasites were indistinguishable from wt (unpublished data). All four Δpblap lines formed ookinetes (both in vitro and in vivo), which appeared morphologically normal as indicated by observations of Giemsa-stained blood films (unpublished data).
All Δpblap parasites were capable of infecting Anopheles stephensi mosquitoes, and on day 10/11 post-infection (p.i.), numbers of oocysts were never less than those observed in wt-infected mosquitoes (Figure 1; Table S3). The diameters of Δpblap1, Δpblap2, and Δpblap6 oocysts were significantly larger than that of wt on day 7, and all mutants were larger on days 14 and 21 of infection (Figure S2).
Light microscopy revealed the presence of two distinct populations of Δpblap1, Δpblap2, Δpblap4, and Δpblap6 oocysts: those that displayed a phenotype reminiscent of immature wt oocysts (i.e., non-sporulated), and those that appeared vacuolated/degenerate compared to wt (Figure 2). Transmission electron microscopy analysis of Δpblap1, Δpblap2, and Δpblap4 oocysts further confirmed these findings and revealed that oocysts of these parasites possessed an endoplasmic reticulum that was highly vacuolated compared to that of wt parasites (Figure 3). On day 13 p.i., the nuclear organization of Δpblap1, Δpblap2, and Δpblap4 oocysts appeared “immature” as indicated by the presence of few but large nuclei (Figure 3). By comparison, wt oocysts of the same age had formed sporozoites, each with their own (haploid) nucleus.
Both light and electron microscopy revealed that some Δpblap4 oocysts were, unusually, melanized from day 13 p.i. onwards (Figures 2 and 3). Melanization was commonly seen in the oocyst wall and overlying midgut basal lamina, but no melanin deposits were observed in the oocyst cytoplasm, suggesting that in these specimens melanization involved neither parasite plasmalemma nor cytoplasm [11]. Unequivocal melanization of Δpblap2 oocysts was not observed on day 13 p.i. From day 18–20 p.i. onwards, a variable proportion of Δpblap2 and Δpblap4 oocysts had been extensively melanized. Melanization was not observed at any time point (day 10–25 p.i.) in wt, Δpblap1, or Δpblap6 infections.
In contrast to wt infections, no midgut sporozoites were observed in Δpblap2, Δpblap4, or Δpblap6 infections on day 10/11 p.i. By day 18 p.i., reduced numbers (typically 0%–12%) of sporozoites were observed in dissected midguts (Figure 1; Table S4). The number of sporozoites in salivary gland preparations from Δpblap2, Δpblap4, and Δpblap6 infections was consistently reduced to <1% of wt. No Δpblap1 salivary gland sporozoites were observed (Table S4), as previously reported [2]. The expression and targeting of the major sporozoite surface protein, circumsporozoite protein, in Δpblap2, Δpblap4, and Δpblap6 midgut sporozoites was indistinguishable from that in wt (unpublished data).
The most sensitive method for the detection of infectious salivary gland sporozoites is xenodiagnosis in naïve mice. To test if the observed Δpblap2, Δpblap4, and Δpblap6 sporozoites were infectious to mice, infected mosquitoes were allowed to feed on mice on days 21 and 28 p.i. Blood stage parasites were observed in all mice bitten by wt-infected mosquitoes when first screened on day 4/5 post-bite. In contrast, mice bitten by Δpblap2-, Δpblap4-, and Δpblap6-infected mosquitoes remained uninfected until sacrificed on day 14 post-bite (unpublished data).
In previous studies, crossing Δpblap1 gametocytes (pblap1−) with wt gametocytes (pblap1+) to form heterokaryotic (pblap1+/pblap1−) oocysts rescued the lethal Δpblap1 phenotype, and produced Δpblap1 sporozoites that were infectious to mice [4]. Our crosses between Δpblap1 and wt gametocytes produced similar results. Crosses between Δpblap2, Δpblap4, and Δpblap6 and a wt clone similarly produced wt numbers of salivary gland sporozoites that were infectious to mice (Figure 4; Table S5). Diagnostic PCR analysis revealed that both wt and either Δpblap2 or Δpblap4 or Δpblap6 parasites were present in the blood stage parasites isolated from the infected mice, indicating that Δpblap sporozoites (i.e., sporozoites which lack the respective pblap gene but which may contain some of the corresponding PbLAP protein carried over from the heterokaryotic oocyst in which they were formed) could be transmitted to mice and that PbLAP2, 4, and 6, like PbLAP1, are not essential for liver or blood stage development (unpublished data).
Given that pblap1+/pblap1−, pblap2+/pblap2−, pblap4+/pblap4−, and pblap6+/pblap6− heterokaryotic oocysts can produce infectious Δpblap1, Δpblap2, Δpblap4, and pblap6 sporozoites, respectively, we hypothesized that crossing each of the Δpblap1, Δpblap2, Δpblap4, and Δpblap6 mutants with each other to produce heterokaryotic oocysts at two gene loci might rescue the mutant phenotypes described above. However, all potential combinations of crosses between Δpblap1, Δpblap2, Δpblap4, and Δpblap6 gametocytes failed to rescue sporozoite production to wt levels (Figure 4; Table S5). In these crosses, both the female and male cells have to provide one functional gene copy each, in contrast to the wt crosses, where the intact gene copy can be supplied by either cell. Recognizing that (some) lap genes are expressed in a sex-specific manner in gametocytes, e.g., PbLAP1, 2, and 3 detected exclusively in female gametocytes [9], we hypothesized that an intact pblap gene may only rescue the mutant phenotype when supplied by either a male or a female cell. To test this hypothesis, we performed genetic crosses in vitro with Δpbs47 and Δpbs48/45 parasites, which (in vitro) are deficient in forming either female or male functional gametes, respectively ([9,12,13]; C. J. Janse and A. P. Waters, personal communication). Following feeding of the resulting 24-h ookinete culture to mosquitoes, similar numbers of oocysts were observed in mosquitoes infected with Δpbs47 X Δpblap and Δpbs48/45 X Δpblap, but sporozoites were only observed in the Δpbs48/45 crosses (Table 1). These sporozoites were infectious to C57BL/6 mice. In contrast, mosquitoes infected with Δpbs47 crosses never transmitted parasites to mice. Diagnostic PCR on genomic DNA prepared from midguts of these mosquitoes demonstrated the presence of the pblap wt, Δpblap (and Δpbs47) alleles, indicating that crosses between Δpbs47 and Δpblap parasites had occurred (the Δpblap parasites thus rescuing the Δpbs47 parasite to the oocyst stage) (Table 1). Therefore, in all crosses between different Δpblap parasites, male gametes (from any Δpblap strain) fail to deliver “in time” appropriate expression of their respective wt gene to the heterokaryon. Thus, any post-fertilization expression of the male-derived pblap1, 2, 4, and 6 genes does not rescue the developmental block at the oocyst stage.
Recognizing that the Δpbs47 and Δpbs48/45 crosses provide a tool to decipher at what point during parasite development the essential PbLAP function occurs (i.e., a time point when a difference in expression of the male and female gene is observed), we undertook RT-PCR analysis of different parasite mosquito stages. RT-PCR analysis on purified ookinetes from ookinete cultures resulting from crosses between either Δpbs47 or Δpbs48/45 and Δpblap parasites detected strong expression of the pblap genes (unpublished data). However, we were unable to prove that this was due to expression of the male gene in a Δpblap X Δpbs47 ookinete, or whether it results from contamination from unfertilized Δpbs47 females, which also express lap genes and inevitably co-purify with ookinetes on anti-Pbs21 magnetic beads. While RT-PCR on young (day 4–5) oocysts is technically challenging and did not give conclusive results, RT-PCR on day 10 oocysts, in which the mutant phenotype becomes apparent, clearly detected expression of the male nucleus-derived pblap genes (Figure 5).
Finally, to test whether genes derived from the male nucleus are accessible to the transcription and translation machinery in the zygote/ookinete, we crossed Δpbs47 and Δpbs48/45 with a parasite line disrupted in a member of the micronemal membrane attack complex/perforin domain containing Plasmodium perforin-like protein (PPLP) family. This parasite is non-infective to mosquitoes due to a loss of midgut-invasion capacity [14]. This mutant phenotype was rescued equally by a cross with either Δpbs47 or Δpbs48/45 (unpublished data).
We therefore conclude that despite a late observable mutant phenotype some 10 d after mosquito infection, essential PbLAP functions occur early in the parasite development in the mosquito, i.e., either in the female gametocyte/gamete or in the zygote/ookinete following fertilization, but before expression of the male gene copy.
Our studies have demonstrated that inheritance of pblap1, 2, 4, and 6 from the female gametoctyte is essential for parasite development, indicating that the critical functions of the LAPs cannot be provided by the male gene copy. While the mutant phenotype in the Δpblap parasites becomes apparent only around day 10 of oocyst development, we demonstrate by RT-PCR analysis of different genetic crosses that both the male and female gene copy are expressed at this time point. As expression of the male gene by day 10 (and possibly day 4; unpublished data) of oocyst development is too late to rescue the defect in oocyst maturation and sporulation, we conclude that the observed mutant phenotype must be a consequence of the absence of protein function earlier in development. More precisely, the critical function must occur at a time point when the male and female gene are expressed in a differential manner. This has so far only been described at the gametocyte stage. Strikingly, it was shown that (at least) PbLAP1, PbLAP2, and PbLAP3 were exclusively detected with high abundance in female gametocytes, and this sex-specific expression was confirmed by reporter studies [9].
Unfortunately, not much is known about the pattern of gene expression from the male and female genome post-fertilization. We attempted to analyse pblap gene expression in ookinetes derived from genetic crosses with male- and female-deficient lines, but were not able to obtain sufficiently pure preparations. Therefore, we could not determine at what time point post-fertilization the male pblap genes are first transcribed and thus whether LAP function is critical pre-fertilization in the female gametocyte/gamete, or post-fertilization during early zygote/ookinete development before the male-derived genes are expressed appropriately. However, a PPLP knockout was complemented with a male-derived pplp gene within the first 24 h post-fertilization (i.e., before midgut invasion), indicating that (some) male genes are expressed before this time point. Clearly, further studies are required to understand the pattern of pblap gene regulation in the sexual and sporogonic stages of Plasmodium development. Specifically, introducing pblap promoter–reporter constructs into male- and female-deficient lines will help narrow down the critical time point of activity.
An early essential function of LAPs at the gametocyte/ookinete stage would be consistent with the high protein expression detected for PbLAP1–5 in these stages [4]. We note that while published studies have failed to detect the expression of PbLAP6 before the sporozoite stage, we have subsequently detected the mRNA in gametocytes, ookinetes, and oocysts by RT-PCR (Figure S3) and the protein in ookinetes by multidimensional protein identification technology analysis (R. Stanway, J. Johnson, J. Yates III, and R. Sinden, unpublished data). Gene expression in the female gametocyte is unusual in that a number of mRNAs are translationally repressed until gametocyte activation, possibly to regulate gene expression during meiosis in the zygote [3,13]. Based on the detection of abundant LAP protein in gametocytes of both P. berghei and P. faciparum in a number of studies [3–10], we consider it unlikely that the majority of lap transcripts are translationally repressed. We therefore note with interest that the mRNAs for pblap4, 5, and 6, but not pblap1, were destabilised in a knockout parasite incapable of translational repression [13].
The precise cellular function of the LAPs remains enigmatic. The morphological characterisation of Δpblap1, Δpblap2, Δpblap4, and Δpblap6 clones in P. berghei has revealed the same mutant phenotype in the oocyst. This is consistent with the suggestion that the LAPs work either in a functional cascade or as a complex [10]. Our data suggests that in these mutants, regulation of the cell cycle is lost. The increase in oocyst size, the large proportion of oocysts displaying a vacuolated/degenerate phenotype, the presence of large but few nuclei, and the reduction (Δpblap2, Δpblap4, and Δpblap6) or complete absence (Δpblap1) of salivary gland–associated sporozoites all suggest that these mutants are unable to regulate the classic nuclear-to-cytoplasmic relationship that reportedly controls cytokinesis. Although few Δpblap2, Δpblap4, and Δpblap6 sporozoites were detected in salivary gland preparations, in this study none could be transmitted to mice via mosquito bite, suggesting either that the number of sporozoites inoculated is too low to establish an infection, or possibly that these sporozoites are non-infectious to mice. Loss of function of the Δpblap parasites is rescued in pblap+/pblap− heterokaryons ([4]; this study), and all resulting haploid Δpblap sporozoites that bud from the polyploid sporoblast are able to complete the exo-erythrocytic, erythrocytic, and sexual phases of the life cycle. This raises the fascinating question as to why putative cell cycle defects are only observed in the oocyst and not in the exo-erythocytic or erythrocytic schizonts.
The probable surface location of the LAP proteins on the surface of gametocytes, gametes, zygotes [4,6,8], and sporozoites [4] supports earlier suggestions that these proteins may be involved in parasite–parasite or parasite–host interactions [2,4]. It will be interesting to determine whether this location makes the molecules vulnerable to immunological or chemotherapeutic attack. We present evidence here that Δpblap2 and particularly Δpblap4 parasites may be more susceptible to immune attack by the mosquito melanization response at the oocyst stage, though whether this is a direct causal interaction or the simple consequence of parasite death cannot be determined. The latter interpretation would be consistent with the vacuolated appearance of the mutant oocysts. The former hypothesis might suggest that LAP2 and LAP4 may be integrated into the oocyst wall, where they may have a protective function suppressing the mosquito's melanotic response. An immunomodulatory role has also been suggested for LAP1 (based on the prediction of scavenger receptor domains in the protein structure [2,6]).
A study in P. falciparum [8] reports phenotypes of Δpflap1 and Δpflap4 different from those of Δpblap1 and Δpblap4 (this study, [2]) in that oocyst numbers and morphology in infections in Anopheles freeborni were similar to wt. Furthermore, the same study also reports that both Δpflap1 and Δpflap4 form sporozoites normally, but that they do not reach the salivary glands during the observation period. However, given that the sample size in their study (between five and 20 infected mosquitoes per experiment) was below that needed to make statistically significant determinations [15], and given the absence of quantitative data, it is impossible to conclude at this stage that the described differences are real. Our data clearly show that it is mainly the formation of sporozoites that is disrupted, although we cannot exclude a role in the transition of the few sporozoites formed from the midgut to the salivary gland. We would also draw attention to the dynamic relationship that exists between parasite and mosquito being dependent upon both parasite strains and mosquito species. Therefore, we do not discount that the mosquito species in which the experiments were conducted (An. stephensi versus An. freeborni) may also be a factor with regard to the observations made. Taken together, we cautiously conclude the phenotypes may be similar in the two parasite species.
In summary, we demonstrate that expression of the female nucleus-derived pblap1, 2, 4, and 6 genes is essential for parasite development in the mosquito, i.e., that the function of PbLAP1, 2, 4, and 6 is critical prior to the expression of protein from the male-derived gene copy during sporogony, possibly in the gametocyte-to-ookinete period of differentiation. The absence of PbLAP gene function at this critical period of activity ultimately results in lethality some 10 d later, at sporulation, which represents the endpoint of several complex developmental cascades [16]. The mutant phenotype detectable by cytological methods suggests a key role of the LAP proteins or LAP-dependent processes in the regulation of the cell cycle and, critically, in the events of cytokinesis. Importantly, this phenotype is not seen in the other dividing forms of the parasite in the liver and blood stages.
Parasite maintenance, ookinete cultures, mosquito infections, mosquito bite-back experiments, diagnostic PCR, pulsed field gel electrophoresis, Southern blotting, and transmission electron microscopy were carried out as previously described [17–21].
Locus names of pblap and pflap genes are listed in Table S1.
Generation of constructs for targeted disruption of pblap2, pblap4, and pblap6 by double homologous recombination were carried out as previously described [17,22]. Briefly, upstream homology regions of 611 bp (pblap2 and pblap4) or 476 bp (pblap6) were PCR amplified from P. berghei ANKA clone 2.34 genomic DNA using primers DR0006F-pblap2/ApaI and DR0006R-pblap2/HindIII, DR0008F-pblap4/ApaI and DR0008R-pblap4/HindIII, or AE25a-pblap6/ApaI and AE25b-pblap6/HindIII, and cloned into pBS-DHFR via ApaI and HindIII. Downstream homology regions of 632 bp (pblap2), 594 bp (pblap4), or 528 bp (pblap6) were PCR-amplified using primers DR0007F-pblap2/EcoRV and DR0007R-pblap2/BamHI, DR0009F-pblap4/EcoRV and DR0009R-pblap4/BamHI, or AE25c-pblap6/EcoRI and AE25d-pblap6/BamHI, and cloned into the plasmids with the respective upstream homology region via EcoRV and BamHI (pblap2 and pblap4) or EcoRI and BamHI (pblap6). The targeting cassette was released by ApaI and BamHI digestion. Parasite transfection using the Human T Cell Nucleofector Kit (amaxa, http://www.amaxa.com) and selection by pyrimethamine and dilution cloning were carried out as previously described [23,24]. Integration of the targeting cassette into the genome leads to replacement of the central 2,950 bp of the coding region of pblap2 (4,848 bp), 4,454 bp of the coding region of pblap4 (5,151 bp) and 4,525 bp of the coding region of pblap6 (4,732 bp) with the drug-selectable marker T. gondii dhfr/ts.
Genetic crosses between gametocytes of different clones were carried out either by mixing equal numbers of gametocytes in normal mouse blood and feeding to mosquitoes via membrane feeding as described previously [4], or by allowing mosquitoes to feed directly on mice infected with different parasite combinations. No difference was seen between these two methods. For the crosses with Δpbs47 or Δpbs48/45, mice were infected with the different parasite combinations, and ookinetes were cultured in vitro and fed to mosquitoes via membrane feeding at a concentration of 800 ookinetes/μl in normal mouse blood. Genomic DNA from oocysts (on midguts) was prepared using the Wizard genomic DNA Purification Kit (Promega, http://www.promega.com) following the protocol for “mouse tail”. The pblap2, pblap4, and pblap6 wt alleles were amplified using primers lapX-KO and lapX-WT. The Δpblap2, Δpblap4, and Δpblap6 alleles were amplified using primers lapX-KO and 248. Diagnostic PCR for pblap1 was as previously described [4].
Total RNA was isolated using TRIzol (Invitrogen) according to the manufacturer's instructions. Contaminant genomic DNA was removed by treatment with TURBO DNA-free (Ambion, http://www.ambion.com) and RNA was cleaned up using the RNeasy Mini Kit (Qiagen, http://www1.qiagen.com). Reverse transcription was performed on 1 μg of RNA using the TaqMan Reverse Transcription Reagents with a mixture of Oligo-dT primers and Random Hexamers (Applied Biosystems, http://www.appliedbiosystems.com), and the resulting cDNA was used in diagnostic PCR reactions. Primers SRCR3 and SRCR5 amplify a 540-bp fragment of pblap1, primers 2RT-F and LAP2WT a 399-bp fragment of pblap2, primers 4RT-F and LAP4WT a 482-bp fragment of pblap4, primers 6RT-F and 6RT-R a 381-bp fragment of pblap6, primers TubF and TubR a 432-bp fragment of the α-tubulin gene, and primers p28F and p28R a 642-bp fragment of pbs21.
For primer sequences, please refer to Table S2.
The PlasmoDB (http://www.plasmodb.org) or National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov) accession numbers for the genes discussed in this paper are pblap1 (PB000977.02.0), pblap2 (PB000652.01.0), pblap4 (PB000504.02.0), and pblap6 (PB000955.03.0). |
10.1371/journal.pcbi.1006381 | Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings | Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions and pathologies. Inferring neuronal connectivity from electrophysiological recordings presents open challenges and unsolved problems. In this work, we present a cross-correlation based method for reliably estimating not only excitatory but also inhibitory links, by analyzing multi-unit spike activity from large-scale neuronal networks. The method is validated by means of realistic simulations of large-scale neuronal populations. New results related to functional connectivity estimation and network topology identification obtained by experimental electrophysiological recordings from high-density and large-scale (i.e., 4096 electrodes) microtransducer arrays coupled to in vitro neural populations are presented. Specifically, we show that: (i) functional inhibitory connections are accurately identified in in vitro cortical networks, providing that a reasonable firing rate and recording length are achieved; (ii) small-world topology, with scale-free and rich-club features are reliably obtained, on condition that a minimum number of active recording sites are available. The method and procedure can be directly extended and applied to in vivo multi-units brain activity recordings.
| The balance between excitation and inhibition is fundamental for proper brain functions and for this reason is precisely regulated in adult cortices. Impaired excitation/inhibition balance is often associated with several neurological disorders, such as epilepsy, autism and schizophrenia. However, estimating functional inhibitory connections is not an easy task and few methods are available to identify such connections from electrophysiological data. Here we present a cross-correlation based method to identify both excitatory and inhibitory functional connections in large-scale neuronal networks. The method is applicable to both in vitro and in vivo spike data recordings. Once a connectivity map (i.e. a graph) is obtained, we characterized the associated topology by means of classical graph theory metrics to unveil functional architecture. In this work, we analyze in vitro cortical networks probed by means of large-scale microelectrode arrays (i.e., 4096 sensors) and we derive network topologies from spike data. The functional organization found is called “small-world and scale-free” and is the same organization found in cortical in vivo brain regions by means of different experimental methods. We also show that to obtain reliable information about network architecture at least a network with a hundred of nodes-neurons is needed.
| Understanding the relationships between structure and function, dynamics and connectivity of neuronal circuits are a challenge of the modern neurosciences, especially as the characterization of neuronal interaction in terms of functional and effective connectivity [1–3] is concerned. Functional connectivity is an observable phenomenon defined as statistical dependency between remote neurophysiological events; it is usually inferred on the basis of correlations among neuronal activity measurements, by means of different approaches ranging from basic cross-correlation[4] to model-based methods[1, 5]. Effective connectivity refers explicitly to the influence that a neuron or neural system exerts on another one, either at synaptic or population level; it can be inferred by perturbing the activity of a neuron, and then by measuring the other neurons activity changes.Structural or anatomical connectivity is related to the physical connections (i.e., synapses) among neurons [2]. In this paper, we refer to the more general framework of functional connectivity, even if, by using the proposed correlation-based method, directed graphs (i.e. causal relationships) can be derived (cf. Materials and Methods sect.).
The complexity of the nervous system and the difficulties of multi-site parallel recordings in in vivo experimental models, hampered the systematic study of emergent properties of complex networks. At the same time, the availability of validated methods able of reliably inferring functional connections down to synaptic level is still limited. To this end, we adopted a reductionist approach making use of in vitro experimental models coupled to Micro-Electrode Arrays (MEAs). In this context, large-scale neural networks developing ex vivo and chronically coupled to MEAs [6], represent a well-established experimental system for studying the neuronal dynamics at population level [7]. Despite their simplicity, they show recurrent synchronized periods of activity, as also observed in vivo during sleep or anesthesia, and even quiet wakefulness [8, 9]. These model systems represent a good trade-off between controllability-observability and similarity to the in vivo counterpart, allowing accessibility and manipulation from both chemical and electrical point of view. Recent advances in multichannel recording techniques have made possible to observe the activities of thousands of neurons simultaneously with the acquisition of massive amount of empirical data [10]. These methods are very attractive since they allow the detailed monitoring of the on-going electrophysiological spatio-temporal patterns of complex networks [11–14].
Reconstructing the detailed functional connectivity of a neuronal network from spikes data is not trivial, and it is still an open issue, due to the complexities introduced by neuron dynamics and high anatomical interconnectivity [15, 16]. Statistical analysis of spike trains was pioneered by Perkel [17] and followed by more than four decades of methodology development in this area [18]. Cross-correlation based methods remain the main statistics to evaluate interactions among the elements in a neuronal network, and produce a weighted assessment of the connections strength. Weak and non-significant connections may tend to obscure the relevant network topology made up of strong and significant links, and therefore they are often discarded by applying an absolute or a proportionally weighted threshold [19]. Correlation-based techniques include independent components analysis, synchrony measures [20], cross-correlation [21, 22], correlation coefficients [7, 23], partial-correlation [24]. Other widespread techniques to infer functional connectivity are based on Information Theory (IT) methods [10, 25, 26], Granger causality [27, 28] and dynamical causal modeling [1]. With few exceptions [29, 30], all the recently introduced and revisited methods concentrate on excitation, ignoring inhibition or admitting the failure in reliably identifying inhibitory links [26].
In this work, we focus attention on cross-correlation histogram (CCH) based methods. We present a new algorithm able to efficiently and accurately infer functional excitatory and inhibitory links; we validate the method on simulated neuronal networks; finally, we study connection properties in large-scale ex vivo neuronal networks showing how to directly and reliably derive the topological properties of such networks.
There are three different connectivity conditions that, theoretically, influence the temporal correlation between neurons: pairs of excitatory neurons, pairs of inhibitory neurons, and inhibitory-excitatory pairs [31]. The first term is the one usually estimated and from which we obtain the inferred functional excitatory network usually represented by a (directed) graph. The second term is negligible as inhibitory-inhibitory links are physiologically very sparse [32]. The last term, when it is exerted by a GABAergic interneuron to cortical excitatory neurons, acts by reducing the activity and decreasing the spontaneous fluctuations (i.e., feedforward inhibition). On the contrary, when it is exerted by cortical excitatory neurons to GABAergic interneurons, it acts by increasing the activity of such neurons that, in turn, form inhibitory synaptic contacts with the glutamatergic cortical cells (i.e., feed-back inhibition) [33]. In other studies [34–36],it was noticed the primary effect of inhibition is a trough in the cross-correlogram: to detect this interaction a background of postsynaptic spiking against which the inhibitory effect may be exercised (i.e., high and tonic firing rates) is needed [22]. From experimental works related to in vivo multi-unit recordings, it was shown the sensitivity to excitation is much higher than the sensitivity to inhibition [37] (due to the low firing rates of neurons).
Finally, it should be underlined the analysis of interactions in neuronal networks is a quite demanding computational process, and all the currently proposed methods for analyzing multiple spike trains rely on quantities that need to be computed through intensive calculations [38]. By using the ad-hoc developed CCH, we could derive functional connectivity maps (both for excitation and inhibition) and to reliably extract topological characteristics from multiple spike trains in large-scale networks (i.e., thousands of neurons) monitored by large-scale MEAs (i.e., thousands of micro-transducers).
Starting from the standard definition of the cross-correlation [22] (cf., Materials and Methods sect.), we adopted the normalization approach described in [21, 39] to obtain the “raw” Normalized Cross-Correlation Histogram (NCCH). We formalized our hypothesis that, the extraction of negative peaks (rather than troughs) obtained by a filtering operation on the NCCH and followed by distinct thresholding operations for excitatory and inhibitory connections allows to identify a significant percentage of inhibitory connections with a high-level accuracy at low computational cost. Theoretically, cross-correlation is able to detect both an increase and a decrease of the synchrony between spike trains related to putative interconnected neurons. However, in real experimental data, the cross-correlogram is very jagged making difficult the detection of small peaks and troughs, and, apart from specific conditions (i.e., high and tonic firing rate) [4], hindering the detection of inhibition. Our approach consists in a simple post processing of the cross-correlation histogram, thus obtaining what we called Filtered and Normalized Cross-Correlation Histogram (FNCCH, curly brackets in Eq (1)).
Stated a reference neuron x and a target neuron y, Eq (1) provides the mathematical definition of the absolute peak of the FNCCH.
FNCCHxypeak=Cxy(τ)|=argmaxt{|Cxy(t)−1W∑v=−W2v=W2Cxy(v)|}
(1)
where W is the time window where FNCCH is evaluated. The filtering procedure (cf. Materials and Methods sect.) consists in subtracting the mean value of the cross-correlogram (in the time window W) from the values of the normalized cross-correlogram Cxy(ν), ν ∈ [-W/2, W/2]. The subsequent peaks extraction operation is performed by considering the absolute values, and it allows to compute the highest peak. In this way, it is possible to distinguish between peaks and troughs by taking into account the original signs: a positive value refers to an excitatory link, and a negative value refers to an inhibitory one. Details about further refinements needed to avoid detection of false inhibitory connections can be found in the Supplementary Information (cf., Sect. S1). In the next sections, we show the validation of the method with the aid of large-scale in silico networks; then, we present the results, in terms of functional connectivity maps and network topology, obtained from the analysis of multi-electrode parallel recordings of in vitro neuronal populations. Such populations are coupled to both 60 channels MEAs (MEA-60) and high-density MEAs with 4096 micro-transducers (MEA-4k) (cf. Materials and Methods sect.).
We applied the FNCCH (time window W = 25 ms and time bin 1.0 ms) to 10 realizations of in silico neural networks made up of 1000 randomly connected neurons, characterized by an average ratio between inhibitory and excitatory connections of 1/4 (cf., Materials and Methods sect.). The model was tuned to reproduce the dynamics exhibited by in vitro neuronal networks. Simulations show the typical signature characterized by a mix of spiking and bursting activities as displayed by the raster plot and the Instantaneous Firing Rate (IFR) traces of the excitatory (red) and inhibitory (blue) neuronal populations of Fig 1A. From a topological point of view, both the excitatory and inhibitory structural sub-networks follow a random connectivity, as the incoming degree distributions of Fig 1B (inset) display. Each neuron receives 100 connections from the other neurons: excitatory neurons receive 80% of excitatory and 20% of inhibitory links, respectively, (reflecting the ratio of the excitatory and inhibitory populations); inhibitory neurons receive only excitatory connections (cf. S2C Fig). Further details about the dynamics and connectivity of the simulated neuronal networks can be found in the Supplementary Information (cf., Sect. S2). Fig 1C and 1D quantify the performances of the FNCCH by means of the Receiver-Operating-Characteristic (ROC) [40] curve and the Matthews Correlation Coefficient (MCC) [41]. Fig 1C shows the ROC curves obtained by comparing the Synaptic Weight Matrix (SWM) of the model (i.e., the ground truth) with the computed Functional Connectivity Matrix (FCM), and Fig 1D shows the MCC curve (cf., Materials and Methods sect.). The ROC curve relative to the detection of inhibitory connections (blue curve in Fig 1C) is very close to the perfect classifier, with an Area Under Curve (AUC) of 0.98 ± 0.01 (blue bar in the inset of Fig 1C). The MCC curve relative to the inhibitory links (blue curve in Fig 1D) has a maximum value of 0.87 ± 0.04, showing a good precision in the identification of inhibition. Then, we compared the sensitivity of the FNCCH for the detection of excitatory links (red curves in Fig 1C and 1D) with the standard NCCH’s one (for excitation, black curves in Fig 1C and 1D) to underline the improved detection capabilities obtained by the filtering procedure. We observed not only a significant (p < 0.001) AUC increase (0.92 ± 0.01 vs. 0.72 ± 0.02, Fig 1C inset), but also significant improvements in both ROC and MCC curve shapes for low values of false positive rates (FPR). In particular, we can notice (Fig 1D), that the FNCCH excitatory curve has a maximum value of about 0.75 with respect to the correspondent NCCH value (for the same false positive rate) that is negative (suggesting a disagreement between prediction and observation). Further details about false and true positive detection can be found in the Supplementary Information (Sect. S5). The above results justify the use of a hard threshold procedure (cf., Materials and Methods sect.) to select the strongest and significant functional connections. The Thresholded Connectivity Matrix (TCM) is thus directly computed from the FCM by using a threshold equal to (μ + 1 σ), (mean plus one standard deviation of the connections strength) for the inhibitory links, and (μ + 2 σ) for the excitatory ones, obtaining estimated links with a very high-level of accuracy (cf. Materials and Methods sect.): R2 = 0.99 for the inhibitory links and R2 = 0.94 for the excitatory ones. To investigate whether the reconstructed functional connectivity network resembles the one of the model, we calculated the excitatory and the inhibitory (Fig 1B) links degree distribution after the thresholding procedure from TCM. The computed degree-distributions fit a Gaussian distribution (Fig 1B, R2 = 0.99 for the inhibitory links and R2 = 0.98 for the excitatory ones), in accordance with the original distributions used to generate the structural (random) connectivity of the model (Fig 1B inset). It can be noticed that the mean and standard deviation values of the functional Gaussian distribution for the excitatory links are in good agreement with the structural ones (μfunct = 87, σfunct = 13.2 and μstruct = 80, σstruct = 19.6). On the other hand, for the inhibitory links, such values are higher than the structural ones due to the presence of many polysynaptic interactions (μfunct = 48, σfunct = 9.3 and μstruct = 25, σstruct = 14.5). Finally, we computed the delay distribution for both the excitatory and the inhibitory links from the TCM (Fig 1E). The extracted delay distribution for the excitatory links qualitatively reflects the one used to generate the model (uniform distribution in the interval [0, 20] ms). The estimated inhibitory distribution, instead, exhibits a more confined range which reflects the one used to produce the model (constant delay set at 1 ms), but with a spread and a median value at about 5 ms (cf., Materials and Methods sect.). The disagreement can be explained by the presence of multiple and polysynaptic interactions (due to the combination of excitatory and inhibitory inputs on a single neuron; cf., Discussion sect.).
Further validation of the proposed method was pursued by implementing a scale-free (with small-world features) network. The results (cf. Supplementary Information, S3 Fig) are less striking than those obtained for random connectivity; nevertheless, FNCCH outperforms standard cross-correlation and the identification of inhibitory links is still maintained with a similar general trend.
The FNCCH was applied to neuronal networks coupled to two different devices: MEA-60 and MEA-4k. Fig 2 shows the two utilized microtransducers (Fig 2A and 2D) and illustrative images of networks coupled to the two (Fig 2B and 2E). Such networks are the morphological substrate originating the complex electrophysiological activity characterized by an extensive bursting dynamics (i.e., highly synchronized network bursts) and a random spiking activity. Fig 2C and 2F show two examples of spontaneous activities recorded by a MEA-60 (Fig 2C) and a MEA-4k (Fig 2F). We can observe silent periods, desynchronized spiking activity, and peaks of activity (of different duration and called network bursts), which cause a rapid increase of the Instantaneous Firing Rate (IFR) (Fig 2C and 2F, bottom panels). More details about the spiking and bursting dynamics originated by networks coupled to MEA-4k are reported in the Supplementary Information (S1 Table). We analyzed three cortical and three striatal networks coupled to the MEA-60 (FNCCH parameters: time window W = 25 ms and time bin 0.1 ms) and three cortical networks coupled to the MEA-4k (FNCCH parameters: time windows W = 24 ms and time bin of 0.12 ms) after they reached a stable stage (i.e., after 21 Days In Vitro, 21 DIV).
Fig 3A and 3G show connectivity graphs of cortical and striatal networks coupled to a MEA-60 device (Fig 3B and 3H and 3C and 3I show the contribution of excitation and inhibition, respectively). All the graphs were obtained by applying the hard threshold approach and the spatio-temporal filtering to prune co-activations (cf., Materials and Methods sect.). Then, we looked, for the cortical networks, the presence of privileged sub-networks constituted by the most connected nodes (i.e., rich club), by computing the Rich Club Coefficient (RCC) curve [42] (cf., Materials and Methods sect., Eq (10)). The nodes of these sub networks are highlighted in yellow and cyan (Fig 3B and 3C). For the striatal culture, the qualitative prevalence of inhibitory connections is clearly visible. To characterize the detected links for the cortical cultures, we computed the box plots of the functional connection peak delays (Fig 3D) and lengths (Fig 3E) of the excitatory (red) and inhibitory (blue) connections. Similar graphs derived from a cortical network coupled to a MEA-4k were obtained (Fig 4A). Links strength is represented by two color codes (arbitrary unit) for excitation (hot-red color code) and inhibition (cold-blue color code). The two detected subnetworks are also shown in Fig 4B and 4C. Moreover, the box plots pointing out the connection peak delays and lengths are depicted in Fig 4F and 4G. Noteworthy it is that the inhibitory links are slower, and with possible slightly longer connections than the excitatory ones, as reported in literature for structural and functional connectivity in brain slices [43]. Considering the high number of connections found by using the MEA-4k, we point out the two hundred strongest connections for excitation and the fifty strongest connections for inhibition (Fig 4D and 4E), illustrating how these main links include both short and long interactions with a prevalence of short interactions for excitatory connections.
We also computed the inhibitory links percentage with respect to the total number of detected links for the three different experimental conditions and three experiments for each condition. As expected, we found that striatal cultures have a higher percentage of inhibition and inhibitory links (about 60%)[44, 45] than cortical ones (about 25%). It is worth noticing that for the cortical cultures the excitatory/inhibitory ratio is detected quite independently of the number of recording sites (Figs 3F and 4H), although it tends to stabilize with a shorter recording time for the MEA-4k. Interestingly enough, the found ratio (about 1/4) in cortical networks between inhibitory and excitatory links is roughly the same as the ratio of inhibitory and excitatory neurons as estimated by immunostaining in similar experimental preparations [8].
In order to derive the topological features [46] of the analyzed cortical networks, we computed the Clustering Coefficient, CC (Fig 5A) and the average shortest Path Length, PL (Fig 5B). Then, we extracted the Small-World Index (SWI) by comparing the CC and the PL of the analyzed networks with the mean values of CC and PL of 100 realizations of a random network with the same degree-distribution, as recently proposed [26]. We found that when cortical networks are coupled to MEA-4ks devices, we can see the emergence of a clear small-world (SW) topology (Fig 5C); on the other hand, for cortical networks coupled to MEA-60s devices, we cannot infer any SW topology. From the measurements performed by MEA-4ks, we can state that both inhibitory and excitatory subnetworks with their small world index, SWI >>1 (9.2 ± 3.5 for the inhibitory links and 5.2 ± 2 for the excitatory ones) contribute to ‘segregation’. Moreover, both inhibitory and excitatory links with their fraction of long connections contribute also to network ‘integration’ (i.e., communication among the SWs). To further characterize the topology of these neuronal assemblies, we also investigated the possible emergence of scale-free topologies [47] by evaluating the presence of hubs[48] and power laws for the excitatory (Fig 5D), inhibitory (Fig 5E) and global (Fig 5E, inset) link degree distributions. In agreement with previously published model systems [49] and other studies [43], we obtained that such distributions fit a power law with R2 higher than 0.92, in all the three cases. Finally, we searched for the presence of privileged sub-networks made up of the most connected nodes (i.e., rich club) of the investigated networks by computing the RCC curve. For the analyzed cortical cultures, we found privileged sub-networks as indicated by the computed RCC values with a maximum value of 2.7 ± 0.5. Fig 4B and 4C show the rich club networks identified for one neural network coupled to the MEA-4k, represented by means of blue circles (for excitatory subnetwork) and pink circles (for inhibitory subnetworks). Fig 3B and 3C are the analogous for a cortical neural network coupled the MEA-60 (yellow for the excitatory nodes and light blue for the inhibitory ones).
Similar cortical networks coupled to the MEA-60 devices show no clear SW topology (Fig 5C); these networks seem to be characterized by a (sub)-random topology with SWI of 0.4 ± 0.1 for the excitatory and 0.2 ± 0.2 for the inhibitory links. These cortical networks are of the same type as the ones coupled to the MEA-4k (i.e., similar density of neurons, same age, same culture medium), and the apparent estimated random topology should be attributed to the low number of recording sites (i.e., 60 channels) that are not enough to reliably infer topological features. To determine how the number and density of electrodes are crucial, we computed the SWI by considering a reduced number of electrodes for the functional connectivity analysis from the MEA-4k recording, as described in Fig 5F. In particular, we started from the full resolution of the MEA-4k (i.e., 4096 electrodes), and we progressively decreased the electrode density to 60 electrodes (inter-electrode distance 189 μm, electrode density 19 electrode/mm2) to obtain a configuration comparable with the MEA-60 devices, as previously reported[50]. The obtained results are shown in Fig 5G: the SWI decreases down to a random topology becoming variable and unstable when the number of the considered electrodes is less than 100. This last result is referred to the excitatory links and the same analysis was not applied to the inhibitory connections. Such inhibitory links are much less than the excitatory ones, thus leading to an inhibitory topology reconstruction that is strongly influenced by the decimation scheme applied to reduce the number of electrodes.
The computation of the correlation of firing activity in the framework of multiple neural spike trains has been introduced since the 1960s. For over thirty years, cross-correlation, its generalizations [51], and its homologue in the frequency domain [52], have been the main tools to characterize interactions between neurons organized into functional groups, or “neuronal assemblies”. A common established technique was to build a cross-correlogram (CCH), describing the firing probability of a neuron as a function of time elapsed after a spike occurred in another one. Nevertheless, in the literature, there is no standard definition of CC, and the strength of a connection can be estimated by different means. To make the correlation coefficient independent of modulations in the firing rate, it is essential to have procedures for correction, normalization and thresholding of the coincidence counts obtained from cross-correlation calculations. Commonly used normalization procedures are related to Normalized Cross-Correlation Histogram (NCCH) [21, 39], event synchronization [53], Normalized Cross-Correlation (NCC–Pearson Coefficient) [23], Coincidence Index of the CC [26]. Once that a Functional Connectivity Matrix (FCM) is obtained, a thresholding procedure is necessary to discard those values that are related to spurious connections. All these approaches present advantages and disadvantages, but none of them have been applied to reliably identify inhibitory connections on large-scale network from spiking activity. In this paper we presented a filtered and normalized CC based algorithm (i.e., FNCCH) from which thresholded functional connectivity matrices and (directed) weighted graphs for excitation and inhibition can be obtained.
From the analysis of the data, we identified both small-world and scale free topologies in cortical networks for the excitatory and inhibitory sub-networks. More specifically, we extracted inhibitory networks in cortical (and striatal) neuronal cultures demonstrating the good performance of the method and offering new understanding of neuronal interactions in large cell populations. Finally, the proposed algorithm strengthens previously results presented in the literature [34], states a new way (i.e., through large-scale MEAs and CCH based analysis) to investigate network topology and opens up new perspective for the analysis of multisite electrophysiological recordings [54].
Generally, by inspecting a CCH, we can notice an increase or a decrease of the fluctuations [22]. In some studies, it was noticed that the primary effect of inhibition on the cross-correlogram is a trough near the origin, and for this interaction to be visible there must be present a background of postsynaptic spiking against which the inhibitory effect may be exercised (high-tonic firing rate regime) [4, 35]. From experimental works related to the analysis of connectivity from cortical multi-unit recordings [55], a good sensitivity for excitation is obtained, while the situation is considerably worse for inhibition.
This is due to a low sensitivity of CCH for inhibition, especially under the condition of low firing rates [4, 56]. The difference in sensitivity may amount to an order of magnitude, and it was demonstrated that for inhibition, the magnitude of the departure relative to the flat background is equal to the strength of the connection, whereas for excitation it involves an additional gain factor [4].
As a whole, the lack of efficiency in the detection of inhibition, simply reflects the disproportionate sensitivity of the analysis tool [57]. In our work, we introduced a cross-correlogram filtering approach (FNCCH) developed to overcome the inhibition detectability issue. As Fig 1 shows, the FNCCH is able to detect, with high accuracy, the inhibitory links when applied to in silico neural networks with similar dynamics with respect to the actual ones. The filtering procedure improves also the detectability of the excitatory links, resulting in a reshaping of the ROC curve (Fig 1A) with an increase of both precision (MCC curve, Fig 1B) and AUC with respect to the standard cross-correlation (NCCH). However, the presented FNCCH, being a CC-based method, has some limitations in the inhibitory links detection that we tried to investigate with our in silico models. The main factor affecting the detectability of inhibition, is the variability of CC. In order to reduce this variability, it is possible to increase the number of coincidences per bin by widening the bin-width (that is, down-sampling with loss of information in the acquired electrophysiological data), or by increasing the number of involved events (which can be obtained with high firing rate and/or by raising the recording time)[58]. Another influencing factor depends on the balance of excitatory and inhibitory neuronal inputs (i.e., balanced model) and it is referred to the relative strength between inhibitory and excitatory inputs. In fact, when the neuron is not balanced, excitation is, on average, stronger than inhibition. Conversely, when the neuron is balanced, both excitation and inhibition are strong and detection of inhibitory links improves [22, 31, 57]. Starting from the in silico model, we were able to investigate the impact of rates variability on excitation/inhibition detectability, and to try to define a reasonable threshold (criterion for detectability [22, 56]). In particular, we varied the firing rate of the inhibitory neurons from 20 spikes/s to 2 spikes/s, while maintaining a firing rate of 2–3 spikes/s for the excitatory neurons. We found that the detectability of the functional inhibitory links is preserved with our method, down to a firing of about 6 spikes/s, and then decreases significantly. We investigated also the inhibition identification with respect to the recording time. Starting from 1 hour of simulation, we reduced (10 min steps) the recording time, and we found that there is a decrease in the inhibition detectability below 30 minutes of recording (cf. S4 Fig). Finally, we investigated the performances of the FNCCH in a scale-free and small-world network. The detection of inhibition was still possible with relatively good results, even if the global performances of the algorithm decreases. This shall not be attributed to the scale-free topology, but to the reduced firing rate for both inhibitory and excitatory neurons and to possible unbalances between inhibition and excitation (cf. S3 Fig). Nevertheless, the method could reliably capture the topology of the network and qualitatively estimate the synaptic in-degree distribution. Thus, the obtained results enabled us to apply the FNCCH to in vitro large-scale neural networks, and allowed us to infer topology and functional organization. The described procedure could be also directly applied to Multi Unit Activity (MUA) from in vivo multi-site measurement recordings. Other methods (e.g., partial correlation, transfer entropy) were not taken explicitly into consideration for comparison, either for their computational costs, or for the inability to identify inhibitory links [59].
The cortical networks probed with MEA-4k showed a clear small-world topology. The inhibitory functional links had a SWI equal to 9.2 ± 3.5, higher than the value extracted from the excitatory links (5.1 ± 1.9). Conversely, the cortical networks coupled to the MEA-60 showed a random organization topology (0.21 ± 0.212 for the inhibitory links and 0.38 ± 0.1 for the excitatory ones). These apparent random organizations are due to the low number of recording sites of the acquisition system; in fact, it is worth to remember that the SWI is computed by comparing cluster coefficient (CC) and average shortest path length (PL) of the analyzed networks to the corresponding values for surrogate random equivalent networks (same number of nodes and links). From the obtained results, unlike recently presented findings [42], we demonstrated that the emergence of small-worldness, cannot be reliably derived or observed in a neuronal population probed by a reduced number (< 100) of recording sites. To characterize connectivity properties, besides the importance of well-defined statistical tools used for the analysis, it is fundamental to probe network activity by using large-scale microtransducer arrays (i.e., with at least 200 electrodes). As a whole, the issue related to the low number of recording sites should be carefully taken into account when extracting dynamical features as well as organizational principles of complex networks.
Finally, it should be underlined that we focused our attention on CC based methods. We mentioned, in the Introduction, the widespread use of Information Theory (IT) based techniques. Beside the relative novelties of such methods, and the good performances (for a review see [38] and references therein), they showed high computational costs and, to our knowledge, the inability to reliably estimate inhibitory connections [26]. Although theoretically, IT based methods such as Transfer Entropy (TE) and Mutual Information (MI) are able to detect inhibitory links, we are not aware of studies consistently reporting a successful identification of inhibitory connections. The problem is in the incapability of distinguishing between excitatory and inhibitory links, rather than in the detection of inhibition as pointed out in the Supplementary Information (S6 Fig).
Primary neurons were obtained from rat embryos (18 days, E18) from Sprague Dawley pregnant rats (Charles River Laboratories). The experimental protocol was approved by the European Animal Care Legislation (2010/63/EU), by the Italian Ministry of Health in accordance with the D.L. 116/1992 and by the guidelines of the University of Genova (Prot. N. 24982, October 2013).
Cross-correlation (CC) [22] measures the frequency at which a neuron or electrode fires (“target”) as a function of time, relative to the firing of an event in another one (“reference”). Mathematically, the correlation function is a statistic representing the average value of the product of two random processes (the spike trains). Given a reference electrode x and a target electrode y, the correlation function reduces to a simple probability Cxy(τ) of observing a spike in one train y at time (t + τ), given that there was a spike in a second train x at time t; τ is called the time shift or the time lag. In this work, we use the standard definition for the cross-correlation computation, following a known normalization approach on the CC values [39]. We define the cross-correlation as follows:
Cxy(τ)=1NxNy∑s=1Nxx(ts)y(ts−τ)
(2)
where ts indicates the timing of a spike in the x train, Nx is the total number of spikes in the x train and Ny is the total number of spikes in the y train. Cross-correlation is limited to the interval [0, 1] and it is symmetric Cxy(τ) = Cyx(-τ). The cross-correlogram is then defined as the correlation function computed over a chosen correlation window (W, τ = [-W/2, W/2]). Different shapes of cross-correlograms can be obtained from pairs of analyzed spike trains. The occurrence of significant departures from a flat background in the cross-correlogram (i.e., a peak or a trough) is an indication of a functional connection[4]. In particular, a peak corresponds to an excitatory connection and a trough to an inhibitory link. The different amplitude of the peaks can be related to the existence of different levels of synchronization between neural spike trains. Generally, a correlogram can reflect a so-called direct excitatory connection between two neurons when a one-sided peak is evident (displaced from the origin of time by latency corresponding to the synaptic delay).
The use of spike train data offers the possibility to optimize the cross-correlation algorithm efficiency. To overcome the lack of efficiency of many of the proposed CC computation strategies, we present an alternative approach based on the “direct” spike time stamps inspection that avoids un-necessary calculations on the binarized spike trains. In fact, the only important information is stored in the bins containing a spike (i.e., spike time stamp), that are significantly less than null bins. If we consider that the average mean firing rate in neural networks oscillates between 0.2 spikes/s and 20 spikes/s [60], at a sampling frequency of 10 kHz, it yields only 2% of bin with spikes and 98% of meaningless bins: thus, we developed a new version of the CCH as indicated in Fig 6E.
Let us consider a reference neuron x and a target neuron y, and let us suppose that we computed the NCCH between x and y. After the NCCH computation, the maximum value (i.e., the peak) is used as a value reflecting the strength of the estimated functional link. If x and y share an excitatory link, this procedure works well (Fig 6A and 6B). On the other hand, when x inhibits y, the inhibitory trough will be discarded in favor of the NCCH peak (Fig 6C), with a misleading excitatory link detection. The CCH shapes are similar also in the correlograms derived from experimental data, although with an even more jagged behavior. Fig 6F and 6G show two examples of detected putative excitatory and inhibitory connections.
Eq (1) gives the mathematical definition of the FNCCH computation that overcomes this problem. We refer to the filtered peak value as entity of the peak. In this way, it is possible to distinguish between peaks and troughs by taking into account the sign. A positive peak is referred to an excitatory links (Fig 6A and 6B), conversely, a negative peak is referred to an inhibitory link (Fig 6D). We implemented and applied also a post-computation filtering procedure to improve the detectability of inhibitory links on noisy spike trains (cf., Supplementary Information, S1 Fig).
The block diagram and pseudocode depicted in Fig 6E show the sequence of operations executed by the FNCCH. The starting point is the first bin containing a spike in the target train. The binning procedure is directly performed on the time stamps. For each couple of neurons, starting from the first spike of the target train, we slide the time stamps of the reference electrode to find the first spike whose correlation window contains the target spike. Then, we continue to move over the target train to build the entire cross-correlogram (for that reference spike). When the correlation window for the reference spike is completed (i.e., when we have counted the number of spikes for all the bin of the target spike train), we move to the next spike of the reference train, and re-iterate the procedure starting from the first target spike into the correlation window, centered at the current reference spike. Then, we normalize the CC and repeat all the aforementioned operations for the other electrodes. Exploiting the symmetry of the CC function we consider only half of the electrodes for the computation. Moreover, for each pair, we select, as target train, the one with the smallest number of spikes to reduce the number of operations. Once the NCCH is obtained, we apply the filtering operation described by Eq (1) to compute the FNCCH values. Finally, we take the maximum absolute value as estimation of the correlation strength between the two electrodes. If it is negative, the found connection is considered a putative inhibitory link, otherwise is considered an excitatory one.
We applied a Spatio-Temporal Filter directly to the functional connectivity matrix (FCM) originated by the FNCCH. The procedure we implemented follows the one devised by Maccione et al.,[61] by defining a distance-dependent latency threshold. More in detail, we evaluated the links length (using the Euclidean distance) and the functional delays for each electrodes pair. We assumed as maximum propagation velocity a value of 400 mm/s[62]. If a functional connection has a temporal delay not compatible with such maximum velocity, it is discarded. Finally, we introduced also a minimum delay of about 1 ms, compatible with fast excitatory AMPA synaptic transmission.
Thus, we refined the FCM by removing all the links related to putative non-physiological connections.
Cross-correlation, as well as any other connectivity method, provides a full n x n Connectivity Matrix (CM), whose generic element (i, j) is the estimation of the strength of connection between electrodes i and j. A thresholding procedure is thus needed to eliminate those values that are only relative to noise and not to real functional connections. In the literature, there are several thresholding procedures, with different levels of complexity: the simplest one is to use a hard threshold, defined as (μ + n · σ), where μ and σ are the mean and the standard deviation computed among all the CM’s elements, respectively, and n is an integer[24]. There are other more sophisticated approaches based on shuffling methods that consist in destroying all the temporal correlations within the spike trains and compute a null hypothesis to test the significance of the connections[63]. However, shuffling procedures require many resources in terms of memory and computational times. In this work we proved, by means of the in silico network model, that a simple hard threshold method is sufficient. We found that significant levels of accuracy can be obtained with a threshold equal to (μ + σ) for both excitatory and inhibitory links (cf., Results sect.).
The ROC curve[40] is a common metrics used to evaluate the performances of a binary classifier by comparing prediction and observation. In our study, the prediction is represented by the computed Thresholded functional Connectivity Matrix (TCM), and the observation corresponds to the Synaptic Weight Matrix (SWM) of the neural network model (i.e., the ground truth).
We can define the True Positive Rate (TPR) and the False Positive Rate (FPR) as follows:
TPR=TPTP+FN
(3)
FPR=FPFP+TN
(4)
where TP are the True Positive links, and TN, FP and FN are the True Negative, False Positive and False Negative connections, respectively. The ROC curve is then obtained by plotting TPR versus FPR. The Area Under Curve (AUC) is a main parameter extracted to have a single number describing the performances of a binary classifier: a random guess will correspond to 0.5, while a perfect classifier will have a value of 1. Another important metrics that can be extracted from a ROC analysis is the accuracy, defined as:
ACC=TP+TNTP+TN+FP+FN
(5)
The MCC curve[40] is a common metrics, alternative to the ROC analysis, used to evaluate the performances of a binary classifier by comparing prediction and observation. Using the quantities defined in the previous paragraph, changing the threshold used to compute the TCM, we can define MCC as:
MCC=TP*TN−FP*FN(TP+FP)(TP+FN)(TN+FP)(TN+FN)
(6)
The MCC assumes values in the interval [–1, 1] and the MCC curve is obtained by plotting the MCC value versus the false positive rate.
Let x be a generic node and vx the total number of nodes adjacent to x (including x). Let u be the total number of edges that actually exist between x and its neighbors. The maximum number of edges that can exist among all units within the neighborhood of x is given by vx(vx -1)/2. The Cluster Coefficient (Cx) for the node x, is defined as:
Cx=2*uvx(vx−1)
(7)
The Average Cluster Coefficient, obtained by averaging the cluster coefficient of all the networks nodes, is a global metric often used to quantify the segregation at network level.
Let x and y be two generic nodes of a network V of n nodes. Let d(x, y) be the shortest distance between the nodes x and y. We define the Average Path Length (L) as:
PL=2n(n−1)∑x≠yd(x,y)
(8)
This topological parameter is commonly used to evaluate the networks level of integration.
To detect the emergence of small-world network [64], it is possible to combine the metrics previously introduced, defining the Small-World Index (SWI):
SWI=CnetCrndLnetLrnd
(9)
where Cnet and Lnet are the cluster coefficient and the path length of the investigated network, respectively, and Crnd and Lrnd correspond to the cluster coefficient and the path length of random networks equivalent to the original network (i.e., with the same number of nodes and links). A SWI higher than 1 suggests the emergence of a small-world topology.
A graph representing a complex network is said to have a rich-club organization if the hub nodes of such a graph are more strongly connected with each other than expected by their high degree alone[42]. It is possible to infer such an organization by computing the Rich Club Coefficient (RCC).
The RCC value at a specific k level is computed by evaluating the cluster coefficient among the nodes with a degree higher than k:
RCC(k)=2E>kN>k(N>k−1)
(10)
where N>k is the number of nodes with a degree higher than k, and E>k represents the edges between them. Evaluating the RCC with k varying from 1 to the maximum degree allows to build the RCC curve. The RCC curve is normalized by the corresponding average value for a set of surrogated random neural networks equivalent to the investigated one (i.e., networks with the same number of nodes and edges). If the maximum RCC normalized coefficient value is higher than one, a privileged sub-network (i.e., a rich club) is found.
The network model was made up of 1000 neurons randomly connected. The dynamics of each neuron is described by the Izhikevich equations[65]. In the actual model, two families of neurons were taken into account: regular spiking and fast spiking neurons for excitatory and inhibitory populations, respectively (S2A Fig). The ratio of excitation and inhibition was set to 4:1 as experimentally founded in cortical cultures [8]. In the model, each excitatory neuron receives 100 connections from the other neurons (both excitatory and inhibitory) of the network. Such incoming connections reflect the same ratio of the neuronal population, i.e., 80% of excitatory and 20% of inhibitory links. (S2C Fig). Each inhibitory neuron receives 100 input only from excitatory neurons. Autapses are not allowed. All the inhibitory connections introduce a delay equal to 1 ms, otherwise excitatory ones range from 1 to 20 ms [66]. Synaptic weights were extracted from a Gaussian distribution with mean equal to 6 and -5 for excitatory and inhibitory weights (S2B Fig). Standard deviations were set to 1. Excitatory weights evolve following the spike timing dependent plasticity (STDP) rule with a time constant equal to 20 ms[67]. The spontaneous activity of the neuronal network was generated by stimulating a randomly chosen neuron at each time stamp injecting a current pulse extracted from a normal distribution (Istm,exc = 11 ± 2; Istm,inh = 7 ± 2). The network model was implemented in Matlab (The MathWorks, Natick, MA, USA), and each run simulates 1 hour of spontaneous activity.
Cortical and striatal neurons were dissociated from rat embryos (E18) Sprague Dawley (Charles River Laboratories). The day before plating, Micro-Electrode Arrays (both MEA-4k and MEA-60) were coated with the adhesion proteins laminin and Poli-Lysine (Sigma-Aldrich). The final density of plating was about 1200 cells/mm2 for the MEA-60 and 700 cells/mm2 for the MEA-4k. MEAs were maintained for four weeks in a humidified incubator (5% CO2, 37°C) in Neurobasal medium supplemented with B27. More details about cell cultures can be found in previous works[50, 68]. Recordings were performed using two kinds of MEAs: (i) MEA-60 (Multi Channel Systems, Reutlingen, Germany) constituted by 60 planar Ti/TiN microelectrodes 200 μm spaced with a diameter of 30 μm and arranged in a 8 by 8 square grid (electrodes in the four corners are not present). (ii) MEA-4k (3Brain, Wadenswill, Switzerland) constituted by 4096 square microelectrodes 42 μm spaced, 21 μm side length, arranged in a 64 by 64 square grid. Recordings of spontaneous activity were performed during the fourth week in vitro. We recorded 1 hour of spontaneous activity at the sampling frequency of 10 kHz (MEA-60) and of 9046 Hz (MEA-4k).
Data analysis was performed off-line using Matlab and C# (Microsoft, US). Spike detection. The algorithm used to detect extracellularly recorded spikes was the Precise Timing Spike Detection (PTSD) [69]. Practically, the detection was performed by setting three parameters: a differential threshold, evaluated as 8 times the standard deviation of the noise of each electrode; a peak life time period (set at 2 ms) and the refractory period (set at 2 ms). Spike sorting was not performed as it is often difficult to distinguish different shapes during bursts due to overlapping spikes [60]. Burst detection. Burst at single electrode level and network bursts were detected by using previously developed and validated algorithms. Single electrode bursting activity was detected by considering at least 5 spikes with a maximum Inter Spike Interval (ISI) of 100 ms [70]. Functional connectivity and topological analysis. The FNCCH used to infer functional connectivity, as well as the metrics used to characterize the topological features of the cortical networks (Small-World Index, Clustering Coefficient, average shortest Path Length) were collected in an update version of the SpiCoDyn software [71].
Data are expressed as mean ± standard deviation of the mean. Statistical analysis was performed using Matlab. Since data do not follow a normal distribution (evaluated by the Kolmogorov-Smirnov normality test), we performed a non-parametric Kruskal-Wallis test. Significance levels were set at p < 0.001. In the box plot representation, the median value and 25th-75th percentiles are indicated by the box, mean value is indicated by the small hollow square, and whiskers indicate 5th-95th percentiles.
The developed FNCCH is available to the scientific community on the Neuroimaging Informatics Tools and Resources Clearinghouse, (NITRC) repository (http://www.nitrc.org/). In particular, the FNCCH has been embedded in a new release (v3.0) of the software tool SpiCoDyn (https://www.nitrc.org/projects/spicodyn/).
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10.1371/journal.ppat.1001192 | MAP Kinase Phosphatase-2 Plays a Critical Role in Response to Infection by Leishmania mexicana | In this study we generated a novel dual specific phosphatase 4 (DUSP4) deletion mouse using a targeted deletion strategy in order to examine the role of MAP kinase phosphatase-2 (MKP-2) in immune responses. Lipopolysaccharide (LPS) induced a rapid, time and concentration-dependent increase in MKP-2 protein expression in bone marrow-derived macrophages from MKP-2+/+ but not from MKP-2−/− mice. LPS-induced JNK and p38 MAP kinase phosphorylation was significantly increased and prolonged in MKP-2−/− macrophages whilst ERK phosphorylation was unaffected. MKP-2 deletion also potentiated LPS-stimulated induction of the inflammatory cytokines, IL-6, IL-12p40, TNF-α, and also COX-2 derived PGE2 production. However surprisingly, in MKP-2−/− macrophages, there was a marked reduction in LPS or IFNγ-induced iNOS and nitric oxide release and enhanced basal expression of arginase-1, suggesting that MKP-2 may have an additional regulatory function significant in pathogen-mediated immunity. Indeed, following infection with the intracellular parasite Leishmania mexicana, MKP-2−/− mice displayed increased lesion size and parasite burden, and a significantly modified Th1/Th2 bias compared with wild-type counterparts. However, there was no intrinsic defect in MKP-2−/− T cell function as measured by anti-CD3 induced IFN-γ production. Rather, MKP-2−/− bone marrow-derived macrophages were found to be inherently more susceptible to infection with Leishmania mexicana, an effect reversed following treatment with the arginase inhibitor nor-NOHA. These findings show for the first time a role for MKP-2 in vivo and demonstrate that MKP-2 may be essential in orchestrating protection against intracellular infection at the level of the macrophage.
| In cells of the immune system are switch-on enzymes called kinases which regulate responses to infectious agents such as Leishmania. However, in the same cells there are switch-off enzymes known as phosphatases which function to turn off the kinases once they have done their work. A lot of studies have focussed on the role of kinases but not phosphatases in response to infection; we therefore generated a novel mouse in which the gene for one of these phosphatases, called MKP-2, has been deleted. We found that in the absence of this phosphatase unexpected things happened. The profile of inflammatory proteins, produced by a special cell of the immune system, called a macrophage, that functions to regulate infection by Leishmania, changed in ways which meant the macrophage could either fight infection very effectively or very weakly. In actual fact, we found that the macrophages with no MKP-2 fought off Leishmania poorly and mice deficient in MKP-2 had a modified immune response favouring the growth of the parasite. This is the first study to give critical insight into the role of MKP-2 in fighting Leishmania infection and demonstrates very well the importance of this class of enzyme in pathogen biology.
| The mitogen-activated protein (MAP) kinase phosphatases (MKPs) are a family of dual specific phosphatases which regulate the functional activity of the major MAP kinase subfamilies through tyrosine and threonine dephosphorylation [1]. At least eleven isoforms exist each with different structures, subcellular distributions, substrate specificity and mechanisms of regulation. For example, the prototypic MKP-1 is induced by a wide variety of extracellular signals, is strictly nuclear located, and is able to dephosphorylate all MAP kinases, whereas MKP-3 is constitutively expressed, cytosolic and selective for extracellular regulated kinase (ERK) above the other major MAP kinases, c-Jun N-terminal kinase (JNK) and p38 MAP kinase. Thus, the action of one or more MKP is essential for the tight regulation of MAP kinase activity and subsequent functional responses mediated by a vast array of extracellular stimuli [1].
A number of MKPs have been implicated in the regulation of disease. Dysfunction or changes in expression of MKPs is a feature of a number of cancers [1], whilst roles in gluconeogenesis, insulin resistance and diabetes have also been established [2], [3]. Recent evidence also implicates a role in the regulation of immune responses [4]. Deletion of MKP-1 [5] and PAC-1 [6] have been shown to both enhance and reduce LPS mediated cellular responses respectively, whilst MKP-5 is thought to regulate adaptive immunity via effects upon T-cells [7]. Furthermore, one of the main anti-inflammatory effects of dexamethasone is attributed to the induction of MKP-1 and the subsequent inhibition of p38 MAP kinase [8].
MAP kinase phosphatase-2 (MKP-2) is a class I DUSP [9], induced by growth factors, hormones and stress agents such as hydrogen peroxide. It is nuclear located due to two nuclear location sequences [10] and dephosphorylates ERK and JNK in vitro [11] whilst being ineffective for p38 MAP kinase despite binding strongly to this kinase [12]. Cellular studies suggest the potential of cell type specificity as stable or conditional over-expression of MKP-2 selectively inhibits JNK in epithelial cells types [13], [14]. Although often described as a surrogate to MKP-1, MKP-2 has been demonstrated to have roles in cellular apoptosis and senescence [13], [15]. A number of recent indirect studies have also indirectly implicated MKP-2 in cancer [16]. However, the function of MKP-2 in the immune system remains uncharacterised due to the lack of suitable MKP-2 (DUSP-4) knockout mouse models.
In this study we examined the function of the MKP-2 (DUSP4) gene using a novel MKP-2 knockout mouse. In macrophages derived from MKP-2−/− mice, we find that its deletion results in enhanced JNK and p38 MAP kinase activation but not, as expected, increased ERK phosphorylation. Increased IL-6, IL-12, TNFα and PGE2 production suggested that MKP-2 may modify the innate immune response in a manner similar to that observed in the MKP-1 deletion model. However, in contrast to these changes we observed marked reduction in inducible nitric oxide (iNOS) expression and enhanced arginase-1 activation indicative of an additional regulatory function. We found that that bone-marrow derived macrophages from MKP-2−/− mice were more susceptible to infection with the intracellular parasite Leishmania (L.) mexicana than macrophages from their wild-type counterparts. The enhanced susceptibility of MKP-2−/− macrophages was reversed using the arginase inhibitor nor-NOHA. Consequently following infection with L. mexicana MKP-2−/− mice had limited ability to control lesion development and parasite growth. This was related to a significant down-regulation of specific Th1 activity in MKP-2 deficient mice. Taken together, our results demonstrate for the first time that MKP-2 plays a functional role in limiting immune responses associated with the macrophage lineage and indicates for the first time that MKP-2 is not a functionally redundant DUSP in vivo.
The MKP-2 deletion strategy is outlined in Figure 1A. Using targeted homologous recombination exons 2–4 were removed. Therefore, most of the open reading frame including the phosphatase catalytic domain and the 3′UTR region of the DUSP4 gene was deleted. Although exon1, which comprises the 5′UTR, the start codon and the KIM domain, remained unchanged, the occurrence of a truncated protein was unlikely since the poly A tail necessary for any kind of protein synthesis was removed. Deletion was confirmed by Southern Blotting (insert), which identified both the wild type 9.9 kB fragment and the mutated 8.3 kB fragment and by PCR (not shown). In Panels B & C, bone marrow derived macrophages were assessed for the presence of MKP-2 protein by Western blotting. In wild type cells, LPS stimulated MKP-2 expression in a concentration-dependent manner, giving a maximum response at approximately 1 µg/ml LPS (Panel B). Induction was also time dependent reaching a peak at 2 h following LPS treatment (Panel C). No induction was observed in macrophages derived from MKP-2−/− mice.
We then examined the effect of MKP-2 deletion on LPS-induced kinase phosphorylation (Figure 2) as MKP-2 has previously been shown to dephosphorylate ERK and JNK in vitro [11]. In MKP-2−/− macrophages, LPS-stimulated JNK phosphorylation was potentiated and prolonged (Panel A *p<0.05). Enhanced JNK activity was confirmed by JNK in vitro kinase assay and Western blotting for serine-63 phosphorylation of c-Jun (Supplementary Figure S1). Surprisingly, LPS-stimulated p38 MAP kinase phosphorylation (Panel B) was also found to be enhanced in MKP-2 −/− mice, despite p38 MAP kinase not being a recognised substrate for MKP-2 in vitro [11]. In contrast, ERK activation was not altered in MKP-2−/− macrophages at any time point studied or over different concentration ranges (Panel C). Preliminary results, however, showed a lack of ERK translocation to the nucleus (results not shown) suggesting a lack of access of ERK to the phosphatase rather than a lack of MKP-2 activity on ERK as the explanation for this finding.
Having established that enhanced kinase activation occurred in macrophages from MKP-2−/− mice, we also assessed the consequences of MKP-2 deletion on the expression and release of key cytokines, known to be regulated by MAP kinase activation. Figure 3 shows cytokine production in macrophages derived from both wild type and MKP-2−/− mice. Panel A shows that LPS induced a substantial increase in IL-6 production manifest at 6–8 h and reaching a peak after 24 h. MKP-2 deletion had little effect upon basal levels, however, but markedly enhanced the rate and magnitude of production stimulated by LPS (24 hr: MKP-2+/+ = 1.83±0.01 ng/ml, MKP-2−/− = 4.44±0.83 n = 4, P<.005). Similar results were observed for both IL-12, which was assayed as IL-12p40/70 (Panel B), and TNFα (Panel C) although production reached a peak at 48 h and the degree of potentiation, whilst significant, was less than that for IL-6. In contrast, LPS stimulated IL-10 production was markedly reduced in MKP-2 deficient macrophages (Panel D).
We then assessed the expression of a number of inflammatory proteins following MKP-2 deletion (Figure 4). Panel A shows that in MKP-2−/− macrophages the rate of onset of cyclo-oxygenase-2 (COX-2) expression was increased in response to LPS, relative to wild type controls, an outcome predicted by previous studies assessing MAP kinase regulation of COX-2 expression and consistent with enhanced JNK and p38 MAP kinase activation. A difference in induction was observed as early as 2 and 4 h and was consistent with increased PGE2 production (Panel A and histogram D). We also assayed inducible nitric oxide synthase (iNOS) expression following incubation with LPS for up to 24 h. However, rather than being enhanced, iNOS expression was strongly inhibited relative to wild type controls and this was reflected in reduced formation of nitric oxide - derived nitrate and nitrite (Figure 4, Panel B and histogram E). This strong inhibitory effect was reproduced when IFN-γ was used to induce iNOS instead of LPS (Supplementary Figure S2). IFN-γ is known to regulate iNOS expression via the JAK/STAT pathway but no differences where found in the activation of these intermediates in MKP-2+/+ and MKP-2−/− macrophages (Supplementary Figure S2). We also determined if other macrophage proteins were down regulated to a similar extent, specifically arginase-1, a protein stimulated through the alternative macrophage activation pathway and utilising the same substrate, L-arginine, as iNOS (Panel C). Basal arginase-1 expression and activity was considerably higher in MKP-2−/− macrophages, and equivalent to 24 h of IL-4 stimulation in MKP-2+/+ samples (Panel C and histogram F). These results contrasted greatly with the expression of iNOS, which is indicative of classical macrophage activation and shows that the MKP-2−/− macrophages intrinsically have a unique profile of inflammatory protein expression.
Changes in MAP kinase signalling and iNOS activity are implicated in the ability of mice to resist infection with Leishmania [17], [18], [19], [20], [21], [22]. However, while the signalling and associated cytokine changes in MKP2−/− mice would favour increased protection, the unexpected changes in iNOS and arginase expression would favour disease progression. As L. mexicana induces an intermediate disease phenotype in C57BL/6 and B6/129 mice whereby lesion growth is controlled, but fails to heal [23], we used this pathogen to test the ultimate influence of MKP-2 on disease outcome.
Initially, we tested the effect of Leishmania mexicana promastigotes on cellular MAP kinase signalling responses and iNOS induction (Figure 5) to confirm that both LPS, via TLR-4, and promastigotes mediate common kinase signalling cassettes and related functional end points. We initially found that in wild type macrophages, promastigotes activated ERK, JNK and p38 MAP kinase (Panel A). However, in TLR-4 deficient macrophages, both ERK and JNK phosphorylation were abolished in response to promastigotes, with a substantial reduction in p38 MAP kinase. In TLR-4−/− macrophages the LPS-induced responses were also reduced, but this not by as much as the reduction seen in promastigote-induced responses. This is probably because our commercial source of LPS may have been contaminated with bacterial lipoproteins resulting in some activation of TLR-2. Nevertheless, these data suggest that both agents act principally through TLR-4. MKP-2 deletion had little effect upon the ERK response (Panel B) or p38 MAP kinase signalling (not shown) in response to promastigotes and JNK phosphorylation was only marginally increased although not significantly (Panel C). Despite activation of these kinases, promastigotes alone failed to significantly induce MKP-2 protein (data not shown) and had no effect upon iNOS synthesis alone or in response to LPS (Panel D). Furthermore, promastigotes did not significantly increase the already high levels of arginase-1 activity in MKP-2−/− macrophages (arginase activity, µg/ml: MKP-2+/+ control = 125.4±14.3, L. mexicana = 101.1±13.0; MKP-2−/− control = 345.8±17.10, L. mexicana = 392±23.5 n = 3).
Nevertheless, despite promastigotes being unable to modify MKP-2 expression per se, we found that MKP-2 deletion had a significant effect in vivo following Leishmania mexicana infection. Upon injection into the footpad with L. mexicana, MKP-2−/− mice developed progressively growing lesions and could not limit lesion growth unlike their wild-type counterparts (Figure 6A). Lesions grew more rapidly in MKP-2+/+ mice compared with MKP-2−/− mice in the first 4 weeks of infection but the parasite burdens remained higher at this site in MKP-2−/− mice than their wild-type counterparts throughout infection (Figure 6B). At week 15, the Th1 response, as measured by antigen specific IgG2a and IFN-γ production (Figures 7A and B), was significantly reduced in MKP-2−/− compared with MKP-2+/+ mice confirming that the MKP-2−/− mice were defective in their ability to control parasite growth. No differences in the Th2 response were noted with specific IgG1 (Figure 7A) and whole IgE as well as IL-4, IL-13, and IL-10 production (data not shown), all being similar in MKP-2−/− and MKP-2+/+ mice. There was no evidence of an intrinsic T cell defect in MKP-2−/− mice as splenocytes from infected MKP-2−/− and MKP-2+/+ mice produced similar levels of IFN-γ upon stimulation with anti-CD3 (Figure 7C).
The growth of L. mexicana is subject to different immunological controls at different sites. The non-healing response to L. mexicana in infected footpads is associated with deficient IFN-γ production, independent of a Th2 response, whereas in the back rump the host response to infection is entirely Th2 dependent [24]. Consequently, we monitored the growth of L. mexicana in the shaven back rump of MKP-2−/− and MKP-2+/+ mice (Figure 8). Lesions appeared earlier and lesion growth was more rapid in MKP-2−/− mice (Figure 8A). The increased susceptibility of MKP-2−/− mice over wild-type animals was associated with expanded Th2 responses in these animals as measured by significantly increased specific IgG1 production (Figure 8B) and increased ConA induced splenocyte IL-4 (Figure 8D) and IL-13 (Figure 8E) production. At the same time there were no significant differences between infected MKP-2−/− and MKP-2+/+ in IFN-γ production (Figure 8C) and specific IgG2a levels (Figure 8B). As with footpad infections there was no indication of an intrinsic T cell defect as measured by anti-CD3 splenocyte cytokine production (results not shown).
To confirm that the intrinsic defect in infectivity was at the level of the host macrophage we therefore compared the growth of L. mexicana parasites in MKP-2−/− and MKP-2+/+ bone marrow derived macrophages. Macrophages were infected with promastigotes at a multiplicity of infection (M.O.I) of 5 parasites/macrophage and growth monitored at 4, 24, 48 and 72 h post-infection in resting as well as LPS+IFN- γ stimulated macrophages (Figure 9). Macrophages from MKP-2−/− mice were significantly more permissive to infection than MKP-2+/+ macrophages as measured by the percentage of cells infected by 4 h (p<0.01) (Figure 9A). Similarly, parasite growth was significantly enhanced in MKP2−/− macrophages compared with MKP-2+/+ macrophages under non-stimulated conditions up to 72 h post-infection (Figure 9B). MKP-2−/− macrophages were, however, able to control parasite growth following IFN- γ+LPS stimulation although only to a level comparable with non-stimulated macrophages derived from MKP-2+/+ bone marrow. This would be consistent with data in figure 5 showing that after 48 h there is measurable iNOS expressed in MKP-2−/− KO macrophages in response to LPS although much less than in MKP-2+/+ macrophages.
Figure 10 shows the effect upon NO production and parasite infection following the treatment with the arginase inhibitor Nω-hydroxy-nor-Arginine (nor-NOHA). Initially, we assessed NO release to confirm that arginase inhibition had been effective in altering, as predicted, the levels of NO in macrophages (Panel A). In LPS+IFN-γ -stimulated MKP-2−/− macrophages NO levels were, as predicted, low, however following nor-NOHA treatment, levels increased significantly equivalent to the level observed for wild type macrophages. In addition, treatment of MKP-2+/+ macrophages with nor-NOHA failed to significantly increase the levels of NO further. When assessing parasite growth in MKP-2−/− and MKP-2+/+ macrophages following nor-NOHA pre-treatment we found that changes in arginase activity altered infectivity (Panel B). In MKP-2−/− macrophages, infectivity was high relative to wild type as expected. However, infectivity was significantly reduced in response to nor-NOHA, to a level which was not significantly different from MKP-2+/+ macrophages. Inhibition of NO using L-NAME also increased the infectivity of the parasite in MKP-2+/+ macrophages suggesting NO is a determinant of macrophage resistance to infectivity with L. Mexicana promastigotes. Furthermore, L-NAME treatment of infected MKP-2−/− macrophages resulted in rapid macrophage destruction and release of parasites (>90%) making it impossible to quantify changes in infectivity. This finding nevertheless re-enforced the idea of increased sensitivity of MKP-2−/− macrophages to infection per se.
This study demonstrates for the first time a novel immune function for MKP-2 in vivo primarily caused by changes in the ability of macrophages to induce innate immune responses. In particular, MKP-2 deletion gives rise to a novel phenotype associated with decreased iNOS and increased arginase-1 activity which makes MKP-2 deficient macrophages more intrinsically susceptible to infection with the intracellular parasite L. mexicana. Infection of MKP-2 deficient mice not only results in increased disease susceptibility and parasite growth in vivo compared to their wild-type counterparts but it is also associated with either inhibition of a Th1 response or promotion of a Th2 response appropriate to enhance infection at the site utilised.
In characterising the novel MKP-2−/− model, we clearly demonstrate that following gene deletion JNK phosphorylation and activity was enhanced. This is consistent with the original studies in vitro [11] and more recent studies using either stable or conditional expression of MKP-2 [13], [14]. ERK activation was, however, not enhanced in MKP-2−/− macrophages despite being a substrate for MKP-2, due to compartmentalisation of ERK to the cytosol, generating a substrate selective function for MKP-2 in this cell type. Unexpectedly, we found that phosphorylation of p38 MAP kinase was also enhanced despite this kinase not being susceptible to dephosphorylation by MKP-2 in vitro. A recent cellular study, however, has indirectly implicated involvement of MKP-2 in regulating AMP kinase and p38 MAP kinase-dependent gluconeogenesis [25] and our findings support this data. Our data show that predictions based on in vitro data can be misleading and indeed study of other MKP deletion mice also shows discordance between substrate specificity in vitro and kinase regulation in whole cells. Whilst MKP-1 is effective against all three major MAP kinases, enhanced p38 MAP kinase is observed in MKP-1−/− macrophages [26], whilst in cells derived from PAC-1 knockout mice, ERK phosphorylation is inhibited despite ERK being a specific substrate for PAC-1 in vitro [6].
Our studies demonstrated that enhanced JNK and p38 MAP kinase activation where reflected at the level of cytokine synthesis. Evidence strongly supports a role for p38 MAP kinase in the expression of IL-6, TNFα and IL-12 [27], [28], [29]. In contrast, a role for JNK is less well defined and cell type specific involvement is implicated [27], [29]. Our studies also correlate well with those obtained with the MKP-1 deletion which implicates p38 MAP kinase as the main in vivo substrate for MKP-1 and shows this kinase to be linked to cytokine release [26], [30]. This would suggest that MKP-2 deletion, in a manner similar to MKP-1 [26], [30] could enhance innate immune responses in vitro and also possibly in vivo.
This possibility was contradicted by other findings. Whilst we found up regulation of COX-2 and associated PGE2 in MKP-2 deletion mice, results also observed in MKP-1 deletion mice, surprisingly expression of iNOS was ablated. Studies show that iNOS expression is enhanced following MKP-1 deletion in vitro [31] and associated with increased mortality in vivo [32]. This suggests the potential for MKP-2 deletion, unlike ablation of MKP-1, to protect against NO induced mortality. Furthermore, we find that basal arginase 1 expression and associated arginase activity is markedly increased in MKP-2 deletion mice, a response which would also mediate a reduction in NO formation, this is again different to the findings in MKP-1−/− macrophages [31]. The molecular mechanisms underpinning the reciprocal regulation of these two inflammatory proteins are at present unclear, as none of the cognate signalling pathways regulating iNOS or arginase induction were found to be negatively affected. Thus, there is a potential for a direct action of MKP-2 within the nucleus which may directly regulate transcription. Recently, MKP-1 has been implicated in the regulation of histone phosphorylation in the nucleus [33] suggesting nuclear functions for the MKPs other than direct MAP kinase regulation.
Overall our studies in vitro therefore reveal that in the absence of MKP-2 macrophages take on a functional profile which can be directed towards either a type-1 or type-2 phenotype. Our in vivo studies enabled us to demonstrate which of these profiles predominate in vivo. Leishmania infection has a well recognized ability to subvert the development of Th1 responses partly via effects upon MAP kinase signalling [17], [20], [34]. Most of these studies have implicated MAP kinase involvement at the level of the host macrophage or dendritic cell, but JNK activation via T cells has also been shown to negatively regulate Th2 cells [34] in a healing response. Therefore, we hypothesised, given the enhanced p38 and JNK activation of MPK-2−/− mice on the C57BL/6 background, that such animals would develop a healing phenotype against this organism. MKP-2 negatively regulated IL-12, IL-6 and TNF-α expression and positively regulates IL-10 confirming this hypothesis. Currently iNOS induction and the subsequent release of NO is considered protective against intracellular infection with many organisms including Leishmania species [19]. In addition, arginase-1, which competes for the same substrate as iNOS, has been shown to promote disease progression not only against Leishmania (in both healing and non-healing strains of mice [18]), but also Mycobacterium and Toxoplasma gondii [35]. As MKP-2 deficiency downregulates iNOS, but upregulates arginase-1 expression and activity this suggests that, counter intuitively, MKP-2−/− animals would be more susceptible to Leishmania infection.
Our studies indeed show that MKP2−/− mice are, in fact, more susceptible to infection with L. mexicana and it is the consequence of changes in iNOS and arginase rather than changes in kinase mediated inflammatory cytokine signalling which dictates the subsequent in vivo response to MKP-2 deletion. The differential effects of Leishmania infection in MKP-2−/− macrophages relative to MKP-2+/+ is not due to Leishmania interacting with the cell in a different way to LPS, both agents utilised TLR-4 during the early stages of stimulation. Furthermore, the fact that Leishmania mexicana alone does not induce MKP-2 points to another potentially indirect effect of MKP-2 deletion not linked to changes in kinase activity. Nevertheless, following footpad infection with L. mexicana, lesion growth was increased and parasite burden enhanced, outcomes associated with reduced IFN-γ production. Of significance it has recently been demonstrated that during L. major infections high local arginase levels at the site of infection mediate L-arginine depletion, which results in impaired local CD4+ T cell function particularly IFN-γ production [21], [22]. The importance of arginase in modulating the virulence of L. mexicana is also highlighted by the fact that arginase null-mutant L. mexicana has attenuated virulence in vitro and in vivo suggesting that the parasite arginase depletes host L-arginine available for iNOS activity [36], [37]. Significantly, mice infected in the footpad with arginase null-mutant L. mexicana have increased antigen induced IFN- γ production. Thus changes in arginase-1 expression in MKP-2−/− macrophage can be easily linked to observed changes in infectivity and immune responses.
Our study is one of the first to reveal an in vivo function for MKP-2 and indicates that MKP-2 does not act as a surrogate to the more extensively studied MKP-1. Our results reveal the potential of MKP-2 to participate in opposing regulatory mechanisms. The first mechanism is based on upregulation of JNK and p38 MAP kinase signalling and is associated with enhanced cytokine expression. The second regulatory mechanism however, involves changes in iNOS and arginase-1 expression, associated with the alternative activated macrophage pathway, is not readily associated with modulation in kinase signalling but possibly involves a different molecular target. The in vivo consequences of this second action can be clearly seen during Leishmania infection where there is down-regulation of Th1 and/or up-regulation of Th2 responses, distinguishing MKP-2 from any of the other MKPs believed to play a role in immune function.
All animal procedures conformed to guidelines from The Home Office of the UK government. All work was covered by two Home Office licences: PPL60/3929, “mechanism of control of parasite infection” and PPL60/3439, “genetic models of cancer and inflammation”.
Dulbecco's modified Eagle's medium and foetal calf serum (FCS) were purchased from Invitrogen. RPMI 164 and LPS were from Sigma Aldrich. Antibodies against p-ERK, and MKP-2 were obtained from Santa Cruz. All other phospho-antibodies were purchased from Biosource International Inc. (USA) and secondary antibodies from Jackson Immuno Research Laboratories Inc (PA, USA). The TLR-4 deficient mice were obtained from Professor Akira S. Osaka University, Japan.
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10.1371/journal.ppat.1004326 | EVM005: An Ectromelia-Encoded Protein with Dual Roles in NF-κB Inhibition and Virulence | Poxviruses contain large dsDNA genomes encoding numerous open reading frames that manipulate cellular signalling pathways and interfere with the host immune response. The NF-κB signalling cascade is an important mediator of innate immunity and inflammation, and is tightly regulated by ubiquitination at several key points. A critical step in NF-κB activation is the ubiquitination and degradation of the inhibitor of kappaB (IκBα), by the cellular SCFβ-TRCP ubiquitin ligase complex. We show here that upon stimulation with TNFα or IL-1β, Orthopoxvirus-infected cells displayed an accumulation of phosphorylated IκBα, indicating that NF-κB activation was inhibited during poxvirus infection. Ectromelia virus is the causative agent of lethal mousepox, a natural disease that is fatal in mice. Previously, we identified a family of four ectromelia virus genes (EVM002, EVM005, EVM154 and EVM165) that contain N-terminal ankyrin repeats and C-terminal F-box domains that interact with the cellular SCF ubiquitin ligase complex. Since degradation of IκBα is catalyzed by the SCFβ-TRCP ubiquitin ligase, we investigated the role of the ectromelia virus ankyrin/F-box protein, EVM005, in the regulation of NF-κB. Expression of Flag-EVM005 inhibited both TNFα- and IL-1β-stimulated IκBα degradation and p65 nuclear translocation. Inhibition of the NF-κB pathway by EVM005 was dependent on the F-box domain, and interaction with the SCF complex. Additionally, ectromelia virus devoid of EVM005 was shown to inhibit NF-κB activation, despite lacking the EVM005 open reading frame. Finally, ectromelia virus devoid of EVM005 was attenuated in both A/NCR and C57BL/6 mouse models, indicating that EVM005 is required for virulence and immune regulation in vivo.
| Poxviruses are large dsDNA viruses that are renowned for regulating cellular pathways and manipulating the host immune response, including the NF-κB pathway. NF-κB inhibition by poxviruses is a growing area of interest and this family of viruses has developed multiple mechanisms to manipulate the pathway. Here, we focus on regulation of the NF-κB pathway by ectromelia virus, the causative agent of mousepox. We demonstrate that ectromelia virus is a potent inhibitor of the NF-κB pathway. Previously, we identified a family of four ectromelia virus genes that contain N-terminal ankyrin repeats and a C-terminal F-box domain that interacts with the cellular SCF ubiquitin ligase. Significantly, expression of the ankyrin/F-box protein, EVM005, inhibited NF-κB, and the F-box domain was critical for NF-κB inhibition and interaction with the SCF complex. Ectromelia virus devoid of EVM005 still inhibited NF-κB, indicating that multiple gene products contribute to NF-κB inhibition. Importantly, mice infected with ectromelia virus lacking EVM005 had a robust immune response, leading to viral clearance during infection. The data present two mechanisms, one in which EVM005 inhibits NF-κB activation through manipulation of the host SCF ubiquitin ligase complex, and an additional, NF-κB-independent mechanism that drives virulence.
| The NF-κB family of transcription factors activate potent pro-inflammatory and anti-viral immune responses that are activated by a variety of signalling pathways [1], [2]. The family consists of five members, p50, p52, p65 (RelA), RelB, and c-Rel, which function as homo- or heterodimers to activate specific genes. The best-characterized NF-κB dimer is the p50/p65 heterodimer, which is held inactive in the cytoplasm by the inhibitor of κB (IκBα) [1], [2]. Signalling cascades initiated by both tumour necrosis factor α (TNFα) and interleukin 1β (IL-1β) trigger the activation of a set of kinases known as the IκB kinase (IKK) complex, which is composed of IKKα, IKKβ and IKKγ/NF-κB essential modifier (NEMO) [2]. Upon activation of the IKK complex, IKKβ phosphorylates IκBα on serines 32 and 36, targeting IκBα for polyubiquitination and degradation by the 26S proteasome [1], [2]. The SCF (Skp1/Cul1/F-box) ubiquitin ligase recruits phospho-IκBα through the F-box domain-containing adaptor protein, β-TRCP, resulting in the degradation of IκBα, and translocation of the p50/p65 heterodimer into the nucleus [1], [2].
Regulation of NF-κB signalling is common amongst most viruses, with each virus employing a combination of specifically tailored strategies [3]–[5]. For example, human immunodeficiency virus (HIV), human T-lymphotropic virus type 1 (HTLV-1), hepatitis B virus (HBV), and Epstein-Barr virus (EBV) activate the NF-κB signalling pathway [4]. Virus activation of the NF-κB pathway could serve several roles. For instance, viruses that lack anti-apoptotic mechanisms may activate NF-κB to prolong the life of the infected cell in order to complete the viral replication cycle. In the case of EBV, constitutive activation of NF-κB leads to the up-regulation of NF-κB-regulated pro-survival proteins during latency [6]. Alternatively, HIV-1 contains NF-κB binding sites in the long terminal repeat (LTR) region of the genome that mediate HIV-1 gene expression [7]. In contrast, other viruses encode proteins that specifically inhibit NF-κB signalling [3]–[5]. For example, the V and C proteins encoded by the Paramyxoviridae associate with the STAT family of transcription factors, thus inhibiting the interferon response and NF-κB activation [8]. Moreover, African Swine Fever Virus encodes a homolog to IκBα that sequesters p65 in the cytoplasm following IκBα degradation [9]. Overall, the varieties of viral proteins that manipulate NF-κB indicate the importance of the long and varied relationship with NF-κB.
The inhibition of NF-κB by poxviruses has become a growing area of interest [5]. The Poxviridae is composed of viruses possessing large dsDNA genomes, encoding between 150 to 300 open reading frames [10]. Poxviruses are unique amongst DNA viruses in that they replicate in the cytoplasm, within DNA-rich regions termed “virus factories” [10]. Members of the Orthopoxvirus genus are well studied, and include variola virus, vaccinia virus (VACV), monkeypox virus, cowpox virus (CPXV), and the mouse-specific pathogen, ectromelia virus (ECTV) [11]. Poxviruses are renowned for the plethora of immune evasion mechanisms they encode; including mechanisms that regulate NF-κB [5], [12], [13]. One of the first identified mediators of NF-κB activation was M-T2, a secreted soluble virus version of the tumor necrosis factor receptor (vTNFR) [14], [15]. Soluble vTNFRs and vIL-1Rs were subsequently identified in a variety of poxviruses [13]. More recently, focus has shifted to the identification of intracellular inhibitors of NF-κB encoded by poxviruses [5]. VACV encodes three proteins, K7, A46, and A52, which contain Toll/IL-1 receptor (TIR) cytoplasmic domains and disrupt NF-κB activation triggered through the IL-1/Toll receptor pathway [16]–[18]. Additionally, the VACV-encoded proteins, B14, M2, K1, A49, and N1, disrupt NF-κB activation triggered through the TNFR pathway [19]–[22]. These proteins function at different points in the signalling cascade, clearly highlighting the importance of NF-κB inhibition during infection [19]–[24].
Recently, we identified a family of four ankyrin/F-box proteins encoded by ECTV, EVM002, EVM005, EVM154 (recently renamed EVM159), and EVM165 (recently renamed EVM170) that interact with the cellular SCF ubiquitin ligase complex [25]. The ECTV-encoded proteins contain N-terminal ankyrin repeats in conjunction with a C-terminal F-box domain; similar viral ankyrin/F-box proteins have been found in a wide range of poxviruses [26]. To date, no cellular F-box proteins have been found in conjunction with ankyrin repeats, suggesting that poxviruses, including ECTV, have evolved a novel set of genes to regulate the cellular SCF ubiquitin ligase. Multiple orthologs for EVM002, EVM154, and EVM165 exist within the poxvirus family; however, EVM005 has only one ortholog, CPXV-BR011, in CPXV virus strain Brighton Red, suggesting that EVM005 and CPXV-BR011 may play an important role that is specific to ECTV and CPXV. Since degradation of IκBα is catalyzed by the SCFβ-TRCP ubiquitin ligase, we investigated the role of EVM005 in regulation of IκBα degradation. Here, we show that cells infected with ECTV and stimulated with TNFα or IL-1β accumulate phosphorylated IκBα, indicating that IκBα is stabilized and not degraded. Ectopic expression of Flag-EVM005 inhibited both TNFα- and IL-1β-stimulated IκBα degradation and subsequent nuclear translocation of NF-κB; however, deletion of the EVM005 F-box domain resulted in activation of NF-κB. ECTV devoid of EVM005, ECTV-Δ005, inhibited NF-κB activation. Finally, we demonstrated that EVM005 is a critical virulence factor, since ECTV-Δ005 was attenuated in both A/NCR and C57BL/6 mice compared to wild type ECTV. These data demonstrate a novel role for poxvirus-encoded ankyrin/F-box proteins in regulation of the SCF ubiquitin ligase and NF-κB signalling.
The NF-κB signalling cascade activates a family of transcription factors responsible for initiating the pro-inflammatory response and antiviral innate immunity [1], [2]. Recent evidence indicates that many poxviruses encode proteins that tightly regulate the activation of NF-κB through the expression of secreted and intracellular factors [5], [12]. Unlike strains of VACV, ECTV lacks M2, K7, B14, A49 and A52, all of which are important inhibitors of NF-κB activation [16], [18], [21], [23], [24]. Given the absence of these inhibitors, we sought to determine if ECTV infection inhibited NF-κB activation. Since the degradation of IκBα is crucial for activation of the NF-κB pathway, we examined the kinetics of IκBα degradation during infection. HeLa cells were mock-infected, or infected with ECTV or VACV for 12 hours and treated with TNFα for up to 120 minutes. Mock-infected cells treated with TNFα showed a typical pattern of IκBα degradation kinetics (Fig. 1A). As early as 10 minutes post-TNFα treatment, mock-infected cells showed phosphorylated IκBα, as indicated by a doublet, which was subsequently degraded (Fig. 1A and B) [27], [28]. ECTV- and VACV-infected cells treated with TNFα also showed obvious phosphorylation of IκBα (Fig. 1A and B); however, the levels of both IκBα and phospho-IκBα were sustained compared to mock-infected cells (Fig. 1A and B). Interestingly, we did observe that lower levels of phospho-IκBα accumulated in cells infected with VACV compared to ECTV, and accumulation was delayed in comparison to ECTV-infected cells (Fig. 1A). Western blotting for I5L, a late poxviral protein, and cellular β-tubulin were used as loading controls (Fig. 1C and D) [25]. Similar observations were also seen following treatment with IL-1β (Fig. 1E–H), indicating that members of Orthopoxvirus genera, including ECTV, sustained levels of phospho-IκBα and inhibited the degradation of IκBα.
Since IκBα appeared to be phosphorylated but not rapidly degraded during infection with ECTV or VACV, we sought to determine if NF-κB p65 was retained within the cytoplasm. HeLa cells were mock-infected or infected with ECTV or VACV, and p65 nuclear accumulation was assayed using immunofluorescence (Fig. 2A and D). Mock-infected cells demonstrated cytoplasmic retention of p65 in the absence of TNFα or IL-1β stimulation, as expected (Fig. 2A panels a–c and D panels m–o). In contrast, mock-infected cells stimulated with TNFα or IL-1β showed nuclear accumulation of p65 (Fig. 2A panels d–f and D panels p–r). Upon infection with ECTV or VACV, p65 was retained in the cytoplasm following treatment with TNFα or IL-1β, indicating that ECTV could inhibit NF-κB despite the lack of orthologs of M2, K7, B14, A49, and A52 (Fig. 2A panels g–l and D panels s–x). These data were confirmed by Western blotting cytoplasmic and nuclear extracts from infected HeLa cells with an antibody specific for p65 (Fig. 2B and E). As expected, p65 was absent from the nuclear extract of mock-infected cells. In contrast, mock-infected cells treated with TNFα or IL-1β showed nuclear p65 (Fig. 2B and E). Little p65 accumulation in the nuclear extract was observed in cells infected with ECTV or VACV and treated with TNFα or IL-1β (Fig. 2B and E). These results were also confirmed in mouse embryonic fibroblasts (MEF) (Fig. 2C and F). Together, these data indicate that NF-κB signalling is inhibited upon infection with members of the Orthopoxvirus genus. Importantly, despite the lack M2, K7, B14, A49 and A52 in ECTV, ECTV infection clearly inhibited p65 translocation to the nucleus.
Recently, we identified a family of four ankyrin/F-box proteins in ECTV (EVM002, EVM005, EVM154 and EVM165), which interact with the cellular SCF ubiquitin ligase (Fig. S1) [25]. The poxvirus family of ankyrin/F-box proteins differs substantially from the cellular F-box proteins. In contrast to the cellular F-box proteins, the poxviral F-box domains are found at the C-terminus in combination with N-terminal ankyrin repeats [25], [26], [29]–[34]. With the exception of EVM005, which has only one ortholog in cowpox virus Brighton Red, CPXVBR011, multiple orthologs exist for EVM002, EVM154 and EVM165. Since the SCFβ-TRCP ubiquitin ligase plays an essential role degrading phospho-IκBα, we sought to determine if EVM005 could inhibit IκBα degradation and NF-κB activation during ECTV infection [27], [28].
We first tested the ability of EVM005 to inhibit the nuclear accumulation of NF-κB p65. HeLa cells were mock-transfected or transfected with full length Flag-EVM005. At 12 hours post-transfection, cells were stimulated with TNFα or IL-1β for 20 minutes and nuclear accumulation of p65 was detected using immunofluorescence (Fig. 3). As expected, unstimulated HeLa cells demonstrated cytoplasmic staining of p65 (Fig. 3A panels a–c and B panels m–o), and strong nuclear accumulation of p65 was seen following TNFα and IL-1β stimulation (Fig. 3A panels d–f and B panels p–r). In contrast, cells expressing Flag-EVM005 that were stimulated with TNFα or IL-1β strongly inhibited p65 nuclear accumulation (Fig. 3A panels g–i and B panels s–u). Given the importance of the F-box domain in associating with the SCF ubiquitin ligase, we wanted to determine whether this domain also contributed to the inhibition of NF-κB activation. To do this, we utilized an EVM005 mutant, Flag-EVM005(1-593), which lacks the C-terminal F-box-like domain and fails to interact with the SCF ubiquitin ligase [25]. Interestingly, cells expressing Flag-EVM005(1-593) displayed strong nuclear staining of p65 following TNFα or IL-1β stimulation (Fig. 3A panels j–l and B panels v–x). Nuclear translocation of p65 was quantified by counting cells from three independent experiments (Fig. 3C). These data indicate that expression of Flag-EVM005 inhibited both TNFα- and IL-1β-induced nuclear accumulation of p65, and that inhibition of p65 nuclear accumulation required a functional F-box domain.
Since transient expression of EVM005 inhibited p65 translocation (Fig. 3), we sought to determine if EVM005 stabilized IκBα. HeLa cells were transfected with Flag-EVM005 and IκBα was visualized using immunofluorescence (Fig. 4A). As expected, in unstimulated cells IκBα was present within the cytoplasm (Fig. 4A panel a–d). Following 20 minutes of treatment with TNFα, the level of IκBα within the cytoplasm dramatically decreased as a result of ubiquitination and degradation of IκBα (Fig. 4A panel e–h) [27], [28]. Expression of Flag-EVM005 in the absence of TNFα stimulation showed that the levels of IκBα were unaffected (Fig. 4A panel i–l). In contrast, cells expressing Flag-EVM005 and stimulated with TNFα demonstrated that ectopic expression of EVM005 stabilized IκBα compared to the surrounding cells (Fig. 4A panel m–p). Levels of IκBα were unaffected by expression of Flag-EVM005(1-593) (Fig. 4A panel q–t); however, upon treatment with TNFα, IκBα was degraded, suggesting that the F-box domain was necessary for EVM005 to inhibit IκBα degradation (Fig. 4A panel u–x). To further confirm these data, HeLa cells were mock-transfected or transfected with Flag-EVM005 in the absence or presence of TNFα and immunoblotted for anti-IκBα, anti-Flag to detect EVM005, and anti-β-tubulin as a loading control. Compared to unstimulated cells, which showed a significant level of IκBα, cells treated with TNFα showed decreased levels of IκBα (Fig. 4B). Expression of EVM005 led to the stabilization of IκBα (Fig. 4B). Finally, we tested the ability of EVM005 to inhibit IκBα degradation by flow cytometry. HeLa cells were mock-transfected, or transfected with Flag-EVM005 or Flag-EVM005(1-593) [25]. Twenty-four hours post-transfection, cells were stimulated with TNFα or IL-1β, and fixed and stained with anti-Flag and anti-IκBα, to detect EVM005 and IκBα, respectively. Flag-positive cells were gated for analysis (Fig. 4C panels b and e). Untransfected cells demonstrated levels of IκBα that were significantly decreased following TNFα or IL-1β stimulation, as indicated by a leftward shift on the histogram (shown in green) (Fig. 4C panels a and d). Pre-treatment of HeLa cells with the proteasome inhibitor MG132, and subsequent TNFα or IL-1β stimulation (shown in blue) inhibited the degradation of IκBα, as expected (Fig. 4C panel a and d) [27]. HeLa cells expressing Flag-EVM005 and stimulated with TNFα or IL-1β inhibited IκBα degradation (Fig. 4C panels b and e); however, expression of Flag-EVM005(1-593) was unable to stabilize IκBα, resulting in degradation of IκBα (Fig. 4C panels c and f). This experiment was repeated in triplicate and these data were quantified by measuring the percentage of cells that underwent IκBα degradation (Fig. 4D). These data indicated that Flag-EVM005 strongly inhibits TNFα- and IL-1β-induced IκBα degradation, while the F-box deletion mutant failed to inhibit IκBα degradation (Fig. 4). Together, these data show that EVM005 expression blocked IκBα degradation in an F-box-dependent manner.
To further examine the role of EVM005 during infection, we generated an EVM005 deletion virus, ECTV-Δ005. In the past, deletion of open reading frames from poxvirus genomes has been performed by inserting drug selection or fluorescent markers into the gene of interest. Instead, we used the novel Selectable and Excisable Marker system that utilizes the Cre recombinase to delete the selection markers resulting a clean deletion of the targeted open reading frame [35], [36]. A cassette containing yellow fluorescent protein fused to guanine phosphoribosyl transferase (yfp-gpt) was inserted into the EVM005 locus. To generate a marker-free EVM005 deletion virus, ECTV-Δ005, we removed the yfp-gpt marker by infecting U20S cells that stably expressed a cytoplasmic mutant of the Cre recombinase (Fig. S2) [36]. Additionally, two revertant viruses were generated by replacing the yfp-gpt cassette with either wild type EVM005 or EVM005(1-593), a mutant lacking the F-box domain. PCR amplification of the EVM005 locus from viral genomes was used to screen for the purity of our viral products (Fig. S2). Using a multi-step growth curve, no growth defects were detected upon infection with ECTV, ECTV-Δ005, ECTV-005-rev or ECTV-005(1-593)-rev (Fig. S2).
To determine if ECTV devoid of EVM005 could still inhibit nuclear accumulation of p65 following stimulation with TNFα, HeLa cells were mock-infected, or infected with ECTV or ECTV-Δ005. Immunofluorescence revealed that infection with ECTV-Δ005 inhibited NF-κB p65 nuclear accumulation (Fig. 5A panels j–l). This was further supported by nuclear and cytoplasmic extracts in both HeLa (Fig. 5B) and MEF cells (Fig. 5C). We also examined the effect of ECTV and ECTV-Δ005 infection on the production of NF-κB-regulated transcripts. HeLa cells were mock-infected, or infected with ECTV or ECTV-Δ005 at a MOI of 5. At 12 hours post-infection, cells were stimulated with TNFα, and RNA samples were collected at 0, 2, 4, and 6 hours post-TNFα treatment. Samples were screened for the relative levels of RNA transcripts corresponding to TNFα, IL-1β, and IL-6; genes known to be upregulated by NF-κB [37]. All samples are presented as relative units compared to GAPDH as well as the unstimulated or 0 hour time point within each sample. Mock-infected cells displayed an increase in TNFα, IL-1β, and IL-6 transcript levels at 2 hours post-TNFα stimulation, as expected (Fig. 6). Transcript levels decreased at 4 and 6 hours post-stimulation, due to the up-regulation of NF-κB inhibitors such as IκBα (Fig. 1A) [38]. In contrast, infection with ECTV and ECTV-Δ005 prevented transcriptional upregulation of TNFα, IL-1β, and IL-6 (Fig. 6). We additionally screened our 0 hour time points to compare basal levels of NF-κB transcripts between samples (Fig. S5). This analysis, demonstrated that basal levels of TNFα, IL-1β and IL-6 were higher in infected cells compared to mock infected cells, however, we were still unable to detect any significant changes between cells infected with ECTV versus cells infected with ECTV-Δ005 (Fig. S5). These data correlated with our previous data indicating that infection with either ECTV or ECTV-Δ005 inhibited the nuclear accumulation of NF-κB p65.
Finally, we looked upstream at IκBα levels. HeLa cells were infected with ECTV, ECTV-Δ005, or ECTV-005-rev. At 12 hours post-infection, cells were stimulated with TNFα, fixed and stained with anti-IκBα or anti-I3L, an early poxvirus protein that is indicative of infection, and analyzed by flow cytometry. Unstimulated cells (shown in black) demonstrated high levels of IκBα that decreased following TNFα stimulation (shown in green) (Fig. 7A panel a). As expected, TNFα-stimulated mock-infected cells that were pre-treated with the proteasome inhibitor MG132 maintained IκBα levels (shown in blue) (Fig. 7A panel a). ECTV-infected cells stimulated with TNFα also indicated no change in the level of IκBα, lending further support that IκBα is not degraded in cells infected with ECTV (Fig. 7A panel b). Additionally, TNFα-stimulated cells infected with ECTV-Δ005 or ECTV-005-rev also inhibited IκBα degradation (Fig. 7A panels c and d). These data were quantified by measuring the percentage of cells with IκBα expression from three independent experiments to obtain standard errors (Fig. 7B). Overall, despite lacking EVM005, ECTV-Δ005 inhibits IκBα degradation.
Since EVM005 is one of four ankyrin/F-box proteins in ECTV, it is possible that deletion of more than one open reading frame may be necessary to render ECTV susceptible to TNFα-induced NF-κB activation and degradation of IκBα. Therefore, we used the Selectable and Excisable Marker system to excise four open reading frames, EVM002, EVM003, EVM004, and EVM005, from the left end of the ECTV genome (Fig. S3 and S4) [36]. Notably, EVM002 and EVM003 are duplicated genes that are encoded on both ends of the ECTV genome. ECTV-Δ002-005 is depleted of both copies of EVM002, but only the left end copy of EVM003 (Fig. S4). EVM002 is an ECTV-encoded ankyrin/F-box protein that interacts with the SCF ubiquitin ligase and inhibits NF-κB activation by interacting with NF-κB1/p105, a member of the IκB family of proteins [25], [31], [39]. Deletion of EVM002 from ECTV led to slightly increased NF-κB levels in vivo, contributing to decreased virulence, potentially through low level paracrine stimulation of interferon and NF-κB in neighbouring cells [40]. Significantly, deletion of the EVM002 ortholog, CPXV006, from CPXV, rendered CPXV susceptible to NF-κB activation [39]. EVM003 encodes a vTNFR, but a copy of this gene is present at both ends of the genome. Thus, even though EVM003 was deleted from the left end of the genome, EVM003 is still expressed from the right end (Fig. S4). EVM004 encodes a BTB-only protein with unknown function [41], [42]. We tested the ability of this virus, lacking two ankyrin/F-box proteins that inhibit NF-κB activation, to inhibit IκBα degradation. HeLa cells were mock-infected, or infected with ECTV, single deletion strains ECTV-Δ002, ECTV-Δ005, or the large deletion strain ECTV-Δ002-005, and analyzed for their ability to protect against TNFα-induced IκBα degradation using flow cytometry (Fig. 7C). Following stimulation with TNFα, ECTV, ECTV-Δ002, and ECTV-Δ005, inhibited IκBα degradation (Fig. 7C panels j–l). Additionally, IκBα was still protected from degradation in cells infected with ECTV-Δ002-005 (Fig. 7C panel m). As before, staining with anti-I3L indicated virus infection (Fig. 7C panels n–r). These data were quantified by measuring the percentage of cells with IκBα expression from three independent experiments to obtain standard errors (Fig. 7D). These data indicate that deletion of more than two ankyrin/F-box proteins, and potentially other ECTV encoded NF-κB inhibitors, may be necessary to render ECTV susceptible to TNFα-induced NF-κB activation.
To determine if EVM005 was required for virulence, we used A/NCR or C57BL/6 mouse strains. A/NCR mice are highly susceptible to lethal infection by all evaluated routes, including the footpad, whereas C57BL/6 mice are only susceptible to lethal infection via the intranasal route [43]–[45]. Groups of five female C57BL/6 mice were mock-infected, or infected with 10-fold escalating doses of ECTV, ECTV-Δ005, ECTV-005-rev, or ECTV-005(1-593)-rev via the intranasal route with doses ranging between 102 and 104 pfu (Fig. S6). Following infection, body weight and mortality were monitored daily. Data from one challenge dose is displayed (Fig. 8A and B). C57BL/6 mice infected with ECTV, ECTV-005-rev, or ECTV-005(1-593)-rev succumbed to infection between day seven and ten; however, mice infected with ECTV-Δ005 survived through day 21 (Fig. 8A). C57BL/6 mice infected with ECTV-Δ005 displayed an initial weight loss through day 13, followed by weight gain similar to naive mice by day 21 (Fig. 8B).
We also assessed the contribution of EVM005 to virulence in the A/NCR mouse strain [44], [45]. Five female A/NCR mice were mock-infected, or infected with ECTV, ECTV-Δ005, ECTV-005-rev, or ECTV-005(1-593)-rev via the footpad [44]. We infected sets of five mice with escalating 10-fold doses between 101 and 104 pfu per mouse and monitored daily changes in body weight, day of death and mortality (Fig. S7). Data from one challenge dose is displayed (Fig. 8C and D). Similar to the data observed in C57BL/6 mice (Fig. 8A and B), ECTV-Δ005 was attenuated in A/NCR mice compared to wild type ECTV, ECTV-005-rev, and ECTV-005(1-593)-rev (Fig. 8C and D). The data demonstrated that mice infected with ECTV, ECTV-005-rev, and ECTV-005(1-593)-rev succumbed to infection by day 7 post-infection. Alternatively, two of five mice infected with ECTV-Δ005 survived through day 21 (Fig. 8C and D). Together, the results suggest that EVM005 is a critical virulence factor for infection of two mouse strains by two different routes of infection. Notably, mice infected with ECTV-005(1-593)-rev displayed similar mortality and weight loss profiles to mice infected with wild type ECTV and ECTV-005-rev, suggesting that although the F-box domain was necessary for inhibition of the NF-κB pathway in vitro, the ankyrin domains alone are sufficient for virulence.
Though EVM005 was a potent inhibitor of NF-κB activation in tissue culture, deletion of EVM005 did not abrogate the ability of ECTV to prevent activation of NF-κB. Since tissue culture assays lack many components of the immune response, we wanted to explore the contribution of EVM005 to immune inhibition and virulence in vivo. To monitor virus spread and activation of the immune response, C57BL/6 mice were infected via intranasal inoculation with ECTV or ECTV-Δ005, and sacrificed at days 3, 4, and 7 post-infection (Fig. 9). Tissue from the spleen, liver, lungs, and kidneys was harvested, and the amount of virus present was determined by plaque assay (Fig. 9A and B). At 4 days post-infection, ECTV and ECTV-Δ005 showed no significant difference in growth rate in all organs tested (Fig. 9A); however, at 7 days post-infection, ECTV had grown to significantly higher levels than ECTV-Δ005 in lung, kidney and liver tissues (Fig. 9B). Notably, the decrease in viral spread correlates well with the decreased mortality previously described (Fig. 8A).
To measure the immune response, whole blood and splenocytes were harvested on days 3 and 7 post-infection and immune cell populations were quantified using flow cytometry (Fig. 9C–F). In mice infected with ECTV-Δ005, there was a significant increase in circulating and splenic NK cells at day 7 compared to ECTV-infected mice (Fig. 9C and E). Additionally, we observed a significant increase in circulating virus-specific CD8+ T-cells at day 7 post-infection in mice infected with ECTV-Δ005 compared to those infected with ECTV (Fig. 9F). Notably, we did not observe an increase in virus-specific CD8+ T-cells in the spleen on day 7 (Fig. 9D). The data suggest that the virus-specific CD8+ T-cells are being activated and expanded in non-splenic tissues before entering circulation. Finally, we assayed for transcriptional upregulation of NF-κB-regulated genes in liver and spleen on day 7 post-infection (Fig. 9G and H). Transcriptional upregulation of TNFα, IL-1β, and IL-6 was determined by harvesting RNA from tissue samples and subjecting it to qRT-PCR. Mice infected with ECTV-Δ005 did not demonstrate an increase in NF-κB-regulated transcripts compared to ECTV-infected mice. These observations support a role for EVM005 in regulating virulence that is independent of its ability to inhibit NF-κB activation. Together these data indicate that mice infected with ECTV-Δ005 displayed boosted immune cell repertoires, increased viral clearance, and decreased mortality compared to mice infected with wild type ECTV.
The NF-κB family of transcription factors regulate a variety of genes involved in inflammation and innate immunity [1]. Not surprisingly, viruses have evolved multiple mechanisms to regulate NF-κB [3], [46], and a growing number of poxviral NF-κB inhibitors can be added to this list [5]. Previously, we identified four ankyrin/F-box proteins in ECTV that interact with the SCF ubiquitin ligase via C-terminal F-box domains; potentially recruiting a unique set of proteins to the SCF ubiquitin ligase [25]. The NF-κB signalling pathway is dependent on the SCF ubiquitin ligase for ubiquitination and degradation of the inhibitory protein, IκBα [47]. Here we demonstrate that IκBα is phosphorylated but not degraded during ECTV infection, suggesting that signalling is inhibited at the point of IκBα ubiquitination, an event mediated by the SCF ubiquitin ligase (Fig. 1). Additionally, we demonstrate that the ECTV-encoded ankyrin/F-box protein, EVM005, inhibits p65 nuclear accumulation and IκBα degradation in a process that requires its C-terminal F-box domain (Fig. 3 and 4). From this, we conclude that EVM005 is an inhibitor of NF-κB activation through manipulation of the SCF ubiquitin ligase. ECTV lacking the EVM005 open reading frame, ECTV-Δ005, was created and tested for its ability to inhibit NF-κB activation (Fig. 5–7). Even though EVM005 was deleted from the genome, ECTV-Δ005 still inhibited p65 nuclear accumulation (Fig. 5), the production of NF-κB-regulated transcripts (Fig. 6), and degradation of IκBα in tissue culture (Fig. 7). Significantly, ECTV lacking EVM005 was attenuated in both A/NCR and C57BL/6 mouse strains, indicating an additional NF-κB-independent mechanism for EVM0005 (Fig. 8). Interestingly, ECTV expressing a mutant of EVM005 lacking the F-box domain was still virulent, demonstrating that the ankyrin domains alone were sufficient for virulence (Fig. 8). Mice infected with ECTV devoid of EVM005 were able to mount a stronger immune response, consisting of higher numbers of NK cells and virus-specific CD8+ T-cells (Fig. 9). A strong immune response is most likely responsible for virus clearance and decreased mortality of mice, and the observed decrease in virus spread to the liver, lungs, spleen, and kidneys (Fig. 9).
EVM005 is one of many open reading frames encoded by ECTV that inhibits NF-κB activation. Given the plethora of poxvirus-encoded inhibitors of NF-κB, the deletion of multiple open reading frames is likely required to render ECTV susceptible to NF-κB activation. In an attempt to create a strain of ECTV that was unable to inhibit TNFα-induced NF-κB activation, we deleted four open reading frames from the left end of the ECTV genome, including EVM002, EVM003, EVM004, and EVM005 [36]. Large deletion strains of VACV, such as VACV811, and modified vaccinia virus Ankara (MVA), have been tremendous tools for the characterization of novel poxvirus-host interactions [48], [49]. Although VACV811 is missing 55 open reading frames, this virus is capable of inhibiting NF-κB activation [50]. MVA is an attenuated strain of VACV that has been passaged over 500 times in chicken embryonic fibroblasts and has acquired numerous gene deletions, truncations, and point mutations [49]. MVA is the only large deletion virus that has been rendered susceptible to TNFα induced NF-κB activation [51]. Our large deletion strain of ECTV, ECTV-Δ002-005, was able to inhibit TNFα-induced IκBα degradation (Fig. 7C and D). EVM002 is an ankyrin/F-box protein that we have previously shown to interact with the SCF ubiquitin ligase and inhibit p65 nuclear accumulation [25], [31]. These data suggest that deletion of more than two ankyrin/F-box proteins, and potentially other ECTV-encoded inhibitors of NF-κB activation, would be required to render ECTV susceptible to NF-κB activation. Creation of an ECTV strain unable to inhibit NF-κB activation would allow us to investigate how ECTV infection triggers NF-κB activation, since little is known about how poxviruses activate this pathway.
Regulation of the NF-κB pathway by poxviruses has been investigated, and a variety of unique NF-κB inhibitors have been found [5], [46]. These inhibitors include poxvirus-secreted proteins, such as the soluble vTNFR and vIL-1R [13]–[15], as well as eight VACV-encoded proteins that act within the cell, including M2, K1, B14, N1, K7, A46, A49, and A52 [16]–[22], [24]. Of the known virus encoded inhibitors of NF-κB, only K1, N1 and A46 contain orthologs in ECTV [16], [17], [19]–[22]. That ECTV is missing many NF-κB inhibitors is perhaps what contributes to the variation observed in phospho-IκBα accumulation between ECTV and VACV, where VACV-infected HeLa cells showed lower levels of phospho-IκBα accumulation, and accumulation was delayed in comparison to ECTV-infected cells (Fig. 1A). This observation may be linked to the additional upstream inhibitors encoded by VACV.
Our data demonstrate an accumulation of phospho-IκBα in ECTV-infected cells that is linked to regulation of the cellular SCF ubiquitin ligase by poxviral ankyrin/F-box proteins. The cellular F-box protein, β-TRCP, recognizes phospho-IκBα in uninfected cells and mediates ubiquitination and subsequent degradation via the 26S proteasome [52]. Though ECTV encodes four ankyrin/F-box proteins [25], [34], we tested the ability of EVM005 to inhibit NF-κB signalling, since it is unique to ECTV and CPXV [25]. Our data demonstrate that EVM005 inhibited IκBα degradation, perhaps through competition with β-TRCP for available Skp1 binding sites at the SCF ubiquitin ligase. This competition would disrupt the association between Skp1 and β-TRCP, an interaction that is required for IκBα ubiquitination and degradation. This idea is consistent with our data demonstrating the requirement of the F-box domain by EVM005 in order to inhibit degradation of IκBα (Fig. 4 and 5). In a similar fashion, HIV-encoded Vpu disrupts the association between β-TRCP and Skp1, thus inhibiting the ubiquitination and degradation of IκBα [53]. Notably, the VACV protein A49 inhibits NF-κB signalling by binding to β-TRCP in a similar fashion to Vpu [24]. This represents a fascinating example of convergent evolution, since both EVM005 and A49 serve similar functions to inhibit NF-κB signalling, but by targeting different proteins within the SCF ubiquitin ligase. Notably, an EVM005 ortholog is not encoded by VACV, and A49 is not encoded by ECTV, demonstrating the importance of regulating NF-κB through the SCF ubiquitin ligase. Of note, our data do not rule out the possibility that EVM005 recruits substrates that are involved in NF-κB activation for ubiquitination; however, we were unable to detect degradation of IκBα, NF-κB1 p50/105, or p65 in ECTV-infected cells (N. van Buuren and M. Barry unpublished data).
Regulation of NF-κB activation by poxviral ankyrin/F-box proteins has been investigated for ECTV-encoded EVM002, CPXV-encoded proteins, CP77 and CPXV006, and the variola protein, G1R [29], [31], [39], [40]. Similar to EVM005, these proteins interact with the cellular SCF ubiquitin ligase [29], [31]. In contrast to EVM005, the mechanism by which G1R inhibits NF-κB activation does not depend on the F-box domain [31], [39]. Instead, G1R and its orthologs, CPXV006 and EVM002, bind to the N-terminus of p105, an inhibitory protein similar to IκBα, to prevent TNFα-induced degradation [31]. Degradation of p105 is generally mediated by the SCFβ-TRCP ubiquitin ligase following TNFα stimulation, similar to IκBα [54]. Deletion of CPXV006, a G1R ortholog encoded by CPXV, rendered CPXV susceptible to NF-κB activation [39]. Additionally, ECTV lacking EVM002 demonstrated decreased virulence and slightly increased levels of NF-κB activation in vivo [40]. We demonstrated that ECTV lacking EVM002 still inhibited IκBα degradation in tissue culture, demonstrating that the ECTV ankyrin/F-box proteins act collectively to inhibit IκBα degradation (Fig. 7C and D). In contrast, ECTV lacking EVM005 was still a potent inhibitor of NF-κB activation in culture and in vivo (Fig. 5–7, and 9). CP77 contains a shortened F-box domain that is necessary to inhibit NF-κB activation [29]. Additionally, CP77 binds to free p65 through its ankyrin repeat domains. The model suggests that CP77 replaces the regulatory protein IκBα, following its degradation, holding the NF-κB transcription factor, p65, inactive in the cytoplasm [29]. In contrast, we were unable to detect an interaction between EVM005 with p65, p50/105 or IκBα (N. van Buuren and M. Barry, unpublished data). Similar to our data with EVM005, CP77 serves a dual role for CPXV as a host range factor in addition to its role in the inhibition of NF-κB activation [55], [56]. It is clear that the ankyrin domains play a major role for most of the ankyrin/F-box proteins described to date, and this is consistent with the virulent phenotype of ECTV-005(1-593)-rev (Fig. 8). Discovery of binding partners for the ankyrin domains of EVM005 will likely provide insight to the mechanism of virulence controlled by EVM005. Together, the data suggest that the poxvirus encoded ankyrin/F-box proteins possess unique mechanisms to regulate NF-κB activation.
Although poxviral ankyrin/F-box proteins associate with Skp1 in the SCF ubiquitin ligase through their F-box domains, identification of substrates recruited for ubiquitination has eluded the field. Additionally, of the sixty-nine cellular F-box proteins encoded in the human genome, substrates have been identified for only nine [57]. The poxviral F-box proteins are suspected to function as substrate adaptor molecules for the SCF ubiquitin ligase, using their unique ankyrin domains to recruit still unidentified cellular or viral target proteins. Although binding partners, other than Skp1, have been identified for several of the poxvirus ankyrin/F-box proteins, none of these identified proteins have been characterized as bona fide substrates for ubiquitination [30], [31], [55]. In support of the substrate hypothesis, the ankyrin-only mutant virus, ECTV-005(1-593)-rev, was still virulent, supporting a critical role for the ankyrin domains, potentially in substrate recruitment. The data presented in this paper suggest a mechanism in which EVM005 inhibits degradation of cellular substrates, such as IκBα. This suggests an alternative mechanism for the poxviral ankyrin/F-box proteins as inhibitors of the SCF ubiquitin ligase.
Finally, we determined that EVM005 was a required virulence factor for ECTV during infection of C57BL/6 and A/NCR mice. However, ECTV-Δ005 was capable of inhibiting IκBα degradation, p65 nuclear accumulation and the synthesis of NF-κB regulated transcripts (Fig. 5, 6 and 7). These data suggest that an EVM005 function independent of NF-κB inhibition is responsible for mediating virulence during ECTV infection. To this end, we demonstrated that ECTV-Δ005 spread in vivo was suppressed compared to ECTV and that this observation correlated with increased immune cell activation (Fig. 9). It is possible that EVM005 regulates the immune response in vivo. At this time, any regulation of the immune response appears to be independent of NF-κB activation as we were unable to detect increased transcription of TNFα, IL-1β or IL-6 in spleens or livers of infected mice on day 7 (Fig. 9). We hypothesize that EVM005 is recruiting substrates to the SCF ubiquitin ligase through its ankyrin domains. Infection of mice with ECTV-005(1-593)-rev demonstrated that expression of the ankyrin domains alone was sufficient for virulence in both the A/NCR and C57BL/6 mice. Potentially the ankyrin-only mutant is still able to bind and sequester these hypothetical SCF substrates and that sequestration alone was sufficient for virulence. If the poxvirus-encoded ankyrin/F-box proteins truly function as substrate adaptors for the cellular SCF ubiquitin ligase, the identification of substrates through proteomics approaches could lead to insight into how EVM005 aids in virulence. Additionally, as mice infected with ECTV-Δ005 demonstrated increased immune responses, we feel that it is therefore likely that these hypothetical target substrates function in immune cell regulation.
In conclusion, our data show that ECTV-encoded EVM005 is a unique inhibitor of NF-κB activation and also suggests the existence of an NF-κB-independent mechanism for EVM005 to contribute to virulence and inhibition of immune activation. In contrast to previously characterized poxviral inhibitors of NF-κB, EVM005 requires its C-terminal F-box domain to manipulate the cellular SCF ubiquitin ligase and inhibit IκBα degradation. Further characterization of the NF-κB-independent mechanism of virulence mediated by EVM005 as well as the identification of ubiquitinated substrate proteins remains a goal of our laboratory.
HeLa, mouse embryonic fibroblast (MEF), and Baby Green Monkey Kidney (BGMK) cells were obtained from the American Type Culture Collection. U20S-Cre cells were generously provided by Dr. John Bell (University of Ottawa, Ottawa, Canada). HeLa and U20S-Cre cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum, 50 U/ml of penicillin, 50 µg/ml of streptomycin and 200 µM glutamine (Invitrogen Corporation). MEF cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum, 50 U/ml of penicillin, 50 µg/ml of streptomycin, 200 µM glutamine, and 10 µM non-essential amino acids (Invitrogen Corporation). BGMK cells were cultured in DMEM supplemented with 10% newborn calf serum, 50 U/ml of penicillin, 50 µg/ml of streptomycin and 200 µM glutamine. Vaccinia virus strain Copenhagen (VACV), and ectromelia virus strain Moscow (ECTV) were propagated in BGMK cells and harvested as previously described [58].
Construction of pcDNA3-Flag-EVM005 and pcDNA3-Flag-EVM005(1-593) were previously described [25]. Construction of pDGloxP-EVM005KO was performed by amplification of 150 base pairs of DNA upstream and downstream of EVM005 from ECTV genomic DNA using Taq polymerase (Invitrogen Corporation). The upstream region of homology was amplified with the following forward 5′-(HindIII)-AAGCTTCTCTACAAAGTATAATATATT-3′ and reverse 5′-(XhoI)-CTCGAGATATTATACATATTAGATGTG-3′ primers. The downstream region of homology was amplified using the following forward 5′-(NotI)-GCGGCCGCTCGT ACCCGCGAACAAAATAG-3′ and reverse 5′-(BamHI)-GGATCCTTTTTTATAAACGATA TTGTT-3′ primers. The 150 base pair fragments were cloned into pGEM-T (Promega). The upstream region of homology was subcloned in to the pDGloxP vector using XhoI and HindIII restriction sites. The downstream region of homology was subcloned into the BamHI and NotI restriction sites, to create pDGloxP-EVM005KO. To clone pGEMT-EVM005-reverant the forward, 5′-ATCAATGGCCGTCTCGAT-3′, and reverse 5′-AAGAAACAAGATACAAGA-3′ primers were used to amplify a 2787 bp PCR product from wild type ECTV viral genomic DNA using LongAmp Taq (New England Biolabs). The resulting PCR product was subsequently cloned into pGEMT (Promega). To clone pDG-loxP-EVM002KO, 150 bp of DNA at the 5′ end of the EVM002 open reading frame were amplified by PCR using Taq polymerase (Invitrogen Corporation) and the forward primer, 5′-(HindIII)-AAGCTTCTCATAATGATTTACTTTTTC-3′ and the reverse primer, 5′-(XhoI)-CTCGAGCGATTCCGTCCAAGATGATAA-3′. The 150 bp of DNA at the 3′ end of the EVM002 open reading frame were amplified with the forward primer, 5′-(NotI)-GCGGCCGCGGTGCTATATCTTTTCCGTTT-3′, and the reverse primer, 5′-(BamHI)-GGATCCTAGAAAGAAAATATTTAAAAA-3′. The 5′ and 3′ 150 bp regions of homology were TA cloned into pGEMT (Promega) following PCR. The 5′ and 3′ regions of homology were then subcloned one at a time into the pDGloxPKO vector using HindIII and XhoI, followed by BamHI and NotI, for the 5′ and 3′ sides, respectively.
BGMK cells were infected with ECTV at a MOI of 0.01 and transfected with 10 µg of linearized pDGloxP-EVM005KO or pDGloxP-EVM002KO using Lipofectamine 2000 (Invitrogen Corporation) [35], [36]. Recombinant ECTV-Δ005-YFP-GPT or ECTV-Δ002-YFP-GPT were selected in BGMK media containing 250 µg/ml xanthine (Sigma-Aldrich), 15 µg/ml hypoxanthine (Sigma-Aldrich), and 25 µg/ml mycophenolic acid (MPA) (Sigma-Aldrich). Drug resistance and YFP fluorescence were used to select recombinant viruses. Removal of the yfp-gpt marker cassette from ECTV-Δ005-YFP-GPT or ECTV-Δ002-YFP-GPT was performed using U20S cells stably expressing a cytoplasmic mutant of the Cre recombinase (U20S-Cre) (provided by Dr. J. Bell, University of Ottawa). White ECTV foci, lacking YFP-GPT expression were selected and purified to create ECTV-Δ005 and ECTV-Δ002. ECTV-005-rev and ECTV-005(1-593)-rev were cloned by infecting BGMK cells with ECTV-Δ005-YFP-GPT at an MOI of 0.01 followed by transfection with pGEMT-EVM005-rev or pGEMT-EVM005(1-593)-rev plasmids. Infected cells were harvested at 48 hours post-infection and recombinant ECTV-005-rev or ECTV-005(1-593)-rev were selected through lack of YFP fluorescence using a fluorescent inverted microscope and a FITC filter (Leica).
PCR analysis of viral genomes verified insertion and deletion of the yfp-gpt cassette. Taq polymerase and forward 5′-(HindIII)-AAGCTTCTCTACAAAGTATAATATATT-3′ and reverse 5′-(BamHI)-GGATCCTTTTTTATAAACGATATTGTT-3′ primers were used to amplify the EVM005 locus. A multi-step growth curve was used to analyze the growth of ECTV-Δ005, ECTV-005-rev and ECTV-005(1-593)-rev on BGMK cells. BGMK cells were infected with ECTV, ECTV-Δ005, ECTV-005-rev or ECTV-005(1-593)-rev at an MOI of 0.05 to perform the multi-step growth curve. Virus growth was assayed using plaque assays from samples collected at indicated time points.
To create the large deletion strain of ECTV, lacking EVM002, EVM003, EVM004 and EVM005 from the left end of the genome, we used sequential insertion and Cre-mediated excision of the yfp-gpt cassette (Fig. S3). Following Cre-mediated excision, one residual loxP site remains in place of the yfp-gpt cassette. BGMK cells were infected with ECTV-Δ002 at a MOI of 0.01 and transfected with 10 µg of linearized pDGloxP-EVM005KO using Lipofectamine 2000 (Invitrogen Corporation). YFP-GPT positive virus was selected as described above to create ECTV-Δ002/Δ005-YFP-GPT. To delete EVM002, EVM003, EVM004 and EVM005, U20S-Cre cells were infected with ECTV-Δ002/Δ005-YFP-GPT at a MOI of 0.01 and white foci were selected using immunofluorescence. The resulting virus, ECTV-Δ002-005, lacks all sequences between the residual loxP site in the EVM002 locus and the new loxP site at the right side of the EVM005 locus introduced during recombination with pDGloxP-EVM005KO. PCR analysis of the EVM002, EVM003, EVM004 and EVM005 loci confirmed the identity and purity of this large deletion strain of ECTV (Fig. S4).
Mouse and rabbit anti-Flag M2 were purchased from Sigma-Aldrich, anti-poly(ADP-ribose) polymerase (PARP) was purchased by (BD Biosciences) and anti-β-tubulin was purchased from ECM Biosciences. Antibodies specific for Skp1 and I5L were previously described [25], [59] Antibodies recognizing the early poxvirus protein I3L were generously donated by Dr. David Evans (University of Alberta). Antibodies recognizing IκBα and phospho-IκBα were purchased from Cell Signalling Technologies. Anti-NF-κB p65 was purchased from Santa Cruz Biotechnology. Antibodies that detected cell surface markers CD45, NK1.1, CD3, and CD8 were purchased from BD Biosciences. An APC labeled tetramer specific to the immunodominant epitope for VACV B8R/CD8 T cell expression was obtained from the NIH tetramer facility.
HeLa cells or MEF cells were mock-infected or infected with ECTV, VACV, or ECTV-Δ005 at a MOI of 5 for 12 hours followed by stimulation with either 10 ng/ml TNFα (Roche) or 10 ng/ml IL-1β (PeproTech Inc) for 20 minutes. Cells were harvested and lysed in cytoplasmic extract buffer containing 10 mM HEPES, 10 mM KCl, 0.1 mM EDTA (pH 8.0), 0.1 mM EGTA (pH 8.0), 1 mM dithiolthreitol (DTT) and 0.05% NP40. Samples were centrifuged at 1000× g for five minutes to remove nuclei. Supernatants were collected and resuspended in SDS sample buffer. The nuclear pellets were washed and resuspended in nuclear extract buffer containing 20 mM HEPES, 25% glycerol, 0.4M NaCl, 1 mM EDTA (pH 8.0), 1 mM EGTA (pH 8.0), and 1 mM DTT and lysis was performed on ice for 30 minutes. Samples were centrifuged at 1000× g for five minutes. Supernatants were collected as nuclear extracts and mixed with SDS sample buffer.
HeLa cells were mock-transfected or transfected with pcDNA3-Flag-EVM005 or pcDNA3-Flag-EVM005(1-593). Alternatively, HeLa cells were mock-infected or infected with ECTV, VACV, or ECTV-Δ005 at a MOI of 5. At 12 hours post-infection or transfection, cells were stimulated with 10 ng/ml TNFα (Roche) or 10 ng/ml IL-1β (PeproTech Inc) for 20 minutes and fixed with 2% paraformaldehyde (Sigma-Aldrich) for 10 minutes at room temperature. Cells were permeablized with 1% NP40 and blocked with 30% goat serum (Invitrogen Corporation). Cells were stained with anti-NF-κB p65 (1∶200) alone or co-stained with either anti-NF-κB p65 (1∶200) and mouse anti-Flag M2 (1∶200), or anti-IκBα (1∶125) and rabbit anti-Flag M2 (1∶200). Cells were stained with secondary antibodies anti-mouse-AlexaFluor488 and anti-rabbit-AlexaFluor546 at a dilution of 1∶400 (Jackson Laboratories). Coverslips were mounted using 4 mg/ml N-propyl-gallate (Sigma Aldrich) in 50% glycerol containing 250 µg/ml 4′,6-diamino-2-phenylindole (DAPI) (Invitrogen Corporation) to visualize nuclei. Cells were visualized using the 40× oil immersion objective of a Ziess Axiovert 200M fluorescent microscope outfitted with an ApoTome 10 optical sectioning device (Ziess). To quantify the number of cells displaying a nuclear localization of p65 greater than 50 cells were counted in three independent experiments.
HeLa cells were transfected with pcDNA3-Flag-EVM005 or pcDNA3-Flag-EVM005(1-593) using Lipofectamine 2000 (Invitrogen Corporation). Alternatively, HeLa cells were mock-infected or infected with ECTV, ECTV-Δ002, ECTV-Δ005, ECTV-005-rev, or ECTV-Δ002-005 at a MOI of 5. At 24 hours post-transfection or 12 hours post infection, mock-infected cells were stimulated with 10 µM MG132. Samples were then left unstimulated or stimulated with 10 ng/ml TNFα (Roche) or 10 ng/ml IL-1β (PeproTech Inc) for 20 minutes. Cells were fixed in 0.5% paraformaldehyde (Sigma-Aldrich) for 15 minutes at 37°C. Cells were permeablized with ice cold 90% methanol for 30 minutes. Transfected cells were co-stained with rabbit anti-Flag M2 (1∶200) and anti-IκBα (1∶400). Cells were stained with anti-rabbit phycoerythrin (1∶1000) and anti-mouse-AlexaFluor488 (1∶400) (Jackson Laboratories) secondary antibodies, and resuspended in PBS. Infected cells were stained with anti-I3L (1∶100) or anti-IκBα (1∶400), followed by anti-mouse-AlexaFluor 488 (1∶400). Data were collected on a Becton Dickinson FACScan flow cytometer and analyzed with CellQuest software. Mean fluorescence intensities were calculated for three independent experiments.
Whole blood or splenocytes were harvested on days 3 or 7 post infection from C57BL/6 mice infected with ECTV or ECTV-Δ005. Whole blood was lysed with water at a 40∶1 water to blood volume ratio for ten seconds then brought to 1X with 10X PBS. The remaining white blood cells were resuspended in PBS with 2% FBS prior to staining. Spleen tissues were disrupted with the Bullet Blender (STL Scientific) for ∼2 minutes at room temperature using the lowest setting in PBS. The cell suspension was pelleted and the red blood cells were lysed with BD Pharm Lyse. The remaining white blood cells were resuspended in PBS with 2% FBS prior to staining. Cells were stained for flow cytometry using Fc block and the described antibody cocktails in PBS with 2% FBS for 20–30 minutes on ice. Cells were washed twice with PBS containing 2% FBS then fixed on ice with PBS containing 2% FBS and 1% methanol free formaldehyde. Stained cells were analyzed on a BD LSRII or BD Canto. NK cells were identified as being CD45 positive, CD3 negative and NK1.1 positive. These are defined in the literature as NK lytic cells and can only be identified in C57BL/6 mice [60]. An APC labeled tetramer specific to the immunodominant epitope for VACV B8R/CD8 T cell expression was obtained from the NIH tetramer facility [61]. Virus-specific CD8+ T-cells were identified as CD45 positive, CD8 positive, and tetramer positive.
HeLa cells were mock-infected or infected with ECTV or ECTV-Δ005. At 12 hours post infection cells were stimulated with 10 ng/ml TNFα and RNA was harvested using Trizol according to the manufacturer's protocol (Invitrogen Corp.). RNA samples were converted to cDNA using Superscript II reverse transcriptase (Invitrogen Corp.). Transcript levels were analyzed by real time PCR using the following primers, TNFα forward, 5′-GGCGTGGAGCTGAGAGATAAC-3′ and reverse, 5′-GGTGTGGGTGAGGAGCACAT-3′, IL-1β forward, 5′-TTCCCAGCCCTTTTGTTGA-3′ and reverse 5′-TTAGAACCAAATGTGGCCGTG-3′, IL-6 forward 5′-GGCACTGGCAGAAAACAACC-3′ and reverse 5′-GCAAGTCTCCTCATCGAATCC-3′ and GAPDH forward 5′-AGCCTTCTCCATGGTGGTGAAGAC-3′ and reverse 5′-CGGAGTCA ACGGATTTGGTCG-3′. Real time PCR was performed using the Sybr-Green master mix (Promega) and a MyIQ (BioRad) thermocycler. Data analysis was performed with IQ-5 software (BioRad). Data was presented as the average of three independent experiments.
Additionally, we measured transcriptional activation of TNFα, IL-1β and IL-6 in infected liver and spleen through isolation of RNA using Trizol (Invitrogen Corporation) as per manufacturer's protocol. RNA samples were subjected to qRT-PCR as described above to quantify transcriptional upregulation with reference to GAPDH.
RNA Transcripts for EVM004, EVM005, EVM058, and GAPDH are analysed as described previously [41]. cDNA was used as a template and gene-specific primers were used to amplify the last 250 nucleotides (at the 3′ end) of each open reading frame. Transcripts were generated with the following primers: ECTV004 forward 5′-GTTTAATATCATGAACTGCGACTATCT-3′, and reverse, 5′-TTAATAATACCTAGAAAATATTCCACGAGC-3′, ECTV005 forward, 5′-TAGTGGTATTAGAGAGAAATGCAATCT-3′, and reverse, 5′-TCATTCATGTGTCTGTGTTTG-3′, I5L forward, 5′ATGGCGGATGCTATAACCGTT-3′, and reverse, 5′-TTAACTTTTCATTAATAGGGA-3′.
To determine the role of the ECTV encoded EVM005 in virulence we infected female C57BL/6 mice. Four to six week old female C57BL/6 mice were obtained from the National Cancer Institute (Frederick, MD). Groups of five mice were infected via the intranasal route with 10-fold escalating doses of wild type ECTV, ECTV-Δ005, ECTV-005-rev or ECTV-005(1-593)-rev. Mice were anesthetized with 0.1 ml/10 g body weight with ketamine HCL (9 mg/ml) and xylazine (1 mg/ml) by intraperitoneal injection. Anesthetized mice were laid on their dorsal side with their bodies angled so that the anterior end was raised 45° from the surface; a plastic mouse holder was used to ensure conformity. Strains of ECTV were diluted in PBS without Ca2+ and Mg2+ to the required concentration and slowly loaded into each naris (5 µl/naris). Mice were subsequently left in situ for 2 to 3 minutes before being returned to their cages. Mice were monitored for body weight daily for up to 21 days. Mice that demonstrated a drop in body weight to 70% of their original mass, or signs of severe morbidity were euthanized. To determine organ titers and immune activation, mice were sacrificed at 2, 3, 4, and 7 days post-infection, and tissue from the spleen, liver, lungs, and kidney were harvested in addition to blood collected by a needle stick in the heart. Tissue was homogenized using a tissue homogenizer (Next Advance), followed by dilution in PBS (10% w/v). Viral titers were determined on BSC-1 cells using a plaque assay. To prevent avoidable suffering, mice demonstrating a drop in body weight to 70% of their original mass, or signs of severe morbidity, were euthanized.
Alternatively, we infected the susceptible A/NCR strain of mice to determine the role of EVM005 during an ECTV infection. Five to ten week old female A/NCR mice were obtained from the National Cancer Institute (Frederick, MD). Groups of five mice with similar body mass were arranged into separate cages. Mice were anesthetized with CO2/O2 prior to infection. ECTV, ECTV-Δ005, ECTV-005-rev, and ECTV-005(1-593)-rev were diluted in PBS without Ca2+ and Mg2+ to the required concentration and 10 µl was used to infect mice via footpad injection. Body weight, day of death and mortality were monitored daily. Mice that demonstrated a drop in body weight to 70% of their original mass, or signs of severe morbidity were euthanized.
To prevent avoidable suffering, mice demonstrating a drop in body weight to 70% of their original mass, or signs of severe morbidity, were euthanized.
Mice were anesthetized with 0.1 ml/10 g body weight with ketamine HCL (9 mg/ml) and xylazine (1 mg/ml) by intraperitoneal injection. Alternatively, mice were anesthetized with CO2/O2. Mice were euthanized by first anesthetizing them with CO2/O2, followed by cervical dislocation.
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Animal experiments were performed at Saint Louis University and approved by the Institutional Animal Care and Use Committee (#IACUC 2082). Additionally, these experiments were performed in accordance with mouse ethics outlined by the Canadian Council on Animal Care and the University of Alberta.
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10.1371/journal.ppat.1007179 | A cancer-associated Epstein-Barr virus BZLF1 promoter variant enhances lytic infection | Latent Epstein-Barr virus (EBV) infection contributes to both B-cell and epithelial-cell malignancies. However, whether lytic EBV infection also contributes to tumors is unclear, although the association between malaria infection and Burkitt lymphomas (BLs) may involve excessive lytic EBV replication. A particular variant of the viral promoter (Zp) that controls lytic EBV reactivation is over-represented, relative to its frequency in non-malignant tissue, in EBV-positive nasopharyngeal carcinomas and AIDS-related lymphomas. To date, no functional differences between the prototype Zp (Zp-P) and the cancer-associated variant (Zp-V3) have been identified. Here we show that a single nucleotide difference between the Zp-V3 and Zp-P promoters creates a binding site for the cellular transcription factor, NFATc1, in the Zp-V3 (but not Zp-P) variant, and greatly enhances Zp activity and lytic viral reactivation in response to NFATc1-inducing stimuli such as B-cell receptor activation and ionomycin. Furthermore, we demonstrate that restoring this NFATc1-motif to the Zp-P variant in the context of the intact EBV B95.8 strain genome greatly enhances lytic viral reactivation in response to the NFATc1-activating agent, ionomycin, and this effect is blocked by the NFAT inhibitory agent, cyclosporine, as well as NFATc1 siRNA. We also show that the Zp-V3 variant is over-represented in EBV-positive BLs and gastric cancers, and in EBV-transformed B-cell lines derived from EBV-infected breast milk of Kenyan mothers that had malaria during pregnancy. These results demonstrate that the Zp-V3 enhances EBV lytic reactivation to physiologically-relevant stimuli, and suggest that increased lytic infection may contribute to the increased prevalence of this variant in EBV-associated malignancies.
| Whether excessive lytic EBV infection increases the risk of EBV-induced cancers is not clear. A particular variant (Zp-V3) of the viral promoter driving expression of the EBV immediate-early BZLF1 (Z) protein that mediates lytic viral reactivation has been reported to be over-represented (relative to the prototype Zp-P form of the promoter) in certain EBV-positive malignancies, but no functional difference between the two promoter variants has been reported. Here we show that the malignancy-associated Zp-V3 variant (but not the Zp-P variant) contains a binding site for the cellular NFATc1 (nuclear factor of activated T cells c1) transcription factor that allows it to be activated by NFATc1-inducing stimuli such as B-cell receptor stimulation. Furthermore, we demonstrate that restoring this NFATc1-motif to the Zp-P variant in the context of the intact EBV genome greatly enhances lytic viral reactivation in response to the NFATc1-inducing stimuli. We also find that the Zp-V3 variant is over-represented in EBV-positive Burkitt lymphomas and gastric carcinomas, and in lymphoblastoid cell lines transformed by EBV-infected breast milk of Kenyan mothers that had malaria during pregnancy. These findings suggest that the Zp-V3 version of the EBV BZLF1 promoter increases the likelihood of EBV-induced malignancies by increasing lytic EBV infection.
| Epstein-Barr virus (EBV) causes infectious mononucleosis and is associated with a variety of different types of human malignancies, including B-cell lymphomas and nasopharyngeal carcinoma. EBV infects approximately 90% of the world’s population, and like all herpesviruses, EBV persists in the host for life. EBV establishes long-term viral latency in the memory B cell compartment of humans [1–3], whereas lytic EBV infection occurs in antigen-stimulated B cells, plasma cells and oropharyngeal epithelial cells [4–6]. While the initial viral infection is often associated with the clinical symptoms of infectious mononucleosis, EBV does not usually cause any subsequent illness in immune competent hosts, although infectious viral particles continue to be periodically shed in the saliva.
Nevertheless, EBV infection sometimes leads to the development of EBV-positive tumors, particularly in immunosuppressed individuals. EBV-induced tumors are thought to be primarily due to the latent form of viral infection. Latent EBV infection transforms primary human B cells in vitro into immortalized lymphoblastoid cell lines (LCLs) that induce lymphomas when injected into immunodeficient mice. Furthermore, the major EBV-encoded oncoproteins are expressed during latent EBV infection, and EBV-positive tumors in humans are composed largely of latently infected cells.
Given the essential roles of the viral latency proteins in EBV-positive tumors, relatively little attention has been paid to the potential role(s) of lytic EBV infection in promoting EBV-induced tumors. Although widespread fully lytic EBV infection of tumor cells would likely be incompatible with tumor growth, increasing evidence suggests that lytic viral infection contributes to the formation of tumors caused by the related human gamma herpesvirus, KSHV (Kaposi’s sarcoma associated herpesvirus) [7]. Excessive lytic EBV infection in humans could potentially increase the likelihood of EBV-positive tumors by increasing the total number of virally infected cells (including the number of latently infected cells), and/or by inducing paracrine effects that help support the growth and/or viability of latently infected tumor cells. For example, B cells with lytic EBV infection have enhanced secretion of the B cell growth factor, IL-6 [8], the angiogenesis factor, VEGF [9], and the immunosuppressive factors, viral and cellular IL-10 [10,11].
Consistent with a role for lytic EBV infection in promoting the development of EBV-associated tumors, increased lytic infection preceding tumor development seems to occur in a number of EBV-positive human cancers. Patients who develop EBV-positive nasopharyngeal carcinoma (NPC) almost universally have extremely high levels of antibodies directed against lytic EBV proteins even before their cancers are clinically symptomatic, and monitoring lytic EBV antibody levels is useful for detecting NPC at early stages [12]. Immunosuppressed organ transplant recipients, who are highly prone to developing EBV-induced lymphoproliferative disease, have a high level of lytic, as well as latent, EBV infection [13]. Malaria infection, which is thought to promote the development of EBV-positive Burkitt lymphoma (BL), greatly increases the amount of lytic EBV infection in children [14–16]. Furthermore, higher levels of infectious EBV are present in the breast milk of malaria-infected, versus malaria-uninfected, Kenyan women [17], and Kenyan infants more frequently become EBV-infected at very young ages (less than 6 months) when residing in areas with a high prevalence of malaria [18].
The latent-to-lytic switch in EBV-infected cells is mediated by the EBV immediate-early BZLF1 gene product (Z). Z is a transcription factor that binds to, and transcriptionally activates, lytic viral gene promoters, resulting in the lytic form of viral DNA replication and assembly of infectious viral particles [19]. Regulation of the Z promoter (Zp) by cellular transcription factors determines whether EBV infection is latent or lytic. B-cell receptor (BCR) stimulation potently induces lytic EBV gene expression in certain Burkitt lymphoma cell lines in vitro, and BCR activation in response to antigen stimulation of EBV-infected B cells is thought to be a biologically important mechanism by which the EBV life cycle is regulated in humans [19]. In addition to its immunosuppressive effect [20], malaria is thought to increase the amount of lytic EBV infection by inducing polyclonal B cell stimulation [16,21]. However, the precise cellular transcription factors that link the BCR signal to EBV lytic reactivation are only partially understood, and EBV-infected cell lines differ substantially in their ability to reactivate in response to BCR stimulation in vitro.
A particular promoter variant of the EBV Z promoter (Zp-V3) has been reported in two different studies to be over-represented in both EBV-infected NPCs [22] and EBV-infected AIDS-related lymphomas [23] in comparison to its frequency in EBV-infected non-malignant tissues obtained from patients in the same geographic regions. However, whether this promoter variant affects BZLF1 transcription or lytic EBV reactivation is not known. Here, we have examined the functional consequences of the malignancy-associated Zp-V3 variant. We show that in comparison to the prototype Zp variant (Zp-P), the Zp-V3 variant responds much more strongly to BCR-ligation and ionomycin stimulation in B cells. Furthermore, we demonstrate that this difference results from a single nucleotide difference in the two promoter variants (which creates an NFATc1 binding motif in the Zp-V3 form of the promoter), and is sufficient to confer greatly enhanced lytic viral protein expression in EBV-infected B cells. Importantly, we find that the Zp-V3 variant is highly over-represented in a set of EBV-transformed lymphoblastoid cell lines (LCLs) that were derived from EBV present in breast milk of Kenyan mothers that had malaria during pregnancy, versus a set of spontaneous LCLs that were derived from the blood of healthy Kenyan individuals living in malaria-high regions. In addition, we show that the Zp-V3 variant is also over-represented in both EBV-positive Burkitt lymphomas, and EBV-positive gastric carcinomas, relative to its frequency in healthy control patients. These findings suggest that the Zp-V3 version of the EBV BZLF1 promoter increases the likelihood of EBV-induced malignancies by increasing the level of lytic EBV infection.
To determine if the Zp-P and Zp-V3 variants of the EBV Z promoter have different activities in a B-cell environment, we inserted the two promoter variants upstream of the luciferase gene in the pCpGL luciferase vector and performed transient reporter gene assays. Although the two promoter variants had similar constitutive activity in BJAB cells (derived from an EBV-negative B-cell lymphoma) (Fig 1A), the Zp-V3 variant was much more efficiently activated when cells were treated with an anti-IgM antibody to stimulate the BCR (Fig 1B). In contrast to the effect of BCR stimulation, the two promoter variants responded similarly to the KLF4 transcription factor (Fig 1C), which binds to and activates the Z promoter [24,25]. These results suggest that the Zp-V3 variant of the Z promoter is more responsive to BCR stimulation than the Zp-P variant.
The promoter sequences located between -668 and +15 (relative to the transcriptional start site) of the Zp-P (from B95.8 strain EBV) and Zp-V3 (from M81 strain EBV) variants differ by only seven nucleotides (Fig 2A). To examine how these differences contribute to the enhanced responsiveness of the Zp-V3 variant to BCR stimulation, we individually switched each of the variant nucleotides in the Zp-V3 luciferase construct to the nucleotides present in the Zp-P promoter. Altering the variant residues located at -100, -106, -274, -365, -460, and -525 (relative to the transcriptional start site) had relatively little effect on the response of the Zp-V3 promoter to anti-IgM treatment in BJAB cells (Fig 2B). However, switching the nucleotide located at -141 from a G to an A almost completely abolished the ability of the Zp-V3 variant to be activated by BCR stimulation, reducing it to the level seen with Zp-P. Thus, a G nucleotide at position -141 in the Zp promoter is required for efficient BCR activation.
To determine if a G nucleotide at -141 is sufficient to increase the BCR responsiveness of the Zp-P version of the Z promoter, we switched the -141 nucleotide from an A to G in the Zp-P luciferase construct. As shown in Fig 2C, this single basepair change conferred strong responsiveness to BCR stimulation. These results suggest that a single nucleotide difference in the two Z promoter variants is largely responsible for the different responses of Zp-V3 and Zp-P to BCR stimulation.
Comparison of the Zp-V3 versus Zp-P sequences surrounding the -141 Zp nucleotide revealed that the Zp-V3 promoter has a consensus NFAT (Nuclear Factor of Activated T cells) motif (TGGAAA) [26] that is not present in the Zp-P version of the promoter (Fig 3A). The NFAT family consists of five cellular transcription factors; NFATc1, expressed in lymphoid tissue, is translocated to the nucleus following T cell receptor and BCR activation [27–30], and is constitutively activated in some B cell lymphomas, including the BJAB cell line [31–33]. Since BCR signaling is known to translocate and activate cellular NFAT transcription factors [30,34], we used a luciferase assay to determine whether pretreating BJAB cells with cyclosporine (an NFAT inhibitor [27]) abolished the ability of BCR stimulation to activate the Zp-V3 promoter. As shown in Fig 3B, cyclosporine treatment prevented anti-IgM activation of Zp-V3, suggesting that the BCR stimulatory effect may be mediated through a NFAT family member. Of note, NFAT activation of promoters commonly requires cooperative binding of NFAT with other transcription factors (in particular, AP1 and Ets family members) [35–38], and the potential NFAT site on the Zp-V3 variant is adjacent to an AP1-like motif (TGAGCCA) known as ZIIIA that has previously been shown to be required for BCR activation of Zp [39,40].
To determine if NFATc1 can bind to the Zp-V3, but not Zp-P, version of the Z promoter, we performed EMSAs using labeled oligonucleotide probes containing the -155 to -127 sequences of either Zp variant, and nuclear extracts harvested from untreated or anti-IgM treated BJAB cells. These probes contain the potential NFAT site but not the adjacent potential AP1 motif. A protein that binds to the Zp-V3, but not Zp-P, version of the probes was observed in the presence and absence of anti-IgM treatment (Fig 3C). Furthermore, two different unlabeled oligonucleotides containing different known binding sequences for NFAT competed for binding with this protein, while oligonucleotides containing Ets, AP1, and ELK1 consensus sites did not (Fig 3D). In addition, pre-incubating the nuclear extract with an antibody against NFATc1 blocked most of the binding to the Zp-V3 probe, while an antibody against another transcription factor, C/EBPα, had little effect (Fig 3E). These results confirm that NFATc1 can bind to the Zp-V3 but not Zp-P form of Zp.
Since NFATc1 is constitutively expressed in BJAB nuclear extracts, and its binding to Zp-V3 is not increased by anti-IgM treatment of cells, we next asked if BCR stimulation of BJAB cells enhances AP1 binding to the adjacent AP1-like (“ZIIIA”) motif, and if this effect is NFATc1-dependent. To confirm that crosslinking of the BCR (which is known to induce expression of AP1 family members [24,25]) results in enhanced AP1 activity in BJAB cells, EMSAs were performed using a labeled consensus AP1 probe and nuclear extracts harvested from untreated or anti-IgM treated cells. A protein binding to the probe was greatly increased in the anti-IgM treated extracts, and this binding was competed by cold oligonucleotide containing a consensus AP1 motif but not by an oligonucleotide containing the NFAT motif (Fig 4A). These results confirm that BCR activation in BJAB cells results in strongly increased nuclear AP1 activity.
We next determined if the Zp AP1-like motif located between -123 to -129 (TGAGCCA versus the AP1 consensus binding sequence TGAGTCA[41]), can bind AP1 in the presence or absence of the adjacent NFAT motif. A previously published paper did not find AP1 binding to this motif [42]; however, this study did not use probes that also contain the adjacent NFAT motif. As shown in Fig 4B, when nuclear extracts from untreated or anti-IgM treated BJAB cells were incubated with a longer oligonucleotide probe that contains both the NFATc1 and ZIIIA motifs, an additional (larger) protein complex bound to the larger probe, and this complex was found only in the anti-IgM treated cells. Furthermore, this larger complex was competed away with both an unlabeled oligonucleotide containing the consensus AP1 motif, and an oligonucleotide containing the consensus NFAT motif. In addition, the anti-IgM dependent complex was super-shifted by pre-incubation with an anti-cFos (AP1) antibody (Fig 4C) but not an anti-XBP1 antibody. These results confirm that BCR stimulation of BJAB cells allows AP1 to bind to the Zp ZIIIA motif in an NFATc1-dependent manner, and that this only occurs on the Zp-V3 version of the promoter.
Given that AP1 is recruited to the Zp-V3 ZIIIA site in an NFATc1-dependent manner, we next asked if the combination of NFATc1 and cFos synergistically activates the Zp-V3 promoter in reporter gene assays. As shown in Fig 5A, the combination of cFos and NFATc1 activated the Zp-V3 luciferase construct much more effectively than either cFos or NFATc1 alone, although the level of transfected NFATc1 was similar with or without co-transfected cFos (Fig 5B). Furthermore, mutation of either the -141 NFAT motif, or the ZIIIA motif, greatly decreased the ability of the NFATc1/cFos combination to activate the promoter. These results confirm that NFATc1 and cFos collaborate to activate the Zp-V3 promoter by binding to the -141 NFAT and ZIIIA sites, respectively.
The EBV-encoded protein LMP2A mimics constitutively active BCR signaling to enhance B cell survival and proliferation [43–50], and in some, but not all, studies has been reported to activate Zp [51]. To determine if LMP2A can activate the Zp-V3 form of Zp, BJAB cells were transfected with the Zp-V3 or Zp-P reporter gene construct in the presence or absence of a co-transfected LMP2A expression vector. As shown in Fig 6A, LMP2A expression greatly enhanced the activity of the Zp-V3 promoter, but only slightly increased that of the Zp-P promoter. This activation of the Zp-V3 promoter was abolished by mutation of either the NFATc1 or ZIIIA binding motifs (Fig 6B). Furthermore, treatment of cells with the NFAT inhibitor cyclosporine also prevented LMP2A from activating the Zp-V3 promoter. Together these results suggest that, similar to the effect of the authentic BCR, LMP2A signals through NFATc1 to increase Zp-V3 but not Zp-P activity.
We next asked if altering a single basepair of the Zp sequence (-141) in the context of the intact approximately 172 Kbp type 1 B95.8 strain EBV genome (which has Zp-P) is sufficient to change the lytic phenotype of the virus. As shown in Fig 7A, B95.8 virus containing the mutated Zp -141 nucleotide (Zp-V3 form) expressed much more Z protein than the WT virus following infection of two different EBV-negative Burkitt lines, BJAB and Akata, although similar levels of the latent EBV protein, EBNA2, were expressed in each cell type.
To determine if the B95.8 Zp-V3 mutant is also more lytic following infection of normal B cells, we infected primary peripheral B cells with the mutant Zp-V3 virus or virus in which the mutation had been reversed back to the WT sequence (revertant virus) using an MOI of 0.1. Since both viruses have the GFP gene inserted into their genomes, we used GFP flow cytometry analysis to examine the number of cells infected with each virus and the level of GFP expression per cell. Although a larger number of B cells (approximately 8% of cells) were infected with the revertant (WT) virus in comparison to the Zp-V3 mutant virus (approximately 3% of cells), the level of Z expression assessed by immunoblot on day 3 after infection was greater in the Zp-V3 mutant infected cells (Fig 7B). Together, these results confirm that a single nucleotide alteration of the BZLF1 promoter is sufficient to confer enhanced lytic gene expression following EBV infection of either primary B cells or Burkitt lymphoma cells.
The expression of early and late lytic EBV genes in B cells requires that the incoming (non-methylated) viral genome becomes highly methylated (since Z preferentially binds to and activates methylated viral promoters) [52–54], and does not occur until at least 2 weeks after EBV infection [53]. To determine if the Zp-V3 sequence enhances the ability of B95.8 virus to lytically reactivate in stably infected cell lines, we infected EBV-negative Mutu cells (derived from a Burkitt lymphoma) with WT, revertant, or Zp-V3 mutant B95.8 viruses, and used hygromycin selection to obtain stably infected cell lines. As shown in Fig 8A, stably infected Mutu cell lines all had type I EBV latency (EBNA1-pos, LMP1-neg, EBNA2-neg), independent of whether cells were infected with the WT, revertant or Zp-V3 mutant viruses. However, when treated with the NFAT-inducing agent, ionomycin, Mutu cells infected with the Zp-V3 mutant had much more Z protein expression, as well as increased expression of an early lytic protein (BMRF1) and a late lytic viral protein (VCA-p18), compared to cells infected with the WT or revertant viruses. Furthermore, the ability of ionomycin treatment to activate lytic EBV protein expression was reversed by cyclosporine treatment (Fig 8A) or NFATc1 siRNA (Fig 8B), confirming that its effect is at least partially mediated through activated NFAT. Likewise, the ability of anti-IgG mediated BCR activation to induce lytic gene expression was increased in Mutu cells infected with the Zp-141G virus (Fig 8C). In contrast, cells infected with the mutant, wildtype, or revertant viruses all induced similar levels of Z expression when treated with the combination of phorbol-12-myristate-13-acetate (TPA) and sodium butyrate (NaBut), and the effect of TPA/sodium butyrate was not reversed by cyclosporine (Fig 8D). Thus, the Zp-V3 variant specifically confers increased Z expression in response to NFAT-inducing agents, and does not globally increase Z expression in response to all lytic inducing agents.
To confirm that NFATc1 binds more efficiently to the Zp-V3 form of the Z promoter in EBV-infected cells in vivo, we performed ChIP assays in ionomycin-treated Mutu cells infected with the wildtype or Zp-141G B95.8 viruses (Fig 8E). Consistent with the in vitro binding assays, these results showed that endogenous NFATc1 binds more strongly to the Z promoter of the Zp-141G (Zp-V3 type) virus in comparison to the wildtype (Zp-P type) virus.
Since excessively lytic EBV infection could be incompatible with the establishment of long-term viral latency and B cell transformation [55], we determined if the Zp-V3 mutant B95.8 virus can transform primary B cells in vitro. Purified adult peripheral B cells were infected with the WT or Zp-V3 mutant viruses using 0.25 infectious EBV unit per cell, and the percentage of wells containing lymphoblastoid outgrowths at day 21 post-infection was examined. 10/10 wells infected with either mutant, wildtype or revertant viruses had such outgrowths, suggesting that the Zp-V3 mutant has similar transforming capacity as the WT B95.8 strain virus, at least when using an MOI of 0.25 (Fig 9). Therefore, the enhanced lytic protein expression that occurs following infection of B cells with viruses containing this form of the Z promoter is not sufficient (at least in the context of B95.8 strain EBV) to inhibit viral transformation of primary B cells.
The frequency of the Zp-V3 variant in non-malignant tissues is variable depending upon the EBV type (type 1 versus type 2), and geographic region. Interestingly, type 2 EBV is most common in areas of the world where malaria and Burkitt lymphoma are endemic, and the Zp-V3 form of the BZLF1 promoter is present in all type 2 EBV genomes sequenced to date [56,57]. In contrast, Zp-V3 is relatively rare in type 1 EBV, except for type 1 EBV genomes isolated from Asian EBV strains [56]. Although the Zp-V3 variant has been shown to be over-represented in type 1 EBV genomes isolated from NPCs in China, and in AIDs-related lymphomas in Italy (relative to its frequency in non-malignant samples obtained from humans living in the same geographic regions [22,23]), whether the Zp-V3 variant is over-represented in type 1 EBV genomes in Burkitt lymphomas (BLs) is not yet known.
To investigate this, we examined the Zp status of EBV-infected BLs obtained from Africa or South America [56,58–60] (detailed in S1 Table) versus EBV genomes derived from non-malignant samples from the same geographic regions (spontaneous LCL samples and infectious mononucleosis samples [56,60]; detailed in S2 Table). Zp sequence alignments from malignant and normal tissues for previously unanalyzed promoter variant sequences are shown in S6 Table.
Type 2 EBV genomes were similarly represented in the Burkitt lymphoma and non-malignant samples (Table 1), and as expected all type 2 EBV genomes had the Zp-V3 form of Zp (Table 2). Importantly, we found that type 1 EBV genomes in BLs are much more likely to contain the Zp-V3 variant of Zp (37%) versus type 1 EBV genomes obtained from non-malignant samples (4%); p <0.003 by Fisher’s exact test (Table 2). This result reveals that Zp-V3 containing T1 EBV is over-represented (and likely selected for) in EBV-infected Burkitt lymphomas.
Up to 10% of gastric cancers worldwide are EBV-infected [61–64] and the genomes of a number of EBV-infected gastric cancers are now available in Genbank and the TCGA database. To determine whether the Zp-V3 variant is also over-represented in gastric carcinomas, we examined the Zp status of 41 type 1 EBV-infected gastric carcinomas obtained worldwide [65–68], versus 113 type 1 EBV genomes derived from non-malignant samples [22,56,69–71] (including spontaneous LCL samples, infectious mononucleosis samples, saliva from healthy individuals, and EBV genome sequences detected in non-tumor control tissues (of any kind) in the whole exome sequence (WXS) TCGA database) as detailed in S3–S5 Tables. Zp sequence alignments from malignant and normal tissues for previously unanalyzed promoter variant sequences are shown in S6 Table. Since the Zp-V3 variant is much more common in type 1 EBV isolates from Asia, and gastric carcinoma is also more common in Asia, we also compared the frequency of Zp-V3 in gastric carcinomas versus non-malignant tissues isolated from samples obtained from either Asian (known Asian individuals or samples obtained from Asian countries) or presumed non-Asian individuals (known Caucasian individuals, or samples obtained from non-Asian countries).
As shown in Table 3, the Zp-V3 variant is significantly over-represented in type 1 EBV-positive gastric cancers worldwide (44% of tumors) relative to its frequency in non-malignant tissues (19%) (p < 0.003 using a Fisher’s exact test). When only samples from Asian (or presumed Asian) patients are examined, Zp-V3 type 1 EBV is still over-represented in gastric tumors (68%) relative to the non-malignant samples (27%) (p < 0.001 using a Fisher’s exact test) (Table 4). Likewise, when only samples from presumed non-Asian patients are examined, Zp-V3 type 1 EBV is still over-represented in gastric tumors (17%) relative to the non-malignant samples (0%) (p < 0.03 using a Fisher’s exact test) (Table 5). Thus, the Zp-V3 variant may also increase the risk of developing EBV-positive gastric cancers.
Finally, given the recent finding that infectious EBV in the breast milk of mothers that had malaria during pregnancy may serve as a source of EBV transmission to neonates [17], we examined whether LCL lines derived from the breast milk of these mothers have a high prevalence of the Zp-V3 variant. For this analysis, we sequenced the Z promoter in the EBV genomes of LCLs transformed by breast-milk-derived EBV from 10 different Kenyan mothers that had malaria during pregnancy; we then compared the frequency of this variant in the breast-milk derived LCLs to that observed in 13 different spontaneous EBV-infected LCLs derived from the blood of healthy Kenyan individuals residing in areas with high frequency malaria transmission (Table 6). We excluded LCLs infected with type 2 EBV (6/19 of the spontaneous LCLs in healthy Kenyan donors, versus 0/10 LCLs derived from breast milk) from this analysis since all type 2 isolates are known to contain the Zp-V3 variant.
Surprisingly, all 10 of the breast milk-derived LCLs contained the Zp-V3 variant, and all also had type 1 EBV. In comparison, 0/13 of type 1 EBV-infected LCLs derived from the blood of healthy Kenya donors contained the Zp-V3 variant (p < 0.001). These results suggest that Zp-V3 containing EBV strains may be more prone to lytic reactivation in breast milk than Zp-P containing strains.
Latent EBV infection transforms primary B cells in vitro and clearly contributes to the development of a number of EBV-associated human malignancies. However, since the most transforming form of EBV latency (type III) is sufficient to induce proliferation and survival of B cells, whether lytic EBV infection also contributes to EBV-associated malignancies is less clear. Here we have examined whether a cancer-associated polymorphism of the viral BZLF1 immediate-early promoter affects lytic EBV gene expression. We demonstrate that this cancer-associated promoter variant contains an NFATc1 binding motif not present in the prototype promoter, and has enhanced lytic gene activation in response to BCR stimulation. Indeed, we find that altering only a single basepair of the BZLF1 promoter (to confer NFATc1 binding) in the context of the intact type 1 EBV genome is sufficient to increase the amount of lytic EBV protein expression in B cells. In addition, we show that the presence of high EBV titers in the breast milk of malaria-infected Kenyan women (measured by the ability of the milk to transform B cells into LCLs in vitro) is highly associated with the presence of the Zp-V3 variant in type 1 EBV strains. Importantly, we also show for the first time that Zp-V3 containing type 1 EBV is likewise over-represented in both Burkitt lymphomas, and gastric carcinomas, relative to non-malignant control samples. Together, the studies presented here suggest that enhanced lytic EBV gene expression increases the likelihood of at least some types of EBV-associated malignancies in humans.
BCR stimulation has been known for some time to induce lytic EBV reactivation in many EBV-infected Burkitt cell lines, but the major BCR effect was previously reported to be mediated through post-translational modification of MEF2 family members that can also bind to the BZLF1 promoter [25,72]. However, many of these previous studies used the Zp-P form of the promoter, which is found in the prototype laboratory EBV strain (B95.8). While a previous paper reported that BCR-mediated stimulation of the Zp-P promoter is largely dependent on a feed-forward loop in which the BZLF1 protein binds to and activates its own promoter (once a small amount of Z gene expression has been stimulated by cellular transcription factors such as MEF2) [39], we show that BCR stimulation directly and strongly activates the Zp-V3 version of the promoter without the requirement for concomitant Z protein expression. We also found that the AP1-like “ZIIIA” motif is required for BCR-mediated activation of Zp-V3, and binds AP1 in an NFAT-dependent manner. In contrast, previous studies using the Zp-P promoter variant found that the ZIIIA motif primarily serves as a binding site for the Z protein itself (rather than AP1 binding) [39,42,73]. Since the NFATc1 binding site in the Zp-V3 variant overlaps the “ZIC” motif previously reported to be important for TPA-induced activation of the Zp-P variant, we cannot exclude the possibility that additional transcription factors can also regulate Z transcription through this motif in one or both promoter variants.
Given the long-appreciated epidemiologic association of malaria infection with EBV-induced Burkitt lymphomas in Africa, and the growing evidence that malaria increases the amount of lytic EBV in co-infected patients, we asked if the presence of infectious EBV in the breast milk of Kenyan mothers that had malaria during pregnancy correlates with the presence of the Zp-V3 form of the EBV promoter. This seems to be the case, since 10/10 of the type 1 EBV LCL lines derived from infectious EBV in the breast milk of these women had this form of the EBV promoter, versus only 0/13 type 1 LCL lines derived from the blood of healthy Kenyan patients in high malaria regions.
B cells in breast milk are more highly activated, and much more likely to differentiate into plasmablasts or plasma cells, than the B cells in peripheral blood [74]. The known propensity of EBV to lytically reactivate in antigen-stimulated plasma cells, and our finding that the Zp-V3 variant is particularly responsive to BCR stimulation, may explain why the Zp-V3 variant is highly over-represented in LCLs derived from infectious EBV in the breast milk of Kenyan mothers that had malaria during pregnancy. Our results suggest that Zp-V3-containing EBV strains may be more easily transferred to nursing neonates than EBV strains containing the Zp-P variant, particularly if the breast milk does not contain enough maternal EBV-neutralizing antibodies to block infection of the neonate (as may occur in malaria). If so, very early EBV infection in the context of an immature immune system may predispose infants to developing Burkitt lymphoma. Clearly, however, larger prospective studies are needed to confirm that Zp-V3 containing EBV strains are more likely than Zp-P containing strains to produce infectious viral particles in breast milk, and/or to be transmitted from breast milk to nursing infants.
Whether the Zp-V3 variant also confers greater Z expression in epithelial cells is not yet clear. However, since we previously showed that B95.8 EBV-infected gastric AGS cells (one of the few epithelial cell lines easily infected with this strain) are remarkably lytic [75], the Zp-P promoter variant is clearly quite active in this cell type. In addition, our finding here that KLF4, a cellular transcription factor required for lytic EBV reactivation during epithelial cell differentiation [24], activates the Zp-P and Zp-V3 variants with similar efficiency suggests that the Zp-P and Zp-V3 versions of the promoter may have similar activity in infected epithelial cells. Furthermore, a recent study showed that similar levels of EBV are contained in the saliva of United Kingdom college students infected with Zp-V3 containing EBV strains versus Zp-P containing strains [56]. Thus, over-representation of the Zp-V3 variant in nasopharyngeal carcinomas [22,23] and gastric carcinomas (shown here) may reflect increased hematogenous delivery of B-cell derived infectious EBV particles to nasopharyngeal and gastric epithelial cells.
There are two major strains of EBV (referred to as type 1 and type 2), and essentially all type 2 EBV strains carry the Zp-V3 version of the BZLF1 promoter, whereas only a minority of the type 1 strains carry this version [56,57]. The major phenotypic differences between the type 1 and type 2 strains of EBV are thought to reflect differences in the sequences of the essential latent transforming genes, EBNA2 and EBNA3A/3C [76–80]. Although type 2 EBV transforms B cells in vitro less efficiently than type 1 EBV [81], there is no evidence that type 2 EBV is less competent for causing EBV-associated Burkitt lymphomas in humans [82]. Indeed, type 2 strain EBV infection is particularly common in regions of Africa that have high rates of Burkitt lymphoma, although even in these regions type 1 strain is still more common [82]. Given the many differences between type 1 and type 2 EBV, it is important to stress that our studies here demonstrate that the Zp-V3 version of the Z promoter is more lytic than the Zp-P version in the context of a type 1 EBV genome (B95.8 strain). Thus our results are not confounded by other potential important differences between the two strains due to alterations in the EBV latency proteins. Nevertheless, since we show here that the Zp-V3 promoter variant produces more lytic EBV reactivation than the Zp-P variant in response to BCR activation, and all type 2 EBV strains sequenced to date contain the Zp-V3 version of the promoter, it is interesting to speculate that increased lytic reactivation by type 2 EBV strains may partially compensate for the less transforming phenotype (at least in vitro) in terms of the ability of type 2 strains to promote Burkitt lymphomas.
An interesting unanswered question is why EBV has evolved to have at least two different BZLF1 promoter variants. It is possible that the Zp-P variant common in the type 1 strain ensures that the virus can establish long-term latency in B cells, whereas the Zp-V3 variant that is universally present in type 2 strains (as well as some type 1 strains) increases the efficiency of lytic virus reactivation and helps ensure efficient horizontal transmission. Many individuals (particularly when immunosuppressed) are co-infected with type 1 and type 2 EBV strains [83], and the breast milk of Kenyan women that had malaria during pregnancy was found to commonly contain both types of EBV [17]. Furthermore, a number of recombinant EBV genomes containing portions of both type 1 and type 2 EBV have now been found in tumors and LCLs [57], suggesting that individual B cells may be simultaneously infected with both types of EBV. In B cells infected with more than one virus type, Z protein transcribed from the Zp-V3 promoter could simultaneously induce reactivation of the Zp-P carrying strain, since once made, Z can auto-activate either form of the Z promoter.
However, an important finding in our studies was that type 1 EBV strains containing the Zp-V3 BZLF1 promoter variant are particularly over-represented in Burkitt lymphomas relative to either Zp-P containing type 1 EBV or Zp-V3 containing T2 EBV strains. Therefore, we speculate that Zp-V3 containing type 1 strains may be especially transforming because they incorporate both the increased lytic activity of Zp-V3 (usually type 2)-containing EBV strains, and the enhanced transforming functions of type 1 EBV strains. Of note, although Zp-V3 incorporation into otherwise type 1 EBV genomes may result from recombination between type 1 and type 2 EBV strains, we identified some Burkitt lymphomas and gastric carcinomas in which only the Zp -141 nucleotide was switched to the Zp-V3 form of the promoter, and the other two variant nucleotides were the same as the Zp-P variant. In contrast, we found no tumors in which one of the other Zp-P specific nucleotides was mutated to the Zp-V3 variant without mutation of the Zp -141 nucleotide. This result suggests that in some instances, mutations which specifically convert the Zp-P -141 sequence to the Zp-V3 sequence may be selected for in EBV-infected tumors in the absence of recombination. Our results also raise the interesting possibility that certain EBV-associated cancers are particularly common in Asia due to the high frequency of Zp-V3 containing T1 EBV strains in this part of the world.
Finally, our findings suggest that additional studies to examine whether the Zp-V3 version of the BZLF1 promoter is associated with increased lytic EBV infection in humans are warranted. If this proves to be the case, it would have important clinical implications, and would buttress the argument that anti-EBV vaccines that inhibit lytic infection without preventing the establishment of viral latency might be useful for preventing EBV-associated malignancies.
The EBV-negative B cell lines Mutu (a gift from Jeff Sample), BJAB (purchased from ATCC), and Akata (a gift from Kenzo Takada), and EBV-positive Raji (ATCC) and Kem III (a gift from Alan Rickinson and Jeff Sample) were grown in RPMI-1640 media (Gibco). All media was supplemented with 10–15% FBS and 1% penicillin-streptomycin (pen-strep). The epithelial line HEK 293 (from ATCC) was maintained in DMEM (Gibco). Mutu cells infected with the EBV p2089 Bacmid (B95.8) were maintained under selection of 300ug/mL Hygromycin B. Primary human peripheral CD19+ B cells from healthy donors (obtained from Stem Cell Technologies (#70033), who used Institutional Review Board (IRB)-approved consent forms and protocols) were EBV-transformed and grown in RPMI. The NOKs cell line (a gift from Karl Munger) is a telomerase-immortalized normal oral keratinocyte cell line that was established and maintained as previously described [84].
Cells were treated with the following drugs for experiments: ionomycin (Calbiochem) at 2.5μg/mL, TPA (Sigma) at 20ng/mL, cyclosporine A (Cell Signaling) at 1μM, sodium butyrate (Sigma) at 3mM, anti-IgM (Southern Biotech) at 10μg/mL, anti-IgG (Sigma I5260) at 10μg/mL. When cyclosporine was added to cells, it was done one hour prior to addition of any other drugs.
Plasmid DNA was prepared using the Qiagen Maxiprep kit according to the manufacturer’s instructions. Plasmid pSG5 was obtained from Stratagene. SG5-LMP2A was a gift from Nancy Raab-Traub. pREP-NFAT2 (NFATc1) was a gift from Anjana Rao (Addgene plasmid # 11788). pLX304-FOS-V5 was a gift from William Hahn (Addgene plasmid # 59140) [85]. The promoterless luciferase reporter gene construct pCpGL-basic (a gift from Michael Rehli) was constructed as previously described [86] and contains no CpG motifs in the entire vector. The BZLF1 promoter (-668 to +15, relative to transcription start site) was PCR amplified from the EBV B95.8 (Zp-P) and M81 (Zp-V3) genomes and cloned upstream of the luciferase gene in pCpGL-basic using the SpeI and BglII restriction sites. The primer sequences are as follows: BZLF1 F SpeI 5’-GCGACTAGTAGGTGTGTCAGCCAAAG and BZLF1 R BglII 5’- GCGAGATCTCCGGCAAGGTGCAATG. Zp mutants in the pCpGL luciferase vector were constructed using the Stratagene QuikChange II XL site-directed mutagenesis kit. Primers are as follows: Zp-V3–100 5’- CTAATGTGCCTCATAGACACACCTAAATTTAGCACGTCC and 5’- GGACGTGCTAAATTTAGGTGTGTCTATGAGGCACATTAG; Zp-V3–106 5’- ACAGGCATTGCTAATGTACCTCAGAGACACACCTA and 5’- TAGGTGTGTCTCTGAGGTACATTAGCAATGCCTGT; Zp-V3–141 5’- CTGCCTCCTCCTCTTTTAGAAACTATGCATGAGCC and 5’- GGCTCATGCATAGTTTCTAAAAGAGGAGGAGGCAG; Zp-V3–274 5’- CTCCCCCCTGACCCCCGAACTTAATGAAATCTTGGA and 5’- TCCAAGATTTCATTAAGTTCGGGGGTCAGGGGGGAG; Zp-V3–365 5’- AGATGGACCTGAGCCACCCGCCCCC and 5’- GGGGGCGGGTGGCTCAGGTCCATCT; Zp-V3–460 5’- GGAGGACCCTGATGAAGAAACCAGTCAGGCC and 5’- GGCCTGACTGGTTTCTTCATCAGGGTCCTCC; Zp-V3–525 5’- CGGTGCCCCAGCCACTTGACCCGG and 5’- CCGGGTCAAGTGGCTGGGGCACCG; Zp-P -141 5’- CTGCCTCCTCCTCTTTTGGAAACTATGCATGAGCC and 5’- GGCTCATGCATAGTTTCCAAAAGAGGAGGAGGCAG. The ZIIIA/AP1 Zp-V3 mutant was mutated sequentially, first with Zp-V3 Ap1 mut 1 5’-TTGGAAACTATGCAGAAGCCACAGGCATTGCTAATGTGCCT and 5’-AGGCACATTAGCAATGCCTGTGGCTTCTGCATAGTTTCCAA, then with Zp-V3 AP1 mut 2 5’-TTGGAAACTATGCAGAATTCACAGGCATTGCTAATGTGCCT and 5’-AGGCACATTAGCAATGCCTGTGAATTCTGCATAGTTTCCAA. All constructs were verified by sequencing.
The EBV p2089 Bacmid was a gift from Henri-Jacques Delecluse and contains the complete genome of the B95.8 strain of EBV in addition to a cassette containing the prokaryotic F-factor as well as the green fluorescent protein (GFP) and Hygromycin B resistance genes in the B95.8 deletion as previously described [87]. p2089 is the parental WT Bacmid to all mutants in this study. EBV Zp-P-141G Bacmid was constructed using the GS1783 E. coli–based En Passant method previously described [88] to change the -141 nucleotide in the B95.8 Zp to Variant 3 Zp sequence. Subsequently a revertant Bacmid, designated EBV Zp-P-141G.REV was constructed, reverting the altered nucleotide to wildtype B95.8 sequence. Finally, the Chloramphenicol cassette in the F-factor of each WT, Zp-P-141G, and Zp-P-141G.REV Bacmids was replaced with Kanamycin. Kanamycin resistance facilitated the transfer of all Bacmids to the Chloramphenicol-resistant BM2710 E. coli [89] used for infection of 293 cells. The integrity of each Bacmid was confirmed by analyzing the restriction digestion patterns with multiple enzymes. Furthermore, all mutations were confirmed by high fidelity PCR amplification and sequencing of the mutated junctions. The list of primers used for generation and confirmation of all mutants is as follows. Zp.T-141C Primer 1 5’- TGAGGTACATTAGCAATGCCTGTGGCTCATGCATAGTTTCCAAAAGAGGAGGAGGCAGTTTTAGGGATAACAGGGTAATCGATTT, Zp.T-141C Primer 2 5’-CTTATTTTAGACACTTCTGAAAACTGCCTCCTCCTCTTTTGGAAACTATGCATGAGCCACAGCCAGTGTTACAACCAATTAACC, Zp.T-141C.REV Primer 1 5’-TGAGGTACATTAGCAATGCCTGTGGCTCATGCATAGTTTCTAAAAGAGGAGGAGGCAGTTTTAGGGATAACAGGGTAATCGATTT, Zp.T-141C.REV Primer 2 5’-CTTATTTTAGACACTTCTGAAAACTGCCTCCTCCTCTTTTAGAAACTATGCATGAGCCACAGCCAGTGTTACAACCAATTAACC, Cam-Ff-Kan Primer 1 5’-CGGGCGTATTTTTTGAGTTATCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGAGCCATATTCAACGGGAAAC, Cam-Ff-Kan Primer 2 5’-CAGGCGTAGCAACCAGGCGTTTAAGGGCACCAATAACTGCCTTAAAAAAATTAGAAAAACTCATCGAGCATC, Zp.-141-Confirm Primer 1 5’-CGGCAAGGTGCAATGTTTAG, and Zp.-141-Confirm Primer 2 5’-GTGTCAGCCAAAGAGGATCA.
BJAB cells were nucleofected using the Amaxa Nucleofector 2b device (Lonza) and program M-013 (with Buffer V) in 12-well dishes with 500 ng of pCpGL-basic promoter construct and 500 ng of vector control, LMP2A, cFos, or NFATc1 plasmid. NOKs were transfected with Lipofectamine 2000 (Thermo-Fisher Scientific). The cells were washed with PBS and harvested in 1× Reporter Lysis Buffer (Promega) at 24–48 h post-nucleofection or transfection. Lysates were subjected to three freeze-thaw cycles, and relative luciferase units were quantified with a BD Monolight 3010 luminometer (BD Biosciences) using Promega luciferase assay reagent. All luciferase assay figures represent two independent experiments, each performed in duplicate.
BJAB cells were treated or mock-treated with anti-IgM for 30 minutes or 6 hours and then harvested. The cell pellet was resuspended in 100uL hypotonic buffer A (10mM HEPES-K+ pH7.9, 10mM KCl, 1.5mM MgCl2, 0.5mM DTT) in the presence of protease inhibitor cocktail (PIC, Roche) and phosphatase inhibitor cocktail II (Calbiochem), then incubated on ice for 10 min with vortexing. 1uL of 10% NP-40 was added and samples were vortexed to assist in the lysis for up to 1 min. The nuclei were centrifuged at 14,000 RPM for 5 min at 4°C. The nuclear pellets were resuspended in 50uL buffer C (20mM HEPES-K+ pH7.9, 420mM NaCl, 0.2mM EDTA, 1.5mM MgCl2, 0.5mM DTT, 25% Glycerol) with PIC. Nuclei were incubated on ice for 40 min, and vortexed periodically. Supernatant containing nuclear protein was collected by centrifuging at 14,000 RPM for 10 min at 4°C and then aliquoted and snap frozen for use in EMSAs.
EMSAs were performed as previously described [90,91]. Consensus binding probes (oligonucleotides) for AP1 (#sc-2501), Ets (#sc-2549), and NFAT (2) (#sc-2577) were obtained from Santa Cruz. All other probes were custom designed and ordered from IDT. Their sequences are as follows: NFAT (1) consensus EMSA 5’- AGAAAGGAGGAAAAACTGTTTCATACAGAAGGCGTT and 5’- AACGCCTTCTGTATGAAACAGTTTTTCCTCCTTTCT; ELK1 consensus EMSA 5’- GGGGTCCTTGAGGAAGTATAAGAAGAAT and 5’- ATTCTTCTTATACTTCCTCAAGGACCCC; Zp-P -155 to -127 EMSA 5’-CCTCCTCCTCTTTTAGAAACTATGCATGA and 5’- TCATGCATAGTTTCTAAAAGAGGAGGAGG; Zp-V3–155 to -127 EMSA 5’-CCTCCTCCTCTTTTGGAAACTATGCATGA and 5’- TCATGCATAGTTTCCAAAAGAGGAGGAGG; Zp-P -155 to -120 EMSA 5’-CCTCCTCCTCTTTTAGAAACTATGCATGAGCCACAG and 5’- CTGTGGCTCATGCATAGTTTCTAAAAGAGGAGGAGG; Zp-V3–155 to -120 EMSA 5’-CCTCCTCCTCTTTTGGAAACTATGCATGAGCCACAG and 5’- CTGTGGCTCATGCATAGTTTCCAAAAGAGGAGGAGG. EMSAs were performed with binding buffer (50 mM KCl, 25 mM Hepes (pH 7.6), 10% glycerol, 1 mM EDTA, 0.5 mM spermidine, 0.5 mM PMSF, and 1 mM DTT) with 2 μg of poly(dI/dC):poly(dI/dC) (Pharmacia) and 2ug BJAB nuclear extract. The protein and binding buffer mixture was allowed to incubate for 5 min at room temperature, and then 20,000 cpm of γ-32P ATP labeled probe were added. The mixture containing labeled probes was allowed to incubate for an additional 20 min. For supershift conditions 1-2ug anti-NFATc1 (Santa Cruz #sc-13033x), anti-XBP1 (Santa Cruz #sc-7160x), anti-cFos (Santa Cruz #sc-52x), or anti-C/EBPα (Santa Cruz #sc-61x) antibodies were added to the protein before addition of radiolabeled probe and allowed to incubate for 20 minutes. For cold competitor conditions 10X excess unlabeled probe was added to the protein before addition of radiolabeled probe and allowed to incubate for 20 minutes.
Immunoblotting was performed as previously described [52]. The following primary antibodies were used: anti-EBNA1 (Santa Cruz #sc-81581), anti-EBNA2 (Abcam #ab90543), anti-LMP1 (Abcam #ab78113), anti-β-actin (Sigma #A5441), anti-GAPDH (Cell Signaling Technology #D16H11), anti-BMRF1 (Millipore #MAB8186), anti-p18 (Thermo Scientific #PA1-73003), anti-BZLF1 (Santa Cruz #sc-53904), anti-R rabbit polyclonal antibody directed against the R peptide (peptide sequence EDPDEETSQAVKALREMAD), anti-NFATc1 (Santa Cruz sc-17834) and anti-tubulin (Sigma T5168). The secondary antibodies used were horseradish peroxidase (HRP)–goat anti-mouse (Thermo Scientific #31430) and donkey anti-goat (Santa Cruz #sc-2056). Image Studio Lite software was used to quantify levels of Z and R relative to loading control tubulin in Fig 7B.
EBV-positive 293 WT, Zp-P-141G and Zp-P-141G.REV were derived using the BM2710 E. coli, which can mediate the transfer of intact recombinant DNA into mammalian cells due to expression of the invasin gene from Yersinia pseudotuberculosis and the listeriolysin O gene from Listeria monocytogenes [89]. Briefly, Bacmids were electroporated using a 0.1 cm gap cuvette (1.5 kV, 200 Ohms, 25 μF) into BM2710 E. coli and selected with Kanamycin and Spectinomycin. BM2710 E. coli containing the respective Bacmid were used to infect EBV-negative 293 cells by co-incubation for 2 hours (approximately 25 bacteria per cell). Cell lines were derived by single-cell cloning and screened for ability to complete the lytic cascade by immunoblotting for viral late protein VCAp18 (product of EBV BFRF3) and titering cell-free virus on Raji cells. Cells were selected and maintained with 100–200μg/ml of Hygromycin B.
Infectious viral particles were produced from 293 cell lines stably infected with the wildtype or mutant B95.8 viruses as previously described [92]. To determine the titer of the virus, Raji cells were infected with serial 10-fold dilutions of virus. After 24 hours, cells were treated with 50 ng/ml TPA and 3 mM sodium butyrate, and the number of GFP-expressing Raji cells was counted 24–48 hours later by fluorescence microscopy.
B cells were centrifuged and resuspended in media containing wildtype, Zp -141 mutant, or revertant B95.8 virus (produced by 293 cell lines and titered on Raji cells) for a total volume of 500uL and an MOI of 0.1 or 0.25 (primary B cells) or 1 (BJAB, Akata and Mutu cells). Cells and virus were incubated for 1–3 h with occasional stirring, then media was increased to 4 mL for overnight incubation. The next day cells were spun down and resuspended in fresh media, except for human peripheral blood CD19+ B cells, which were not centrifuged. Peripheral blood CD19+ B cells and Akata cells infected with EBV were harvested at day 3 post-infection, and BJAB cells at 6 days after infection, and extracts containing equal amounts of protein used for immunoblots. Mutu cells were selected with 300 ug/mL hygromycin B starting at day five post-infection to create stable cell lines; Mutu lines were under selection for at least two months before other experiments were performed.
To determine the number of EBV-infected CD19+ B cells, and the mean fluorescence intensity, a portion of cells was analyzed with a LSRII flow cytometer (BD Biosciences) three days post-infection. Data analysis was performed using FlowJo software.
EBV-infected Mutu cells were nucleofected with 120 pmol control siRNA (Santa Cruz sc-37007) or NFATc1 siRNA (29412) using Amaxa program N-16 in Buffer V. Ionomycin or DMSO control was added after 48 hours. Cells were harvested 72 hours post-nucleofection and immunoblots performed using equal amounts of protein.
EBV-infected Mutu cells treated with ionomycin for 3hrs were harvested and fixed with 1% formaldehyde in PBS for 8 minutes at room temperature followed by addition of glycine to 125mM for 5 minutes at room temperature to quench the reaction. Fixed cells were pelleted, washed once with PBS and twice with Cell Lysis/Wash Buffer (150mM NaCl, 50mM Tris pH 7.4, 5mM EDTA pH 8.0, 0.5% NP-40, 1% Triton X-100). Pellets were resuspended in ChIP Lysis Buffer (50mM Tris pH 8.0, 10mM EDTA pH 8.0, 1% SDS) and chromatin was sheared by sonication using a QSonica Q700 sonicator (3 rounds of 10 cycles of 30sec on/30sec off at 95% amplitude in an ice water bath). Debris was cleared by centrifugation at 11,500 x g for 10 min at 4°C. Supernatant was then diluted 1:5 in ChIP Dilution Buffer (16.7mM Tris pH 8.0, 167mM NaCl, 1.2mM EDTA, 1.1% Triton X-100, 0.01% SDS) and chromatin from approximately one million cells was incubated with 3ug of rabbit anti-NFATc1 antibody (Bethyl Laboratories A303-508A) or rabbit IgG control antibody (Millipore 12–370) overnight at 4°C. Chromatin/antibody complexes were isolated with Magna ChIP Protein A+G magnetic beads (Millipore 16–663) and subsequently washed with Low Salt Buffer (20mM Tris pH 8.0, 150mM NaCl, 2mM EDTA, 1% Triton X-100, 0.1% SDS), High Salt Buffer (20mM Tris pH 8.0, 0.5M NaCl, 1% Triton X-100, 0.1% SDS), LiCl Buffer (10mM Tris pH 8.0, 0.25M LiCl, 1mM EDTA pH 8.0, 1% NP-40, 1% DOC), and TE (10mM Tris pH 8.0, 1mM EDTA pH 8.0). Crosslinks were reversed and DNA was isolated with an IBI Gel/PCR DNA fragment extraction kit (IB47030; IBI) and quantitated by qPCR using iTaq Universal SYBR Green Supermix (172–5124; Bio-Rad) and primers to the Z promoter (FWD 5′-GCCATGCATATTTCAACTGGGCTG-3′ and REV 5′-TGCCTGTGGCTCATGCATAGTTTC-3′) and analyzed using an ABI 7900HT real-time PCR system with SDS2.4 software (Applied Biosystems).
Transformation titration assays were performed by infecting human peripheral blood CD19+ B cells (10,000 cells/well in a 96-well microtiter plate) with 0.25 infectious GFP Raji Units/cell (10 replicates per virus) and culturing with RPMI complete medium. Wells with clearly growing LCLs (lymphoblastoid cell lines) were scored microscopically after at least 3 weeks of culture.
A portion of the Zp sequences analyzed in this paper (many of which were also previously analyzed for Zp type) were derived from publicly available sequences of EBV genomes deposited in Genbank. Z promoter sequences that were not previously analyzed for the type of Zp variant present are aligned in S6 Table. Zp promoters were considered to have Zp-V3 if they contained the Zp-V3 specific nucleotide located at position -141.
In addition to Genbank EBV sequences, the TCGA database was interrogated for EBV-positive gastric cancers (stomach adenocarcinoma or STAD) by using the Genomic Data Commons Application program interface (GDC-API) to perform BAM slicing on harmonized TCGA data. Reads mapping to "chrEBV" were sliced and Samtools was used to quantify the number of reads with MAPQ of 20 or greater. Tumor EBV read count was a bimodal distribution with 27 tumors having 2,888–53,779 reads and 379 having 0–217 reads and one tumor with 534 reads. All tumors with >500 reads were treated as EBV-positive.
Zp variant calling was performed on all 28 EBV-positive samples obtained from the TCGA database using any informative reads that could be obtained from RNAseq and whole exome sequencing (WXS) data for each tumor. The GDC-API was used to perform BAM slicing on GDC harmonized TCGA data from chrEBV in the region 91006–47. Zp-P or Zp-V3 calls were made based on the following position: nt91006 "-100" Zp-P = A Zp-v3 = C; nt91012 "-106" Zp-P = T Zp-v3 = C; nt91047 "-141" Zp-P = T Zp-V3 = C. For 5 samples for which neither RNAseq nor WXS provided informative coverage of the Z promoter, whole genome sequencing data was obtained from the GDC legacy archive. Reads mapping to NC_007605 in the BAM files were manually reviewed in IGV 2.3.82 and Zp-P or Zp-V3 calls were made as above. To estimate the background prevalence of each Zp genotype in the TCGA database, blood and normal tissue WXS (whole exome sequencing) datasets from TCGA were interrogated using the GDC-API to perform BAM slicing for reads mapping to "chrEBV." Samtools was used to identify samples containing reads from chrEBV: 91006–91047 with MAPQ of 20 or greater. These BAM files were then manually viewed in IGV 2.3.82 and Zp-P or Zp-V3 calls were made based as for tumor tissues. Additional criteria included requiring base quality of 10 or greater at the call position and the mate read had to also map to chrEBV.
Available RNA-seq data from endemic Burkitt tumors were also downloaded from sequence read archive (SRA) PRJNA292327 [58]. Fastq files were then aligned to the type 1 and 2 EBV genomes (AJ507799.2 and NC_009334.1) using BWA’s backtrack algorithm with default settings.
Zp alignment for TCGA samples was performed as follows. BAM slices from GDC for each sample were evaluated in IGV v2.3.82. Consensus quality scores were calculated by summing individual Phred scores when reads had different mates. Bases with consensus quality scores less than 20 or without read coverage were represented with a (-). Bases with a consensus quality score of 20 or more were represented with an uppercase letter.
LCLs were transformed by infectious EBV particles present in the breast milk of Kenyan women that had malaria during pregnancy living in a rural region of Kisumu County, as previously described [17]. DNA was isolated from these LCLs and the EBV Zp amplified by PCR using the following primers: Zp+34 primer 5’-GCAAAGATAGCAAAGGTGGC and Zp-561 primer 5’-GAACCGGTCGGATCCCTAAC. Sequencing of the Zp promoter was performed using the Zp +34 primer.
The program Mstat, Version 6.1, was used for all statistical analyses (N. Drinkwater, McArdle Laboratory for Cancer Research, School of Medicine and Public Health, University of Wisconsin) and is available for downloading (http://www.mcardle.wisc.edu/mstat).
The primary human peripheral CD19+ B cells from healthy donors used in this study are considered exempt by the University of Wisconsin-Madison Institutional Review Board (IRB). These cells were purchased from Stem Cell Technologies (#70033), which obtained written donor consent using IRB-approved protocols.
The anonymized EBV breast milk samples used for this study have been previously documented [17]. The original study received approval from the Kenya Medical Research Institute (KEMRI), the University of Colorado COMIRB, and Upstate Medical University (where R. Rochford was at initiation of study) Review Boards. Written informed consent was obtained from all study participants before any sample collection.
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10.1371/journal.pmed.1002337 | Low-intensity cognitive-behaviour therapy interventions for obsessive-compulsive disorder compared to waiting list for therapist-led cognitive-behaviour therapy: 3-arm randomised controlled trial of clinical effectiveness | Obsessive-compulsive disorder (OCD) is prevalent and without adequate treatment usually follows a chronic course. “High-intensity” cognitive-behaviour therapy (CBT) from a specialist therapist is current “best practice.” However, access is difficult because of limited numbers of therapists and because of the disabling effects of OCD symptoms. There is a potential role for “low-intensity” interventions as part of a stepped care model. Low-intensity interventions (written or web-based materials with limited therapist support) can be provided remotely, which has the potential to increase access. However, current evidence concerning low-intensity interventions is insufficient. We aimed to determine the clinical effectiveness of 2 forms of low-intensity CBT prior to high-intensity CBT, in adults meeting the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for OCD.
This study was approved by the National Research Ethics Service Committee North West–Lancaster (reference number 11/NW/0276). All participants provided informed consent to take part in the trial. We conducted a 3-arm, multicentre randomised controlled trial in primary- and secondary-care United Kingdom mental health services. All patients were on a waiting list for therapist-led CBT (treatment as usual). Four hundred and seventy-three eligible patients were recruited and randomised. Patients had a median age of 33 years, and 60% were female. The majority were experiencing severe OCD. Patients received 1 of 2 low-intensity interventions: computerised CBT (cCBT; web-based CBT materials and limited telephone support) through “OCFighter” or guided self-help (written CBT materials with limited telephone or face-to-face support). Primary comparisons concerned OCD symptoms, measured using the Yale-Brown Obsessive Compulsive Scale–Observer-Rated (Y-BOCS-OR) at 3, 6, and 12 months. Secondary outcomes included health-related quality of life, depression, anxiety, and functioning. At 3 months, guided self-help demonstrated modest benefits over the waiting list in reducing OCD symptoms (adjusted mean difference = −1.91, 95% CI −3.27 to −0.55). These effects did not reach a prespecified level of “clinically significant benefit.” cCBT did not demonstrate significant benefit (adjusted mean difference = −0.71, 95% CI −2.12 to 0.70). At 12 months, neither guided self-help nor cCBT led to differences in OCD symptoms. Early access to low-intensity interventions led to significant reductions in uptake of high-intensity CBT over 12 months; 86% of the patients allocated to the waiting list for high-intensity CBT started treatment by the end of the trial, compared to 62% in supported cCBT and 57% in guided self-help. These reductions did not compromise longer-term patient outcomes. Data suggested small differences in satisfaction at 3 months, with patients more satisfied with guided self-help than supported cCBT. A significant issue in the interpretation of the results concerns the level of access to high-intensity CBT before the primary outcome assessment.
We have demonstrated that providing low-intensity interventions does not lead to clinically significant benefits but may reduce uptake of therapist-led CBT.
International Standard Randomized Controlled Trial Number (ISRCTN) Registry ISRCTN73535163.
| Many people suffer from obsessive-compulsive disorder (OCD), and if they do not get treatment, it can be a long-term problem.
Although cognitive-behaviour therapy (CBT) with a therapist is effective, many people struggle to get access because of limited numbers of therapists.
Low-intensity versions of CBT (written or web-based materials with limited therapist support) may increase access to care, but evidence of their effectiveness is limited.
We tested 2 low-intensity versions of CBT in a trial (guided self-help and supported computerised cognitive-behaviour therapy [cCBT]), testing their impact on patients with OCD when provided prior to CBT with a therapist.
Neither low-intensity version of CBT led to clinically significant benefits in patient outcomes.
Access to “low-intensity” interventions led to significant reductions in uptake of CBT with a therapist over 12 months.
More patients were satisfied with guided self-help than supported cCBT.
Providing low-intensity interventions does not lead to clinically significant benefits but may reduce uptake of therapist-led CBT.
These findings from a large pragmatic trial showing no clinical benefit from low-intensity treatments are in contrast to other studies published recently.
| Obsessive-compulsive disorder (OCD) has an estimated lifetime prevalence of 2%–3% and is rated among the top 10 causes of disability worldwide, with an estimated US$8.4 billion attributable to OCD in the United States [1]. Providing accessible and effective care for OCD is a priority worldwide.
However, there is evidence that people with OCD struggle to access treatment, with consistent reports of a marked delay between OCD onset and management. One study found a 10-year gap between onset and seeking help and 17 years between onset and receiving effective help [2].
In OCD, both medication and psychological therapy are effective, with the “gold standard” psychological therapy intervention being therapist-led cognitive-behaviour therapy (CBT) [3], with 1-hour weekly sessions delivered predominantly face-to-face over 12–16 weeks. However, it is relatively costly, and the limited availability of specialist therapists means that access can be poor, with long waiting times. Additionally, OCD is characterised by intrusive, unwanted, recurrent, and distressing thoughts, images, or impulses (i.e., obsessions) and repetitive actions or rituals (compulsions). These obsessions and compulsions can make it more difficult for patients to engage with treatment because of fears of contamination or causing harm to others.
Conventional ways of delivering psychological therapy are being challenged. Health systems under financial pressure need to manage demand more effectively through new methods of delivery [4], and innovation is needed to meet the needs of diverse patient populations with complex needs. Research has shown how conventional therapist-led CBT can be delivered effectively in low-intensity forms including guided self-help (written CBT materials with limited telephone or face-to-face support) or computerised CBT (cCBT; web-based CBT materials and limited telephone support). Both forms are potentially cheaper and more accessible than conventional therapist-led CBT and demonstrate evidence of effectiveness in a range of disorders [5,6]. Low-intensity CBT interventions for OCD may provide more rapid relief of symptoms, reduce the need for more expensive therapist-led CBT, and encourage more efficient use of healthcare resources when delivered as part of a stepped care system [7].
At the time this study was commissioned, the evidence base for low-intensity interventions in OCD was far from definitive. Much of the evidence for guided self-help was based on small open or uncontrolled studies [8,9] or comparisons of nonguided self-help with guided self-help [10]. A systematic review of cCBT for OCD found only 4 studies [11]. cCBT reduced rituals and obsessions and improved functioning and was more effective than attention control [12], but not as effective as therapist-led CBT. Clearly, the potential benefits of both guided self-help and cCBT need to be demonstrated in large-scale trials.
We conducted a randomised trial for patients with OCD, allocating patients to either (a) guided self-help prior to therapist-led CBT, (b) cCBT prior to therapist-led CBT, or (c) a waiting list for therapist-led CBT only. We aimed to provide a definitive answer to the following questions:
This study was approved by the National Research Ethics Service Committee North West–Lancaster (reference number 11/NW/0276). All participants provided informed consent to take part in the trial.
We conducted a pragmatic trial, delivered in routine service settings, to provide a balance between internal and external validity and maximise relevance to clinical guidelines [13,14]. The study protocol has been published [15]. The study is reported as per Consolidated Standards of Reporting Trials (CONSORT) guidelines, as described in the CONSORT checklist (S2 Text). Potential participants were most frequently identified by administrative and clinical staff in primary-and secondary-care screening waiting lists, although self-referral was used at 1 site to increase recruitment (via adverts in newspapers, community facilities, and social media). Potential participants were provided with an information pack. Those who provided consent to contact took part in a telephone eligibility screen to determine if they were over 18 years old and not currently receiving a psychological therapy for OCD or experiencing severe and distressing psychotic symptoms. Participants passing the initial screen were offered a face-to-face eligibility appointment.
We included patients who were (1) aged 18+ years, (2) able to read English, (3) currently waiting for access to therapist-led CBT, (4) meeting DSM-IV criteria for OCD (assessed using the Mini-International Neuropsychiatric Interview [16]), and (5) scoring 16+ on the Yale-Brown Obsessive Compulsive Checklist–Self-Report (Y-BOCS-SR [17]).
We excluded patients (1) experiencing active suicidal or psychotic thoughts, (2) meeting DSM-IV alcohol or substance dependence criteria, (3) receiving psychological treatment for OCD, or (4) with language difficulties that would preclude participation.
Patients were randomised (ratio 1:1:1) via a secure web system independently administered by a trials unit to ensure concealment of allocation. Allocation was minimised on OCD severity, OCD duration, antidepressant use, and depression severity. It was not possible to mask participants or clinical staff to treatment allocation. Research staff undertaking assessments were masked to allocation: unmasking was reported in 30%, 22%, and 26% of the 3, 6, and 12 month interviews, respectively. When this occurred, subsequent assessments were done by another researcher to limit bias.
cCBT was delivered using OCFighter (www.ccbt.co.uk), a commercial OCD program. OCFighter involves 9 steps (focussed on exposure and response prevention) to help people with OCD carry out treatment and monitor progress. Participants received a secure login and were advised to use cCBT at least 6 times over 12 weeks. Participants received six 10-minute telephone calls, for risk assessment, progress review, and problem solving.
Guided self-help was delivered using the book Obsessive Compulsive Disorder: A Self-Help Book [18], which is focussed on exposure and response prevention. Participants received weekly guidance, with an initial session (60 minutes face to face or by telephone, dependent on patient preference) followed by 10 30-minute sessions over 12 weeks. The support involved an explanation of the workbook, help devising goals, risk assessment, support for conducting CBT homework, progress review, and problem solving.
Support for both cCBT and guided self-help was provided by “psychological well-being practitioners.” These are graduates with no prior clinical qualifications who receive 12 months training and who are responsible for delivering guided self-help CBT and general education for anxiety and depression in England. Most have limited OCD-specific training. They were trained in both interventions over 3 days by the research team (with additional support from the company supplying cCBT). All staff received telephone supervision every fortnight from the research team or from experienced therapists within routine services. In total, 93 psychological well-being practitioners managed patients in the trial (range 1–18 patients), with 46 practitioners allocated patients in both interventions. Psychological well-practitioner characteristics are reported in Table A in S1 Appendix.
Psychological well-being practitioners recorded dates, length, and mode of contact for all sessions and were asked to record sessions to examine fidelity. We also received automated recordings of cCBT use. Fidelity was evaluated by an independent rater blind to outcome. We defined tasks to be carried out in both interventions, which were coded from recordings as “implicit,” “explicit,” or “absent,” and an overall rating was generated using a 5-point scale (“unacceptable” to “excellent”).
The comparator was waiting list for therapist-led CBT. As the trial was a pragmatic design within routine services, we were unable to mandate a waiting period for therapist-led CBT. We expected that most patients would start therapist-led CBT 3–6 months following allocation, after receiving their low-intensity interventions (where allocated). Therapist-led CBT was typically 8–20 face-to-face, 45–60-minute weekly sessions.
In this pragmatic trial, we placed no restrictions on treatment after randomisation. Before seeing a therapist, patients on waiting lists sometimes received interventions other than our low-intensity interventions, including education, medication, or nonspecific interventions (such as “stress management”). All additional care outside the trial protocol was recorded as part of the economic evaluation.
We conducted follow-up assessments at 3 months (primary outcome timepoint), 6 months, and 12 months following randomisation. The primary outcome measure, Yale-Brown Obsessive Compulsive Scale–Observer Rated (Y-BOCS-OR) [17], was collected face to face at baseline. At follow-up time points where face-to-face collection was not possible following a highly structured standardised operating procedure, telephone or postal assessment using the Y-BOCS-SR was attempted.
The Y-BOCS-OR is an interview-administered structured assessment that provides an indication of OCD symptom severity. It consists of 2 comprehensive symptom checklists exploring current and past symptoms of obsession and compulsion (over the past week and past symptoms) and a 10-item severity scale exploring current obsessive and compulsive symptoms. Impairment over 5 clinical domains is identified: time consumed, functional impairment, psychological distress, efforts to resist, and perceived sense of control on a 5-point Likert scale from 0 (none) to 4 (extreme). Scores from the 10 items are summed to identify level of severity (0–7 subclinical, 8–15 mild, 16–23 moderate, 24–31 severe, and 32–40 extreme).
Secondary outcomes were collected at baseline and at the 3, 6, and 12-month follow-up. Outcomes included the Y-BOCS-SR, a self-report version of the Y-BOCS-OR scale. When it was not possible to complete the Y-BOCS-OR, the Y-BOCS-SR was used as a proxy.
Other secondary outcomes (all self-report) included the Short Form-36 (SF-36) [19] for health-related quality of life; Clinical Outcomes in Routine Evaluation (CORE-OM) [20] for distress; Patient Health Questionnaire (PHQ-9) [21] for depression; Generalized Anxiety Disorder 7-item (GAD-7) scale [22] for generalised anxiety disorder; Work and Social Adjustment Scale (WSAS) [23] for functional impairment; IAPT Employment Status Questions (A13-A14) [24] for employment rates and receipt of statutory sick pay; and the Client Satisfaction Questionnaire (CSQ-8-UK) [25] for satisfaction. Comorbidities (Clinical Interview Schedule-Revised; CIS-R) [26] and demographics were collected at baseline only.
With 3 pair-wise comparisons, alpha was set at 1.67%. We assumed a standard deviation for the primary outcome of 7.3, a correlation between baseline and follow up of 0.43 [27], and a therapist intracluster correlation (ICC) between therapists (0.06) and within therapist (0.015). Assuming 85% retention, 432 patients were required. Trial monitoring suggested a lower follow-up rate, and thus, the sample size was increased to 473 to retain power. In total, 366 patients at follow-up provided power greater than 80% to detect a clinically significant difference of 3 Y-BOCS points for each comparison.
Preliminary modelling determined methods for handling missing data (full details provided in the statistical analysis plan: https://dx.doi.org/10.6084/m9.figshare.3503885). There was no deviation from the prespecified plan, all analyses were conducted, and those not present in this manuscript are reported in the Health Technology Assessment (HTA) report [28]. Missing baseline covariates were imputed by single imputation [29] using other covariates. Analyses of the primary outcome were based on a linear mixed model with random effects for psychological well-being practitioners. As practitioners were crossed with treatment, correlated random effects were included for each treatment, enabling estimation of the ICC for cCBT and guided self-help. We included the following covariates: OCD duration and severity; anxiety, depression, antidepressant use; and gender. Binary outcomes (e.g., uptake of therapist-led CBT) used logistic regression to estimate adjusted odds ratios (ORs), with the same baseline covariates. Analysis used intention-to-treat subject to the availability of data. Distributional assumptions of the models were checked. All outcomes included in the Obsessive Compulsive Treatment Efficacy Trial (OCTET) protocol are detailed.
Patients and members of the public were involved throughout the trial, including the design, management, and conduct of the trial. From the outset, the chief executive of a national user-led organisation (Anxiety UK) was involved as a coapplicant and collaborator. Members of an OCD self-help group assisted with the development of the guided self-help manual and adaptations to one of the trial outcome measures. A service user with OCD was a member of the trial steering committee, while another conducted some of the patient acceptability interviews. The findings have been disseminated to trial participants, and the results presented at a national user conference.
We opened recruitment in 15 clinical sites in England between February 2011 and May 2014, with the last follow-up in May 2015. There were 2 postrandomisation exclusions: one aged under 18 years and one at risk of suicide. In total, 473 eligible patients were randomised (see Fig 1). Baseline sociodemographic characteristics are presented in Table 1, with baseline clinical data presented in Table 2. Data were indicative of severe OCD, mild-to-moderate depression, and moderate anxiety. Just over half reported previous professional help with OCD, and around half were using antidepressants. More than half reported OCD for more than 10 years. There were no substantial differences at baseline.
Patient flow is shown in Figs 1 and 2. Retention rates were 81% at 3 months, 75% at 6 months, and 71% at 12 months and were broadly similar across arms (Fig 1). Contrary to expectation, approximately 29% of patients started to access therapist-led CBT prior to the 3-month assessment (Fig 2). More detailed data on CBT uptake and predictors of uptake are detailed separately (Tables B and C in S1 Appendix).
Fifty-nine percent of participants had at least 1 session with a psychological well-being practitioner in cCBT. The mean number of sessions was 2.3 (SD 2.5), and the average length was 13.4 minutes (93% by telephone). Of the 9 cCBT steps, the mean number completed was 3.7 (SD 3.2). Sixty-five percent of participants had at least 1 session with a psychological well-being practitioner in guided self-help. The mean number of sessions was 4.1 (SD 4.3) over 57 minutes for session 1 and 31 minutes for sessions 2–11 (48% face-to-face, 26% telephone, 22% both, 4% missing). Rates of recording for fidelity assessment were low (26% guided self-help, 17% cCBT). Of the sessions recorded in cCBT, 11 (65%) were rated “good” and 6 (35%) as “excellent.” Of the sessions in guided self-help, 9 (21%) were rated “satisfactory,” 24 (56%) rated “good,” and 10 (23%) “excellent.”
Table 3 gives the summary statistics for the primary (Y-BOCS-OR) and selected secondary outcomes (Y-BOCS-SR, CSQ-8, and EuroQol five dimensions questionnaire [EQ-5D]). Complete outcomes are reported separately (Table D in S1 Appendix).
There was no significant benefit of access to cCBT (adjusted mean difference = −0.71, 95% CI −2.12 to 0.70, p = 0.325). There was statistically significant benefit of guided self-help (adjusted mean difference = −1.91, 95% CI −3.27 to −0.55, p = 0.006), although the effect was less than the prespecified “clinically important difference” of 3 points.
Analyses of secondary outcomes (Tables E, F, and G in S1 Appendix) showed only 1 significant difference, an effect of cCBT on anxiety (adjusted mean difference = −1.50, 95% CI −2.67 to −0.33, p = 0.012).
Satisfaction data are shown in Table 3. There were no differences in patient satisfaction among patients receiving cCBT compared to those allocated to a waiting list (adjusted mean difference = −0.31, 95% CI −2.07 to 1.45, p = 0.732). Patients receiving guided self-help tended to be more satisfied than those allocated to a waiting list for therapist-led CBT (adjusted mean difference = 1.69, 95% CI −0.04 to 3.42, p = 0.055), although the estimate did not reach significance according to the corrected significance level. Patients receiving cCBT were less satisfied than those receiving guided self-help (adjusted mean difference = −2.00, 95% CI −3.63 to −0.37, p = 0.016).
There was no significant long-term benefit from access to either guided self-help or cCBT (cCBT adjusted mean difference = −1.37, 95% CI −3.59 to 0.84, p = 0.224; guided self-help adjusted mean difference −2.37, 95% CI −4.37 to −0.38, p = 0.02; Table 2).
As a post hoc analysis, we tested whether low-intensity interventions were formally noninferior to waiting list for therapist-led CBT at 12 months. A 98.33% confidence interval corresponds to a 1.67% significance level that we have used for hypothesis testing. For the comparison of cCBT against waiting list, the 98.33% confidence interval is −4.07 to 1.33, and for guided self-help against waiting list, it is −4.81 to 0.06. Given that the upper limits are substantially smaller than the prespecified criterion for a clinically important difference (3 points), we conclude that both interventions are noninferior to waiting list at 12 months.
Therapist-led CBT uptake is shown in Fig 2. Both interventions were associated with significantly lower uptake of therapist-led CBT at 12 months (cCBT adjusted OR = 0.34, 95% CI 0.15 to 0.79 p = 0.011; guided self-help adjusted OR = 0.27, 95% CI 0.12 to 0.60 p = 0.001) (Table 4).
Post hoc, we compared intervention use and 12-month OCD outcomes among guided self-help and cCBT patients who did and did not access therapist-led CBT (Table H in S1 Appendix). Although lacking randomisation, the data do not suggest that those who accessed only guided self-help or cCBT demonstrated markedly worse outcomes than those who accessed both a low-intensity intervention and therapist-led CBT (Table I in S1 Appendix).
We assessed the role of 2 low-intensity interventions (guided self-help and cCBT) in OCD. Prior to access to therapist-led CBT, guided self-help demonstrated statistically significant benefits over the waiting list, but the difference did not meet the prespecified criterion for clinical significance. cCBT did not demonstrate significant benefit at the 3- or 12-month follow-up. Access to low-intensity interventions does not provide more rapid symptom relief.
Over 12 months, access to low-intensity interventions prior to therapist-led CBT did not significantly augment the effects of therapist-led CBT on OCD symptoms in the longer term. Rapid access to low-intensity interventions did lead to significant reductions in uptake of therapist-led CBT, which did not compromise patient outcomes at 12 months.
To our knowledge, we conducted the largest trial of psychological therapy for OCD worldwide. We achieved acceptable levels of retention. When patients were not able to provide the primary clinical outcome using the observer-reported version, we used self-report as a proxy. These different measures show high associations [30,31], with some evidence of lower scores in the self-reported version, but proxy measures were only used in 8% and 11% of cases at 3 and 12 months, with minimal differences in rates of use between arms. In this pragmatic trial, recruitment was over multiple sites and involved a large number of psychological well-being practitioners. This enhances external validity, as delivery was not restricted to a small number of specialised sites or highly selected professionals. However, many psychological well-being practitioners only saw a few patients, which restricted the opportunity to practice their skills. Uptake of the interventions was reasonable (65% guided self-help and 59% cCBT). Collecting detailed data on fidelity proved difficult, but analysis of the data provided some evidence that delivery of guided self-help and cCBT was in line with protocols.
Several issues are worth noting in this pragmatic design. First, we did not mandate a defined waiting time for therapist-led CBT, although the expectation was 3–6 months. In practice, around 40% of patients allocated to a waiting list for therapist-led CBT started to receive some contact with their therapist before 3 months, compared with just over 20% in the guided self-help and cCBT groups. This would reduce differences in outcomes between guided self-help, cCBT, and the waiting list comparator at 3 months, leading to conservative estimates of effect. Still, these data refer to patients receiving any contact with therapist-led CBT, which in most cases would involve an initial session or two, rather than a full “dose” of treatment. Nevertheless, relatively quick access to CBT in the waiting list arm would have reduced short-term benefit associated with the low-intensity interventions. The effects of low-intensity interventions may have been more pronounced at 3 months if CBT was less accessible than in the current trial. The longer-term analyses are less affected, as all patients were expected to receive both a low-intensity intervention (where allocated) and therapist-led CBT over 12 months.
We have shown that uptake of therapist-led CBT was lower in the groups allocated to low-intensity treatment. This could reflect positive outcomes from some aspects of the low-intensity interventions, although our analysis showed that this was largely restricted to patient satisfaction rather than clinical benefits. Even in the absence of significant clinical benefits, providing low-intensity treatments may give patients a sense of support and progress. When combined with natural improvement in symptoms over time (as found in all groups), this may mean that patients do not feel a need for further intensive support or no longer wish to engage with services. However, the numbers of patients attending therapist-led CBT increased in all groups over time. It is possible that, had our follow-up been longer than 12 months, eventual uptake of therapist-led CBT across all groups would be the same.
Secondly, we placed no restrictions on medication use, and in line with most psychological therapy trials in OCD, a proportion of patients were taking medication. Baseline self-reported use of antidepressant treatment is provided in Table 1, showing that about half the patients reported using antidepressants, with no differences between groups. Data on antidepressant use after allocation showed that, over the 12-month period of the trial, self-reported use of antidepressants decreased (cCBT 26%, guided self-help [GSH] 32%, and waiting list 27%) [28]. We do not have details of the nature or quality of antidepressant prescription. Although antidepressants are effective in OCD [3,32], it seems unlikely that such small differences between arms would be a major driver of study outcomes.
Thirdly, there was no attempt to match the level or type of clinician contact across the 2 low-intensity interventions: indeed, the study was specifically designed to test the relative value of 2 different forms of low-intensity intervention. GSH involved both a different delivery format and more clinician contact, so our trial is not a strict comparison of paper and digital interventions. Although increased clinician contact may well enhance acceptability and effectiveness, its additional costs were accounted for in the economic analysis.
Fourth, we did not undertake quality assurance of the therapist-led CBT provided to all patients. As noted above, therapist-led CBT was provided by a range of practitioners in a range of areas and is likely to be reasonably representative of the treatment provided in the National Health Service (NHS) in England, which remains optimal for a pragmatic trial. Formal measurement of quality would have been preferable but logistically complex.
The trial adopted aspects of a stepped care model, whereby patients are offered a low-intensity intervention first, with a proportion progressing to therapist-led CBT. However, unlike true stepped care, there was no regular assessment of outcome, and access to therapist-led CBT was available to all, rather than as part of a defined “stepping” mechanism following nonresponse to treatment or deterioration. Therefore, our analysis does not assess the benefits of low-intensity treatment in a full stepped care model.
At the initiation of this trial, the evidence base was very limited [11]. While the current trial was being delivered, a number of additional studies were published. One small trial (n = 56) used similar interventions to the present study (GSH and supported cCBT) and compared their effects in a group of volunteers recruited through a website. Very large effects were found post-treatment [33]. A second trial (n = 86) exploring a minimally supported cCBT intervention again found very large effects in a sample recruited online through a research centre [34]. A third trial randomised 34 patients with OCD to a supported internet-based writing therapy and again found very large effects among a sample recruited via public notices. A long-term follow-up [35] of a previous trial [12] showed enduring effects for cCBT and that “booster” treatments were effective in maintaining gains. Finally, one trial (n = 128) explored the effects of unsupported written material about metacognitive therapy in patients with OCD from internet groups, self-help organisations, and clinical facilities and found small benefits [36].
The picture from these trials is more positive about the clinical benefits of “low-intensity” treatments, especially supported cCBT, with most effect sizes over 0.5 and some exceeding 1.0. This contrasts markedly with the modest clinical impacts observed in the current study. There are a number of reasons that could account for these differences. The interventions do vary, although it is not clear that the variation is large enough to account for the large variation in effects. The current sample of patients have more severe symptoms at baseline (Y-BOCS scores of 25, compared to 20–21 in the other studies), although again it is not clear that such modest differences would be expected to lead to such profound variation in impact. Our current study is far larger than the other trials, and some report quite large differences at baseline, which can occur when small numbers of patients are randomised [33,36]. A potentially important issue is the method of recruitment. The vast majority of the patients in the current study were recruited through routine clinical services, whereas a number of the other trials used recruitment through the internet; this may produce a sample with different clinical features and one that is much more amenable to online cCBT interventions. Similar differences in effects between large pragmatic trials in routine services and smaller trials recruiting through the internet have been recently reported in depression [37].
The trial demonstrated that neither form of low-intensity CBT was responsible for clinically significant improvements in OCD symptoms among patients on the waiting list.
In the absence of any significant clinical benefit over waiting list only, readers may have concerns about reductions in the use of therapist-led CBT at 12 months, as this might reflect the substitution, or delay, of an evidence-based treatment. It may be that access to GSH or cCBT means that patients are put off from engaging in subsequent therapist-led CBT. We found no evidence that lower uptake of therapist-led CBT was associated with worse outcome over 12 months. Concerns that GSH or cCBT inappropriately discourages patients from engaging in subsequent therapist-led CBT are not supported by the wider literature, which shows an increased likelihood of help seeking and greater healthcare use following self-help [38,39]. It is possible that some patients who are offered GSH or cCBT improve so that they do not need subsequent therapist-led CBT or make an informed choice to discontinue therapy sooner rather than later. However, as noted earlier, differences in uptake between arms may have reduced if a longer follow-up had been possible.
Our results raise questions about the targeting of low-intensity interventions. Recruiting from waiting lists identified a sample with severe symptoms and a relatively long history of treatment. Although most showed significant improvements over time (around 8–9 points on the Y-BOCS across groups), the means at 12 months still showed significant symptoms (around 16 points), meaning many would continue to be eligible for the trial. Routine provision of CBT within the NHS in line with clinical guidelines clearly leaves many patients with clinically significant residual symptoms. Low-intensity treatments may be better targeted at a less severely ill group, closer to the onset of their OCD. However, as this patient group is characterised by late presentation to services, the viability of this is unclear.
Neither cCBT nor GSH showed clinically significant benefits at 3 months. Further development of more effective low-intensity interventions may be required. Uptake of the interventions was relatively low, although not abnormally so for a pragmatic trial. Both interventions may benefit from enhancements that might improve motivation or adherence, which might translate to greater clinical benefit.
The clinical results alone do not support an important role for low-intensity interventions in the care pathway for OCD. However, full interpretation of the benefits of low-intensity interventions for OCD also demands consideration of cost-effectivness, using comprehensive assessments of costs, as well as appropriate measures of the impact of these interventions on health-related quality of life and associated utility. We report the results of this analysis separately [28].
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10.1371/journal.ppat.1006659 | Disrupting assembly of the inner membrane complex blocks Plasmodium falciparum sexual stage development | Transmission of malaria parasites relies on the formation of a specialized blood form called the gametocyte. Gametocytes of the human pathogen, Plasmodium falciparum, adopt a crescent shape. Their dramatic morphogenesis is driven by the assembly of a network of microtubules and an underpinning inner membrane complex (IMC). Using super-resolution optical and electron microscopies we define the ultrastructure of the IMC at different stages of gametocyte development. We characterize two new proteins of the gametocyte IMC, called PhIL1 and PIP1. Genetic disruption of PhIL1 or PIP1 ablates elongation and prevents formation of transmission-ready mature gametocytes. The maturation defect is accompanied by failure to form an enveloping IMC and a marked swelling of the digestive vacuole, suggesting PhIL1 and PIP1 are required for correct membrane trafficking. Using immunoprecipitation and mass spectrometry we reveal that PhIL1 interacts with known and new components of the gametocyte IMC.
| Transmission of the malaria parasite from humans to mosquitoes relies on the formation of the specialised blood stage gametocyte. Plasmodium falciparum gametocytes mature over about 10 days, during which time they undergo a remarkable morphological transformation, eventually adopting a characteristic crescent shape. The shape changes are thought to facilitate the mechanical sequestration of maturing gametocytes within the bone marrow and spleen, as well as the eventual release into the circulation. Failure to mature correctly leads to a failure to transmit. Despite the importance of this process, little is known about the molecular basis of elongation. In this work, we introduce 3D Electron Microscopy of P. falciparum gametocytes and use it, in a combination with super-resolution optical microscopy, to elucidate the genesis and expansion of the molecular structures that drive gametocyte elongation. We use protein interaction profiling to identify some of the proteins that help drive the shape change and employ inducible gene knockdown strategies to show that these proteins play a role in remodeling membranes, and are needed for gametocyte elongation. This work points to potential targets for the development of transmission-blocking therapies.
| Plasmodium falciparum, the deadliest of the human malaria parasites, continues to impose an enormous economic and public health burden globally, resulting in about 480,000 deaths each year [1]. Nonetheless, considerable progress has been made in preventing and treating malaria infections and in targeting the mosquito vector, as evidenced by a decrease in mortality rates of more than 50% in some parts of Africa in recent years [1]. To cement these gains, there is an urgent need to target the transmission-competent sexual stage gametocyte, because asymptomatic carriers with persistent low level gametocytemia serve as a parasite reservoir during the low transmission season, leading to a resurgence of infections when mosquito numbers increase [2]. Transmission-ready P. falciparum gametocytes appear in the bloodstream 2–3 weeks after the first appearance of asexual stage parasites [3]. Ingestion of gametocytes by the Anopheles vector triggers their release from red blood cells (RBCs) and sexual reproduction in the mosquito gut. The resultant infectious sporozoites migrate to the salivary glands, and are transferred to the bloodstream of a new host when the mosquito next feeds.
P. falciparum gametocytes develop through five distinct stages over a period of 10–12 days as they prepare for transmission. Later stage gametocytes adopt a characteristic crescent or falciform shape unique to P. falciparum (see [4] for review). These shape changes are hypothesized to facilitate mechanical sequestration of developing gametocytes in privileged sites that include the bone marrow [5], thereby avoiding scrutiny and clearance from the circulation by the spleen. Upon maturation, stage V gametocytes release from their sites of sequestration and re-enter the circulation.
Gametocyte elongation is driven by a network of microtubules that assemble underneath flattened cisternal membrane compartments, known as the inner membrane complex (IMC) [6–8]. While related to the IMC of the invasive or motile stages (merozoite, ookinete and sporozoites), the gametocyte IMC has stage-specific functions that likely involve an as-yet poorly defined specialized set of proteins. Moreover, little is known about the genesis, development and organization of the supporting microtubule network and its interplay with the IMC.
To investigate the process of IMC assembly, we undertook a detailed ultrastructural survey of this organelle across the five stages of gametocyte development, highlighting the formation and expansion of the IMC plates as the gametocyte matures. We have investigated a previously identified IMC protein from Toxoplasma gondii, the Photosensitized 5-[125I] iodonaphthalene-1-azide Labeled protein-1 (PhIL1) confirming it as a component the IMC in Plasmodium falciparum [9]. Our experiments revealed the presence of PhIL1 at the IMC in very late stage schizonts and all stages of gametocyte development. We performed immunoprecipitation experiments with PhIL1-GFP parasites identifying eight interacting proteins, including three known IMC proteins and three new putative IMC proteins. The top PhIL1 Interacting Protein, PIP1, localizes to the IMC, but shows a more restricted localization than PhIL1 in stage IV gametocytes. Functional characterization of PhIL1 and PIP1 by gene knockdown show that upon depletion of either of these proteins, gametocyte elongation is ablated and the gametocytes fail to reach maturity. The IMC of knockdown parasites is under-developed and they exhibit a swollen digestive vacuole, indicating a role for PhIL1 and PIP1 in the membrane trafficking events that drive IMC genesis, expansion and gametocyte elongation.
Serial block-face scanning electron microscopy (SBF-SEM) generates high-resolution 3D images of larger sample volumes at ~50 nm resolution [10, 11]. We employed SBF-SEM to obtain a detailed map of membrane organization and organelle placement across gametocyte development (Fig 1). The cellular features are revealed in individual SBF-SEM “sections” (Fig 1 top panels). Rendering of these features in successive sections provides a 3D view of parasite ultrastructure (Fig 1 bottom panels). Features such as the RBC membrane (RBC), parasite plasma membrane/parasitophorous vacuole membrane (PM, blue) and nucleus (N, yellow) are readily identified. From late stage II gametocytes the mitochondrion (M, red) can be seen in addition to the apicoplast (A, orange) (Fig 1D–1H). Mature stage IV and V gametocytes have osmophillic bodies (Ob, light blue) (Fig 1G and 1H). These are best appreciated by viewing the translations through the SBF-SEM sections (S1–S3 Videos) and the 3D rotations of the 3D rendered images (S4–S6 Videos). Parasite elongation (length to width ratio) reached a maximum value (4.6) at stage IV, and then decreased (3.3) at stage V. Nuclear elongation reached a maximum value (3.3) at stage III, then decreased at stage V (1.2). Mitochondrial elongation reaching a maximum value (3.5) at stage IV, then decreased at stage V (1.4) (S1 Fig). Analysis of 35 individual stage III/IV gametocytes revealed no evidence for apical-basal polarity, as is observed in the invasive or motile stages of development (merozoites, sporozoites and ookinetes) where the IMC is also present. That is, there is no apical complex, the nucleus was roughly centrally located, and we observed a roughly even distribution of the mitochondrion and digestive vacuole complex with respect to the two ends and two sides of the cell (Supp S2 Fig, S1 Table).
We next investigated IMC formation by SBF-SEM. The phospholipid-rich (double membrane) nature of the IMC promotes binding of the reduced osmium stain, thus permitting its detection by intensity thresholding and semi-automatic segmentation (Fig 2; see S1–S6 Videos). In early stage II gametocytes, regions of thickened membranes are observed (Fig 2A, SBF-SEM section, yellow arrow). Rendering of this structure reveals a narrow semi-circular strip of thickened membrane at the parasite periphery (Fig 2A, purple, yellow arrow). Pockets of deposited membrane material are also observed in other regions of the cell (Fig 2A, blue arrow). The zoom image shows the formation of a disk-like structure (Fig 2A, zoom, yellow arrow). As the parasite elongates to the “lemon-shaped” stage IIb, the initial line of membrane expands to form a ribbon (Fig 2B, yellow arrow). The striped appearance of the IMC is already evident at this stage (Fig 2B zoom, yellow arrow IMC plates). By stage III, the IMC begins to expand and wraps further around the parasite (Fig 2C and 2D). Regions of extra thick membrane are evident along the growing edge of the IMC (Fig 2C and 2D, yellow arrow and zooms). These membrane thickenings are seen within the SBF-SEM sections (Fig 2C and 2D, yellow arrows). This suggests that the IMC plates are extended laterally by deposition of membrane material at the leading edge. By stage IV, the parasite is almost completely enclosed by the IMC and elongation is most pronounced (Fig 2E). Rotations at 0 and 90° of the rendered model highlight the structure of the IMC plates and the expansion of the IMC structure (Fig 2E, yellow arrows). These rotations illustrate the opening where the IMC plates have not fused (Fig 2E, 90° white arrow and see S6 Video for rotations). Our 3D-SIM analysis revealed that the IMC is made up of 13 cisternal plates in total of which the two end plates are larger. Examination of the SBF-SEM reveal that the 11 internal plates (Fig 2E, yellow arrow) have an average width of 0.85 ± 0.27 μm, while the 2 plates near the tips of the gametocytes (Fig 2E, blue arrow) have an average width of 2.2 ± 1.6 μm. By stage V the ends of the parasite are rounded and the IMC stripes are no longer evident in SBF-SEM (Fig 2F), potentially due to a relaxing of the gametocyte upon disassembly of the microtubule network. Indentations of the parasite surface and IMC, which occur during fixation, may represent areas of weakness in the IMC and parasite plasma membrane, as the microtubule skeleton is disassembled, in the mature stage V parasite (S3 and S6 Videos).
To further study IMC genesis and organization, we characterized the P. falciparum homologue (PF3D7_0109000.1) of T. gondii PhILl, which shares 56% sequence identity within the C-terminal region (amino acids 130–217). S3 Fig provides a protein alignment of the sequences for a number of PhIL1 homologues in EupathDB. The N-terminal region is highly divergent and contains a motif (amino acids 17–91) that is found in non-muscle myosins. To study the cellular location of PhIL1 a recombinant protein corresponding to full-length PhIL1 (predicted molecular mass of 25,477 Da), was expressed in E. coli and used to generate rabbit polyclonal antiserum. Immunofluorescence microscopy of stage IV gametocytes revealed a strong PhIL1 signal at the periphery, where it is closely associated with β-tubulin (Fig 3A) and was located close to the known IMC marker, the glideosome-associated protein 50 (GAP50) (Fig 3B). Likewise, PhIL1 was found at the IMC in late stage schizonts (S4A and S4B Fig). The specificity of the immune serum is indicated by its recognition of a single band of ~28 kDa in Western blots of the pellet fraction of saponin-permeabilized gametocytes (3D7 WT and GAP50-GFP transfectants, Fig 3C). No bands were observed when protein extracts were probed with pre-immune sera (Fig 3C). This sample was also positive for the IMC marker, GAP45 (Fig 3C). Probing with anti-GFP confirmed the correct expression of the GAP50-GFP chimeric protein and the ER marker PfERC was used as a loading control (Fig 3C; see S10 Fig for full-length blots).
To further investigate the location of PhIL1 in P. falciparum we generated transgenic parasites in which full length GFP-tagged PhIL1 was episomally expressed under the control of the gametocyte-specific Pfs16 promoter (PhIL1-GFP) [12]. A PhIL1-HA line was also generated by integrating 3xHA into the 3’ end of the PhIL1 locus. Immunofluorescence signals for HA and GFP overlapped with the signal from anti-PhIL1 antibody, confirming the correct location of the tagged proteins (Fig 3D and 3E). Western blotting of extracts of transfected gametocytes with anti-PhIL1, anti-GFP and anti-HA revealed the expected bands of 28–30 kDa in the PhIL1-HA line (which sometime appeared as a doublet), and 48 kDa in the PhIL1-GFP line (S4C Fig). Both samples were positive for PfERC (S4C Fig).
The PhIL1 antiserum recognizes a band of ~28 kDa in schizont stage asexual parasites, plus a possible breakdown product at ~20 kDa (S4C Fig). Immunofluorescence microscopy revealed a signal at the periphery of merozoites in very late stage schizont-infected RBCs and in free merozoites (S4A and S4B Fig), colocating with the IMC marker GAP45 (S4B Fig). No PhIL1 signal was observed in ring and trophozoite-infected RBCs and no fluorescence signal was observed with the pre-immune serum.
Saponin-permeabilized PhIL1-HA gametocytes were subjected to solubility profiling using different extraction agents. PhIL1 was only partially extracted with carbonate buffer and Triton X-100, but fully extracted in 8M urea and 2% SDS. This is suggestive of an association with the cytoskeleton and peripheral association with the IMC (Fig 3F). Blots were re-probed with anti-GAP45 to validate the solubility profile (Fig 3F) [9].
To investigate the formation and expansion of the IMC we made use of the PhIL1-HA parasite line and performed 3D-SIM super-resolution microscopy at different stages of early gametocyte development. The initial phase of IMC formation (in stage I) is associated with the deposition of a semi-circular ribbon of PhIL1 at the parasite periphery (Fig 4A, yellow arrow). Co-staining with anti-β-tubulin antiserum reveals punctate buds that form an alternating pattern within the PhIL1 ribbon (Fig 4A, blue arrow). Puncta of β-tubulin are also observed in other regions of the cell; these may represent microtubule “seeds” (Fig 4A). The spatial organization of the PhIL1 and β-tubulin puncta are best appreciated in the zoom in images (Fig 4A, zoom) and in the 3D rotations of the 3D-SIM images (S7 Video). As the cell transitions to stage II, the PhIL1 puncta form 13 small circular structures that represent the nascent IMC plates (Fig 4B, yellow arrow. S7 Video). An accumulation of β-tubulin is observed at the center of each of these nascent plates (Fig 4B, blue arrow, zoom). The data indicate that the nascent IMC plates may stabilize microtubule nuclei, thereby promoting microtubule polymerization and growth; however it is also possible that IMC assembly is driven by microtubule polymerization.
As the gametocyte develops further, bundles of microtubules extend along the nascent plates (Fig 4C, blue arrow). In addition, extended microtubules are observed traversing the parasite cytoplasm (Fig 4C, white arrow). Electron microscopy analysis of freeze-substituted stage II-III gametocytes confirms that microtubules lie closely opposed to the IMC (S5A Fig, yellow arrows) as well as crossing the cytoplasm (S5B Fig, blue arrows).
In stage III gametocytes, the IMC plates expand laterally (Fig 4D and 4E, yellow arrow). The leading edge of the IMC can be seen at the tip of the gametocyte with closely associated β-tubulin (Fig 4D, zoom). The parasite starts to elongate due to the formation of extended microtubules underneath the IMC plates (Fig 4D and 4E, blue arrow) and in the regions away from the IMC plates crossing the parasite cytoplasm (white arrow). The organization of the IMC plates, the sutures (white arrows) and microtubules can be seen in the zoom image of Fig 4E.
The expansion of the IMC plates is completed in stage IV where they almost cover the entire parasite, with a small opening present where the plates have not yet fused (Fig 4F; best appreciated by examining the rotation of the 3D-SIM images; S8 Video). The stage IV microtubules are all tightly associated with the IMC, forming a dense network (Fig 4F, blue arrow, zoom). Electron microscopy reveals long continuous microtubules running the length of the gametocyte (S5E Fig, blue arrows) and arranged in a tightly packed array at the tips of the stage IV gametocyte (S5E and S5F Fig, yellow arrows).
In stage V of development, the microtubule skeleton is depolymerized, leaving only a weak, diffuse labeling with anti-β-tubulin (Fig 4G). The plates of the IMC are still visible delineated by the darker suture lines (Fig 4G, zoom). Electron microscopy of stage V gametocytes reveals the presence of small microtubule stubs bound to the IMC, often in small bundles (S5G and S5H Fig, yellow arrows). This depolymerisation of the microtubules coincides with the previously reported increase in cellular deformability at stage V of development [13, 14].
To assess the function of PhIL1, we generated an inducible gene knockdown using the glmS riboswitch system [15, 16]. Integration of the PhIL1-HA-glmS plasmid into the endogenous PhIL1 locus in NF54 parasites was confirmed by PCR (S6A and S6B Fig). Immunofluorescence microscopy confirmed the location of PhIL1-HA at the gametocyte periphery where it overlaps with β-tubulin (Fig 5A). Treatment of PhIL1-HA-glmS parasites with glucosamine was performed for 6 days from a ring stage culture containing both asexual and gametocyte rings. Apparent knockdown of PhIL1-HA to a level of 85% (by densitometry) was achieved with 5 mM glucosamine (Fig 5B). Knock-down was associated with a 62% reduction (t-test, P = 0.001) in the numbers of gametocytes (Fig 5C) and the remaining gametocytes had significantly altered morphology (Fig 5D and 5E, S6D Fig). In wildtype NF54 parasites or in untreated PhIL1-HA-glmS parasites, more than 75% of the parasites reach stage IV/V by day 7 (Fig 5E, S6C Fig), while less than 15% of the remaining gametocytes in the treated PhIL1-HA-glmS line have progressed morphologically past stage III (Fig 5E). Untreated PhIL1-HA-glmS transfectants reached an average cell length of 10.3 ± 0.3 μm by day 6, similar to the treated or untreated NF54 (S6F Fig). By contrast, glucosamine-treated PhIL1-HA-glmS parasites had an average length of only 4.9 ± 0.4 μm. Taken together these data indicate that PhIL1 is needed for gametocytes to progress morphologicaly past stage III of development. By contrast, treatment of asexual stage parasites with 5 mM glucosamine had no effect on parasite growth (S7A Fig), demonstrating that PhIL1 is not needed (or needed only at low level) for asexual reproduction. Immunofluorescence analysis of the late stage marker Pfs48/45 indicated that this protein in produced in the few PhIL1 knock-down parasites that survive treatment with glucosamine (S7B Fig).
Immunofluorescence microscopy of PhIL1-HA-glmS reveals that GAP45 and β-tubulin are still located at the periphery of the knockdown parasites, suggesting that the initial stages of formation and attachment of the microtubules to the IMC are not affected (S6E Fig). SBF-SEM imaging confirmed the presence of the IMC, however the width of the plates was less than in the untreated controls, suggesting an inability of the IMC plates to expand (Fig 5F). A striking feature of the PhIL1-HA-glmS knockdown parasites was a greatly enlarged digestive vacuole (Fig 5F). While the parasite volume remained roughly similar (untreated, 45.2 ± 6.1 fL; treated, 51.4 ± 6 fL), the DV volume was markedly increased (Untreated, 1.9 ± 0.2 fL; treated, 10.8 ± 1.9 fL; P = 0.01) (Fig 5G). This may suggest mistrafficking of membrane destined for IMC expansion. Similarly, labeling live gametocytes with the pH-reporter, LysoSensor, reveals small, evenly distributed acidic compartments in control gametocytes and swollen acidic compartments in treated parasites (Fig 5H).
To identify PhIL1-interacting proteins, we solubilized PhIL1-GFP stage IV gametocytes using Triton X-100 and performed immunoprecipitation using GFP-Trap. The IMC proteins, GAP45 and GAP50 were significantly enriched in the pull-down from the PhIL1-GFP transfectants, when compared to wildtype 3D7 (Fig 6A). To investigate PhIL1-interacting partners more globally, immunoprecipitated proteins were subjected to in-solution trypsin digestion and analysed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). We identified eight parasite proteins that were significantly enriched (≥2 significant MS/MS spectra in two independent experiments) in PhIL1-GFP compared to wildtype 3D7 (Fig 6B, S2 Table). Precipitated proteins included PhIL1 itself, GAP50, two members of the glideosome-associated protein with multiple membrane spans (GAPM)1 and GAPM2, and Heat Shock Protein 110c (HSP110c) (Fig 6B, S2 Table). In addition, we identified three previously uncharacterized proteins PF3D7_1355600, PF3D7_1431100 and PF3D7_1430800, which we have termed PhIL1 Interacting Proteins (PIPs)1-3 respectively (Fig 6B, S2 Table). PIP1 appears to be restricted to human malaria species with no homologue identified in the rodent malaria species, nor in Toxoplasma. Interrogation of transcriptional data (PlasmoDB) indicates that PIP1 is more highly transcribed in gametocytes than in asexual parasites [17]. In contrast, PIP2 and -3 are found in all malaria species, and exhibit low level similarity to the T. gondii alveolin domain-containing intermediate filament proteins, IMC7 and IMC12 (Fig 6B, S2 Table). PIP2 and PIP3 have been previously identified as putative IMC proteins [18]. Interactions with other known IMC and cytoskeleton proteins were also observed, including GAP40, GAP45, GAPM3, IMC1c, β-tubulin and actin-I, as well as with several uncharacterized proteins, but these failed to reach the applied significance criteria. A full list of identified proteins is provided in S2 Table.
We generated a parasite line expressing a HA-glmS fusion of the top PhIL1 interacting protein (PF3D7_1355600; PIP1). Integration was confirmed by PCR (S8A and S8B Fig) and the expected band of ~66 kDa was observed in Western blots of both asexual and sexual stage parasites (S8D Fig). Immunofluorescence microscopy revealed that PIP1-HA is present at the IMC during early stages of development, where it largely co-locates with PhIL1 (Fig 7A). PIP1-HA extends completely around the parasite periphery by stage III of development (Fig 7A, top panels), but following transition to stage IV it is depleted from the tips of the gametocyte (Fig 7A, yellow arrows), and wanes significantly in stage V gametocytes (Fig 7A, bottom panel). Immunofluorescence microscopy reveals that PIP1-HA is located at the periphery of the nascent merozoites within late stage schizonts, in a similar location to PhIL1 (S8C Fig).
3D-SIM confirmed that PIP1 is associated with IMC plates in stage II—IV gametocytes (Fig 7B, yellow arrows). However, the plates closest to the gametocytes tip lose PIP1 fluorescence from stage III (Fig 7B, blue arrows). Some PIP1 fluorescence is retained at the apex of the tips in stage IV gametocytes (Fig 7B, white arrows). This is in contrast to PhIL1, which remains associated with all of the IMC plates throughout development (Fig 3B and 3E).
To confirm the interaction of PIP1 with PhIL1 we immunoprecipitated extracts of wild type 3D7 and PIP1-HA-glmS parasites, using anti-HA agarose beads. The precipitated proteins were subjected to in-solution tryptic digestion and analysed by mass spectrometry. This analysis identified PhIL1 as the top interacting protein, thus confirming this interaction. PIP2 and GAPM2 were also enriched (≥2 significant MS/MS spectra in two independent experiments) (S9F Fig and S2 Table). Other proteins enriched in one of the two experiments include PIP3, GAP50, GAP45 and α-tubulin II (S2 Table).
Treatment of the PIP1-HA-glmS parasites with 5 mM glucosamine was associated with ~90% apparent knockdown (Fig 7C). Immunofluorescence microscopy confirms the loss of the PIP1-HA signal (S8E Fig). Knockdown was associated with a 68% reduction in gametocyte numbers on day 6 of the gametocyte assay (P = 0.01) (Fig 7D), and an apparent arrest of development (Fig 7E). While the late stage marker Pfs48/45 is produced, less than 10% of the surviving PIP1-depleted parasites achieved stage IV morphology by day 7, compared with >50% of untreated parasites (Fig 7F, S9 Fig). The average parasite length decreased from 10.5 ± 0.5 μm to 5.2 ± 0.4 μm in the glucosamine-treated group (S9C Fig). As with PhIL1, electron microscopy revealed arrested development of the IMC and swelling of the digestive vacuole in the knockdown cell line (S9D Fig). Quantification of the SBF-SEM images showed no significant difference in the glucosamine-treated (46.6 ± 1.8 fL) and untreated (42.3 ± 2.3 fL) parasite volumes but a significant increase in the digestive vacuole volume following glucosamine treatment (untreated 0.9 ± 0.2 fL; treated 8.5 ± 2 fL; P = 0.01) (S9E Fig). By contrast, treatment of the parental 3D7 line with 5 mM glucosamine had no effect on gametocyte development (S9A Fig). Similarly, treatment of asexual PIP1-HA-glmS parasites had no significant effect on parasite growth (S9G Fig), suggesting that PIP1 is not essential (or only needed at a low level) for asexual replication.
The IMC is a cisternal compartment that is assembled under the plasma membrane of Plasmodium parasites, in the invasive or motile merozoite, sporozoite and ookinete stages, and in non-invasive/motile gametocytes. An IMC is also present in other Apicomplexan parasites, such as T. gondii, and related structures (more generally referred to as alveolar sacs) are found in all groups of the Alveolates (which unites Apicomplexan parasites, dinoflagellate algae, and the ciliates) [18, 19]. The IMC plays a range of structural roles, including underpinning the shape and stability of cells, providing a scaffold during cell division and harboring the machinery for gliding motility and cell invasion [19]. While some components of the IMC have shared functions in different stages of Apicomplexan development [20, 21], it is clear that some proteins carry out stage- and species-specific functions.
In the invasive or motile stages of Plasmodium, and in T. gondii, the IMC and the structural subpellicular microtubules are connected to tubulin-based annuli that lie under the plasma membrane at the apex of the cell, called the polar rings [22–24]. The three apical polar rings serve as a microtubule organizing center, and the subpellicular microtubules radiate from there toward the basal end of the cell [22, 25–27]. In cells with an apical complex the IMC appears to be laid down from the apical end by the deposition of material originating from the ER, with gradual expansion of the IMC towards the basal end of the cell [28, 29].
By contrast, the gametocyte has no apical complex, and no obvious polarity, and we show here that the genesis of the IMC and the assembly of the underpinning microtubules occurs via a distinct mechanism (Fig 8). The initial event appears to be the deposition of IMC material in a thin semi-circular string of plates at the periphery of the roughly spherical stage I gametocyte. The nascent IMC appears to be segmented from the earliest stage, with individual segments acting to seed microtubule formation (Fig 8). Once established, the IMC expands laterally via the deposition of membrane at the leading edge, as evidenced by the thickened regions at the ends of the plates, observed in our SBF-SEM images. As the IMC tiles expand laterally, the microtubule stubs grow longitudinally, forming long filaments that appear to stretch the length of the cell (Fig 8). Microtubules also form in regions of the cell away from the IMC, but as the IMC expands laterally around the girth of the parasite, it associates with these microtubules and, as a consequence, the gametocyte constricts (laterally) and extends (longitudinally). By stage IV of development, the microtubules form a tight, sometimes slightly twisted array of longitudinally aligned microtubules around the entire, highly elongated parasite. The microtubules are depolymerized in stage V, but small membrane-bound stubs remain at the parasite periphery, generating an IMC-based carapace that retains a somewhat elongated shape, but is capable of deformation.
In this work, we characterized PhIL1 as a novel component of the gametocyte IMC and defined some of its interacting partners. PhIL1 has no homologues outside the phylum Apicomplexa. It is expressed at the periphery of the daughter merozoites in very late stage schizonts, consistent with being a component of the merozoite IMC. However the level of expression is low and the protein appears to be processed and knockdown does not affect asexual growth, indicating that it does not play a critical role in merogony or merozoite invasion. In cultures that have been induced to form gametocytes, PhIL1 is first observed at the periphery of the small membrane plates that initiate the IMC ribbon in stage I. As the plates expand, PhIL1 is distributed more uniformly across the plates, where it remains through to stage V of development. PhIL1 has no signal sequence and no transmembrane domain but appears to form a partially soluble (Triton X-100) protein complex with other components of the IMC as well as with cytoskeletal components, and may be lipid-modified, as reported for T. gondii PhIL1 [30]. Upon genetic knockdown of PhIL1, gametocytes form, but fail to develop morphologically beyond stage III and exhibit a swollen digestive vacuole and a significant loss of total gametocyte numbers. SBF-SEM imaging of wild type parasites shows an accumulation of extra membrane at the edges of the plates at the stage III-IV transition (Fig 8). It is possible that PhIL1 plays a role in recruiting membrane from the digestive vacuole to the IMC. The defect in elongation is similar to, though more dramatic than, that previously reported for T. gondii PhIL1. Genetic disruption of TgPhIL1 resulted in tachyzoites that were significantly shorter and wider than wild type parasites, a phenotype that translated into a significant loss of virulence in a mouse model of the disease [31].
PhIL1 homologues are broadly represented within (but not outside) the Apicomplexa phylum. However only the C-terminal region of PhIL1 is conserved. The N-terminal region of PfPhIL1 diverges from that of other Apicomplexan PhIL1 proteins, but is more well conserved amongst Plasmodium species. This region includes a domain that is recognized as a myosin and kinesin motor domain motif; however it appears to lack the P-loop motif characteristic of this family of NTPases. Further work is needed to determine the precise role of PhIL1, but a motor/tethering function could facilitate trafficking or docking of membrane vesicles to the IMC.
PIP1 was identified as the top-ranked PhIL1-interacting protein. PIP1 is rich in asparagine (15.8%) and lysine (13.2%) but contains no recognized motifs. Interestingly one of the top interacting proteins HSP110c has been previously shown to stabilize proteins containing asparagine repeat regions [32], and may play a role in maintaining correctly folded IMC and cytoskeleton proteins and trafficking cargo. While PhIL1 is an acidic protein (predicted pI = 4.81), PIP1 is highly basic (predicted pI = 9.68), which may contribute to their association. PIP1 is highly conserved across all Plasmodium species. Unlike PhIL1, PIP1 has no homologues outside the Plasmodium genus. Like PhIL1, PIP1 is present in the merozoite IMC, but does not appear to play an important role in asexual growth. In gametocytes, PIP1 is located in the IMC plates from their initiation to the final stages of development, but wanes later in development. As for PhIL1, knockdown of PIP1 expression was associated with a significant reduction in the number of gametocytes, and the surviving gametocytes fail to progress morphologically past stage II-III of development, indicating a critical role for PIP1 in IMC expansion (Fig 8).
In this work, we expanded the repertoire of potential gametocyte IMC proteins using immunoprecipitation to identify PhIL1-interacting proteins. Proteins that were significantly enriched in the PhIL1 immunoprecipitations include GAP50, GAPM-1 and -2, HSP110c and three new proteins termed PhIL1 interacting proteins (PIP)1-3. In addition, a number of known IMC proteins, including GAPM-2 and -3 and the cytoskeletal components, β-tubulin and actin-I, were identified but failed to reach significance due to the cut off level employed.
It is interesting to consider the roles of the different IMC components. The GAPM proteins (1–3) possess six transmembrane domains and are thought to be present in large, oligomeric complexes, potentially associated with 9 nm particles on the innermost (cytoplasmic) surface of the IMC [33, 34] and have been reported to interact with the alveolins [33]. The alveolins vary in size and contain multiple repeat domains with a motif that is predicted to form extended coiled-coil domains [21]. PIP2 and PIP3 share sequence similarity with the Toxoplasma proteins, IMC7 and IMC12, which contain alveolin domains. GAP50 is an integral membrane protein that is targeted via the endomembrane system to the IMC and is oriented with its N-terminal (non-functional) phosphatase domain facing the lumen of the IMC [28, 35]. GAP50 is thought to recruit pre-complexed GAP45-MTIP-MyoA on to the nascent IMC [28, 36]. It has been suggested that GAP45 spans the IMC and the plasma membranes, interacting with both, via myristyl and palmityl modification, thereby maintaining the integrity of the IMC during invasion [20]. Interactions with GAP45, GAP50 and PhIL1 may help stabilize the IMC, holding it close to the parasite plasma membrane as it expands and develops. TgPhIL1 has been shown to be palmitylated [30] and this may provide a means of connecting the protein to the IMC. The nature of the interacting partners suggests that PhIL1 may be located on the cytoplasmic surface of the IMC and may participate in a higher order complex that links the microtubule-interacting proteins through to the structural components of the glideosome complex (Fig 8). This is supported by the solubility profile for PhIL1, which is consistent with a peripherally-located protein that is associated with the underlying actin/tubulin skeleton.
In summary, we have elucidated details of the genesis and elaboration of the gametocyte IMC and identified two novel gametocyte IMC proteins that appear to be involved in this process. This work gives new insights into the fascinating cell biology events that drive gametocyte elongation and facilitate preparation of the gametocyte for transfer to a mosquito host. Gametocyte formation and maturation represents a bottle-neck in parasite development and inhibition of this process would ablate disease transmission.
Asexual stage parasites were grown in O+ RBCs (Australian Red Cross blood service) at 5% hematocrit. The parasites were maintained in complete culture media containing RPMI-GlutaMAX-HEPES (Invitrogen) supplemented with 5% v/v human serum (Australian Red Cross blood service), 0.25% w/v AlbuMAX II (Invitrogen), 200 μM hypoxanthine, 10 mM D-glucose (Sigma) and 20 μg/ml gentamicin (Sigma). To obtain ring stage cultures sorbitol (5%) synchronization was performed [37]. Transfectants were maintained in media supplemented with WR99210 (5 nM). High gametocyte producing NF54 and 3D7 parasite lines were used [38, 39].
Gametocytes were generated as previously described [13, 40, 41]. Asexual stage parasites were sorbitol synchronized to produce a ring stage culture and the parasitemia adjusted to 3% parasitemia at 5% hematocrit (day -4). The culture was grown in complete culture media until day -1 resulting in a culture containing 8–10% trophozoite stage parasites. The culture was adjusted to 2% parasitemia 5% hematocrit by addition of fresh media and RBCs. The spent culture media was left on the culture at a ratio of 1:4 with fresh media to induce gametocyte induction. N-acetyl-D-glucosamine (GluNac; 62.5 mM final concentration) was added from day 0 of the culture [40]. This protocol results in synchronized gametocyte production. Gametocyte stage development was monitored by thin blood films and Giemsa staining. The following timing of stage progression is used as a guide but can vary depending on the cell line and age of asexual cells when the assay was established. Day 0–1: stage I; Day 2–3: stage II; Day 4–5: stage III, Day 6–7: stage IV; day 8 –onwards: stage V. Gametocytes were purified from culture at the required development stage by Percoll density gradient or magnet separation [40, 41].
The pPhIL1-3xHA plasmid was created by amplifying full length PhIL1 sequence from genomic 3D7 DNA with the P1FHA (GCAGATCTAAAATGCTTTCTTCCATATCACCAAAAAG) and P1RHA (AACTGCAGCCATATCTTGGTTATAATTTTCTTGATC) primers (restriction enzyme sites in bold). The resulting PCR product was directionally cloned into the BglII and PstI sites of the pD3HA plasmid. The pPfs16-PhIL1-GFP construct is an episomally maintained plasmid that utilizes the promoter region of the early gametocyte specific protein Pfs16 [12]. The full-length coding sequence of PhIL1 was amplified from genomic 3D7 DNA with the PIGFPF (GCGTCGACAAAATGCTTTCTTCCATATCACCAAAAAG) and PIGFPR (AACTGCAGTGCTGCTGCTGCTGCTGCTGCTGCCATATCTTGGTTATAATTTTCTTGATC) primers containing BglII and PstI restrictions sites respectively. This product was directionally cloned into the p16proPfs16-GFP Entry plasmid [12] which had been predigested with BglII and PstI to remove the Pfs16 coding sequence. The resulting plasmid pPfs16pro-PhIL1-GFP was recombined with the pHH1 Destination vector as previously described to obtain the pHH1-Pfs16pro-PhIL1-GFP plasmid [12].
The pGLMS-PhIL1-HA inducible knockdown construct was made as described above for the pPhIL1-3xHA plasmid, with cloning of the same full-length PhIL1 fragment into the pGlmS plasmid. To generate the pGlmS-PIP1-HA plasmids the coding sequence of PIP1 was PCR amplified from 3D7 genomic DNA using the PIPF (GTCGACGGATCCATGAATAACGGATCTAATAAA) and PIPR (CTGCAGTGCTCTTTTTTTAAATTGCATAGG) primers and directionally cloned into the pGLMS-HA plasmid using the BamHI and PstI restriction sites.
Parasite transfections were performed by electroporation as previously described [42]. Drug cycling was performed to enrich for integrated parasites. Integration of the lines was confirmed by PCR using the PHILINT (GAACCTTCCATAACAGAC) or PIPINT (CCGCGGCCTATATTATTTTCATTAAACATTGAC) primers that target genomic sequence upstream of the targeting sequence in combination with the vector specific HArev (CGAACATTAAGCTGCCATAT) reverse primer.
A codon optimized (for E. coli) sequence of full length PhIL1 (PlamsoDB ID: PF3D7_0109000.1) was commercially synthesized by GenScript. This sequence was directionally cloned into the PstI and BglII sites of the pQE-30 protein expression plasmid, which will place a 6xHis tag at the N-terminus of the resulting protein. The plasmid was transformed into BL21 (DE3) E. coli (Bioline) and grown in the presence of 100 μg/ml Ampicillin. The expression of recombinant PhIL1 was induced with the addition of 2 mM isopropyl-D-1-thiogalactopyranosid (IPTG). After 3 h, cells were pelleted via centrifugation at 4,000 x g for 20 min at 4°C and the pellet was then re-suspended in lysis buffer (50 mM NaH2PO4, 300 mM NaCl, 10 mM imidazole, pH 8.0). Lysozyme (1 mg/mL) was also added and cells were incubated on ice for 30 min. The lysate was then sonicated and drawn through a 21-gauge syringe before being frozen at -80°C overnight. After thawing, the lysate was centrifuged at 10,000 x g for 20 min at 4°C and the supernatant containing the soluble protein fraction was discarded. The pellet was re-suspended in a denaturing lysis buffer (100 mM NaH2PO4, 10 mM Tris-Cl, 8 M urea, pH 8.0) and after a 1 h incubation period, the E. coli lysate was centrifuged at 10,000 x g for 20 min. His-tagged PhIL1 was purified from the supernatant fraction using a batch purification method.
The bacterial lysate was mixed with 50% Ni-NTA agarose resin for 1 h on a rotary mixer and then placed into a column. The agarose resin was washed three times using a wash buffer (100 mM NaH2PO4, 10 mM Tris-Cl, 8 M urea, pH 6.3). Recombinant PhIL1 was eluted using elution buffer A (100 mM NaH2PO4, 10 mM Tris-Cl, 8M urea, pH 5.9) and then elution buffer B (100 mM NaH2PO4, 10 mM Tris-Cl, 8 M urea, pH 4.5). All elution fractions were collected. Those containing PhIL1 protein were pooled and the buffer was exchanged for 1x PBS via dialysis. The resultant protein was then sent to the Walter and Eliza Hall Institute antibody facility, Bundoora. Rabbits were immunized to produce polyclonal antibodies against P. falciparum PhIL1.
Glass coverslips were primed for immunofluorescence assays by incubating with 0.1 mg/mL PHAE (erythroagglutinating phytohemagglutinin, Sigma Aldrich) for 15–30 min in a humid chamber at 37°C [43]. Parasites were harvested by centrifugation at 2,000 x g for 2 min, washed in 1x PBS and placed onto the PHAE coated coverslip and incubated at room temperature for 15 min. Washing of samples with 1x PBS was carried out between each step of the following protocol. Cells were incubated on the slides and then fixed in a solution of 4% v/v paraformaldehyde and 0.0065% v/v glutaraldehyde. Parasite permeabilization was carried out using 0.1% Triton X-100. Immobilized cells were stained with primary antibodies for 1 h at room temperature (RT) (anti-PhIL1 rabbit (1:500), anti-HA rat (1:250, Sigma Aldrich) anti-HA mouse (1:250, Sigma Aldrich), anti-GAP45 rabbit (1:1000, [44]) anti-GFP mouse (1:500, Roche) anti-β-tubulin mouse (1:500, Sigma Aldrich), anti-Pfs16 mouse (1:1000, [12]). The secondary antibody was added for 1 h at RT (goat anti-mouse Alexa Fluor 488, 568, 647; anti-rabbit 488, 568, 647; anti-rat 647, were used at 1:250). Parasite nuclei were stained with DAPI (2 μg/mL) for 10 min at room temperature. The slides were mounted in p-phenylenediamine antifade and kept at 4°C. Samples were imaged on a DeltaVision Elite Restorative Widefield Deconvolution Imaging System (GE Healthcare) using the 100x UPLS Apo (1.4NA) objective lens under oil immersion. Samples were excited with solid state illumination (Insight SSI, Lumencor). The following filter sets with excitation and emission wavelengths were used: DAPI Ex390/18, Em435/48; FITC, Ex475/28, Em523/26; TRITC, Ex542/27, Em594/45; Cy5 Ex 632/22, 676/34 nm. For higher resolution imaging via 3D Structured Illumination microscopy (3D-SIM), the DeltaVision OMX V4 Blaze was used (GE Healthcare). Samples were excited using 488, 568 or 642 nm lasers and imaged using band pass filters at 528/48, 609/37 and 683/40 nm with a 60X Olympus Plan APO N (1.42 NA) oil immersion lens. Images were processed using the Fiji ImageJ software [45].
For the solubility assay, a culture enriched in gametocytes from stage III to stage V was magnet purified. Gametocytes were lysed in 0.03% saponin and the pellet fractions incubated with 0.1 M sodium carbonate, pH 11 (4°C) or in 8 M urea (RT) or 1% Triton X-100 in PBS, pH 7.4 (4°C) or in 2% SDS (RT) for 30 min. The soluble supernatant and the insoluble pellet were collected and taken up in 1x SDS loading buffer. Proteins fractions were analysed by immunoblotting.
For SDS-PAGE, protein samples were prepared by lysis with 0.03% saponin for 25 min on ice. Protein samples were separated on 4–12% Bolt Bis-Tris gels (Invitrogen) and transferred to 0.2 μm nitrocellulose membrane using the iBlot system (Invitrogen). The nitrocellulose membrane was blocked in 3.5% skim milk/1xPBS for 1 h at RT prior to antibody probing. Primary and secondary antibodies were prepared in 3.5% skim milk and incubated with the membranes for 1 h at RT. The membranes were washed with 1x PBS/0.05% Tween20 after the primary and secondary antibody incubations. The following primary antibodies were used in this study: anti-PhIL1 pre/post-immune (1:500), anti-HA mouse (1:500, Sigma Aldrich), anti-ERC (1:1000), anti-GAP45 (1:1000, [44]), anti-GAP50 (1:500, [44], anti-GFP, 1:500, Roche)). Horseradish peroxidase conjugated anti-mouse or anti-rabbit antibodies were used (1:25,000, Millipore). The washed immunoblots were incubated with enhanced chemiluminescent (ECL) reagents before being imaged using the FujiFilm LAS3000 digital imaging system (GE Healthcare).
Gametocytes were fixed using 2% paraformaldehyde and 0.1% glutaraldehyde. Cells were mounted to membrane carriers (Leica Microsystems) with a cell depth of 200 μm. The cells were then rapidly frozen by liquid nitrogen jet under 2100 bar pressure in a high pressure freezer (EM PACT2, Leica Microsystems) and quickly transferred to liquid nitrogen. A quick freeze substitution method was used (McDonald and Webb, 2011). The samples were transferred to cryotubes containing fixative: 1% osmium tetroxide, 0.5% uranyl acetate 5% water in acetone at liquid nitrogen temperature. The cryotubes were mounted on a cooled aluminum heat block in a polystyrene box filled with liquid nitrogen. To initiate the freeze substitution, the liquid nitrogen was replaced with dry ice pellets. The box was placed on a rotary shaker at 125 rpm. The dry ice was removed after 2 h and the shaking continued. After 1 h the cryotubes were removed from the block.
The samples were washed three times with pure acetone after returning to RT. They were then infiltrated and embedded with EPON resin. Polymerization of the resin was carried out at 60°C before 60 nm sections were prepared with an ultramicrotome (Leica EM UC7, Leica Microsystems). The specimens were post-stained with 7% uranyl acetate in methanol and Reynold’s lead citrate. Samples were observed on Transmission Electron Microscope (TEM) at 200 kV (Tecnai G2 F30, FEI).
The staining method (ROTO) was adapted from a previous publication [46]. Parasite pellets were fixed with 2.5% glutaraldehyde in PBS for 1 h at 4°C. Following agarose pre-embedding and washing with 0.175 M sodium cacodylate buffer, cells were stained in ferrocyanide-reduced osmium tetroxide in 0.1 M cacodylate buffer for 1 h on ice. After washing with double distilled H2O, the cells were incubated with freshly prepared 1% thiocarbonhydrazide solution in H2O for 20 min at RT. The cells were then rinsed and further stained with 2% osmium tetroxide in H2O for 30 min at RT. Subsequently the cells were rinsed and en-bloc stained with 1% uranyl acetate in H2O overnight at RT. In the final step of staining the specimens were rinsed and treated en-bloc with Walton’s lead aspartate for 30 min at 60°C [47]. Following the removal of lead aspartate and several washes, the cells were dehydrated in a graded series of ethanol-H2O mixes, followed by several washes in dry ethanol and dry acetone and then progressively infiltrated with EPON resin. After resin polymerization, a 200 x 200 x 200 μm resin block was trimmed off using the mesa trimming method [48] on an ultramicrotome (Leica EM UC7, Leica Microsystems). The resin block was mounted on a microtome stub and immobilized using silver glue. After the silver glue was completely dried, the resin block was coated with a layer of gold and the top surface was then cleansed by diamond knife. The serial images (every 50 nm) were collected by a serial block face-scanning electron microscope (SEM), which was equipped with an in-chamber diamond knife, (Teneo VolumeScope, FEI Company) using back scattered electron signals at 3 kV under low vacuum conditions.
Serial sections were optimized and aligned using IMOD software (Boulder Laboratory for 3D Electron Microscopy of Cells). The regions of interest were segmented and reconstructed into 3D models, using a semi-automatic method described previously [49]. Briefly, the pixel size of the greyscale images was binned to 20–50 nm, the images were subjected to Gaussian smoothing and a threshold value was selected manually from each reconstructed tomogram using a noise-estimate variance criterion, after marking some of the relevant cellular features, as described by others [50]. Connected regions were identified semi-automatically using connected component analysis [51]. Where required, a rough bounding area was drawn manually on every a few 2D sections. The regions were labeled with different colors, and 3D models were generated and rendered [52]. The volumes of the 3D models were determined for the organelles, while the parasite volume was determined after manual segmentation.
For polarity assessment, the IMOD Slicer tool was used to orient slices at the maximum length or width of the cells and organelles and the relative distances from the ends were measured using open contours in IMOD. The correct positioning of the open contours was confirmed on 3D models.
Purified gametocytes were harvested and washed in 1xPBS for co-immunoprecipitation experiments. Parasites were solubilized using 1% TX-100/PBS (in 150 mM NaCl, 50 mM Tris, 2 mM EDTA) plus Complete protease inhibitors (Roche) on ice for 30 min, re-suspending every 10 min. Insoluble material was pelleted by centrifugation 16,000 x g for 10 min at 4°C. The supernatant was also re-pelleted to remove any residual insoluble material. The soluble fraction was incubated with 50 μL of Pierce protein A agarose beads (Pierce) for 30 min at 4°C to remove non-specific binding proteins. The resin was pelleted by centrifugation at 3,420 x g for 2 min and supernatant was incubated for 2 h with 25 μL GFP-Trap A (Chromotek) or anti-HA agarose (Sigma) depending on parasite cell line, at 4°C. After pelleting the resin at 3,420 g for 2 min, the beads were washed five times with 1% Triton X-100. Proteins were then either eluted using SDS and analysed via Western blotting or the beads were washed two times with 1 mM Tris and analysed via mass spectrometry.
For mass spectrometry, proteins was eluted from beads with 5 μL trifluroethanol and 20 μL 0.1% formic acid (pH 2.5) and incubated at 50°C for 5 min. Beads were pelleted at 3,420 x g for 2 min and 20 μL of the supernatant collected. The sample was neutralized with the addition of 1 μL of 1 M tetraethylammonium bicarbonate. To reduce disulphide bonds, 0.2 μL of tris(2-carboxyethyl)phosphine (final concentration of 5 mM) was added to the supernatant and the solution was incubated at 70°C for 10 min. Proteins were digested with trypsin overnight at 37°C. Mass spectrometry was performed using a Thermo NanoLC/ OrbiTRAP ELITE ETD for GFP-Trap samples, and a Thermo NanoLC/ Q Exactive Plus for the anti-HA Agarose samples. Mass spectra were searched in MASCOT (Matrix Science) against a custom database comprised of P. falciparum (PlasmoDB) and H. sapiens (UniProt) non-redundant proteomes. Protein hits were considered significant if they were present in two biological replicates, had ≥2 significant peptide MS/MS spectra, and were at least 5 times enriched compared to the 3D7 or NF54 parental control. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [53] partner repository with the dataset identifier PXD007564.
All statistical analyses were performed using the Graph Pad Prism 5 software package. P values were calculated using an unpaired t-test, the mean values plus or minus the standard error are shown. This information is provided in the figure legends. The numbers of repeat experiments, internal replicates and cell numbers measured are noted in the figure legends.
Red Blood Cells and serum was obtained from the Australian Red Cross blood service. All blood products were anonymous and individual donors could not be identified. This work was approved by the University of Melbourne Human Research Ethics Committee (Approval number 1135799).
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10.1371/journal.pgen.1002780 | An Engineering Approach to Extending Lifespan in C. elegans | We have taken an engineering approach to extending the lifespan of Caenorhabditis elegans. Aging stands out as a complex trait, because events that occur in old animals are not under strong natural selection. As a result, lifespan can be lengthened rationally using bioengineering to modulate gene expression or to add exogenous components. Here, we engineered longer lifespan by expressing genes from zebrafish encoding molecular functions not normally present in worms. Additionally, we extended lifespan by increasing the activity of four endogenous worm aging pathways. Next, we used a modular approach to extend lifespan by combining components. Finally, we used cell- and worm-based assays to analyze changes in cell physiology and as a rapid means to evaluate whether multi-component transgenic lines were likely to have extended longevity. Using engineering to add novel functions and to tune endogenous functions provides a new framework for lifespan extension that goes beyond the constraints of the worm genome.
| We used bioengineering to extend the lifespan of C. elegans by expressing genes acting in critical aging pathways. We overexpressed five genes that act in endogenous worm aging pathways, as well as two genes from zebrafish encoding molecular functions not normally present in worms. For example, we used zebrafish genes to alter mitochondrial function and innate immunity in ways not normally available to C. elegans and extended worm lifespan by ∼40%. Next, we used a modular approach to extend lifespan by 130% by combining up to four components in the same strain. These results provide a platform to build worms having progressively longer lifespans. This project is conceptually similar to using engineering to increase the useful lifespan of a primitive machine (1931 Model T) using both parts from the model T as well as parts from a more advanced machine (2012 Toyota Corolla). Our results open the door to use engineering to go beyond the constraints of the C. elegans genome to extend its lifespan by adding non-native components.
| Recent advances in genome technology and systems biology have made it possible to use engineering approaches to create new biological systems. Examples include the construction of a synthetic genetic oscillator in bacteria [1], engineering quorum sensing (the ability to respond to population density) in yeast by integrating signaling components from the plant A. thaliana [2], and creating an artificial bacterial cell using a genome consisting only of chemically-synthesized DNA [3]. Here, we expand bioengineering to a complex phenotype, longevity, in a multicellular animal, C. elegans.
Despite being extremely complex, aging has at least three features that make it an attractive trait to improve by engineering. First, many pathways are involved in aging, such as stress response, repair of oxidative damage, protein quality control, developmental drift and innate immune response to pathogens [4]–[6]. The diverse nature of these aging pathways allows multiple avenues to engineer changes that may extend lifespan. Second, there is a great diversity in lifespan between different species, from two weeks for C. elegans, to 80 years for humans, to over 200 years for whales or clams [6]–[9]. This observation shows the remarkable dynamic range of over a thousand fold in lifespan encoded by different genomes. Third, most animals in the wild (including C. elegans) die from predation and disease rather than old age [10]–[11]. Thus, aging is not under the force of natural selection and represents the system-wide degeneration of processes due to evolutionary neglect. As a result, an engineering approach to slow aging seems more feasible than engineering improvements in other biological processes (e.g. development) because it may be easier to repair damaged processes in old animals rather than to improve highly-functional pathways in young animals.
We chose to use C. elegans because it has a short lifespan of two weeks and a strong genetic toolkit making it a good platform for engineering longer lifespan. We first used a variety of approaches to identify genes with well-characterized roles in critical aging pathways that can be used as components to extend lifespan in transgenic worms. In particular, we were able to extend lifespan by expressing genes from zebrafish with cellular functions that are not normally found in worms. Having created a list of components that each extends lifespan singly, we then used a modular approach to increase lifespan by increments. We generated transgenic worms that contain an increasing number of aging components, and showed that there was a corresponding increase in lifespan.
The framework and goal of our engineering approach to aging are fundamentally different from those in a study of the biology of aging. The main goal of our approach is to add components in order to extend the worm lifespan without a direct need to understand the mechanisms underlying this lifespan extension. For example, our modular approach aims to combine lifespan-extending components without aiming to determine whether these components act in the same or in different pathways. Additionally, in our engineering approach, we are not constrained to genes or pathways derived only from the worm genome. Rather, we can use novel molecular functions derived from long-lived organisms in order to extend worm lifespan.
Our goal is to use an engineering approach to generate C. elegans strains that are long-lived but that develop normally, are fertile, and are generally healthy. We began by accumulating a set of genes that individually extend lifespan. The first and easiest way to obtain an aging component is to select genes that have already been shown to extend lifespan when overexpressed; we generated expression vectors for four such genes (hsf-1, activated aakg-2, sod-1, daf-16) [12]–[15]. Transgenic worms were generated by microinjection of the gene of interest, a co-transformation marker (unc-119(+)) and an aging biomarker (sod-3::mCherry). We compared the lifespan for each of the transgenic strains to the lifespan from a control transgenic strain containing unc-119(+) and sod-3::mCherry alone (see Table S1 for complete list of components). In most cases, we generated two separate transgenic strains and measured their lifespan to verify reproducibility.
Three of the genes (hsf-1, activated aakg-2, sod-1) showed extended lifespan. hsf-1 encodes heat shock transcription factor that induces expression of many stress-resistance genes that can extend lifespan [16]. aakg-2 encodes the gamma subunit of AMP-activated protein kinase, a regulatory signaling molecule that responds to low ATP/AMP ratios and plays a key role in the stress response [17]. sod-1 encodes cytosolic superoxide dismutase that catalyzes the dismutation of superoxide radicals (O2−) into hydrogen peroxide [18], which could reduce damage accumulation and extend lifespan. Consistent with previous results [12]–[14], we found that overexpression of hsf-1, activated aakg-2 and sod-1 extended lifespan by ∼30%, ∼45%, and 25%, respectively (Figure 1a–1c, Table 1, Table S3).
The second way to obtain an aging component is a candidate gene approach using C. elegans genes that act in known aging pathways. One such aging pathway is proteostasis, which counteracts damage accumulation to proteins by removing old, damaged proteins [19]–[20]. Increasing the rate of protein turnover should lower accumulation of damaged proteins and may extend lifespan [21]. We overexpressed a gene involved in chaperone-mediated autophagy (lmp-2) [22] and a gene involved in proteostasis (uba-1). We found that that lmp-2 but not uba-1 resulted in extended lifespan compared to a control strain (Figure 1d, Table 1, Table S3). lmp-2 is the ortholog of mammalian lamp2A, which encodes the lysosome-associated membrane protein type 2A receptor involved in chaperone-mediated autophagy that is responsible for the degradation of approximately 30% of cytosolic proteins in conditions of stress [23]. Overexpression of lamp2A in old mice results in lower intracellular accumulation of damaged proteins and improved organ function [22].
The third approach was expressing orthologous genes from either the zebrafish or the human genome that act in known aging pathways. We selected genes from zebrafish and humans as they have much longer lifespans than worms (4 years or 80 years vs 2 weeks, respectively) [6], [8], [24]. We expected that vertebrate genes from aging pathways may be more efficient at delaying aging than orthologous genes from worms. Furthermore, zebrafish live at a similar range of temperatures as C. elegans and therefore zebrafish proteins should be capable of functioning at the ambient temperature used to grow worms (20°C).
We selected four zebrafish and one human gene that are orthologous to C. elegans genes that act in known aging pathways: D. rerio sod1, D. rerio msra, D. rerio foxo3A, D. rerio psmb1 and human aldh2. We used upstream regions from C. elegans genes that were homologous to the zebrafish gene to drive expression of zebrafish cDNAs. For each construct, we generated transgenic worms and measured their lifespan under normal growth conditions. Of the five genes tested, only expression of D. rerio sod1 in transgenic worms resulted in longer lifespan than a control strain (Figure 1e, Table 1, Table S3). D. rerio sod1 encodes superoxide dismutase and is the ortholog of C. elegans sod-1. D. rerio sod1 and C. elegans sod-1 extended lifespan to a similar extent.
The fourth approach to find an aging component was to select zebrafish genes with functions that are absent from the worm genome, and test whether adding them to worms can have a beneficial effect. One such function is mitochondrial uncoupling, which allows protons to leak into mitochondria without producing ATP [25]. According to the uncoupling to survive hypothesis, mitochondrial proton leakage may be beneficial because reduction of the proton motive force should reduce production of reactive oxygen species and thereby reduce damage accumulation during aging [25]. We chose to introduce mitochondrial uncoupling activity into worms using the zebrafish ucp2 gene, which encodes one of the mitochondrial uncoupling proteins. A similar experiment to add human ucp2 to Drosophila has been done previously, although it is not clear whether Drosophila has endogenous uncoupling activity and thus it is unclear if this previous experiment involves adding a new function to Drosophila [26].
The evolutionary tree for ucp genes shows that ucp-4 is the most ancient, contained in the genomes of all animals (Figure S1a). Worms contain only a single related gene (ucp-4) that encodes a protein that does not have mitochondrial uncoupling activity but rather is a transporter for succinate [27]–[28].
We generated transgenic worms that express zebrafish ucp2 from the worm ucp-4 promoter. As a control, we also generated worms that overexpress worm ucp-4. We found that expression of zebrafish ucp2 extended the median lifespan of worms by about 40% (Figure 1f, Table 1, Table S3). In contrast, overexpression of worm ucp-4 did not extend lifespan in two independent transgenic strains (Figure 1f, Table S2).
Another example of new functionality added to the worm is the addition of vertebrate lysozyme activity. Worm lifespan is limited by mild pathogenic effects from E. coli, which is used as a standard food source [29]–[30]. All lysozymes have bacterial cell wall hydrolase activity that degrades peptidoglycans and thus are key players of the innate immune defense system providing protection against bacterial pathogens [31]. There are ten lysozyme genes in C. elegans, all belonging to a clade shared with microbes such as D. discoideum and E. histolytica (Figure S1b). Vertebrates contain a large number of lysozyme genes, including a second clade that is derived solely from metazoans. Lysozyme genes from this clade contain a distinct anti-bacterial activity besides cell wall hydolase activity, which involves direct interaction of lysozyme with the bacterial cell membrane resulting in membrane leakage [31].
We generated transgenic worms expressing a zebrafish lysozyme gene from the second clade (lyz). We found that zebrafish lyz extended the median lifespan of worms by about 30% (Figure 1g, Table 1, Table S3). In contrast to D. rerio lyz, overexpression of worm lys-1 did not extend lifespan in two independent transgenic strains (Figure 1g, Table S2), consistent with previously published results [32].
We used various worm- and cell-based assays to validate that the aging components were expressed and to determine what changes in cell physiology and stress were induced. One reason for this is to provide evidence that the aging components extended lifespan by the expected mechanism. Since we tested each of the seven aging components with each of the assays, another reason is to also determine whether the aging component induced unexpected changes in cell physiology, which might indicate indirect activation of secondary aging pathways. A third reason is that the cellular assays could be used as a rapid and practical means to identify transgenic worms that are likely to have extended lifespan.
To examine expression of the transgenes, we performed RT-PCR experiments using RNA extracted from fourth larval stage hermaphrodites. We found that the three vertebrate genes were expressed and that the four worm aging genes were over-expressed 5–18 fold in the transgenic strains compared to the endogenous gene in the control strain (Table S4).
ATP production by the mitochondria is directly related to the production of reactive oxygen species and damage accumulation. Furthermore, ATP levels is thought to be associated with dietary restriction and the subsequent induction of protective pathways [33]–[34]. We measured ATP levels in extracts from fourth larval stage worms for each of the transgenic worms and found that worms expressing ucp2 or aakg-2(sta2) had lower overall levels of ATP compared to controls (Figure 2a). Uncoupling protein would be expected to lower ATP levels by lowering the proton gradient in mitochondria, and thus lowering ATP production. Our results are consistent with previous experiments showing that vertebrate ucp2 genes have mitochondrial uncoupling activity when expressed in yeast and in flies [26], [35]–[36]. Activation of AMPK is thought to increase catabolic pathways that generate ATP while decreasing ATP-consuming processes [17]. Thus, aakg-2(sta2) transgenic worms were expected to have increased ATP levels, opposite to the observed result.
Lysozymes are anti-bacterial enzymes that could extend lifespan by combating bacterial pathogenicity. If lysozyme acts by combating mild pathogenicity stemming from E. coli, then it should not be able to extend lifespan when worms are grown on non-pathogenic B. subtilis. We determined the lifespan of control and lyz transgenic worms when grown on B. subtilis, and found no difference (Figure 2b, Table S5). This result strongly indicates that the mechanism of lifespan extension by zebrafish lysozyme involves combating mild pathogenicity from E. coli.
Mild stress can extend lifespan by inducing protective pathways, a phenomenon referred to as hormesis. We tested for induction of the stress-responsive genes hsp-16.2 and hsp-16.11 using RT-PCR. We found that hsf-1 and aakg-2(sta2) transgenic worms showed higher expression of hsp-16.2 and hsp-16.11 than controls (Figure 2c). hsf-1 but not aakg-2(sta2) was expected to induce expression of stress response genes.
Next, we examined resistance to oxidative damage, which accumulates with age. One way to measure susceptibility to oxidative damage is to measure resistance to oxidative stress from paraquat, a chemical that generates superoxide ions. We found that C. elegans sod-1, zebrafish sod1, aakg-2(sta1) and hsf-1 conferred resistance to paraquat (Table 2). C. elegans sod-1 and zebrafish sod1 encode superoxide dismutase, an enzyme that reduces levels of oxygen free radicals that could directly counteract the effects of paraquat. aakg-2(sta2) and hsf-1 were not expected to affect oxidative damage directly.
Another way to examine oxidative damage in worms is to directly detect oxidized residues in proteins from a whole worm extract in a Western blotting assay. In old worms, ucp2 and hsf-1 worms showed lower levels of oxidative damage compared to controls (Figure 2d). ucp2 could decrease levels of oxidative damage by decreasing the proton motive force in mitochondria and reducing production of reactive oxygen species. Reduced levels of oxidative damage in hsf-1 transgenic worms was not anticipated. C. elegans sod-1 and D. rerio sod1 might be expected to reduce oxidative damage by reducing levels of reactive oxygen species, but neither showed an effect in this assay. Tallying the results from both assays for oxidative damage, we found expected evidence for reduced oxidative damage in three strains (ucp2 and C. elegans sod-1, D. rerio sod1) as well as unanticipated results for two strains (hsf-1 and aakg-2(sta2)).
We next examined activation of the FOXO transcription factor DAF-16, which is a key regulator of aging [6]. Activation of DAF-16 can be measured by expression of a sod-3 reporter gene, which is one of its downstream targets [37]. We compared the level of expression of a sod-3::mCherry reporter in each of the seven long-lived worms to control worms in middle-aged hermaphrodites. We observed that aakg-2(sta2) and hsf-1 transgenic worms showed increased expression of sod-3::mCherry whereas zebrafish lyz showed decreased expression (Figure 2e). aakg-2 encodes a kinase that phosphorylates DAF-16, and would be expected to induce sod-3 expression [13], [38]. For zebrafish lyz, one possibility is that lysozyme could reduce pathogenicity from E. coli used as food. Mild pathogenicity is known to activate DAF-16 and induce expression of the downstream target sod-3.
Lastly, lower ATP levels in ucp2 transgenic worms might extend lifespan using mechanisms shared by dietary restriction. If so, then worms that receive both dietary restriction and ucp2 might not live longer than worms receiving either condition alone. We found that dietary restriction alone extended median lifespan 18%, ucp2 alone extended lifespan 40% and that dietary restriction of ucp2 worms extended lifespan 40% compared to controls (Figure 3a, Table S6). Thus, dietary restriction did not further extend the lifespan of ucp2 worms that were fed normally.
Table 3 provides a summary of the results from the seven cell- and worm-based assays using the seven transgenic lines expressing aging components. Except for lmp-2, we obtained either direct or indirect evidence that each of the components was expressed and acting as expected. Furthermore, we also obtained evidence that some aging components produced changes in cell pathways that were indirect, providing evidence for cross-talk between different aging pathways in C. elegans. For example, the aakg-2(sta2) strain also shows induction of the two hsp protein chaperones that function in a protective stress pathway.
According to the disposable soma theory, evolution of organisms in the wild requires a balance between allocation of metabolic resources for somatic maintenance or reproduction [10]. We tested whether there was a reduction in brood size in our engineered strains. Five transgenic strains with long lifespan had similar brood size and two showed a decrease in fertility compared to the control strains (Figure S2). These results show that the aging components can extend lifespan without reducing fertility.
The seven aging components are individually capable of extending lifespan 25–50%. Because aging is a complex phenomenon affected by many pathways, our strategy to extend lifespan further was to use a modular approach by combining different aging components in a single transgenic strain to progressively extend lifespan. Additionally, we needed to develop a scheme to rapidly test whether or not combining genes in a new transgenic strain has a beneficial effect. This is because lifespan analysis requires four weeks for normal worms, and becomes even more tedious as lifespan increases. Our approach was to first use the cell- and worm-based assays described above to rapidly test whether worms expressing multiple aging components show a beneficial effect. Results showing that a multi-component strain shows protective changes in several pathways or stronger effects in a single aging pathway compared to single-components lines would be encouraging that it will live a long time.
We started by generating two transgenic worm strains that each contain two components; one combination (dual-1) contains aakg-2(sta2) and zebrafish ucp2 and the other combination (dual-2) contains hsf-1 and zebrafish lyz (Table 4). These four components include two zebrafish genes that add new functionality to the worm (ucp2 and lyz) and two C. elegans genes that showed the largest increase in lifespan (aakg-2(sta2) and hsf-1). We used qRT-PCR to show that the components in the dual-module worms were expressed at levels equivalent to those from the single-module worms (Table S4).
One reason for choosing the two components in dual-1 was that six assays were affected by either aakg-2(sta2) or ucp2 single-expressing worms, suggesting that this combination might be able to affect a large number of aging pathways. We analyzed dual-1 worms with all six assays to examine changes in cell physiology resulting from expression of the two aging components. For four assays (ATP level, paraquat resistance, oxidative damage and sod-3 expression), the change relative to controls seen for the dual-1 strain was greater than the changes seen with either the aakg-2(sta2) or ucp2 single strains (Figure 4a, 4b, 4d; Table 2). For the dietary restriction assay, the results with dual-1 were similar to aakg-2(sta2) and ucp2 single-module worms; specifically, the lifespan of dual-1 was not further extended by dietary restriction (Figure 3a–3c). For induction of the hsp genes, the dual-1 strain showed a smaller change than the aakg-2(sta2) strain by itself (Figure 4c). Thus, the dual-1 strain showed changes in a greater number of assays than in either of the single-expressing lines, and oftentimes the changes were larger in magnitude.
The dual-2 strain contains components (hsf-1 and D. rerio lyz) that showed changes in a total of five cell assays when expressed singly (Table 3). We tested dual-2 in four of the cell assays (paraquat resistance, hsp induction, oxidative damage and sod-3 expression), and saw changes in the first three but not sod-3 expression with respect to control worms (Figure 4a–4d, Table 2). Paraquat resistance was greater in dual-2 than in each of the two single lines, but changes in hsp induction and oxidative damage relative to control worms was less or equivalent to the changes found in the hsf-1 and D. rerio lyz single-module lines (Figure 4c, 4d; Table 2). For sod-3, hsf-1 increases but D. rerio lyz decreases its expression. In dual-2, sod-3 expression is not different than in control worms suggesting that the opposite effects from these two genes cancel out (Figure 4b).
Finally, we measured the lifespan of the dual-expressing lines, and compared them to the lifespan of the single-expressing lines and controls. Two independent dual-1 lines had an increase in median lifespan of ∼80%, compared to an increase of 35–48% from either of the single components (Figure 5a, Table 4, Table S7). Two independent dual-2 lines had an increase in median lifespan of ∼60%, compared to an increase of 28–37% from either of the single components alone (Figure 5b, Table 4, Table S7). These results show that expression of two components can have an additive effect on lifespan.
We extended the method by generating transgenic lines that each express three components. Specifically, triple-1 was generated based on dual-1 with the addition of D. rerio lyz, and thus contains aakg-2(sta2), D. rerio ucp2 and D. rerio lyz. Triple-2 was based on dual-2 with the addition of aakg-2(sta2), and thus contains hsf-1, D. rerio lyz and aakg-2(sta2). We examined expression of the components in the triple-expressing lines using qRT-PCR, and found that each of the genes in the triple-expressing lines was expressed at levels comparable to those from the corresponding single-expressing line (Table S4).
For triple-1 and triple-2, the three constituent aging components can affect each of the seven assays when expressed individually. We examined triple-1 and triple-2 using five of the cell assays (ATP level, hsp induction, paraquat resistance, oxidative damage and sod-3 expression). Triple-1 and triple-2 showed changes in all five assays with respect to control worms (Figure 4a–4d, Table 2). We then measured the lifespan of the two triple-expressing lines and found that two independent triple-1 lines showed 97–105% increased lifespan and two independent triple-2 lines showed 84–92% increased lifespan (Figure 5a, 5b; Table S7).
Finally, we generated a quadruple-expressing line containing four different components: hsf-1, D. rerio lyz, aakg-2(sta2) and D. rerio ucp2. We measured expression of these genes in the quadruple line to determine if their expression was as high in the quadruple-expressing as in the single-expressing lines. We found that hsf-1 was expressed at a comparable level but that aakg-2(sta2), Dr ucp2 and Dr lyz, were expressed at about 50% of the previous levels (Table S4). The quadruple-expressing line showed changes in all five cell assays with respect to control worms, and was the most resistant to paraquat among all strains (Table 2 and Figure 4a–4d). The lifespan of this quadruple-expressing line was increased 130% compared to control, and this result was verified in a second quadruple-expressing line (125% increased)(Figure 5c, Table S7). Taken together, our results show a monotonic increase in lifespan: single-expressing lines (28–47%), double-expressing lines (57% to 84%), triple-expressing lines (84–105%) and quadruple-expressing lines (125–130%).
Besides living for the longest time of any of the engineered strains, the quadruple also has a long health span. Quadruple worms reach the L4 larval stage in approximately 72 hours, similar to control worms. Young adult quadruple worms appear and move normally when viewed using a dissecting microscope. For control worms, the median lifespan is about 18 days at which point most of the animals still alive show limited mobility. For quadruple worms, the median lifespan is 40 days but most of the surviving animals move well, similar to 14 day-old control worms (Videos S1, S2, S3). These observations indicate that we have extended the time that quadruple worms are mobile and healthy. This is important for lifespan engineering, as one would optimally want to extend the healthy portion over the morbid time at the end of life.
This paper uses an engineering approach to build healthy and long-lived worms. Our approach was to choose genes from well-studied aging pathways that can be used as components to extend lifespan, and then validate that they are active using a variety of molecular and cellular assays. In this initial study, we used four approaches to find seven aging components. In the first approach, three components (hsf-1, aakg-2(sta2) and sod-1) were obvious choices because they were previously known to extend lifespan when overexpressed in worms [12]–[14]. Future genetic studies of aging are likely to reveal many more genes that extend lifespan when overexpressed, each time providing a new aging component.
Secondly, one component was chosen based on prediction from theory; specifically, lmp-2 was selected because it is involved in chaperone-mediated autophagy [23]. Overexpression of this component is expected to increase protein degradation, reduce steady-state levels of protein damage, and thereby extend lifespan. In this case, not only did we generate an aging component that can be used in our study, but we were also able to validate a prediction made from theory and thus provide support for the role of protein turnover in aging.
In the third approach, we used orthologous genes from zebrafish rather than genes from C. elegans. We found D. rerio sod1 and C. elegans sod-1 extended lifespan to a similar extent. It will be interesting to continue to compare orthologous genes from D. rerio and C. elegans in order to determine whether there may be a systematic advantage to selecting genes from a longer-lived species.
Lastly, we showed that we can extend lifespan by expressing new functions in C. elegans. The first function is mitochondrial uncoupling activity, which is absent from C. elegans [28]. Previous work has shown that human ucp2 extends the lifespan of D. melanogaster, although it is not clear whether this involves adding a new activity to flies because it is not known whether flies have an endogenous mitochondrial uncoupling activity [26]. The second novel function is vertebrate lysozyme, which has an additional anti-bacterial function not found in C. elegans lysozymes [31]. We found that worms expressing either uncoupling protein or lysozyme from D. rerio have a longer lifespan than control worms.
Each of the aging components can extend lifespan about 30–50% by themselves. To extend lifespan beyond this amount, we combined four different aging components in the same line and extended lifespan to 130%. As the number of components increases beyond four, lifespan assays will become more time-consuming and less practical. For practical purposes, we showed that we can rapidly use cell- and worm-based assays to assess whether a multi-component strain is a good candidate for extended longevity, before having to perform the lifespan assay itself. There was generally good agreement between the results from the cell assays and lifespan; e.g, high levels of sod-3 expression correlated well with extended lifespan in the multi-component strains. It will be interesting to determine how much further one can extend lifespan by adding additional components. Previous studies have already shown that daf-2 mutant worms that lack a germline have a five-fold increase in longevity [39]. Future engineering efforts may also be able to achieve extreme longevity.
Although data from our cell assays indicate that a certain aging pathway may be active, it is difficult to formally conclude that the observed activity is the cause for longer lifespan. This is because any of the aging components may have an unknown second activity. For instance, our results show that expression of superoxide dismutase results in paraquat resistance, consistent with a reduction in oxidative damage. However, recent evidence suggests that this enzyme extends lifespan not through an oxidative damage pathway but by another undefined mechanism [40]. Whether or not the precise mechanism is reduction of oxidative damage, superoxide dismutase does indeed extend lifespan and serves our purposes as an aging component to engineer longer-lived worms.
This work provides a proof of principle that one can engineer longer lifespan in C. elegans by adding new components. New technologies in DNA construction, increased knowledge of aging pathways, and improved methods to fine-tuning gene expression will add powerful tools to engineering lifespan. For instance, it will soon be possible to synthesize large stretches of DNA containing many genes from any organism, worm or otherwise, in order to express multiple genes from a genetic pathway. For example, the innate immune system of vertebrates is a source of new anti-bacterial proteins that could significantly improve resistance to pathogenicity in C. elegans [41]. Additionally, expression of vertebrate-specific chaperones and mitochondrial proteins in worms may improve their proteostasis and energy balance pathways, respectively [42]–[43]. Adding exogenous components from vertebrates is a powerful strategy that goes beyond the natural constraints of the C. elegans genome to engineer worms with increased lifespan and healthspan.
All C. elegans strains were maintained and handled as previously described [44]. 5-fluoro-2′-deoxyuridine (FUDR, Sigma) plates were made by supplementing NGM agar media with 30 µM of FUDR.
Genes from C. elegans used in this study were amplified by PCR from N2 worm genomic DNA. Generation of constructs containing zebrafish or human cDNA used worm upstream regulatory sequence as defined by the promoterome [45]. If the required promoter was not part of promoterome, all intergenic sequence upstream of the gene of interest was used. cDNA of the gene of interest was obtained from Open Biosystems. The 3′ UTR was from the intron-containing unc-54 gene.
Transgenic strains were made by microinjecting unc-119 worms with the gene of interest at 10 ng/µl and PD4H1 (unc119(+); sod3::mCherry) [46] at 80 ng/µl. To generate transgenic worms containing two or three genes, each of the genes of interest was injected at 10 ng/µl and PD4H1 at 70 ng/µl. sod-3::mCherry is a reporter for daf-16 activity.
Life span analyses were conducted on FUDR plates at 20°C as previously described [47]. At least 80 worms were used for each experiment. Age refers to days following adulthood, and p-values were calculated using the log-rank (Mantel-Cox) method. Individuals were excluded from the analysis if their gonad was extruded or if they desiccated by crawling onto the edge of the plate.
Fourth larval stage worms were washed with M9 buffer and pelleted in a centrifuge. RNA was extracted by addition of 500 µl of Trizol (Invitrogen) to 50 µl of worm pellet, followed by six freeze-thaw cycles in liquid nitrogen. RNA extraction was performed according to the Trizol protocol from the manufacturer. Gene expression was determined by reverse transcription of 0.5 µg total RNA with the Superscript III kit (Invitrogen) followed by quantitative PCR analysis on a Step One Plus real time PCR machine (Applied Biosystems) with iQ SYBR green (Bio-Rad) using act-1 RNA as a control. The experiments were conducted in triplicate. Expression level of a gene of interest relative to act-1 was determined by calculating the difference in the number of cycles between the gene of interest and act-1. The level of expression in a transgenic strain compared to a control strain is the difference in the normalized number of cycles, with one cycle being equivalent to two-fold difference in expression.
Assays were performed in triplicate as previously described [48]. Briefly, four day old adult hermaphrodites were immersed in S-basal media containing 50 mM or 200 mM of paraquat. The number of dead worms was scored every hour by touch-provoked movement until all worms were dead. For each strain, median survival was determined by plotting Kaplan Meier survival curves containing 150 worms.
About 200 L4 hermaphrodites were collected, washed four times with S-basal buffer in an eppendorf tube, boiled for 20 minutes and quickly frozen in −80°C. All samples were processed on the same day. A Roche ATP Bioluminscent HSII kit was used to measure ATP concentrations, which measures bioluminescence emitted by the ATP-dependant oxidation of D-luciferin catalyzed by luciferase. ATP concentrations were determined using a standard curve derived from bioluminescence of known ATP concentrations (HSII kit). A Wallac 1420 multilabel counter luminometer (Victor2, Perkin Elmer) was used to measure levels of bioluminescence. A BioRad protein assay kit was used to measure protein concentrations using a Beckman Coulter DU 640 spectrophotometer. ATP concentrations were normalized to absolute protein concentrations. Each assay was repeated in triplicate, and the average ATP concentration and SD were calculated.
The sDR method was performed as described in [49] with slight modifications. Overnight cultures of E. coli OP50 were grown at 37°C and collected by centrifugugation at 3,000 rpm for 30 minutes (Sorvall Legend RT) to collect bacterial cells. DR plates were prepared by adding 0.75×108 cfu of OP50 and ad libitum (AL) plates were prepared by adding 0.75×1011 cfu of OP50 to NGM-FUDR plates. Worms were grown on NGM plates and synchronized hermaphrodites were transferred overnight to fresh NGM plates with OP50 and 30 µM of FUdR in order to prevent growth of progeny. Day 1 adult animals were then transferred to DR or AL plates. To maintain bacterial concentration, worms were transferred to fresh DR or AL plates every other day.
Oxidative damage was assessed using an Oxyblot assay kit (Millipore) to detect carbonylated proteins as previously described [50]. About 100 worms synchronized at day 1 or day 14 of adulthood were collected, washed twice with M9 buffer and boiled for 20 min in lysis buffer [50]. Carbonyl groups were derivatized to 2,4-dinitrophenylhydrazone (DNP-hydrazone), and were then detected by Western blotting with a DNP-specific antibody. Total protein levels in the extract were measured by nanodrop and 9 mg of protein lysate was loaded in each lane. Quantification of carbonylated proteins was achieved by taking the ratio of DNP staining to tubulin staining. Levels of carbonylated protein were compared in three independent samples of one day old and 14 day old adult worms.
Fluorescence images of sod-3::mCherry were taken as described [30]. Briefly, 10 age-synchronized worms at day 9 of adulthood were transferred to 1 mM aldicarb-NGM plates for 2–3 hours to induce paralysis [51]. Worms were then photographed using a 20× lens on a Zeiss AxioPlan Fluorescent Microscope. Levels of mCherry expression (in the head and the first two pairs of intestinal cells) were analyzed using ImageJ [30]. For any given comparison, all pictures were taken on the same day with the same microscope settings. Results from three independent sets of 10 worms were used to calculate the average expression level and SD.
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10.1371/journal.pntd.0006558 | Multi locus sequence typing of Burkholderia pseudomallei isolates from India unveils molecular diversity and confers regional association in Southeast Asia | Burkholderia pseudomallei, the causative agent for melioidosis, has become a public health problem in India and across the world. Melioidosis can be difficult to diagnose because of the inconsistent clinical presentations of the disease. This study aims to determine the genetic diversity among the clinical isolates of B. pseudomaelli from India in order to establish a molecular epidemiology and elucidate the Southeast Asian association.
Molecular typing using multi locus sequence typing was performed on thirty one archived B. pseudomallei clinical isolates, previously characterised from specimens obtained from patients admitted to the Christian Medical College & Hospital, Vellore from 2015 to 2016. Further investigations into the genetic heterogeneity and evolution at a regional and global level were performed using insilico tools.
Multi locus sequence typing (MLST) of the isolates from systemic and localized forms of melioidosis, including blood, pus, tissue, and urine specimens, revealed twenty isolates with novel sequence types and eleven with previously reported sequence types. High genetic diversity was observed using MLST with a strong association within the Southeast Asian region.
Molecular typing of B. pseudomallei clinical isolates using MLST revealed high genetic diversity and provided a baseline molecular epidemiology of the disease in India with a strong Southeast Asian association of the strains. Future studies should focus on whole genome based Single-Nucleotide-Polymorphism (SNP) which has the advantage of a high discriminatory power, to further understand the novel sequence types reported in this study.
| Burkholderia pseudomallei, a gram negative bacteria, is the causative agent for melioidosis. Annually, around 165,000 people suffer from melioidosis worldwide. B. pseudomallei is present in wet soil and stagnant water. It enters the human body via percutaneous inoculation, inhalation, aspiration, and occasionally ingestion. Clinical presentations of B. pseudomallei vary by geographical region. Melioidosis occurs predominantly in Southeast Asia, northern Australia, South Asia (including India), and China. Occasional cases occur in other countries around the world. Melioidosis has become a public health problem in India, due to the increasing numbers of people affected in various parts of the country. This study provides baseline data on the genetic diversity among B. pseudomallei isolates from different clinical samples (blood, pus, tissue and urine) of patients admitted to a tertiary care hospital using signature nucleotide sequences via multi locus sequence typing (MLST). Further, this study shows a relationship among B. pseudomallei previously reported in various Southeast Asian countries over the years from 1935 and 1947 with those seen in current clinical cases.
| Burkholderia pseudomallei, the causative agent of the infectious disease melioidosis, is estimated to cause 165,000 cases of human melioidosis per year worldwide [1]. B. pseudomallei, an environmental saprophyte is commonly found in wet soil and stagnant water throughout endemic regions. The mode of infection is by inhalation, through cuts in the skin, and occasionally through ingestion [2]. The most severe clinical manifestation is melioidosis septic shock, which is often associated with pneumonia and bacterial dissemination to distant sites [3]. Melioidosis often affects individuals with one or more pre-existing conditions associated with an altered immune response,the most common being diabetes mellitus [4,5].
Melioidosis is endemic to Southeast Asia and Northern Australia, but still can be recognized in other countries worldwide [6]. Regional variations in clinical presentation of melioidosis are widely observed such as the predominance of pivotal swelling seen in Australia but not in Thailand. The contributing factors for this diversity are still unclear whether bacterial, host or environmental. There is no correlation between the clinical presentations and genotypes to date, even though environmental partitioning between Australian and Asian population of B. pseudomallei have been reported previously [7]. Melioidosis has become a public health problem in India, due to the steady rise in case detection rates from various parts of the country. Moreover, no consistency has been observed in the forms of melioidosis (clinical presentations) reported among the sporadic cases across the country in the last two decades. A recent report reveals genetic diversity among clinical isolates of B. pseudomallei from South India [8]. This study focuses on the clinical manifestations and genetic diversity of B. pseudomallei isolated from patients across India using the multi locus sequence typing (MLST) scheme for B. pseudomallei as described by PubMLST, with an attempt to establish the molecular epidemiology in Southeast Asian region.
A total of 31 B. pseudomallei clinical isolates that were previously characterised from different clinical specimens (blood, pus, tissue and urine) obtained from patients admitted to the Christian Medical College & Hospital, Vellore from different parts of the country, during 2015 to 2016 were included in this study. The total genomic DNA was extracted using automated method (QIASymphony SP, QIAGEN, Germany). MLST was performed by PCR amplification of seven house-keeping genes (ace, gltB, gmhD, lepA, lipA, narK, ndh). The primer sets used for PCR amplification were obtained from B. pseudomallei MLST scheme as described in PubMLST (https://pubmlst.org/bpseudomallei/). Sequencing PCR using the same primer set was performed using the Big Dye Terminator (v3.1) cycle sequencing kit (Applied Biosystems, Thermo Fisher Scientific Company, Waltham, MA.) under the manufacturer’s protocol, purified and resolved on ABI 3500 Genetic Analyzer (Applied Biosystems, Thermo Fisher Scientific Company, Waltham, MA). The complete sequences of the seven loci of the house keeping genes were assigned allelic number and defined a sequence type based on the allelic profile match on the PubMLST database. New allele numbers and STs were assigned to sequences not reported previously by submission to the database. Genetic relatedness of the isolates in comparison to the global isolates was analysed using the goeBURST algorithm of the PHYLOVIZ open source software to establish a clonal association. The nucleotide diversity was calculated using the allelic sequences on the DNA sequence polymorphism software (v6.10.01). SplitsTree4 (version 4.14.6) was used to derive a comparative phylogenetic relationship among the study isolates and isolates previously reported from India (http://www.ub.edu/dnasp/).
The B. pseudomallei are from clinical specimens (blood, pus, tissue and urine) from patients admitted to the Christian Medical College and Hospital, Vellore. This study was approved with ethical clearance to use the clinical isolates by the Institutional Review Board with IRB MIN 16044. The clinical samples are anonymized.
Within the thirty one clinical isolates obtained during 2015 to 2016, Systemic forms of melioidosis (blood) contributed to 51.6% (n = 16) and localized to 45% (n = 14) with one urinary tract infection (3.2%). In the case of the localised infections, the number of isolates from pus and the urinary tract infection were 29% (n = 9), tissue 16% (n = 5) respectively. Infected patients were from the states of Tamilnadu (n = 11; 35.5%), West Bengal (n = 5; 16.1%), Andhra Pradesh (n = 6; 19.5%), Jharkhand (n = 5; 16.1%), Kerala (n = 1; 3.2%) and Tripura (n = 1; 32%). Twenty isolates had distinct allelic profiles from the existing database and were assigned new sequence types (ST) (13 new STs, ST1630-ST1642). Eleven isolates in this study were found to be previously reported STs (ST51, ST1364, ST1099, ST300, ST1552, ST375, ST56, ST71, ST228 and ST99).
The genetic diversity among the identified STs was low as four isolates had ST1630 (12.9%), three had ST1639 (9.7%), two had ST51, ST1637, 1641 (6.5%) and the remaining 19 isolates having different STs. Isolate identifiers with geographical location, type of specimen and the corresponding STs are represented in Table 1. Interestingly in two patients with both systemic and localized infections (VBBP002 –Systemic–ST1630 and VBBP006 –localized—ST1631; VBBP011—Systemic–ST51 and VBBP023—localized–ST375), different STs were observed with respect to the site of isolation—they are single loci variants. goeBURSTanalysis clustered 18 of the study STs into a single major clonal complex of founder ST300. Four STs (1634, 1632, 1636, and 1639) were found to be singletons and are outliers (Fig 1).
Thirty five percent (n = 11) of the identified STs in this study have been previously reported and were found to be associated with Singapore (ST51), China (ST51, ST1099), Thailand (ST51, ST99, ST375, ST228, ST300), Malaysia (ST51, ST99), Burma (ST51), Bangladesh (ST56), Cambodia (ST56), Vietnam (ST56), Philippines (ST99) and Sri Lanka (ST1364) of Southeast Asia (Fig 2) [9, 10].
Except for the isolates with the ST51 (6.5%) and ST 56 (3%), no association was found between the epidemiological year and the prevalence of the isolates. ST51 and ST 56 were first observed in 1935 and 1947 and are still seen in clinical cases (Table 2).
Nucleotide diversity among the study isolates as calculated by DNA SP6 was 0.00212 and within the Indian isolates was 0.00182 (Table 3). Splits tree analysis depicts 80% of the isolates associated with Southeast Asia into one group wherein the rest of the study isolates are grouped differently (Fig 3).
The burden of B. pseudomallei in India and across the world is of a great concern due to its wide distribution in the community as an environmental etiological agent. Molecular typing by MLST method serves as a powerful epidemiological tool to determine the source of infection (local epidemiology) and understand the diversity and evolution of the pathogen population. Though this study involved a small number of isolates (n = 31), the identified STs in this study provide information on regional and country wide sequence diversity.
The different types of melioidosis in the Southeast Asian region include bacteraemia, skin/soft tissue infections, localized abscesses (splenic, prostatic, liver, prostatic, parotid), pneumonia and genitourinary tract infections [11]. There was no association found between the different types of melioidosis and the sequence types among the study isolates, which showed high diversity.
Thirty five percent (n = 11) of the study isolates are confined to STs of South East Asia conferring a regional association and the remaining are novel STs. Though there is high diversity among B. pseudomallei across India and South East Asia, this study provides insights into the regional STs corresponding to both systemic and localized infections being consistent over a long period of time. The correlation between a few of the identified STs (51, 56) and the epidemiological years denote persistent strains causing infection across the continent.
The variation among isolates (VBBP002:VBBP006 and VBBP011:VBBP023) in both systemic and localized infections from the same patient with a single loci variation shows possible evolution over a short period of time; however genome wide studies are needed to provide valid information. Nucleotide diversity and splits network analysis within the house keeping genes shows the least differences for the 6 housekeeping genes, reducing the possibilities of recombination events but more of single nucleotide polymorphisms. The nucleotide diversity of the gmhD gene was found to be (0.00306) and has the maximum number of allelic profiles within the study population. The existence of the parallelogram type of split tree network of all the south Indian isolates signifies the possibility of recombination events, but the Southeast Asian study isolates did not show a typical parallelogram and lie on the same group conferring the absence of recombination events [12].
Though Multi locus sequence typing (MLST) is one of the most commonly used Single-Nucleotide-Polymorphism (SNP) based phylogeny with the use of seven housekeeping genes, it represents only 0.05% (3401 nucleotides) of the total bacterial genome of 7.4 million nucleotides, with less discriminatory potential being the main limitation in a closely related group or within the sequence type. However, whole genome based SNP typing provides phylogeny with high discriminatory power, which could further type the isolates belonging to same sequence types and/or clonal group. This was substantiated by the studies done by Price et al., 2015, to show the differences between same sequence types but polyclonal by Whole Genome Sequencing in a patient with chronic melioidosis across years unveiling the genome plasticity [13], but the study did not indicate the non synonymous nucleotide polymorphisms. Additionally, Chapple et al 2016 describe the differences in B. pseudomallei by Whole Genome Sequences within the same sequence types being persistent across years and different regions, but the mutations not correlating to the environment factors [14]. This evidence gives a glimpse of the high evolution in B. pseudomallei, with conserving the core genome having strong ancestral relationships as derived in this study.
Prospective studies based on whole genome phylogeny would provide higher resolution over the genome plasticity of B. pseudomallei in India and the regional association through the conserved regions on this pathogen. Continent wide large scale genomic studies would enable us to establish a regional association of the strains [15]. To conclude, future studies must focus on whole genome based SNP typing in order to understand the phylogeny and evolution of this bacterium.
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10.1371/journal.pbio.0050039 | Absence of Ret Signaling in Mice Causes Progressive and Late Degeneration of the Nigrostriatal System | Support of ageing neurons by endogenous neurotrophic factors such as glial cell line–derived neurotrophic factor (GDNF) and brain-derived neurotrophic factor (BDNF) may determine whether the neurons resist or succumb to neurodegeneration. GDNF has been tested in clinical trials for the treatment of Parkinson disease (PD), a common neurodegenerative disorder characterized by the loss of midbrain dopaminergic (DA) neurons. BDNF modulates nigrostriatal functions and rescues DA neurons in PD animal models. The physiological roles of GDNF and BDNF signaling in the adult nigrostriatal DA system are unknown. We generated mice with regionally selective ablations of the genes encoding the receptors for GDNF (Ret) and BDNF (TrkB). We find that Ret, but not TrkB, ablation causes progressive and adult-onset loss of DA neurons specifically in the substantia nigra pars compacta, degeneration of DA nerve terminals in striatum, and pronounced glial activation. These findings establish Ret as a critical regulator of long-term maintenance of the nigrostriatal DA system and suggest conditional Ret mutants as useful tools for gaining insights into the molecular mechanisms involved in the development of PD.
| What does a neuron need to survive? Our body produces its own survival factors for neurons, so-called neurotrophic factors, which have additional roles in neuron differentiation, growth, and function. Declining production of a neurotrophic factor or impaired signal transduction in ageing neurons may contribute to pathological neurodegeneration in humans. Glial cell line–derived neurotrophic factor (GDNF) and brain-derived neurotrophic factor (BDNF) have been suggested as survival factors for midbrain dopaminergic neurons, a group of neurons primarily affected in Parkinson disease.
To investigate the physiological requirements for GDNF and BDNF to establish and maintain an important output pathway of these neurons—the nigrostriatal pathway—in the intact brain, we generated mutant mice with regionally selective ablations of the receptors for these survival factors, Ret (receptor of GDNF and related family members) or TrkB (BDNF receptor). Surprisingly, these mice survive to adulthood and show normal development and maturation of the nigrostriatal system. However, in ageing mice, ablation of Ret leads to a progressive and cell-type–specific loss of substantia nigra pars compacta neurons and their projections into the striatum. Our findings establish Ret and subsequent downstream effectors as critical regulators of long-term maintenance of the nigrostriatal system.
| The ventral mesencephalon contains the majority of dopaminergic (DA) neurons in the vertebrate brain with important functions for maintaining the mental and physical health of the organism. They form two prominent pathways: DA neurons of the substantia nigra pars compacta (SNpc) extend their axons mainly into the dorsal striatum (caudate-putamen) to form the nigrostriatal pathway essential for the control of voluntary motor behavior. DA neurons of the ventral tegmental area (VTA) project their fibers mostly into the ventral striatum (nucleus accumbens), olfactory tubercle, septum, amygdala, hippocampus, and cortex collectively referred to as the mesocorticolimbic system. This system has important function in controlling emotion-based behavior such as motivation and reward. Pathological changes in the DA systems result in psychosis, schizophrenia, attention deficit/hyperactivity disorder (ADHD), depression, addiction, and, most prominently, Parkinson disease (PD).
PD is the most common neurodegenerative movement disorder, clinically characterized by resting tremor, rigidity, postural imbalance, and bradykinesia. The underlying pathological event in PD is the progressive loss of DA neurons in the SNpc, often accompanied by intracytoplasmic proteinaceous inclusions termed Lewy bodies [1] and by neuroinflammatory processes [2]. Because of presymptomatic compensation [3], behavioral symptoms appear by a threshold effect, when 50%–60% of SNpc neurons and 70%–80% of striatal dopamine are lost [4,5]. Healthy individuals also experience continuous loss of DA neurons, but they remain asymptomatic as long as the critical threshold is not reached. The questions about the molecular etiology of PD and the selective neuronal vulnerability have not been answered satisfactorily.
Endogenous neurotrophic factors regulate natural cell death during development and maintain target innervations and cell survival during postnatal life. Declining production of a neurotrophic factor or impaired signal transduction in ageing neurons may contribute to pathological neurodegeneration [6]. Glial cell line–derived neurotrophic factor (GDNF) is a member of the GDNF family of neurotrophic factors that signal through a two-component receptor complex consisting of the Ret (rearranged during transfection) receptor tyrosine kinase and the GPI-linked GDNF family receptor alphas (GFRα) [7]. GDNF was suggested to be a target-derived neurotrophic factor for developing DA neurons [8] and a postnatal survival factor for midbrain DA neurons (reviewed in [9,10]). Genetic evidence, however, is limited, because GDNF and Ret null mutant mice die at birth [10]. GDNF protects DA neurons from the effects of neurotoxins such as 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) (reviewed in [7,11]). GDNF is currently tested in clinical trials (using different delivery systems) with the hope that it will ameliorate PD symptoms, but the results are so far conflicting [12,13]; see also [14] and [15].
Brain-derived neurotrophic factor (BDNF) is a member of the neurotrophin family and signals through the TrkB receptor tyrosine kinase and the p75 receptor. BDNF and TrkB are widely expressed throughout the adult and ageing brain, including midbrain DA neurons and the striatum [16,17], but age- and PD-related decreases in the expression of BDNF and reduced responsiveness to BDNF have been observed (reviewed in [6,18]). DA neuron loss after BDNF ablation during development [19] suggested that impaired signaling through TrkB may compromise DA neuron survival. BDNF modulates nigrostriatal functions and rescues DA neurons in PD animal models [9,20,21]. BDNF and TrkB null mutant mice do not survive to adulthood, preventing the genetic analysis of their roles in long-term DA neuron survival [22,23].
To investigate the physiological requirements for Ret and TrkB to establish and maintain the nigrostriatal pathway, we generated mice with regionally selective Ret and TrkB ablations that are compatible with postnatal survival of the mice. We find that Ret, but not TrkB, regulates long-term maintenance of the nigrostriatal DA system. Ret ablation causes progressive and late loss of DA neurons in SNpc, degeneration of DA nerve terminals in striatum, pronounced reactive gliosis, microglial activation, and reduced levels of evoked dopamine release. Together, these data establish Ret as an important signaling receptor for nigrostriatal DA system preservation and suggest conditional Ret mutants as an interesting model to study presymptomatic compensatory mechanisms in this system and early PD-related pathologies.
To disrupt the genes encoding Ret and TrkB in a regionally selective manner, we used mice with floxed alleles of Ret [24] and TrkB [25] in combination with Dopamine transporter (DAT)-Cre mice [26] (DAT-Retlx/lx and DAT-TrkBlx/lx mice, respectively) and Nestin-Cre mice [27] (Nes-Retlx/lx mutants). DAT-Cre mice had been generated by knocking Cre into the 5′ UTR region of the endogenous mouse DAT locus [26], whereas Nestin-Cre mice express Cre from a transgene [27]. For DAT-Cre mice, it was previously shown that virtually all (95%) of tyrosine hydroxylase (TH)-positive cells in SNpc and the nearby VTA regions express Cre and show Cre-mediated recombination in adult mice, whereas weak lacZ reporter activity was seen in DA neurons of the olfactory bulb and hypothalamus. No reporter activity was seen in the striatum [26]. We confirmed these observations and extended the analysis to other time points, including embryonic day 15 and 2-y postnatal (Figure 1A–1D). Our results indicate that DAT-Cre–mediated recombination is region selective from late embryonic stages to aged mice. Ret expression is high in the SNpc and VTA of adult control mice (Figure 1E and 1F) and is efficiently removed in DAT-Retlx/lx and Nestin-Retlx/lx mice (Figure 1G–1J). Western blot analysis of Ret protein revealed a nearly complete loss of the protein in SN of Dat-Retlx/lx mice, and complete loss of Ret in the striatum of DAT-Retlx/lx mice (Figure 1K and 1L). TrkB expression is found in nigral DA neurons [17], but also in other neuronal populations in the entire ventral midbrain (unpublished data). The conditional trkBlx allele has previously been used for region-specific removal of TrkB in several studies [25,28], indicating that this locus can efficiently be modified by Cre recombination. We were unable to visualize loss of TrkB by immunostaining and Western blotting in DAT-TrkBlx/lx mice, because the TH-positive subpopulation expresses low amounts of TrkB and is a minor population within the TrkB expression domain. However, we detected the recombined TrkBlx allele by PCR specifically in SNpc, but not in striatum, of DAT-TrkBlx/lx mice (Figure S1A). In addition, using laser capture microdissection combined with single-cell RT-PCR, we found a 65% decrease of TrkB mRNA–positive DA neurons in the SNpc in DAT-TrkBlx/lx mice compared to controls (Figure S1B–S1D).
DAT-Retlx/lx and DAT-TrkBlx/lx knock-outs are viable and fertile. To detect morphological alterations in the nigrostriatal system, brain tissue sections of mutant and control mice (floxed Ret and/or TrkB mice; heterozygote DAT-Cre mice) were immunostained for TH and subjected to stereological quantification (Figure 2A). A significant decrease of approximately 25% in the number of TH-positive neurons in the SNpc was found in 1-y-old DAT-Retlx/lx, but not DAT-TrkBlx/lx mice, compared to age-matched control mice (Figure 2B). Similar reductions of SNpc neurons were also observed in a more widespread knock-out of Ret using Nestin-Cre mice (Figure 2C). Combined loss of Ret and TrkB did not significantly enhance the loss of TH-positive neurons, suggesting that TrkB signaling has a minor, if any, role in the survival of a subset of nigral DA neurons (Figure 2B). A time-course analysis starting at 3 mo of age revealed that the nigrostriatal system of DAT-Retlx/lx mutants had developed normally, despite the fact that DAT-Cre–mediated recombination started during late embryogenesis (Figure 2B). At 2 y of age, DAT-Retlx/lx mice have lost 38% of TH-positive neurons, compared to age matched control mice (Figure 2B), whereas in 2-y-old DAT-TrkBlx/lx mice, no reduction of TH-positive neurons was detected (Figure 2B). We used the general neuronal marker NeuN and additional independent markers of nigral DA neurons to further characterize the defects in DAT-Retlx/lx mutants. Anti-NeuN immunostaining combined with a weak TH staining to visualize the SNpc revealed 14% and 17% loss of NeuN-positive cells in the SNpc of 1-y-old and 2-y-old DAT-Retlx/lx mutants, respectively (Figure 2D and 2E). Nissl staining, which labeled approximately five times more cells than TH staining, did not reveal significant changes in DAT-Retlx/lx mutants (Figure S2A–S2C). Anti–dopa-decarboxylase (DDC) and anti-Pitx3 immunostaining [29] revealed reductions of 35%–40% of immunopositive nigral DA neurons in DAT-Retlx/lx mutants compared to controls (Figure 2F–2J). In summary, the data suggest that loss of Ret leads to the loss of neurons rather than to reduced TH expression in a subpopulation of the SNpc. The observed defects in Ret mutants were region specific: The nearby VTA region was not affected in DAT-Retlx/lx mice, and the locus coeruleus (LC) was not affected in Nes-Retlx/lx mutants (despite the fact that Nestin-Cre recombines in and Ret is expressed in the LC) (Figure 2K and 2L). The loss of SNpc DA neurons in PD is often associated with the formation of α-synuclein–containing aggregates, so-called Lewy bodies. We were not able to detect accumulation or aggregates of α-synuclein in the cell bodies of Ret or TrkB or double-mutant mice (Figure S3A–S3I and unpublished data).
We next sought to evaluate the possibility that Ret and TrkB signaling would be required for maintenance of target innervation of nigral DA neurons. The quantification of TH-positive fiber density revealed an approximately 40% decrease in the dorsal striatum of 1-y-old DAT-Retlx/lx mice, compared to age-matched controls (floxed Ret and/or TrkB mice; heterozygote DAT-Cre mice) or DAT-TrkBlx/lx knock-outs (Figure 3A–3E). Similar reductions in TH fiber density (38%) were seen in 1-y-old DAT-Ret/TrkB double knock-outs and in Nestin-Retlx/lx mutants (Figure 3E). The reduction was somewhat more pronounced in dorsal versus ventral striatum (40% versus 28% in DAT-Retlx/lx mice, and 38% versus 20% in DAT-Ret/TrkB double-mutant mice and Nestin-Retlx/lx mutants), correlating with the innervation preference by nigral DA neurons [30] (Figure 3E, ventral). To exclude the possibility that the reduction of TH fiber density reflected a reduction of TH protein, rather than fibers, we used DAT protein as an independent and selective marker for DA terminals. Because the aged DAT-Cre knock-in mice have reduced levels of DAT protein (unpublished data) due to the loss of one functional copy of the DAT gene, we analyzed Nes-Retlx/lx instead and found a similar reduction of DAT fiber density in mutants versus control mice (Figure 3F–3H). A time-course analysis between 3 mo and 2 y revealed that this was an age-dependent process that started at around 9 mo of age and was most pronounced in 2-y-old mice (63% reduction in DAT-Retlx/lx mice versus controls) (Figure 3I). In conclusion, Ret signaling is required for maintenance of target innervation of midbrain DA neurons.
We next asked if loss of nigrostriatal innervation by midbrain DA neurons would cause non-cell autonomous dysfunction of striatal target neurons that do not express Ret. Staining with the neuronal marker NeuN, which detects all striatal neurons including interneurons, revealed a decreased staining intensity and a small, but significant reduction of NeuN-positive cells (8%; n = 5 for each group; p < 0.05, Student t-test) in 2-y-old DAT-Retlx/lx compared to age-matched control striatum (Figure 4A–4C). More importantly, expression of DARPP-32 (dopamine and cyclic adenosine 3′,5′-monophosphate-regulated phosphoprotein, 32 kDa), a protein expressed by nearly all dopaminoceptive striatal projection neurons [30], was also reduced and the number of positive cells decreased by 20% (n = 3 for each group; p < 0.01, Student t-test) in DAT-Retlx/lx mutants compared to controls, suggesting the existence of postsynaptic dysfunction including unhealthy, atrophic cells and perhaps cell loss as consequence of Ret ablation in DA neurons (Figure 4D–4F). In contrast, expression of the calcium-binding protein parvalbumin, which labels local GABAergic interneurons, was not reduced in DAT-Retlx/lx mutants compared to controls (Figure 4G–4I). These results suggest that loss of nigrostriatal innervation indirectly affects a fraction of dopaminoceptive striatal neurons.
Having established substantial loss of nigrostriatal innervation and some striatal dysfunction in DAT-Retlx/lx mutants, we next asked if these degenerative processes would cause gliosis by invading reactive astrocytes. We used immunoreactivity against glial fibrillary acidic protein (GFAP) as an indicator of the astroglial response to genetically induced DA nerve terminal damage (Figure 5). Staining of 2-y-old brains revealed a massive reactive gliosis in dorsal striatum of DAT-Retlx/lx mutants compared to controls (Figure 5D–5F; n = 5 mice per group, p < 0.0001, Student t-test). No increased GFAP immunoreactivity was observed in 2-y-old DAT-TrkBlx/lx striatum (Figure 5F; n = 4 per group, p < 0.0001, Student t-test), or in younger (12-mo-old) DAT-Retlx/lx mutants (Figure 5A–5C; n = 3 mice per group, p = 0.90, Student t-test), or in other brain regions such as the neocortex of 2-y-old DAT-Retlx/lx mutants (unpublished data). GFAP immunoreactivity in SNpc of 2-y-old DAT-Retlx/lx mutants was not significantly enhanced compared to controls (Figure 5G–5I; n = 3 per group, p = 0.24, Student t-test) despite the marked loss of TH-positive cells in this structure. Because Ret is not genetically ablated in astrocytes, these results suggest that the gliosis in the striatum of DAT-Retlx/lx mice is non-cell autonomously caused by degenerating DA nerve terminals.
Inflammatory processes are often associated with and activated by a variety of neuronal insults including PD and Alzheimer disease. We used immunohistochemistry for ionized binding calcium adapter molecule (Iba)-1 to detect microglia in brains of DAT-Retlx/lx mice. The numbers of Iba-1 immunopositive cells were not significantly increased in dorsal striatum of 2-y-old DAT-Retlx/lx mice compared to controls (Figure 6A–6C; n = 4 mice per group, p = 0.065, Student t-test). In contrast, we observed an approximately 45% increase in the number of Iba-1 immunopositive cells in SNpc of 2-y-old DAT-Retlx/lx mice compared to controls and DAT-TrkBlx/lx mice (Figure 6D–6J; n = 3 mice for DAT-TrkBlx/lx and n = 5 mice for controls and DAT-Retlx/lx, p < 0.05, Student t-test). Similar results were obtained using macrophage antigen alpha (MAC1, CD11b, or CR3) as a second, independent marker (Figure 6K–6M; n = 3 mice per group, p < 0.05, Student t-test). No differences in the numbers of Iba-1–positive microglial cells were detected in 1-y-old DAT-Retlx/lx mice compared to controls (unpublished data). Similar to reactive astrocytes, the Ret gene was not subjected to recombination in microglia of DAT-Retlx/lx mice, suggesting that the neuroinflammation occurred as a result of neuronal cell death.
PD is clinically defined by a decrease in dopamine levels that result in motor impairments. To determine the effects of nerve terminal loss in DAT-Retlx/lx mice on the DA output capacity of the system, we measured total levels and evoked dopamine release in the striatum of mutant mice. Striatal levels of dopamine and one of its major metabolites, dihydroxyphenylacetic acid (DOPAC), were similar in DAT-Retlx/lx and control mice at 3, 12, and 24 mo (Figure 7A and unpublished data). The somewhat lower values in all mice (mutants and controls) carrying the DAT-Cre transgene compared to DAT-Cre–negative controls is due to the reduced levels of DAT protein, which regulates dopamine transport and metabolism [31]. Evoked dopamine release was measured by fast-scan cyclic voltammetry (FSCV) following electrical stimulation of coronal slice preparations of mutant and control mice [32]. Electrical stimulation resulted in a stimulus intensity–dependent overflow of DA in the striatum. DA overflow is the result of released DA minus the DA reuptake by DAT. We observed a marked reduction of evoked DA overflow in the striatum of all mice carrying the DAT-Cre transgene (Figure 7B–7E) as described before [31]. Interestingly, the evoked DA overflow was further reduced in DAT-Retlx/lx mice compared to DAT-Retlx/+control mice in both 1-y-old and 2-y-old mutants (n = 5 per genotype, p < 0.05 and p < 0.01 for 1-y-old and 2-y-old mutants, respectively, one-way analysis of variance and post-hoc Student t-test). Together with the unchanged input–output curves of the FSCV experiment (Figure S4), these data suggest that the reduced dopamine release and reuptake in the Ret mutants is likely due to the reduced number of DA fibers in the striatum. To determine to what extent these histological and physiological alterations change the behavior of the DAT-Retlx/lx mice, we tested DAT-Retlx/lx and control mice for behavioral alterations in open-field and rotarod tasks, and in voluntary and forced swimming tasks (Protocol S1). The behavior was essentially unaffected in the mutants (Figure S5).
In the present study, we show that signaling by Ret and TrkB receptors is not essential for establishment of the nigrostriatal system. TrkB signaling appears to play a minor, if any, role in maintaining long-term cell survival or target innervation of midbrain DA neurons in aged mice. In contrast, Ret ablation leads to a progressive and cell-type–specific loss of SNpc neurons and their afferents with adult onset, with subsequent alterations in physiology and appearance of neuroinflammatory responses. These findings establish Ret and subsequent downstream effectors as critical regulators of long-term maintenance of the nigrostriatal DA system. Because similar alterations are observed in the early phases of PD, DAT-Retlx/lx mice might be useful for gaining insights into the molecular mechanisms involved in the development of PD.
The apparently normal development and maturation of the nigrostriatal system in DAT-Retlx/lx, DAT-TrkBlx/lx, and DAT-Ret/TrkB mice was rather surprising in light of the known in vitro neurotrophic effects of the respective ligands GDNF and BDNF on DA neurons [9]. Consistent with our results, transgenic overexpression of GDNF or knock-down of GDNF in mice transiently altered the number of DA neurons in the early postnatal days; however, these alterations did not persist into adulthood (see [8] and references within). Ablation of the BDNF gene in the developing mid-hindbrain region using Wnt1-Cre–mediated recombination resulted in reductions in the number of TH-positive neurons in the SN of newborn mice [19]. In contrast to our study, BDNF ablation was earlier, not cell-type specific, and more widespread (mid-hindbrain region), and may have caused alterations in other non-DA neurons and progenitor cells that influenced the development of the nigrostriatal system.
We found that Ret is specifically required for long-term target innervation and cell survival of a significant fraction of SNpc DA neurons. In aged Ret mutant mice, the extent of target innervation loss exceeded the degree of cell loss. This is consistent with observations from PD patients and MPTP-treated animals, which led to the current model of a “dying back” process to explain the DA neuron degeneration [4]. The requirement for Ret appears to be topographically specific: The nigrostriatal pathway from SNpc to dorsal striatum was more dependent on Ret than the mesolimbic pathway from the VTA to ventromedial striatum. At first glance, this seemed surprising because VTA neurons were shown to be more responsive than SN neurons to overexpressed GDNF, and the number of VTA neurons, but not SNpc neurons, persistently increased to adulthood in these mice [33]. GDNF is the likely Ret ligand in this system, because it promotes neurite outgrowth and sprouting of adult midbrain DA neurons more efficiently than do related family members [9,34], and its expression is maintained at detectable levels in the adult [35]. This suggests that GDNF/Ret signaling might be limiting, but not essential, for VTA neurons.
Differences in sensitivity toward stresses between VTA and SNpc neurons have been described previously. For example, SNpc neurons are more sensitive than VTA neurons to 6-OH-dopamine treatment and overexpression of human α-synuclein [36,37]. The presence of functional Kir6.2, a K-ATP channel, promotes cell death of SNpc, but not VTA neurons in two chronic mouse models of DA degeneration [38]. Aphakia mice, deficient for the transcription factor Pitx3 with important function for the establishment of the DA cell fate, preferentially lose SNpc, but not VTA neurons [32]. These data suggest different physiological features of SNpc and VTA neurons in vivo, including possible differences in their cell death pathways and survival factor requirements. It is possible that other neurotrophic factors such as members of the TGF-β superfamily of cytokines [39] or MANF [40] are required for the survival of VTA neurons. Future genetic experiments will hopefully help to answer this question. Also, in PD patients and neurotoxin-based animal models of PD, the nigrostriatal pathway is most affected [4]. The reason for this specificity is not well understood, but it suggests that the molecular death pathways activated in PD, by MPTP and by loss of Ret, share similarities.
We also observed reduced levels of evoked dopamine release, but not of total dopamine amounts in the striatum of DAT-Ret mutants. With respect to total dopamine tissue content, it appears that reductions are generally observed when the majority of SNpc neurons are lost. For example, the recently published En1+/−;En2−/− mutant mice [41] show more than 60% loss of SNpc neurons and a 39% reduction in total striatal dopamine content. The DAT-Ret mutants do not show such a strong decrease in SNpc neurons and, not surprisingly, have normal levels of total dopamine content. In contrast to the En mutants, DAT-Ret mutants lose neurons during ageing which leaves more time for compensatory mechanisms (En mutants lose SNpc neurons before P15). The physiologically more important reduction in dopamine release after electric stimulation parallels the age-dependent loss of striatal innervation. It also parallels changes in postsynaptic neurons such as the decrease of DARPP-32 expression. Whether or not postsynaptic neurons become atrophic or eventually die in PD patients and animal models is not clear and has not been studied in great detail. There is evidence that loss of DA neurons in the basal ganglia or dopamine depletion can lead to changes in the striatum, such as loss of spines and glutamatergic synapses [42] and may eventually lead to cell death [43]. The mild postsynaptic alterations observed in the DAT-Retlx/lx mice are consistent with the idea that presynaptic decrease in DA fibers can indirectly lead to pathological changes in postsynaptic striatal neurons.
Apoptosis contributes to PD neuronal loss [44] and is the predominant cell death mechanism of neurotoxin-based models using prolonged administration of low doses of MPTP, but the detection of apoptotic cells is difficult because of the very low frequency of dying cells and their rapid clearance from the tissue [4]. Apoptosis is most probably the cell death mechanism underlying neurotrophic factor deprivation; although, due to the late and selective degeneration of nigral DA neurons, we were unable to detect apoptotic cells in DAT-Retlx/lx mice. What may the signaling pathways downstream of Ret be that maintain target innervation and mediate cell survival? Besides the well-documented importance of the PI3K/AKT pathway for neuronal survival in response to GDNF [7], GDNF also increases dopamine release and influences synaptic transmission [45,46], and might thereby influence the vulnerability of SNpc neurons. Axon degeneration often begins with breakdown of microtubules whose assembly and disassembly is regulated by microtubule-associated proteins (MAPs) and may involve collapsin-response-mediator-protein-2 (CRMP2). The expression of CRMP2 is induced by GDNF [47], and CRMP2 promotes axon growth and branching as a partner for tubulin heterodimers [48]. Work is currently in progress to investigate the role of downstream mediators of Ret in maintaining DA afferents and cell survival.
Using single-cell RT-PCR, we detected TrkB mRNA in only 50% of nigral DA neurons. Ablation of TrkB expression in the majority of the TrkB+;TH+ pool in the SNpc did not cause any morphological alterations. Moreover, under sensitized conditions in the absence of GDNF/Ret signaling, the additional reduction of TrkB did not cause significant alterations beyond those evoked by lack of Ret alone. From these results, we conclude that the physiological role of TrkB in the nigrostriatal system is minor at best. Cell and fiber loss reported in previous studies using nonconditional alleles of TrkB [49,50] have to be interpreted with care, since several TrkB-positive cell types in this region are not DA. The discrepancies between constitutive and conditional mutants may be partially due to non-cell autonomous effects from cells outside the SNpc. Additional Cre lines and markers for specific cell populations will have to be employed to settle the issue completely.
Activated glial cells (astrocytes and microglia) have been associated with central nervous system (CNS) injuries and PD [2,51]. It is a matter of debate, whether activated microglial cells are neuroprotective by the release of trophic factors or participate in the propagation of neurodegenerative processes. The mechanisms that lead to microglial and astroglial cell responses in PD patients are not understood. Here, we found that Ret ablation resulted in gliosis in the striatum and to microglial activation in the SNpc. What could be the underlying mechanisms? First, degenerating axons may be a stronger signal for astrocytes than for microglia. Such a conclusion is based on previous studies on injured CNS axons, including peripheral motor axons. It was found that astrocytes participated in the removal of presynaptic boutons, whereas microglial participation was not required for this process (reviewed in [52]). Second, apoptotic CNS neurons may be sending signals that preferentially activate microglia. Previous studies have shown that axotomy of retinal ganglion cells in adult rats leads to protracted degeneration that can be delayed by the application of compounds that suppress macrophage and microglia activity, suggesting that the microglial system has a key role in eliminating severed neurons in the CNS [53]. Third, there may be intrinsic differences between striatum and SNpc that result in gliosis versus microglia activation. In MPTP-treated mice, the astrocytic reaction is consecutive to death of neurons, and astrocyte accumulation is observed primarily in the striatum rather than in the SNpc [44,54]. Likewise, in PD patients, astroglial responses are generally weak and microglial responses are dramatic in the SNpc; they culminate in subregions that are most affected by the neurodegenerative process (reviewed in [55]).
The nigrostriatal pathologies following Ret ablation display several features of presymptomatic PD including (1) specific and progressive degeneration of the nigrostriatal pathway, with adult onset (so far unique among genetic PD models), (2) greater loss of DA neurons in SNpc than in VTA, (3) greater degeneration of DA nerve terminals in dorsal than ventral striatum, (4) the presence of substantial neuroinflammation and gliosis, and (5) reduced levels of evoked dopamine release in striatum.
However, our DAT-Retlx/lx mice are not a perfect model of symptomatic PD since they lack several hallmarks of the disease, the first being the lack of cytoplasmic inclusions containing α-synuclein. This suggests that SNpc neuron cell death occurs in the absence of α-synuclein aggregates similar to MPTP-based models and PD cases caused by parkin mutations [56]. The absence of behavioral deficits in DAT-Retlx/lx mice could be explained by incomplete destruction of the nigrostriatal pathway below the reported threshold level for symptom appearance in human PD patients and the presence of compensatory mechanisms maintaining DA homeostasis [3]. The unaltered total amounts of striatal dopamine in the aged DAT-Retlx/lx mice support the idea that these mice are still in a phase in which dopamine-dependent or -independent mechanisms stabilize the system. Genetic experiments are in progress to investigate whether the chronic GDNF deprivation stress in DAT-Retlx/lx mice would make nigral DA neurons more susceptible to other cellular stresses ultimately leading to a more complete destruction of the nigrostriatal pathway. Preliminary data suggest that mild transgenic overexpression of human mutant Ala30Pro α-synuclein using the TH promoter did not aggravate the defects seen in DAT-Retlx/lx mice (L. Aron, E. R. Kramer, P. Kahle, C. Haass, and R. Klein, unpublished data).
DAT-Retlx/lx mutants may be useful in studies of age-related neurodegeneration. Neurotrophins are thought to improve the body's resistance to neurodegeneration. Environmental factors such exercise, dietary energy restriction, and cognitive stimulation protect neurons against dysfunctions and death. This may happen, in part, by induction of a mild stress response that induces the production of BDNF and GDNF [6]. Mice that lack neurotrophin responses in specific neuronal subpopulations should be excellent models to test these hypotheses. DAT-Retlx/lx mutants could also be used for the identification of biomarkers associated with the first phases of nigrostriatal pathway degeneration. So far there is no evidence that PD can be caused by mutations in the GDNF and Ret gene because analysis of polymorphisms in the GDNF and Ret gene have not shown any association with PD [57]. But perhaps GDNF/Ret signaling is reduced as a secondary consequence in PD and leads to the increased vulnerability of SNpc neurons. Further experiments are required to clarify this issue. However, the physiological requirement of Ret signaling for the maintenance of the nigrostriatal system is an important issue, considering the potential for stem cell therapy to replace DA neurons in PD patients, and argues for further investigations toward optimizing the ongoing clinical trials using activators of the Ret pathway as potential therapy for PD.
The generation of floxed Ret (Retlx) [24], TrkB (TrkBlx) [25], and DAT-Cre [26] alleles, and the Nestin-Cre transgenic mice [27] was described previously. Cre mice were crossed with ROSA26R reporter mice [58] to test for proper Cre expression. The mice used in this study were kept on a C57Bl6/J genetic background with contributions of 129/sv from the embryonic stem cell culture and the different Cre mouse lines. Because both Cre lines show significant recombination in germ cells, the floxed allele derived from the parent that carries the Cre recombinase is often constitutively recombined; therefore, the homozygous Ret mutants carry one Ret allele recombined in a regionally specific manner and one Ret allele recombined constitutively. Unless specifically mentioned, the control mice for all experiments carried floxed alleles of Ret (Retlx/lx, Retlx/+), TrkB (TrkBlx/lx, TrkBlx/+), or one copy of Cre (DAT-Cre or Nestin-Cre).
For β-galactosidase stainings, mice were perfused with PBS and 30% sucrose; for immunohistochemistry, mice were perfused with PBS and 4% paraformaldehyde. Subsequently, brains were removed from the scull, postfixed overnight in the same fixative, and cyroprotected by incubating them in 15% and 30% sucrose solutions. Left and right brain halves were embedded separately in egg yolk with 10% sucrose and 5% glutaraldehyde, and kept frozen at −80 °C until analyzed. The 30 μm–thick coronal sections were cut on a cryostat, collected free floating, and then directly used for stainings or stored in a cryoprotection solution at −20 °C until utilized. For fluorescent immunohistochemical stainings, sections were premounted; for all other stainings, free-floating sections were used. Primary antibodies used were goat anti-Ret (1:25; RDI, Flanders, New Jersey, United States, or Neuromics, North Field, Minnesota, United States), monoclonal mouse anti-tyrosine hydroxylase (1:2,000; DiaSorin, Stillwater, Massachusetts, United States), rabbit anti-dopa decarboxylase (1:100; Chemicon/Millipore, Billerica, Massachusetts, United States), monoclonal mouse anti–β-galactosidase (1:50; Sigma, St. Louis, Missouri, United States), rat anti-dopamine transporter (1:500; Chemicon/Millipore), rabbit anti-Pitx3 (1:1,000, provided by M. P. Smidt [29]), monoclonal mouse anti-NeuN (1:200; Chemicon/Millipore), rabbit anti-GFAP (1:500; DakoCytomation, Glostrup, Denmark), rabbit anti–DARPP-32 (1:50; United States Biological, Swampscott, Massachusetts, United States), monoclonal mouse anti-parvalbumin (1:10,000; Swant, Bellinzona, Switzerland), rabbit anti–Iba-1 (1:1,000; Wako, Neuss, Germany), monoclonal rat anti-MAC1 (1:200; Serotec, Kidlington, United Kingdom). For diaminobenzidine detection of the primary antibody, different Vectastain ABC kits (Vector Laboratories, Burlingame, California, United States) were used according to the provider's instructions. For NeuN/TH double labeling, we first stained for NeuN as described above, followed by a weak TH staining with more-diluted primary (1:20,000) and secondary antibodies (1:2,000) and avidin-HRP/biotin complexes (1:2,000). DA fiber density in the striatum was assessed on every third section spanning the striatum (between Bregma +1.10 mm and −0.10 mm) [59]. The mounted sections were blocked for 1 h in 5% BSA, 0.3% Triton X-100 in TBS, and incubated with the first antibody diluted in 2% BSA, 0.1%Triton X-100 in TBS at 4 °C overnight. The sections were washed three times in TBS for 5 min, incubated in biotinylated secondary antibody (1:200 anti-mouse or anti-rat, Vectastain) for 2 h at room temperature, again washed as described above, and treated with streptavidin-Cy3 (1:500; Sigma) for 2 h. After another three washing steps, sections were mounted in aqueous mounting medium with anti-fading reagent (Biomedia, Foster City, California, United States, or DakoCytomation), and pictures were taken with a fluorescent microscope (Axioplan; Zeiss, Göttingen, Germany) at 63×. For every section, three pictures in the dorsal striatum and two pictures in the ventral striatum were acquired. In order to automatically delineate the fibers and to increase the signal-to-noise ratio, the images were first thresholded and subsequently quantified with an automatic counting-grid macro implemented in the Metamorph software (Molecular Devices, Sunnyvale, California, United States). Stereological countings were done with the StereoInvestigator program (MicroBrightField, Williston, Vermont, United States) on every third section for the LC and at least every sixth section for the SNpc and VTA.
For Western blot analysis from the substantia nigra and the striatum and for total dopamine measurements in the striatum, mouse brain tissue isolation was done by cutting 2 mm–thick coronal sections (2-mm rostral or caudal to the interaural line) from a freshly frozen brain and punching out 2-mm2 (substantia nigra) or 3-mm2 (striatum) tissue circles with sample corers (Fine Science Tools, Heidelberg, Germany). Western blot analysis was done according to standard techniques with a rabbit anti-Ret (1:250; Santa Cruz Biotechnology, Santa Cruz, California, United States) and a mouse monoclonal anti–α-tubulin antibody (1:500; Sigma). Total striatal dopamine was measured as described previously [60] with a few modifications.
Evoked release of dopamine was measured in 200 μm–thick coronal slices containing the striatum of control mice (Retlx/lx and Retlx/− mice), heterozygous DAT-Retlx/+ mice, and homozygous DAT-Retlx/lx mice of 1 y or 2 y of age. The slice preparation was done as previously described [32]. Dopamine release was evoked by a single pulse (0–1,000 μA, 300 μs) applied through a bipolar stimulation electrode (bipolar stainless steel, 100 μm, insulated except for the tip) every 30 s. Dopamine was detected with 5-μm carbon-fiber disk electrodes insulated with electrodeposition paint (ALA Scientific Instruments, Westbury, New York, United States) using FSCV. Cyclic voltammograms (ramps from −500 mV to +1,000 mV and back to −500 mV versus a Ag/AgCl, 300 V/s) were repeated every 100 ms using an EPC10 amplifier (HEKA Electronic, Lambrecht, Germany). Stimulus-evoked dopamine overflow was measured by subtracting the background current obtained before stimulation (average of ten pre-stimulus responses) from the current measured after stimulation, using IgorPro software (Wavemetrics, Lake Oswego, Oregon, United States). The resulting voltammogram showed a typical dopamine profile, with an oxidation peak between 500 and 700 mV and a smaller reduction peak around −300 mV. The concentration of the dopamine overflow was calculated after calibrating the recording electrode in known concentrations of dopamine. The amount of dopamine released depends on the stimulation intensity, and the input–output relation was fitted with a sigmoid function [dopamine]/[dopamine]max = 1/{1 + exp[-(S.I. − S.I.1/2)/k]} where S.I. = stimulation intensity.
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10.1371/journal.ppat.1006469 | T-dependent B cell responses to Plasmodium induce antibodies that form a high-avidity multivalent complex with the circumsporozoite protein | The repeat region of the Plasmodium falciparum circumsporozoite protein (CSP) is a major vaccine antigen because it can be targeted by parasite neutralizing antibodies; however, little is known about this interaction. We used isothermal titration calorimetry, X-ray crystallography and mutagenesis-validated modeling to analyze the binding of a murine neutralizing antibody to Plasmodium falciparum CSP. Strikingly, we found that the repeat region of CSP is bound by multiple antibodies. This repeating pattern allows multiple weak interactions of single FAB domains to accumulate and yield a complex with a dissociation constant in the low nM range. Because the CSP protein can potentially cross-link multiple B cell receptors (BCRs) we hypothesized that the B cell response might be T cell independent. However, while there was a modest response in mice deficient in T cell help, the bulk of the response was T cell dependent. By sequencing the BCRs of CSP-repeat specific B cells in inbred mice we found that these cells underwent somatic hypermutation and affinity maturation indicative of a T-dependent response. Last, we found that the BCR repertoire of responding B cells was limited suggesting that the structural simplicity of the repeat may limit the breadth of the immune response.
| Vaccines aim to protect by inducing the immune system to make molecules called antibodies that can recognize molecules on the surface of invading pathogens. In the case of malaria, our most advanced vaccine candidates aim to promote the production of antibodies that recognize the circumsporozoite protein (CSP) molecule on the surface of the invasive parasite stage called the sporozoite. In this report we use X-ray crystallography to determine the structure of CSP-binding antibodies at the atomic level. We use other techniques such as isothermal titration calorimetry and structural modeling to examine how this antibody interacts with the CSP molecule. Strikingly, we found that each CSP molecule could bind 6 antibodies. This finding has implications for the immune response and may explain why high titers of antibody are needed for protection. Moreover, because the structure of the CSP repeat is quite simple we determined that the number of different kinds of antibodies that could bind this molecule are quite small. However a high avidity interaction between those antibodies and CSP can result from a process called affinity maturation that allows the body to learn how to make improved antibodies specific for pathogen molecules. These data show that while it is challenging for the immune system to recognize and neutralize CSP, it should be possible to generate viable vaccines targeting this molecule.
| Malaria caused by Plasmodium falciparum causes the deaths of around 430,000 people each year [1]. The most advanced vaccine candidate for malaria is the RTS,S/AS01 vaccine which consists of a truncated version of the sporozoite-surface circumsporozoite protein (CSP), packaged in a Hepatitis C core virus-like particle delivered in AS01—a proprietary liposome based adjuvant [2]. Phase II and Phase III clinical trials have repeatedly demonstrated that the vaccine gives around 50% protection against clinical malaria in field settings for the first year following vaccination [3]. The bulk of protection is attributed to antibodies targeting the CSP repeat epitope included within the vaccine, with some contribution from CD4+ T cells [4]. It is still unclear why the antibody response to CSP is only partially protective. We lack structural information about how neutralizing antibodies bind to CSP and knowledge on the breadth and nature of the B cell response elicited.
Antibodies to CSP were first identified as potential mediators of protection following seminal studies that showed that immunization with irradiated sporozoites could induce sterile protection against live parasite challenge [5,6]. In the early 1980s, monoclonal antibodies (mAbs) isolated from mice immunized with sporozoites were found to be capable of blocking invasion of hepatocytes [7] and directly neutralizing parasites by precipitating the surface protein coat (a process known as the circumsporozoite reaction) [8]. These antibodies were then used to clone CSP, one of the first malaria antigens identified [8,9]. The N- and C-terminal domains of CSP from all Plasmodium species are separated by a repeat region, which was the target of the original mAbs [9–11]. In the 3D7 reference strain of P. falciparum, the CSP repeat has 38 repeats: 34 asparagine-alanine-asparagine-proline (NANP)-repeats interspersed with 4 asparagine-valine-aspartate-proline (NVDP) repeats that are concentrated towards the N-terminus [12] though different isolates can contain slightly different numbers of repeats [13]. One of the most effective P. falciparum sporozoite neutralizing antibodies identified in these early studies was 2A10 which can block sporozoite infectivity in vitro [7] and in in vivo mouse models utilizing rodent P. berghei parasites expressing the P. falciparum CSP repeat region [14,15].
While CSP binding antibodies have been shown to be able to neutralize sporozoites and block infection, it has also been proposed that CSP is an immunological “decoy” that induces a suboptimal, but broad, T-independent immune response due to the CSP repeat cross-linking multiple B cell receptors (BCRs) [16]. However, it remains unknown if the repetitive regions of CSP can cross-link multiple BCRs as they are not as large as typical type-II T-independent antigens [17]. Moreover, the ability to induce a T-independent response does not preclude a T-dependent component to immunity as well: various oligomeric viral surface proteins can induce both short-lived T-independent responses and subsequent affinity matured IgG responses [18,19]. Furthermore, the very little published data on the sequences of CSP binding antibodies does not convincingly support activation of a broad B cell repertoire: a small study of five P. falciparum CSP mouse monoclonal antibodies (mAbs) identified some shared sequences [20]. In humans, a study that generated mAbs from three individuals who received RTS,S found that the three antibodies studied had distinct sequences though these all used similar heavy chains [21].
We therefore set out to test the hypothesis that the CSP repeat can bind multiple antibodies or BCRs and drive a T-independent immune response. To do this we undertook a comprehensive biophysical characterization of the 2A10 sporozoite-neutralizing antibody that binds to the CSP repeat. We found that this antibody binds with an avidity in the nano-molar range which was unexpected as previous studies using competition ELISAs with peptides predicted a micro-molar affinity [22,23]. Strikingly, isothermal titration calorimetry (ITC), structural analyses, and mutagenesis-validated modeling revealed that the CSP repeat can be bound by around six antibodies suggesting that the repeat may potentially crosslink multiple BCRs on the surface of a B cell. However, analysis of CSP-specific B cells revealed that CSP-specific B cells can enter Germinal Centers (GCs) and undergo affinity maturation contradicting the notion that the response to CSP is largely T-independent. Moreover, we found that the BCR repertoire of CSP-binding B cells is quite limited which may restrict the size and effectiveness of the immune response.
We began our analysis by performing isothermal titration calorimetry (ITC) to understand the interaction between 2A10 and CSP. For ease of expression we used a recombinant CSP (rCSP) construct described previously which was slightly truncated with 27 repeats compared to 38 in the 3D7 reference strain [12,24]. ITC experiments were run on the purified 2A10 antibody and the purified 2A10 antigen-binding fragment (FAB) fragment to test the thermodynamic basis of the affinity of 2A10 FAB towards CSP. Experiments were also performed on the 2A10 FAB fragment with the synthetic peptide antigen (NANP)6, which is a short segment of the antigenic NANP-repeat region of CSP (Table 1; Fig 1). The binding free energies (ΔG) and dissociation constants (KD) were found to be -49.0 kJ/mol and 2.7 nM for the full 2A10 antibody with CSP, -40 kJ/mol and 94 nM for the 2A10 FAB with CSP, and -36.4 kJ/mol and 420 nM for the 2A10 FAB with the (NANP)6 peptide.
Surprisingly, we did not observe a typical 1:1 antibody/FAB domain:antigen binding stoichiometry (Table 1). We found that each (NANP)6 peptide was bound to by ~2 FAB fragments (2.8 repeats per FAB domain). With the rCSP protein we observed that ~11 FAB fragments could bind to each rCSP molecule, (2.5 repeats per FAB domain. Finally, when the single-domain FAB fragment is replaced by the full 2A10 antibody (which has two FAB domains), we observe binding of 5.8 antibodies per rCSP molecule (4.7 repeats per antibody). Therefore all complexes exhibit approximately the same binding stoichiometry of two FAB fragments/domains per ~5 repeat units. These results suggest that the antigenic region of CSP constitutes a multivalent antigen and that repeating, essentially identical, epitopes must be available for the binding of multiple FAB domains.
It is not possible to separate affinity from avidity in this system, although it is apparent that there is a substantial benefit to the overall strength of binding between the antibody and antigen through the binding of multiple FAB domains. The FAB:rCSP complex and the 2A10:rCSP complex had similar enthalpy and entropy of binding (Table 1), but the 2A10:rCSP complex had a lower overall ΔG binding, corresponding to a lower dissociation constant (2.7 nM vs. 94 nM for FAB:rCSP). The observation that this antibody-antigen (Ab-Ag) interaction is primarily enthalpically driven is consistent with the general mechanism of Ab-Ag interactions [25]. It is clear that the dissociation constant (Kd) of a single FAB domain to the (NANP)6 peptide is substantially higher (420 nM), and that the avidity, the accumulated strength of the multiple binding events between the FAB domains of the antibody and the CSP repeat, is the basis for the lower Kd value observed in the 2A10:rCSP complex. Thus, the characteristic repeating pattern of the epitope on the CSP antigen allows multiple weak interactions with 2A10 FAB domains to accumulate, which yields a complex with a high avidity dissociation constant in the low nM range.
To better understand the molecular basis of the multivalent interaction between 2A10 and rCSP, we performed structural analysis of the components. Previous work indicated that the NANP-repeat region of CSP adopts a flexible rod-like structure with a regular repeating helical motif that provides significant separation between the N-terminal and the C-terminal domains [26]. Here, we performed far-UV circular dichroism (CD) spectroscopy to investigate the structure of the (NANP)6 peptide. These results were inconsistent with a disordered random coil structure (S1 Fig). Rather, the absorption maximum around 185 nm, minimum around 202 nm and shoulder between 215 and 240 nm, is characteristic of intrinsically disordered proteins that can adopt a spectrum of states [27].
The lowest energy structures of the (NANP)6 repeat were predicted using the PEP-FOLD de novo peptide structure prediction algorithm [28]. The only extended state among the lowest energy structures that was consistent with the reported spacing of the N-and C-terminal domains of CSP [26], and which presented multiple structurally similar epitopes was a linear, quasi-helical structure, which formed a regularly repeating arrangement of proline turns (Fig 2A). The theoretical CD spectrum of this conformation was calculated (S1 Fig), qualitatively matching the experimental spectra: the maximum was at 188 nm, the minimum at 203 nm and there was a broad shoulder between 215 and 240 nm. To investigate the stability of this conformation, we performed a molecular dynamics (MD) simulation on this peptide, which showed that this helical structure could unfold, and refold, on timescales of tens of nanoseconds, supporting the idea that it is a low-energy, frequently sampled, configuration in solution (S1 Movie, S2 Fig). We also observed the same characteristic hydrogen bonds between a carbonyl following the proline and the amide nitrogen of the alanine, and the carbonyl group of an asparagine and a backbone amide of asparagine three residues earlier, that are observed in the crystal structure of the NPNA fragment [29]. Thus, this configuration, which is consistent with previously published experimental data, is a regular, repeating, extended conformation that would allow binding of multiple FAB domains to several structurally similar epitopes.
To better understand the interaction between the 2A10 and the (NANP)-repeat region, we solved the crystal structure of the 2A10 FAB fragment in two conditions (S1 Table), yielding structures that diffracted to 2.5 Å and 3.0 Å. All of the polypeptide chains were modeled in good quality electron density maps (Fig 2B), except for residues 134–137 of the light chain. This loop is located at the opposite end of the FAB fragment to the variable region and not directly relevant to antigen binding. The 2.5 Å structure contained a single polypeptide in the asymmetric unit, whereas the 3.0 Å structure contained three essentially identical chains. Superposition of the four unique FAB fragments from the two structures revealed that the variable antigen binding region is structurally homogeneous, suggesting that this region might be relatively pre-organized in the 2A10 FAB. This is consistent with the observation that antibodies typically undergo relatively limited conformational change upon epitope binding [25]. Indeed, a recent survey of 49 Ab-Ag complexes revealed that within the binding site, the heavy chain Complementarity Determining Region (CDR)-3 was the only element that showed significant conformational change upon antigen binding and even this was only observed in one third of the antibodies [30].
Attempts to obtain a crystal structure of a complex between 2A10 FAB and the (NANP)6 peptide were unsuccessful; unlike binary Ab-Ag interactions, in which the Ab will bind to a single epitope on an antigen and produce a population of structurally homogeneous complexes that can be crystallized, in this interaction we are dealing with an intrinsically-disordered peptide, the presence of multiple binding sites on the peptide, and the possibility that more than one 2A10 FAB domain can bind the peptide. Therefore it is difficult to obtain a homogeneous population of complexes, which is a prerequisite for crystallization. Attempts to soak the (NANP)6 peptide into the high-solvent form of 2A10 FAB, in which there were no crystal packing interactions with the binding-loops, caused the crystals to dissolve, again suggesting that the heterogeneity of the peptide and the presence of multiple epitopes produces disorder that is incompatible with crystal formation.
Although it was not possible to obtain a crystal structure of the 2A10-(NANP)6 peptide complex, the accurate structures of the 2A10 FAB fragment, the (NANP)6 peptide, and the knowledge that antibodies seldom undergo significant conformational changes upon antigen binding [30], allowed us to model the interaction, which we tested using site directed mutagenesis. Computational modeling of Ab-Ag interactions has advanced considerably in recent years and several examples of complexes with close to atomic accuracy have been reported in the literature [31]. Using the SnugDock protein-protein docking algorithm [31], we obtained an initial model for binding of the peptide to the CDR region of the 2A10 FAB fragment (Fig 2C). We then performed, in triplicate, three 50 ns MD simulations on this complex to investigate whether the interaction was stable over such a time period (S2 Movie, S3 Fig). These simulations confirmed that the binding mode that was modeled is stable, suggesting that it is a reasonable approximation of the interaction between these molecules. To experimentally verify whether our model of the 2A10 FAB:(NANP)6 peptide interaction was plausible, we performed site directed mutagenesis of residues predicted to be important for binding. Our model predicted that the interaction with (NANP)6 would be mainly between CDR2 and CDR3 of the light chain and CDR2 and CDR3 of the heavy chain (Fig 2C).
In the light chain (Fig 3A and 3B), Y38 is predicted to be one of the most important residues in the interaction; it contributes to the formation of a hydrophobic pocket that buries a proline residue and is within hydrogen bonding distance, via its hydroxyl group, to a number of backbone and side-chain groups of the peptide. Loss of this side-chain abolished binding. Y56 also forms part of the same proline-binding pocket as Y38, and loss of this side-chain also resulted in an almost complete loss of binding. R109 forms a hydrogen bond to an asparagine residue on the side of the helix; mutation of this residue to alanine results in a partial loss of binding. Y116 is located at the center of the second proline-binding pocket; since loss of the entire side-chain through an alanine mutation would lead to general structural disruption of the FAB fragment, we mutated this to a phenylalanine (removing the hydroxyl group), which led to a significant reduction in binding. Finally, S36A was selected as a control: the model indicated that it was outside the binding site, and the ELISA data indicated that had no effect on (NANP)n binding.
Within the heavy chain (Fig 3C and 3D), mutation of N57 to alanine led to complete loss of binding, which is consistent with it forming a hydrogen bond to a side-chain asparagine but also being part of a relatively well packed region of the binding site that is mostly buried upon binding. T66 is located on the edge of the binding site and appears to provide hydrophobic contacts through its methyl group with the methyl side-chain of an alanine of the peptide; mutation of this residue resulted in a partial loss of binding. Interestingly, mutation of E64, which is location in an appropriate position to form some hydrogen bonds to the peptide resulted in a slight increase in binding, although charged residues on the edge of protein:protein interfaces are known to contribute primarily to specificity rather than affinity [32]. Specifically, the cost of desolvating charged residues such as glutamate is not compensated for by the hydrogen bonds that may be formed with the binding partner. Y37 is located outside the direct binding site in the apo-crystal structure; the loss of affinity could arise from long-range effects, such as destabilization of the position of nearby loops. In general, the effects of the mutations are consistent with the model of the interaction.
The binding mode of the FAB fragment to the (NANP)6 peptide is centered on two proline residues from two non-adjacent NANP-repeats (Fig 3A and 3C). These cyclic side-chains are hydrophobic in character and are buried deeply in the core of the FAB antigen binding site, into hydrophobic pockets formed by Y38 and Y56 of the light chain and the interface between the two chains. In contrast, the polar asparagine residues on the sides of the helix are involved in hydrogen bonding interactions with a number of polar residues on the edge of the binding site, such as N57 of the heavy chain. Due to the twisting of the (NANP)6 repeat, the binding epitope of the peptide is 2.5–3 alternate NANP repeats, with a symmetrical epitope available for binding on the opposite face (Fig 4A). Thus, this binding mode is consistent with the stoichiometry of the binding observed in the ITC measurements, where we observed a stoichiometry of two 2A10 FAB fragments per (NANP)6 peptide. To investigate whether this binding mode was also compatible with the indication from ITC that ~10.7 2A10 FAB fragments, or six antibodies (containing 12 FAB domains) could bind the CSP protein (Table 1), we extended the peptide to its full length. It is notable that the slight twist in the NANP helix results in the epitope being offset along the length of the repeat region, thereby allowing binding of ten 2A10 FAB fragments (Fig 4B). Six 2A10 antibodies can bind if two antibodies interact by a single FAB domain and the other four interact with both FAB domains. The observation that the FAB fragments bind sufficiently close to each other to form hydrogen bonds also explains the observation from the ITC that the complexes with rCSP, which allow adjacent FAB fragment binding, have more favorable binding enthalpy, i.e. the additional bonds formed between adjacent FAB fragments further stabilize the complex and lead to greater affinity (Table 1). Thus, the initially surprising stoichiometry that we observe through ITC appears to be quite feasible based on the structure of the NANP-repeat region of the rCSP protein and the nature of the rCSP-2A10 complex. It is also clear that the effect of antibody binding to this region would be to prevent the linker flexing between the N- and C-terminal domains and maintaining normal physiological function, explaining the neutralizing effect of the antibodies.
We next set out to determine the implications of our structure for the B cell response to CSP. Because the CSP protein could conceivably cross-link multiple B cell receptors (BCRs) we hypothesized that the B cell response might be T-independent. As a tool to test this hypothesis we used (NANP)n-based tetramers to identify and phenotype antigen specific B cells in mice immunized with P. berghei sporozoites expressing the repeat region of the P. falciparum CSP (P. berghei CSPf) [15]. The tetramers are formed by the binding of 4 biotinylated (NANP)9 repeats with streptavidin conjugated phycoerythrin (PE) or allophycocyanin (APC). To validate our tetramer approach, mice were immunized with either P. berghei CSPf or another line of P. berghei with a mutant CSP (P. berghei CS5M) that contains the endogenous (P. berghei) repeat region, which has a distinct repeat sequence (PPPPNPND)n. (NANP)n-specific cells were identified with two tetramer probes bound to different conjugates to exclude B cells that are specific for the PE or APC components of the tetramers which are numerous in mice [33]. We found that mice immunized with P. berghei CSPf sporozoites developed large tetramer double positive populations, which had class switched (Fig 5A and 5B). In contrast, the number of tetramer double positive cells in mice receiving control parasites was the same as in unimmunized mice; moreover these cells were not class switched and appeared to be naïve precursors indicating that our tetramers are identifying bona-fide (NANP)n-specific cells (Fig 5B and 5C). Further analysis of the different populations of B cells showed that most B cells present at this time-point were GL7+ CD38- indicating that they are GC B cells in agreement with results from a recent publication [34] (Fig 5B and 5D). Given that T cells are required to sustain GC formation beyond ~3 days these data indicate that a T-dependent response can develop to CSP following sporozoite immunization [35].
Our previous data showing GC formation among (NANP)n specific B cells was indicative of a T-dependent response. To determine whether there might also be a T-independent component to the B cell response we immunized CD28-/- mice as well as C57BL/6 controls with P. berghei CSPf radiation attenuated sporozoites (RAS) and measured serum (NANP)n specific antibody by ELISA and the B cell response using our Tetramers. CD28-/- mice have CD4+ T cells but they are unable to provide help to B cell responses [36]. Interestingly 4 days post immunization there were comparable IgM and IgG anti-(NANP)n responses in the CD28-/- mice and control animals (Fig 6A), indicative of a T-independent component to immunity. However by day 27 post immunization there was no detectable IgM or IgG antibody specific for (NANP)n in the CD28-/- mice suggesting the T-independent response is short-lived. We further analyzed (NANP)n specific B cell responses using our tetramers, in particular examining the number and phenotype (plasmablast vs GC B cell) of activated IgD- Tetramer+ cells (Fig 6B). In agreement with our antibody data, similar numbers of antigen specific B cells were seen at 4 days post immunization in the CD28-/- and control mice and most of these cells were plasmablasts (Fig 6C). However by 7 days post immunization the number of antigen specific cells declines in the CD28-/- mice as the T dependent GC reaction begins to predominate. Thus CSP on the surface of sporozoites is able to induce short-lived T-independent B cell response, but subsequently T-dependent responses predominate.
We wanted to know if to induce a T-independent response it was necessary for CSP to be presented on the surface of the sporozoite or if free rCSP was sufficient. We found that indeed rCSP could induce a T-independent response as evidenced by similar IgM and IgG levels and IgD-Tetramer+ responses 4 days post immunization in control and CD28-/- mice (Fig 6D and 6E). Finally we were concerned that there may be some residual CD4+ T cell help in the CD28-/- mice so we performed experiments in which we used the antibody GK1.5 to deplete CD4+ T cells [37]. In agreement with our previous data we found that sporozoites (live or RAS) and rCSP induced IgM responses in CD4 depleted mice, though we were unable to detect a significant IgG response (S4 Fig). We also detected primed antigen specific B cells in GK1.5 treated mice following RAS or rCSP immunized mice 4 days post-immunization, albeit at lower levels than in mice treated with isotype control antibodies (S4 Fig). Overall our data with GK1.5 depleted mice support our results in the CD28-/- model.
Our ability to identify and sort (NANP)n specific B cells with our tetramers also allows us to examine the repertoire of antibodies that can bind the (NANP)n by sequencing the BCRs of the identified cells. While the repeat structure of CSP has been hypothesized to induce a broad polyclonal response based on data that the CSP repeat can absorb most of the sporozoite binding activity of human sera from immune individuals [23,38], an alternative hypothesis is that the antigenically simple structure of the repeat epitope might only be recognized by a small number of naïve B cells. We therefore sorted (NANP)n-specific cells 35 days post immunization of BALB/C mice with sporozoites. We performed this analysis in BALB/C mice as this is the background of mice from which the 2A10 antibody was derived. We then prepared cDNA from the cells and amplified the heavy and kappa chain sequences using degenerate primers as described previously [39,40]. Heavy and kappa chain libraries were prepared from 4 immunized mice as well as from 3 naïve mice from which we bulk sorted B cells as controls. We obtained usable sequences from 3 of the 4 mice for both the heavy chain and kappa chain. Analysis of the heavy chain revealed that in each mouse 3 or 4 V regions dominated the immune response (Fig 7A). The V regions identified (IGHV1-20; IGHV1-26; IGHV1-34 and IGHV5-9) were generally shared among the mice. As a formal measure of the diversity of our V region usage in the (NANP)n specific cells and the bulk B cells from naïve mice we calculated the Shannon entropy for these populations. This analysis formally demonstrated that the diversity of the antigen specific B cells was significantly lower than the diversity of the repertoire in naïve mice (Fig 7B). We further found that each V region was typically associated with the same D and J sequences even in different mice. For example, IGHV1-20 was typically associated with J4, IGHV5-9 with J4 while in different mice IGHV1-34 was variously paired with J1 or J4 (Fig 7C). Similar results were obtained for the kappa chain with the response dominated by IGKV1-135; IGKV5-43/45; IGKV1-110; IGKV1-117 and IGKV14-111 (Fig 7D and 7E). The V regions were typically paired with the same J regions even in different mice (Fig 7F), for example IGKV5.43/45 was typically paired with IGKJ5 or IGKJ2 and IGKV1-110 was typically paired with IGKJ5, although IGKV1-135 was typically more promiscuous. One limitation of our high throughput sequencing approach is that the degenerate primers only amplified ~70% of the known IGHV and IGKV sequences in naïve mice, suggesting that we may not capture the full diversity of the response. However, comparison with the 5 published antibody sequences (S2 and S3 Tables) that include IGHV-1-20, IGKV5-45 and IGKV1-110 reveals that we are likely capturing the bulk of the antibody diversity. Together these data suggest that the number of B cell clones responding to CSP may be limited, potentially reducing the ability of the immune system to generate effective neutralizing antibodies.
Finally we were interested in knowing if the GC reaction we could see following sporozoite immunization was inducing higher affinity antibodies. We therefore examined our deep sequencing data to determine if CSP-specific antibodies had undergone somatic hypermutation (SHM) that would be indicative of B cells specific for CSP entering the GC. Taking advantage of the fact that our kappa chain primers capture the entire V-J sequences of the antibodies we sequenced we asked: 1) if the kappa chains shared between immune animals differed from the germline (providing evidence of SHM) and 2) if the mutations were conserved between different mice indicative of directed selection. Analysis of the reads from the kappa chains of the three immune mice showed that these had a much higher degree of mutation than bulk B cells from naïve mice, demonstrating SHM in the CSP-specific antibodies (Fig 8A). We further examined each specific common kappa chain in turn (IGVK1-110; IGKV1-135; IGVK5-43/45) comparing the sequences obtained from naïve B cells and (NANP)n specific cells in immune mice. This analysis showed that while, as expected, sequences from naïve mice contained few mutations, the sequences from immune mice had much higher levels of SHM. Importantly mutations were found to be concentrated in the CDR loops, and were frequently shared by immunized mice providing strong circumstantial evidence for affinity maturation (Fig 8B; data for IGVK1-110 only shown).
To directly test if CSP-binding antibodies undergo affinity maturation we expressed the predicted germline precursor to the 2A10 antibody (2A10 gAb) in HEK293T cells. We identified the predicted germline precursors of the 2A10 heavy and light chains using the program V-quest [41] (S5 and S6 Figs). This analysis identified the heavy chain as IGHV9-3; IGHD1-3; IGHJ4 and the light chain as IGKV10-94;IGKJ2, with the monoclonal antibody carrying 6 mutations in the heavy chain and 7 in the light chain. The 2A10 gAb had considerably lower binding in ELISA assays compared to the 2A10 mAb itself (Fig 8C), indicative that affinity maturation had taken place in this antibody. To determine the relative contribution of mutations in the heavy and light chain to enhancing binding we also made hybrid antibodies consisting of the mAb heavy chain and the gAb light chain and vice versa. Interestingly mutations in the light chain were almost entirely sufficient to explain the enhanced binding by the mAb compared to the gAb (Fig 8C).
To identify the specific mutations that were important we introduced the mutations individually into the gAb light chain construct. We prioritized mutations that were shared with the 27E antibody which has previously been found to be clonally related to 2A10 having been isolated from the same mouse and which shares the same germline heavy and light chains as the 2A10 mAb [20]. We found that two mutations (L114F and T117V) in the CDR3 of the light chain appeared to account for most of the gain in binding (Fig 8C). The effect of these antibodies appeared to be additive rather than synergistic as revealed by experiments in which we introduced these mutations simultaneously (Fig 8D). A further mutation close to the light chain CDR2 (H68Y) also caused a modest increase in binding. As expected mutations in the heavy chains appeared generally less important for increasing binding though M39I, N59I and T67F all gave modest increases in binding (Fig 8E). Collectively our data suggest that CSP repeat antibodies can undergo SHM in GCs resulting in affinity maturation, however the antibody response may be limited by the number of naïve B cells that can recognize and respond to this antigen.
Here we provide an analysis of the structure of a Plasmodium falciparum sporozoite-neutralizing antibody (2A10). Having obtained this structure we further modeled the binding 2A10 with its antigen target, the repeat region of CSP, and provide a thermodynamic characterization of this interaction. Finally, we used novel tetramer probes to identify and sort antigen specific B cells responding to sporozoite immunization in order to measure the diversity and maturation of the antibody response. We found that the avidity of 2A10 for the rCSP molecule was in the nanomolar range, which was much higher than the affinity previously predicted from competition ELISAs with small peptides [22,23]. This affinity is a consequence of the multivalent nature of the interaction, with up to 6 antibodies being able to bind to each rCSP molecule. Our model suggests that to spatially accommodate this binding the antibodies must surround CSP in an off-set manner, which is possible due to the slight twist in the helical structure that CSP can adopt. It is notable that the twisted, repeating arrangement of the CSP linker is the only structure that would allow binding in the stoichiometry observed through the ITC. We further found that the diversity of the antibody repertoire to the CSP repeat was limited, perhaps due to the relative simplicity of the target epitope. However, these antibodies have undergone affinity maturation to improve affinity, potentially allowing protective immune responses to develop.
Using ITC we determined the dissociation constant of 2A10 for rCSP to be 2.7 nM, which is not unusual for a mouse mAb. However it is a tighter interaction than that predicted from competition ELISAs, which predicted a micro-molar affinity [22,23]. However, these competition ELISAs were performed with short peptides rather than rCSP. Indeed, when we performed ITC with a short peptide and FAB fragments we too obtained a dissociation constant in the micro-molar range (0.42 μM). The difference between the FAB binding to the peptide and the tight interaction of the antibody binding to full length CSP appears to be driven by a high avidity, multivalent interaction. There is also additional enthalpic stabilization (per FAB domain) in the 2A10:CSP complex, although this is partially offset by the increased entropic cost associated with combining a large number of separate molecules into a single complex. One caveat of these data is that we used a slightly truncated repeat in our recombinant CSP, however it is likely that longer repeats will have further stabilization of the interaction that could result in even higher affinity interaction between CSP and binding antibodies.
The mechanism of sporozoite neutralization remains unclear, however our structural data may provide some insights. Repeat specific antibodies can directly neutralize sporozoites (without complement or other cell mediators) in the circumsporozoite reaction [8,42]. Morevoer FAB fragments alone are sufficient to block invasion [42,43]. However, it is well established that activation of complement and cell mediated immunity is important for the action of blood stage-specific antibodies [44,45]. It has also been suggested that the CSP repeat might act as a hinge allowing the N-terminal domain to mask the C-terminal domain which is believed to be important for binding to and invading hepatocytes [10]. Cleavage of this N-terminal domain is therefore required to expose the C-terminal domain and facilitate invasion [10]. Antibody binding as observed here may disrupt this process in several ways, either by opening the hinge to induce the premature exposure of the C-terminal domain. Alternatively since the repeat region is directly adjacent to the proteolytic cleavage site, anti-repeat antibodies might function by sterically hindering access of the protease to CSP, thus preventing sporozoite invasion of the hepatocyte. One possible consequence of the requirement for mutivalency to increase the avidity of the antibody, is that antibodies with different binding modes may interfere with each other limiting their effectiveness.
Our results uncovering how neutralizing antibodies bind to CSP has several implications for understanding the development of the immune response to CSP. Notably the finding that the CSP molecule can be bound by multiple antibodies/B cell receptors raises the possibility that this molecule can indeed crosslink multiple BCRs and potentially act as a type-II T independent antigen [17]. We find that indeed there is a T-independent component to the response to CSP, though T cells are required to sustain the immune response beyond day 7. As such the response to CSP appears follow a similar process to that seen for several oligomeric viral entry proteins, which induce a mix of T-independent and T-dependent responses [18,19]. It maybe that T-independent responses are driven by the density of CSP molecules on the sporozoite surface; however, rCSP can also induce a small T-independent response. This suggests that the CSP protein alone is sufficient to crosslink multiple BCRs on the B cell surface which is consistent with our structural model. Interestingly, the RTS,S/AS01 vaccine based on that contains 18 CSP repeats and does appear to induce high titers of anti-CSP antibodies which initially decline rapidly and are then more stable [4,46]. This may be consistent with the induction of a short-lived a type-II T-independent plasmablast response (accounting for the initial burst of antibodies), followed by a T-dependent response (which may be the basis of the more sustained antibody titers). The relative contributions of short-lived antibody production and long-term B cell memory to protection is an area for future investigation.
The finding of a limited repertoire in the BCR sequences specific for the (NANP)n repeat contradicts previous suggestions that the response to CSP might be broad and polyclonal [38]. One explanation for this limited antibody diversity is that the antigenic simplicity of the CSP repeat region limits the range of antibodies that are capable of responding. A prior example of this is the antibodies to the Rhesus (Rh) D antigen. The RhD antigen differs from RhC by only 35–36 amino acids, resulting in the creation of a minimal B cell epitope [47]. The repertoire of antibodies that can bind this epitope are accordingly limited and mainly based on the VH3-33 gene family [48]. Another potential explanation for a limited antibody repertoire could be that the (NANP)n repeat shares structural similarity with a self-antigen as is speculated to happen with meningococcus type B antigens [49], however it is not clear what this self-antigen might be. One potential outcome of this finding is that if each B cell clone has a finite burst size this may limit the magnitude of the overall B cell response.
One area for future investigation is to determine the binding modes and sporozoite neutralizing capacities of other antibodies in the response. It is clear that not all CSP-repeat binding antibodies have the same capacity for sporozoite neutralization [7]. As such the finding of a limited repertoire of responding B cells may lead to the possibility that some people have holes in their antibody repertoires limiting their ability to make neutralizing antibodies. This may explain why, while there is a broad correlation between ELISA tires of antibodies to the CSP repeat and protection following RTS,S vaccination, there is no clear threshold for protection [4].
While our work has been performed with mouse antibodies, there are major similarities between mouse and human antibody loop structure [50]. The main difference between the two species is the considerably more diverse heavy chain CDR3 regions that are found in human antibodies [51]. Consequently, this leads to a much larger number of unique clones found in humans compared to mice. However, the number of different V, D and J genes and the recombination that follows are relatively similar between humans and mice [52]. From our data it can be observed that while the BCR repertoire was restricted in the V gene usage, these different V gene populations were represented in multiple unique clones, suggesting that increasing the number of clones is unlikely to substantially increase V-region usage. Our analysis was performed on inbred mice which may also limit repertoire diversity, however studies on the human IGHV locus reveal that in any given individual ~80% V region genes are identical between the maternal and paternal allele i.e. heterozygosity is not a major driver of human V region diversity [53,54]. It is notable that all 4 human monoclonal antibodies described to date from different volunteers share the use of the IGHV3-30 gene family [21,22], suggesting that in humans as well as mice there may indeed be a constrained repertoire of responding B cells.
Overall our data provide important insights into how the antibody response to CSP develops. Our results also help explain why relatively large amounts of antibodies are required for sporozoite neutralization and suggest that the ability to generate an effective B cell response may be limited by the very simplicity of the repeat epitope. These data support previous suggestions that CSP may be a suboptimal target for vaccination. However, we also find that CSP binding antibodies can undergo somatic hypermutation and reach high affinities. This suggests if we can develop vaccination strategies to diversify the repertoire of responding B cells and favor the GC response it may be possible to generate long-term protective immunity targeting this major vaccine candidate antigen.
All animal procedures were approved by the Animal Experimentation Ethics Committee of the Australian National University (Protocol numbers: A2013/12 and A2016/17). All research involving animals was conducted in accordance with the National Health and Medical Research Council's (NHMRC) Australian Code for the Care and Use of Animals for Scientific Purposes and the Australian Capital Territory Animal Welfare Act 1992.
BALB/C, C57BL/6 or CD28-/- [55] mice (bred in-house at the Australian National University) were immunized IV with 5 x 104 P. berghei CS5M sporozoites expressing mCherry [56] or 5 x 104 P. berghei CSPf sporozoites dissected by hand from the salivary glands of Anopheles stephensi mosquitoes. Mice were either infected with live sporozoites and then treated with 0.6mg choloroquine IP daily for 10 days or immunized with irradiated sporozoites (15kRad). For immunization with rCSP, 30ug rCSP was emulsified in Imject Alum according to the manufacturer’s instructions (ThermoFisher Scientific) and delivered intra-peritoneally. All mice received only a single immunization in these experiments. To deplete CD4+ T cells mice were treated with two doses of 100ug GK1.5 antibody on the 2 days prior to immunization (BioXCell); control mice received an irrelevant isotype control antibody (LTF2; BioXCell).
Single cell preparations of lymphocytes were isolated from the spleen of immunized mice and were stained for flow cytometry or sorting by standard procedures. Cells were stained with lineage markers (anti-CD3, clone 17A2; anti-GR1, clone RB6-8C5 and anti-NKp46, clone 29A1.4) antibodies to B220 (clone RA3-6B2), IgM (clone II/41), IgD (clone 11-26c2a), GL7 (clone GL7), CD38 (clone 90), CD138 (clone 281–2) and (NANP)9 tetramers conjugated to PE or APC. Antibodies were purchased from Biolegend while tetramers were prepared in house by mixing biotinylated (NANP)9 peptide with streptavidin conjugated PE or APC (Invitrogen) in a 4:1 molar ratio. Flow-cytometric data was collected on a BD Fortessa flow cytometer (Becton Dickinson) and analyzed using FlowJo software (FlowJo). Where necessary cells were sorted on a BD FACs Aria I or II machine.
Single cell suspensions from the spleens of immunized mice were stained with (NANP)n tetramers and antibodies to B cell markers as described in the supplementary experimental procedures. Antigen specific cells were sorted on a FACS ARIA I or II instrument prior to RNA extraction with the Arturus Picopure RNA isolation kit (Invitrogen) and cDNA preparation using the iScript cDNA synthesis kit (BioRad). BCR sequences were amplified using previously described heavy and kappa chain primers including adaptor sequences allowing subsequent indexing using the Nextera indexing kit (Illumina). Analysis was performed in house using R-scripts and the program MiXCR as described in supplementary experimental procedures.
Variants of the 2A10 antibody were expressed in HEK293 T cells (a kind gift of Carola Vinuesa, Australian National University) as described in the supplemental experimental procedures. Binding to the CSP repeat was tested by ELISA and ITC using standard techniques as described in the supplementary experimental procedures.
Statistical analysis was performed using Prism6 (GraphPad) for simple T tests and one-way ANOVAs from single experiments. Where data were pooled from multiple experiments, analysis was performed using linear mixed models in R version 3.3.3 (R foundation for Statistical Computing). Linear mixed models are a regression analysis model containing both fixed and random effects: fixed effects being the variable/treatment under examination, whilst random effects are unintended factors that may influence the variable being measured. If significance was found from running a linear mixed model, pair-wise comparisons of the least significant differences of means (LSD) was undertaken to determine at which level interactions were occurring. Statistical significance was assumed if the p-value was < 0.05 for a tested difference. (ns = not significant, * = p < 0.5, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001).
Sequence data generated in this paper is deposited at the NCBI sequence read archive (SRA) with accession number SRP092808 as part of BioProject database accession number PRJNA352758. Atomic coordinates and related experimental data for structural analyses are deposited in the Protein Data Bank (PDB) with PDB codes 5SZF and 5T0Y.
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10.1371/journal.pgen.1000092 | Novel Roles for MLH3 Deficiency and TLE6-Like Amplification in DNA Mismatch Repair-Deficient Gastrointestinal Tumorigenesis and Progression | DNA mismatch repair suppresses gastrointestinal tumorgenesis. Four mammalian E. coli MutL homologues heterodimerize to form three distinct complexes: MLH1/PMS2, MLH1/MLH3, and MLH1/PMS1. To understand the mechanistic contributions of MLH3 and PMS2 in gastrointestinal tumor suppression, we generated Mlh3−/−;Apc1638N and Mlh3−/−;Pms2−/−;Apc1638N (MPA) mice. Mlh3 nullizygosity significantly increased Apc frameshift mutations and tumor multiplicity. Combined Mlh3;Pms2 nullizygosity further increased Apc base-substitution mutations. The spectrum of MPA tumor mutations was distinct from that observed in Mlh1−/−;Apc1638N mice, implicating the first potential role for MLH1/PMS1 in tumor suppression. Because Mlh3;Pms2 deficiency also increased gastrointestinal tumor progression, we used array-CGH to identify a recurrent tumor amplicon. This amplicon contained a previously uncharacterized Transducin enhancer of Split (Tle) family gene, Tle6-like. Expression of Tle6-like, or the similar human TLE6D splice isoform in colon cancer cells increased cell proliferation, colony-formation, cell migration, and xenograft tumorgenicity. Tle6-like;TLE6D directly interact with the gastrointestinal tumor suppressor RUNX3 and antagonize RUNX3 target transactivation. TLE6D is recurrently overexpressed in human colorectal cancers and TLE6D expression correlates with RUNX3 expression. Collectively, these findings provide important insights into the molecular mechanisms of individual MutL homologue tumor suppression and demonstrate an association between TLE mediated antagonism of RUNX3 and accelerated human colorectal cancer progression.
| Approximately one million people every year are diagnosed with colorectal cancer worldwide, and about five hundred thousand of these people subsequently perish from the disease. Colorectal cancer is thought to develop through a series of early and later stages (called cancer initiation and progression, respectively). Deaths from colorectal cancer are particularly tragic because the disease can usually be cured if discovered before full-blown progression. However, our knowledge of how these tumors progress remains very limited. DNA mismatch repair is known to be an important process in preventing ∼15% of colorectal cancer initiation. In this study we describe how two of these genes (Mlh3 and Pms2) that have partial functional redundancy and therefore individually are rarely mutated are also important in preventing colorectal cancer progression. Additionally, we describe a new gene (Tle6-like) that, when overactive, makes these cancers progress more rapidly. The overall goal of this study is to understand colorectal cancer progression better so that we can come up with new ways to block it at the later stage.
| Colorectal cancer (CRC) is one of the common malignancies in industrialized countries. Lynch syndrome, a highly penetrant disorder that confers predisposition to cancer of the colorectum, endometrium and other extra-colonic sites [1], is caused by germline mutations in DNA Mismatch Repair genes (MMR). Including sporadic forms, defective MMR underlies ∼12–15% of CRC [2]. MMR plays critical roles in the maintenance of genomic stability in both prokaryotes and eukaryotes [3]. The study of model organisms has yielded great insights into the mechanisms through which MMR prevents cancer [1],[3],[4],[5],[6],[7],[8]. Briefly, there are nine mammalian MMR genes (MLH1, MLH3, PMS1-2, MSH2-6). The mammalian E coli MutS homologues (MSH) directly contact DNA, scanning along the genomic DNA for mismatches analogous to a “sliding clamp” until they encounter a base-pair containing a mismatch [9],[10]. MSH2-MSH6 primarily recognizes single-base substitutions and 1 base-pair insertion-deletion loop (IDL) mutations, while MSH2-MSH3 recognizes 1–4 base-pair insertion-deletion mutations [1],[3].The IDL repair deficiency is commonly referred to as Microsatellite Instability (MSI). The MSH proteins interact with multiple proteins including the mammalian E coli MutL homologues (MLH) and yeast post-meiotic segregation (PMS) homologue proteins (which have significant amino acid identify and structural similarity to the MLH proteins), as well as RPA, EXO1, RFC, HMGB1, POLDC and other proteins [1],[8],[11],[12]. MLH1-PMS2 is the primary MutL complex that interacts with both MSH2/6 and MSH3 complexes. MLH1–MLH3 is less well characterized, but is believed to participate in IDL repair [13],[14], DNA damage response [13], and possibly single-base point mutation repair (SBR)[15]. MLH1-PMS1 exists in mammalian cells but currently has no clearly defined roles in processes related to cancer prevention [16],[17].
To study the precise mechanisms through which MMR suppresses carcinogenesis in vivo, we and others [16],[18],[19],[20],[21],[22],[23],[24] previously developed several mouse models carrying mutations in different MMR genes. Mlh1−/− and Msh2−/− mice develop early onset GI epithelial cancers, lymphomas and other types of cancer. Pms2−/− mice develop lymphomas, but not GI epithelial cancers. Mlh3−/− mice develop GI and extra-GI tumors, have decreased survival when compared with Wt mice, but with later onset than Mlh1−/− [13]. Mlh3−/−;Pms2−/− mice have increased cancer incidence, resistance to apoptosis and MSI [13]. However, the precise mechanisms in which Mlh3 and Pms2 participate to suppress GI epithelial tumorigenesis and progression remain poorly characterized.
Germ-line mutations in tumor suppressor gene APC lead to familial adenomatous polyposis (FAP) [25],[26]. Mutations in APC are found in the majority of sporadic CRC and many Lynch syndrome tumors [27],[28]. APC complexes with AXIN and CK1/2 and destabilizes β-Catenin by enhancing proteasomal destruction. Mutated APC proteins are unable to down-regulate β-Catenin, and the stabilized β-Catenin translocates into the nucleus where it acts as a transcriptional coactivator of the DNA binding protein TCF-4 [29],[30]. More than 95% of APC germ-line mutations are truncating or nonsense mutations and most of the pathogenic mutations are located within the first 1500 codons. Apc mutations cooperate with MMR deficiency in both tumorigenesis and tumor progression. Apc1638N mice are a well characterized model that develops GI cancer [31]. Mlh1−/−;Apc1638N mice showed significantly increased GI tumor multiplicity and accelerated progression to adenocarcinoma compared to either mutation separately. Analyses of GI tumors from Mlh1−/−;Apc1638N and Msh3−/−;Msh6−/−;Apc1638N mice revealed that both single-base substitutions and MSI induced frameshift mutations in repetitive sequences were responsible for most mutations found in the remaining wild-type (Wt) Apc allele [32],[33]. In contrast, tumor-associated Apc mutations found in the Wt Apc allele in Msh6−/−;Apc1638N tumors were predominantly single-base point mutations.
To understand more precisely the mechanistic roles that Mlh3 and Pms2 play in GI tumor suppression, we generated Mlh3−/−;Apc1638N (MA) and Mlh3−/−;Pms2−/−;Apc1638N (MPA) mice. We show that in vivo Mlh3 mutations significantly increase frameshift mutation rates in Apc, and increase GI tumorigenesis. Unlike typical MSI-induced mutations, Mlh3 deficiency also results in frameshift mutations in non-repetitive sequences, a unique mutational signature among MMR deficient mice found only in Mlh3 deficient mice. Consistent with the role of Pms2 in SBR, combined Mlh3 and Pms2 mutations proportionally increase point mutations and show a sequence preference for a CpG mutation hotspot also previously seen in Mlh1−/− mice. Because MPA mutant mice also have significantly increased rates of GI adenocarcinomas vs. Apc1638N or MA mice, we investigated mechanisms of tumor progression. Using array-CGH, we identified a recurrent 5-Mb amplification on chromosome 12 in GI tumors from MPA mice. We defined the amplicon critical interval and demonstrated that it contains a previously uncharacterized member of the Transducin enhancer of Split (TLE)/Groucho family of transcriptional co-regulators, Tle6-like, that contributes to tumor progression. Tle6-like overexpression in colon cancer cell lines increases cell proliferation, colony-formation ability, cell migration and xenograft tumorigenicity. Human TLE6D, an alternatively spliced isoform of TLE6, with a domain structure similar to Tle6-like, has functional activity similar to Tle6-like. Both Tle6-like and TLE6D interact with GI tumor suppressor, RUNX3 [34], and antagonize RUNX3 gene target tranactivation. TLE6D is overexpressed in multiple human microsatellite stable (MSS) and microsatellite unstable (MSI-H) CRCs, and TLE6D expression levels correlate with RUNX3 expression levels. Collectively, these findings provide important insights into the molecular mechanisms through which MMR-deficiency contributes to GI tumorigenesis and implicate a novel association between TLE6 isoforms and antagonism of RUNX target gene expression in CRC tumor progression.
By 9.5 months of age, MA mice develop >50% more tumors than Apc1638N mice (P<0.001; Mann-Whitney) (Figure 1A and C). However, the relative ratios of GI adenomas to carcinomas in Apc1638N mice (65% and 35% respectively) were very similar to that seen in MA mice (70% and 30% respectively) and overall survival is not significantly affected (9.5 vs. 10.5 months). No significant effect was seen on extra-GI cancer incidence or progression. These data suggest the primary role of Mlh3 is in suppression of GI tumor initiation and not tumor progression.
To study the effects of combined Mlh3 and Pms2 mutations in vivo, we generated MPA mice. MPA mice had significantly shorter survival vs. Apc1638N or MA mice (P<0.01, Mann-Whitney test; Figure 1A, C) and developed significantly more adenocarcinomas than MA or Apc1638N mice (Figure 1B, C) (P = 0.022 MPA vs. MA and p = 0.0003 MPA vs. Apc1638N). These are consistent with a role for Mlh3;Pms2 combined loss both to increase GI tumor initiation and accelerates progression. However, mean overall survival of MPA mice is longer than that previously seen in Mlh1−/−;Apc1638N mice [35].
In vitro studies have alternatively suggested that Mlh3 participates in either IDL repair [13] or SBR [15]. To understand the role of Mlh3 in these processes, we used the wild type Apc allele as a tumor-associated in vivo reporter gene to analyze the mutation spectrum from MA GI tumors. A total of 49 tumors from MA mice and 28 tumors from Apc1638N littermates were analyzed for Apc truncation mutations by IVTT analysis. Truncated Apc products were detected in 27 of 49 (55%) MA tumors while only 9 of 28 (32%) were found in Apc1638N tumors. The current observed incidence of Apc somatic mutations of Apc1638N tumors is in agreement with the previous results (7 of 22, 32%) [36], hence for better understanding of mutational differences between the two strains, this and the previous data for Apc1638N tumors were combined and used for further comparisons. This 23% increase in somatic Apc mutations in MA mice was significant (P<0.0048; Fisher exact test) and was attributable to increased small insertion/deletion frameshift mutations (62.5%) vs. Apc1638N (33.3%) mice (P<0.001; Fisher exact test; Figure 2B and Tables 1 and 2). MA mice had one recurrent insertion/deletion mutation “hotspot” also observed in Mlh1;Apc1638N mice (amino acid 1464) (Figure 2A). Furthermore, examination of the sequences surrounding each Apc mutation site in MA tumors showed that, unlike in other mismatch repair deficient tumors such as Mlh1−/−;Apc1638N or Msh6−/−;Msh3−/−;Apc1638N [32],[33], about 40% of frameshift mutations occurred at non-repetitive sequences within the Apc coding region. These data are consistent with a primary in vivo role for Mlh3 in DNA repair of small insertion/deletion mutations in GI epithelial cells.
We also studied the tumor-associated Apc mutations in GI tumors from MPA mice. The overall incidence of Apc truncation mutations in MPA tumors were similar to that observed in MA tumors, yet the nature of mutations characterized was distinct. Compared with MA mice (37.5%), combined Mlh3;Pms2 deficiency caused a significant increase in the proportion of single-base point mutations (57.2%, P<0.01; Figure 2 and Table 2). Within the types of single-base point mutations, MPA tumors showed higher frequency of C∶G→T∶A transition mutations (12 of 16, 75%) compared to MA tumors (7 of 12, 58.3%). However, this high frequency was not as prominent as that of Mlh1;Apc1638N tumors which showed the majority (23 of 25, 92%) of base substitutions to be transition mutations[32]. The C∶G→T∶A transition mutations found in tumors, irrespective of genotypes, occurred at either CpG dinucleotides or CpNpG sites, typical targets for DNA methylation. Among these, Apc codon R854 seems to be a preferential target for base substitution mutation, which was not only demonstrated to be a mutational hotspot in Mlh1;Apc1638N mice [32] but also in MPA mice.
Apc mutation is thought to be an early event in CRC carcinogenesis. The significantly increased number of adenocarcinomas vs. adenomas seen in MPA vs. MA or Apc1638N mice suggested that MPA tumors have accelerated tumor progression. While there is extensive evidence that increased mutation rates and decreased apoptosis contribute to MMR defective CRC, it is likely that additional mechanisms participate in tumor progression as well. Because chromosomal and segmental aneuploidy has been described in a subset of MMR deficient adenocarcinomas [37],[38],[39], we performed array comparative genomic hybridization (aCGH) analyses of GI tumor vs. E18.5 C57BL/6 embryonic control DNA from Apc, MA, and MPA mice to identify specific genetic changes that accelerate MPA GI tumor progression. Comparison of aCGH profiles revealed a recurrent 5-Mb base pairs amplification on chromosome 12F2 (66.7%∼83.3%; see Table 3 for detail; Figure 3A and B) in MPA GI tumors not seen in Apc1638N or MA tumors (Figure S1). To define the critical interval for this amplification on chromosome 12F2 we bred a new cohort of MPA mice and quantified copy number variation in the tumor using real-time quantitative PCR (qPCR) (Figure 3C and Table 4). Using qPCR with primer sets for the six genes within the amplified region and two flanking genes, we identified one gene that showed recurrent increased level of genomic DNA in tumor tissues (Figure 3C), Transducin-like enhancer protein 6-like, (Tle6-like). TLE family members act as transcriptional corepressors [40],[41] without any intrinsic DNA-binding activity. They are recruited to specific gene regulatory sequences in a context-dependent manner by forming complexes with different DNA-binding transcription factors. Two evolutionarily conserved domains define the TLE gene family: an N-terminal glutamine-rich (Q) domain that mediates TLE family member heterodimerization, and a C-terminal domain of WD motif repeats that mediates direct interactions with sequence specific DNA binding transcription factors (Figure 4)[40],[41]. Previously TLE family members have been described containing only the Q domain, such as Grg1-S [42], or only the WD repeat motif, such as Grg6/Tle6[43]. Tle6-like similarly contains only the C-terminal WD repeat domain and had highest amino acid identity (84.4%) with TLE6 (Figure 4A and Figure S2).
To understand the impact of gene amplification on Tle6-like expression, we isolated total RNA from tumor and normal tissues from MPA mice and used qPCR to quantify relative Tle6-like mRNA expression. As a result of copy number amplification, Tle6-like mRNA levels were significantly increased in tumors compared with adjacent normal GI tissue (Figure 3D). To understand whether Tle6-like protein levels are subsequently increased, we generated anti-Tle6-like specific antisera. Western blot analysis with this antisera demonstrated that Tle6-like protein levels are significantly increased in GI tumors compared to surrounding normal GI epithelial tissue from MPA mice (Figure 3E). Overall, these data suggest increased genomic DNA copy number of Tle6-like causes increased mRNA and protein expression of Tle6-like in MPA tumors.
Gene diversity can be generated by several mechanisms, including gene duplication and paralogue evolutionary divergence, and the generation of alternative mRNA splice isoforms that modify coding sequence. The mouse Tle6-like-containing amplicon is syntenic to human chromosome 14q33, but amplification of this chromosomal region is not associated with CRC. Upon further analysis, we discovered that 14q33 contains no human ortholog of mouse Tle6-like, or any other TLE family member. However, when we analyzed TLE6 mRNAs bioinformatically, we identified a previously identified alternative spliced isoform of TLE6 (TLE6D) (Genbank Accession #BX375733) that contains only the C-terminal WD repeat domain of TLE6, and therefore has the same domain structure as mouse Tle6-like (Figure 4B) To understand expression of TLE6A (full-length isoform) and TLE6D in human CRC, we generated three sets of RT-PCR primers: one for the TLE6D N-terminus, one crossing the splice junction that is specific for TLE6D and one that detects TLE6A but not TLE6D (Figure 4B). We then calculated expression of these transcripts in 40 human CRC samples and normal tissue. Compared to adjacent normal tissue, the TLE6D-specific and TLE6 C-terminus qPCR showed significantly increased expression in a subset of human CRCs (Figure 5A), but not for the TLE6 N-terminal or TLE6A qPCR (data not shown). These data suggest that the TLE6D isoform specifically is overexpressed in a subset of human CRCs.
Because GI tumors from MPA mice showed increased number of adenocarcinoma than Apc1638N or MA mice, we evaluated whether increased levels of Tle6-like can contribute to mechanisms that underlie tumor progression. We generated stable cell 293 cell lines that express Tle6-like or TLE6D. For both Tle6-like and TLE6D overexpressing cell lines, cell proliferation rates were significantly increased compared with vector-transfected control cells (Figure 6A). Similar results were also seen in HCT116 and 3T3 cells (data not shown). We next tested the effect of Tle6-like/TLE6 expression on the ability to form colonies in vitro. Mouse embryonic fibroblasts transfected with Tle6-like or TLE6D significantly increased colony formation (four-fold and two-fold, respectively) compared with empty vector-transfected control cells (Figure 6B and C). We also tested the mobility of the cells transfected with Tle6-like/TLE6D by in vitro migration assay. Cell lines stably expressing Tle6-like or TLE6D were able to migrate a significantly longer distance when compared with control cell lines expressing only the vector (Figure 6D). In contrast, no effect of Tle6-like or TLE6D ectopic expression was seen on induction or resistance of apoptosis induced by serum-depletion in culture medium (data not shown). In summary, these results are consistent with a proliferation and migration advantage for tumor cells expressing Tle6sh or TLE6D.
Because Tle6-like or TLE6D ectopic expression increased cell proliferation and migration in vitro, we evaluated their impact in vivo. We injected HCT116 cells stably expressing Tle6-like, TLE6D or vector s.c. into nude mice and quantified tumor growth. As expected, HCT116 cells transfected with vector formed xenograft tumors. In parallel, HCT116 cells expressing Tle6-like and TLE6D formed significantly larger tumors (Figure 7). These results suggest that Tle6-like and TLE6D expression increases CRC cell proliferation and growth, in vivo.
RUNX genes encode transcription factors that activate or repress transcription of key regulators of growth, survival and differentiation pathways [44],[45]. This gene family is defined by the Runt domain, which mediates both protein-DNA and protein-protein interactions with transcriptional co-regulators. TLE proteins interact with, and regulate the function of, RUNX proteins through direct interactions between the TLE WD domain and the Runt domain and the interactions antagonize RUNX-mediated transactivation [44],[45],[46],[47],[48]. RUNX3 has been shown to play important roles in GI epithelial cell development and tumorgenesis. Loss of Runx3 predisposes knockout mice to gastric hyperplasia, indicating a tumor suppressor-like role for this gene [34],[49],[50],[51],[52]. In human gastric cancers, hypermethylation of RUNX3, hemizygous deletion and truncating point mutations have been observed [34],[52],[53],[54],[55],[56],[57],[58]. To test whether Tle6-like/TLE6D interact with RUNX3, we first evaluated sub-cellular localization using immunofluorescence staining in 293 cells co-transfected with Tle6-like or TLE6D and native RUNX3 (Figure S3). Using anti-Myc, anti-Xpress and anti-RUNX3 antibodies, we observed that highest levels of Tle6-like and TLE6 and are in the nucleus overlapping with nuclear RUNX3 staining. Furthermore, in 293 cells, transiently transfected with Tle6-like or TLE6D, endogenous RUNX3 co-immunoprecipitated with anti-Myc or anti-Xpress antibodies (Figure 8A and B), suggesting an interaction between Tle6-like/TLE6D and RUNX3. Similar co-localization and co-immunoprecipitation results were seen in HCT116 and 3T3 cells (data not shown). Finally, to evaluate the functional consequences of Tle6-like/TLE6D interaction on RUNX3 transcriptional regulation we used a well characterized RUNX3 transactivation on promoter target, osteocalcin (OC), fused to a luciferase reporter gene [47]. As expected, transfected RUNX3 activated luciferase expression in 293, Hela or HCT116 cells (Figure 8C, lane 1 and 2). Co-transfection of Tle6-like or TLE6D decreased RUNX3 transcriptional reporter activity in a dose-dependent manner (Figure 8C), whereas Tle6-like/TLE6D transfection had no effect on promoters lacking RUNX3 binding sites, such as the TOPFLASH/FOPFLASH system (data not shown). Taken together, these results are consistent with a model whereby Tle6-like/TLE6D expression antagonizes RUNX3 GI tumor suppressor mediated target gene transactivation through an interaction between the Tle6-like/TLE6D and RUNX3, providing a selective growth advantage for cell proliferation and migration.
In gastric cancer, RUNX3 activity is most commonly reduced through a mechanism involving RUNX3 promoter hypermethylation and subsequently decreased mRNA expression. However, its expression levels in CRC have not been well characterized. We therefore used qPCR to evaluate RUNX3 expression in 40 human CRC and matched normal GI epithelial samples, normalized to GAPDH expression. In many CRCs, RUNX3 expression is low, consistent with a role in GI tumor suppression. However, in a subset of CRCs RUNX3 expression is paradoxically increased (Figure 5B). To test whether elevated TLE6D expression is associated with RUNX3 activation, we used qPCR to analyze TLE6D expression levels in the same matched sets of CRCs and normal mucosa. We observed a clear correlation of RUNX3 and TLE6D expression levels (R = 0.723; Figure 5C). However, at the same time no clear correlation was seen for RUNX3 and TLE6D expression levels with regard to MSI-H/MSS status or for expression levels of the full length TLE6 and RUNX (data not shown). Overall, in combination with the functional antagonism of RUNX3 activity by TLE6D observed in colon cancer cells, the correlation of RUNX3 and TLE6D expression in human CRCs suggests that TLE6D may interact with the RUNX3 GI epithelial tumor suppressor and inactivate RUNX3 in a subset of CRCs independent of MSI status. However, further experiments will be required to analyze the association between RUNX3 and TLE6D expression levels and functional interactions in more detail.
Because APC is a common mutation target in MMR-deficient CRC, we created novel mouse models combining different mutations in these genes to analyze their roles in MMR-deficient GI carcinogenesis and progression. The observation that MA mice have increased tumor multiplicity but no accelerated tumor progression or decreased survival vs. Apc1638N mice suggests a primary role for the Mlh1–Mlh3 heterodimer in suppression of GI tumor initiation. While previous in vitro studies have alternatively suggested that Mlh1–Mlh3 participates in IDL repair [13] and SBR[15],[59], our study provides the first in vivo evidence that Mlh3 deficiency significantly increases IDL mutation frequency. This type of mutation occurred both at repetitive and non-repetitive Apc sequences, implicating its role in repair of both types of IDL (Figure 2). Previous studies of Pms2−/−;ApcMin mice have shown a primary role for Mlh1-Pms2 in GI tumorgenesis suppression but not tumor progression[60]. We therefore combined these mutations to create MPA mice. Like Mlh1−/−;Apc1638N mice, MPA mice have significantly increased GI tumor multiplicity, accelerated tumor progression and decreased overall survival[61] . MPA tumors harbor proportionally more C∶G→T∶A (at either CpG or CpNpG sites) transition mutations than MA tumors, showing recurrence in certain arginine codons, one of which was at Apc codon 854, a SBR hotspot that was also previously seen in Mlh1−/−;Apc1638N mice.
In addition to Mlh1-Pms2 and Mlh1–Mlh3, several lines of evidence from our study suggest a potential role for Mlh1-Pms1 in suppression of GI tumorigenesis. First, MPA mice have later mean GI tumor onset compared to previous studies of Mlh1−/−;Apc1638N mice[32]. Second, the multiplicity of GI tumors is decreased vs Mlh1−/−;Apc1638N mice. Third, two Apc insertion/deletion mutation hotspots seen in Mlh1−/−;Apc1638N mice have not been detected in MPA tumors. These data are consistent with previous studies of yeast Mlh2p (orthologue of mammalian PMS1) that demonstrate a minor role for this protein in IDL repair [62].
Because the combination of Mlh3, Pms2 and Apc mutations accelerates tumor progression, we searched MPA GI tumor specific genetic changes associated with progression using high-resolution aCGH. MPA tumors contained a recurrent 5-Mb amplicon with a critical interval containing a novel, poorly characterized member of the TLE family of transcriptional co-repressors, Tle6-like. Unexpectedly, this MPA recurrent amplification hotspot is not detected by aCGH in GI tumors from Mlh1−/−;Apc1638N mice (data not shown). The reason for this difference is unclear, but again suggests that Mlh1-Pms1 may play a role in causing chromosomal instability.
TLE genes are the mammalian homologues of Drosophlia groucho that play critical roles in a wide range of developmental and cellular pathways [40]. TLE proteins are transcriptional corepressors for specific families of DNA-binding transcription factors, including RUNX proteins[48]. In addition, Tle1/Grg1 has been shown to act as a lung-specific oncogene in a transgenic mouse model [63]. Mouse Tle6/Grg6 has been shown to synergize with the E2A-HLF oncoprotein in antagonism of Runx1 transactivation in murine pro-B cells, causing acute leukemogenesis [64]. Tle6/Grg6 also participates in developmental mechanisms of neurogenesis [43]. Here, we provide data that a previously uncharacterized TLE family member containing only the WD repeat domain, Tle6-like, has amplified gene copy number, mRNA and protein levels in GI epithelial tumors from MMR deficient/Apc mutant mice, and is associated with accelerated tumor progression. Consistent with this observation, in functional studies Tle6-like/TLE6D enhances cell proliferation, colony-formation, migration and xenograft tumorgenicity. While TLE family members have previously been shown to repress Wnt/β-catenin signaling [42],[65],[66],[67], we were unable to demonstrate any Tle6-like/TLE6D protein-protein interactions with β-catenin or effect of Tle6-like/TLE6D overexpression on β-catenin reporter gene activity using TOPFlash in transient transfection in colon cancer cell lines (data not shown), suggesting that Tle6-like/TLE6D might not be involved in canonical Wnt pathway.
RUNX family genes regulate lineage and stage specific gene transcription by direct binding to DNA promoters and enhancer elements [44],[45]. Loss of Runx3 in the mouse results in the development of gastric mucosal hyperplasia, decreased apoptosis and attenuated TGF-β anti-proliferative signaling. Consistent with previous observations of interactions between RUNX3 and TLE family members mediated through the Runt and WD repeat domains, respectively [46],[48], we detected an interaction between RUNX3 and Tle6-like/TLE6D by co-immunoprecipitation. Furthermore, we demonstrated that Tle6-like/TLE6D antagonized RUNX3 regulated transcriptional targets. However, while these experiments show an association between RUNX3∶TLE6D interactions and tumor progression, they do not demonstrate mechanistically the functional importance of this interaction in accelerating tumor progression.
Alternative mRNA splicing allows multiple gene products to be produced from a single coding sequence, and through this mechanism a higher diversity of mammalian genes is generated [68]. Several distinct TLE/Grg gene alternative splice forms, such as Grg-1s, QD of TLE4, and Grg3b [42],[69],[70], have been reported. While the human genome does not encode a TLE6-LIKE ortholog, a structurally equivalent protein, TLE6D, is generated through alternative splicing. The observation that GI adenocarcinomas from both humans and mice use two very distinct mechanisms to amplify Tle6-like/TLE6D activity suggests a strong growth advantage and selective pressure for this TLE isoform in tumor progression. Similarly, the correlation between TLE6D and RUNX3 expression in human CRC suggests a model whereby RUNX3 inactivation by TLE6D could be an important factor driving this growth advantage in both MSI-H and MSS CRC. Future studies will be required to understand the mechanistic implications of the interaction between these two proteins in CRC progression in more precise detail.
Wild-type (Wt), Pms2+/− and Mlh3+/− mice were maintained on the 129 Sv/Ev genetic background and intercrossed to generate Mlh3+/−;Pms2+/− mice as described before [13]. Apc1638N mice were backcrossed four times to 129 Sv/Ev and subsequently intercrossed with Mlh3+/−; Pms2+/− to generate Mlh3−/−;Apc1638N and Mlh3−/−;Pms2−/−;Apc1638N mice. Kaplan-Meier survival curves were generated and statistical significance between genotypes was determined using the Log Rank test as previously performed [13]. All lines of mice were necropsied when they became morbid or moribund. Sacrificed mice were surveyed for tumors and suspicious masses were histology analyzed as previously performed. Statistical analyses of tumor onset and incidence among the different mouse lines were performed using the Mann-Whitney test as previously described [23],[32],[33],[35],[71],[72],[73],[74],[75],[76]. Tumors from stomach, small intestine, and colon were cut into two parts. One part of the tumor was processed for histopathological analysis and the other part was used for DNA/RNA extractions. Genomic DNA samples were extracted using Puregene DNA Isolation kit (Gentra Systems, Minneapolis, MN) and subjected to mutational analysis of Apc gene between codons 677–1674 as previously described [33].
Genomic DNAs were isolated from tumor tissue and tail tissue from each mouse using PUREGENE DNA Isolation kit (Gentra Systems, Minneapolis, MN). DNAs were digested with DpnII and subsequently purified using the QIAquick PCR Purification kit (Qiagen). The quality of the DNA samples was evaluated using the Agilent 2100 BioAnalyzer. The purified fragmented DNA samples were random-prime labeled with either Cy5 or Cy3 and hybridized as previously described [77]
Briefly, for each labeling reaction, 2 µg of purified digested DNA were used. Each sample was dye-swap labeled for hybridization to mouse 70-mer oligonucleotide microarrays (Agilent Technologies, Palo Alto, CA) containing 20,281 clones. After hybridization, the arrays were scanned using an Agilent Microarray DNA scanner (Agilent Technologies) and the spot intensity was extracted from slide images using Agilent Feature Extraction Software 7.0. The data were further analyzed using the procedures of Automatic Data Analysis Pipeline (ADAP). Only spots with fluorescence intensities statistically different from the surrounding background (P<0.001) were considered reliable, taking up >85% of total spots on the chip. For further analysis the fluorescence intensity values of reliable spots were transformed to log2. To minimize the effect of the variations, the log2 intensity ratios of remaining spots were subjected to normalization by Lowess fitting. Gene copy number changes for each sample was calculated by taking the median of the normalized log2 intensity ratios of dye-swapped chip experiments for the corresponding sample. The gene copy numbers were ordered along chromosomes by the map positions of corresponding genes. To eliminate systematic noise, gene copy number changes (log2Ratios) along the chromosomes were smoothed by taking a moving median of symmetric 5-nearest neighbors, followed by Lowess fitting (f = 0.2). The mean and standard deviation (SD) of smoothed log2Ratios for all genes in all the samples were calculated. The copy number profiles of at least 5 consecutive genes that deviated significantly above mean+3SD were interpreted as regional gains, below mean-3SD as regional losses. The threshold for whole chromosomal gain/loss was mean±2SD. The ideograms of chromosomal aberrations were drawn using mapping information of cytogenetic bands to the mouse genome (NCBI Mapview Build 32).
For RNA extractions, Trizol reagent (Invitrogen) was used to isolate total RNA. RNA were further digested with RNAse-free DNAseI (Promega) and cleaned with RNeasy Mini kit (Qiagen). High Capacity cDNA Archive kit from Applied Biosystems was used to make cDNA from the RNA samples. Real-time quantitative PCR was performed with either SYBRGreen PCR master mix or Taqman PCR master mix (Applied Biosystems) following the manufacture's protocol on ABI 7900 machine. Primers used for SYBR Green assays are listed in Table 1. Each gene was normalized to the internal control gene Gapdh and then compared to a known single copy gene (Alkbh), which is located on non-amplified region on chromosome 12 D3 in the MPA tumors.
The whole Tle6-like gene (encoded 240 amino acids) was cloned in to pET28b vector and Tle-6like protein was induced and purified from E. coli. Rabbit anti-serum was raised against Tle6-like protein. The anti-serum was further purified using affinity column, in which Tle6like protein was covalently bound to CNBr-activated Sepharose 4B (Sigma). The purified antibody was used in immunoblotting at 1∶100 dilutions.
HCT116, 293, Hela or 3T3 cells were maintained in DMEM with 10%FBS and transfected using Lipofectamine 2000 (Invitrogen). The human isoform TLE6D cDNA clone was purchased from Invitrogen (Full-length Human Clones CS0DC017YC05; Accession number BX375733). Tle6-like was cloned from cDNA samples from MPA mice. We subcloned Tle6-like and TLE6D into either Xpress-epitope-tagged pcDNA6/HisA vector (Invitrogen) or Myc-tagged pCS2+MT vector. Cells were transfected with following plasmids: pcDNA6/HisA, pcDNA6/HisA-Tle6-like, pcDNA6/HisA-TLE6D, pCS2+MT, pCS2+MT-Tle6-like, pCS2+MT-TLE6D. Stable cell lines from each transfectant were generated with the selection medium containing 10 µg/ml blasticidin (Calbiochem) for 10 days. The pooled populations of cells that survived were used in the experiments for MTT assay and cell mobility assay. The transient-transfected cells were used for colony formation assay, immunoprecipitation, and reporter assay.
For the cell proliferation assay, 4000 cells were plated in 96-well plates and MTT assay were used to determine the cell numbers in a time-course experiment. Briefly, cells were washed with PBS and treated with 5 µg/ml MTT ([3-(4,5-dimethylthiazol-2-yl)- diphenyltetrazolium bromide]Sigma, St. Louis, MO) for 5 hours. After removal of MTT, DMSO was added to dissolve the dark purple formazam crystals in the viable cells and absorbance of 600 nm were determined by a multiwell scanning spectrophotometer. The cell numbers were calculated with a control standard curve. For colony-formation assay, MEF cells were seeded in 6 well plates and transient-transfected with 1 µg of the respective plasmids in the next day. After 24 h, cells were trypsinzed, transferred to 10-cm plates and allowed to grow with the selection medium containing 10 µg/ml blasticidin for 2 weeks. Survived cells were fixed in 30% ethanol and stained with 0.25% methylene blue. Colonies containing more than 50 cells were counted. Both assays were repeated three times in three independently-derived cell lines.
The monolayer “wounding assay” was used to demonstrate the in vitro cell migration. Human colon cancer HCT116 cells stably expressing corresponding plasmids were plated on glass microscopy slides and cultured to confluence. A “wound” was generated by scratching the slide with a razor blade, clearing a portion of adherent cells on the slide. Photo documentation was taken at day 4 and the migration of cells from the cut edge of the monolayer into the clear portion of the slides was assessed. Two independently-derived stable cell lines for each plasmid were used in this assay.
Transient-transfected 293 cells in 10-cm plate were lysed with 1 ml of NP-40 lysis buffer and prepared as described before [13]. Five hundred µl of lysates were pre-cleared with 50 µl ProteinA/G agarose beads (Santa Cruz) for 1 h. After spinning down the ProteinA/G beads, the collected supernatants were incubated with 5 µg anti-Xpress or anti-myc monoclonal antibody (Invitrogen) and 50 µl ProteinA/G beads overnight at 4°C. The next day, the beads were washed with NP-40 buffer 5 times and incubate with 4× protein loading dye (Invitrogen) 10 min at 95°C to elute the binding proteins. These samples were resolved by SDS-PAGE and the immunoblotting was used as previously described to detect the corresponding proteins. The antibodies used in immunoblotting are: mouse monoclonal anti-Xpress and anti-myc (1∶2000, Invitrogen), rabbit anti-RUNX3 (1∶1000, Abcam) and goat anti-β-actin (1∶1000, Santa Cruz Biotechnologe).
293, Hela or 3T3 cells were transient-transfected accordingly with the Flag-RUNX3 (a kind gift from Dr. Yoshiaki Ito) and rat Osteocalcin promoter fused to luciferase reporter construct (OC-Luci, a kind gift from Dr. Gary Stein), and plasmids as described above. Luciferase activities were determined using Dual-Luciferase reporter assay systems kit (Promega) on the luminemeter.
Female 6-week-old nude mice (Charles River Laboratories, Wilmington, MA) were divided into four experimental groups, five for each. One million HCT116 cells stably transfected with vectors (pCS2+MT or pCDNA6/HisA), pCS2+MT-Tle6sh, or pCDNA6/HisA-TLE6D were injected subcutaneously in the flanks of each mice. Mice were monitored daily for palpable tumors. Because of rapid growth, tumors were dissected out 3 weeks after injection and were analyzed. |
10.1371/journal.pcbi.1002633 | On Genetic Specificity in Symbiont-Mediated Host-Parasite Coevolution | Existing theory of host-parasite interactions has identified the genetic specificity of interaction as a key variable affecting the outcome of coevolution. The Matching Alleles (MA) and Gene For Gene (GFG) models have been extensively studied as the canonical examples of specific and non-specific interaction. The generality of these models has recently been challenged by uncovering real-world host-parasite systems exhibiting specificity patterns that fit neither MA nor GFG, and by the discovery of symbiotic bacteria protecting insect hosts against parasites. In the present paper we address both challenges, simulating a large number of non-canonical models of host-parasite interactions that explicitly incorporate symbiont-based host resistance. To assess the genetic specialisation in these hybrid models, we develop a quantitative index of specificity applicable to any coevolutionary model based on a fitness matrix. We find qualitative and quantitative effects of host-parasite and symbiont-parasite specificities on genotype frequency dynamics, allele survival, and mean host and parasite fitnesses.
| Coevolution between hosts and parasites is believed to be central to a number of biological phenomena, most notably the observed patterns of biodiversity and the origins of sexual reproduction. However, classical mathematical models of host-parasite coevolution account neither for the hosts' use of bacterial symbionts for protection from parasites, nor for the potential and observed complexity of genetic interactions between the coevolving species. In this article we address both challenges by simulating a large number of models of host-symbiont-parasite coevolution based on randomly generated genotype interaction patterns. We demonstrate that the degree of “specificity” between the genotypes of the interacting species is a major factor influencing the outcome of coevolution. We also observe that the symbionts may take over from the hosts the coevolutionary arms race against the parasites. Overall, our results make clear that the complex interaction patterns and the defensive symbionts can both play vital roles in host-parasite coevolution. An additional contribution of the article is a numerical index of specificity, applicable to a wide range of existing and future coevolutionary models.
| Parasitism is one of the main lifestyles in nature and a major source of evolutionary pressure. Despite its central place in evolutionary ecology, however, the details of the genetic architecture underlying resistance and infectivity are not known for most host-parasite associations. Mathematical models of host-parasite coevolution compensate for the missing data by making explicit, first-principle assumptions about the interaction of host and parasite genotypes. Two such classic assumptions, and consequently two classic families of models, are known as Matching Alleles (MA) and Gene For Gene (GFG). The MA models, inspired by vertebrate immune systems [1], assume that an exact, lock-and-key match between host and parasite genotypes is required for successful infection. The GFG models, based on studies of plant disease [2], [3], postulate that an infection takes place if every “resistance” allele of the host is countered by a “virulence” allele of the parasite. Perhaps the farthest-reaching difference between the two is in the genetic specificity of the interaction. Under MA parasites exhibit full genetic specialisation to their host: a single parasite genotype can only infect a single host genotype. Under GFG, on the other hand, the number of host genotypes that a parasite may infect depends of the number of virulence alleles it has, and ranges from one genotype to all. The perfect specificity of MA interactions readily results in negative frequency-dependent selection and persistent cyclic dynamics of genotype frequencies in hosts and parasites (“Red Queen dynamics”), which in turn underpin the Red Queen Hypothesis (RQH) for the evolution of sexual reproduction [4], [5]. In contrast, in the GFG models the parasite carrying all virulence alleles takes over the population, at least until costs of infectivity and resistance are assumed [3], [6].
One problem with the existing theory is that there is a mounting number of natural systems for which the interactions between host and parasite genotypes have been disentangled and found to be neither of the MA nor the GFG kind [7]–[10]. Coupled with the uncertainty as to the extent to which plant disease data supports the GFG model [3], [11], these findings cast doubt on the generality and explanatory power of interaction models as simple as MA or GFG. Agrawal and Lively [12] tackled this problem by considering a range of non-standard genetic interactions spanning a particular continuum between MA and GFG, and found that MA-like behaviour, in particular Red Queen dynamics, is sufficiently common among non-standard models to support the generality of the RQH. Their work has since been generalised by Engelstädter and Bonhoeffer [13], who sampled a much broader range of interaction patterns. While they were able to confirm the pervasiveness of Red Queen dynamics, they also found effects that are entirely absent from the MA and GFG models, showing that non-standard models should not be ignored.
We aim to build on these two studies to address a new challenge to the existing theory of host-parasite interactions: the discovery of bacterial endosymbionts that increase their hosts' resistance to parasites [14]–[17]. When a symbiont protects a host against a parasite, one has to consider the coevolution of three, not two, species, with all the resulting complications. In particular, it is now important to distinguish between host-parasite and symbiont-parasite genetic interactions. It is for example possible for one to be of the GFG, and the other of the MA kind. Indeed, specialisation of symbiont strains to parasite genotypes and apparent lack of host-parasite specialisation characterises the protection against parasitoid wasps that the endosymbiotic bacterium Hamiltonella defensa lends to aphids [18], [19]. Another important aspect that models of such systems should address is the spread of symbionts in, and loss from, the host populations, and the coevolutionary impact of these processes.
In this paper we build a generic model of the coevolution of hosts, symbionts and parasites. We incorporate as independent, tunable parameters the strength of the reciprocal selection acting on hosts and parasites, the fitness penalty for harbouring symbionts, the efficiency of horizontal and vertical transmission of symbionts, and the genetic interactions among all players. In a straightforward extension of the standard approach, the last factor is subsumed in a real-valued matrix, and we randomly sample many such matrices and simulate our model for each. In this way we are able to cover a range of potential host-symbiont-parasite systems and, importantly, decouple the effects of genetic specialisation patterns from the other factors. In addition, we analyse separately a collection of matrices describing protective symbionts acting within the established MA and GFG frameworks. Throughout, we do not treat genetic specialisation as a binary property; instead, we devise a numerical index of specificity. We find that specificity as defined in this paper strongly influences important coevolutionary outcomes of the models, such as the genotype frequency dynamics, maintenance of allelic diversity and mean host and parasite fitness. Our simulations show that these characteristics depend also on symbiont-related processes, especially the reliability of their maternal inheritance.
We assume that hosts and parasites reproduce asexually and consider one haploid locus and two alleles in each of the three protagonists: host, symbiont and parasite; we also model hosts without symbionts. This gives rise to two parasite genotypes: and , and six combined host-symbiont genotypes or associations: , , , , and . The blank “” denotes absence of symbiont, and so the symbiont-free hosts and are also formally considered to be associations. We subsume any individual interaction pattern of the host-symbiont and parasite genotypes in a 62 master matrix denoted . Each entry in this matrix falls in the interval and is interpreted as the degree of resistance of the particular host-symbiont association to the particular parasite genotype. To give a concrete example, if , then every host carrying the symbiont will suffer only 20% of the maximal potential fitness damage from the parasite. For the majority of the analyses we rely on random generation of such matrices in order to cover a wide range of possible host-parasite relationships (see “Model sampling and simulation”).
We use three additional parameters in our coevolutionary setup: the maximum strength of selection that the parasites can exert on hosts (e.g. means the host can have zero fitness as a result of infection, that is be sterilised or killed before it reproduces), the corresponding parameter representing the strength of selection on parasites (which can be interpreted as the maximal fitness penalty for failing to infect a host), and the fitness penalty the hosts pay for harbouring protective symbionts; see Table 1 for an overview of parameters and their values. These parameters are used to derive the host and parasite fitness matrices, and respectively, from the master matrix as follows (see also Figure 1):(1)(2)(Here, and throughout the paper, we use lowercase letters to refer to the entries of the matrix denoted by the corresponding uppercase letter.) Each entry of the fitness matrix specifies the relative fitness consequence of the interaction between a particular host-symbiont association and a particular parasite genotype. Again, to give a concrete example, means that the fitness of the host-symbiont association when faced with the parasite genotype is half that of a symbiont-free uninfected host. By definition, host and parasite fitness values are fully anti-correlated in our model, reflecting the antagonistic nature of host-parasite relationships (but see also [13]).
Instead of generalising the concept of genetic specialisation to three species, we prefer to analyse the specificity of genetic interactions between pairs of species separately. To this end we transform the master matrix into three matrices , and , each time averaging out the contribution of one species (symbiont, host and parasite, respectively). Thus, each of these matrices serves as a proxy for the genetic interaction of the remaining two species. Formally, they are defined by:(3)(4)(5)
In this paper we focus on and . Figure S1 contains the corresponding results for .
Our basic assumption in formalisation of specificity is that a relationship between two coevolving species and is specific if there are two genotypes of the species, say and , and two of the species, and , such that is better adapted than to , but is better adapted than to —or analogously with s and s swapped around. This is the same as saying that there is a genotypegenotype interaction between and , or that the reaction norms for two or two genotypes cross. This definition is also easily expressible in terms of interaction matrices. Taking the as an example, we say that the interaction it subsumes is specific if and only if but , or this condition holds with / or / switched in a consistent manner. When is specific, we define its index of specificity, , to be the minimal additive disturbance necessary to bring into a non-specific form. Formally:(6)where is the set of matrices such that is non-specific.
The above definition can be generalised to cover arbitrary and matrices. These constructions are described in Text S1. In the remainder of the paper we are only concerned with the specificity of and . For these, and for any matrices in general, observe that ranges between and , with only for non-specific matrices such as the GFG matrix , and only for the MA matrix and the Inverse Matching Alleles matrix . The IMA model [20] assumes that the host is resistant if and only if it can match all parasite alleles; in the 22 case it is formally equivalent to the MA model because their matrices are mirror images of each other. For square matrices with , the GFG and IMA matrices acquire intermediate specificity, while the MA matrix remains the most specific.
With the exception of the MA- and GFG-based host-symbiont-parasite relationships analysed in “Protective symbionts in the MA and GFG frameworks”, we kept the range of investigated relationships as broad as possible by generating a large number of random master matrices. Because we are interested in the effects of specificity, our goal was to have two collections of matrices, each uniformly distributed with respect to one of the specificity scores. Ideally, one would generate enough matrices by sampling the entries independently from the uniform distribution (i.u.d.) on [0,1], and then select the two matrix collections from this sample. Unfortunately, i.u.d. sampling yields no high-specificity matrices in reasonable time, because their entries have extreme values and are highly dependent on each other (see Text S2). To overcome this problem, we separated the [0,1] interval into ten non-overlapping sub-intervals of length 0.1, and for each sub-interval we randomly generated master matrices until we had 400 with the specificity score falling in it. For intervals up to [0.6,0.7] the matrices were obtained by i.u.d. sampling. For the remaining three the matrices were independently sampled from a symmetric bimodal distribution with modes 0 and 1 (probability density of being for , for , and otherwise), ensuring polarised matrix entries, which is a characteristic property of high-specificity matrices. Of all matrices we further required that the symbionts do not impair host resistance, which translates into the simple criterion: for all , and . We performed this procedure twice, once for HP-specificity and once for SP, obtaining two sets of 4000 matrices distributed in an approximately uniform fashion with respect to and ; see Figure 2.
We considered three values of each of the three supplementary parameters, , and (see Table 1 for an overview of parameters), thus deriving 27 pairs of fitness matrices for each master matrix (see Figure 1). For each pair of fitness matrices we simulated 20000 generations of coevolution, the first 10000 of which were considered the burn-in period and discarded. We worked with infinite population sizes, meaning that we tracked frequencies of host-symbiont and parasite genotypes rather than population sizes. At each generation the symbionts colonised the symbiont-free hosts at the mass-action basal rate , then selection was allowed to operate, and finally the constant fraction of of symbiont-harbouring hosts lost the symbionts. The selection step was performed as follows (see also [13]): assume that is the vector of host-symbiont association frequencies, and the vector of parasite genotype frequencies. Then the post-selection frequencies and are given by:The numerator in the above expressions gives the fitness of genotype , obtained as the sum of the th row of the fitness matrix entries, weighed by the frequencies of the genotypes of the antagonists. The underlying assumption is that the more common a particular opponent is, the more the performance against it contributes to fitness. The denominator, which is independent of , is the mean host or parasite fitness and ensures that the frequencies add up to one after selection.
For each master matrix we simulated and analysed 108 models, because there are 108 auxiliary parameter combinations (Table 1). Each model was first simulated for 10 randomly chosen starting frequency vectors, and then once more for equal starting frequencies of all genotypes, with the results of the last run used for evaluation. Approximately 10% of the models were ambiguous, in that the results of the 11 simulations were not consistent with each other. We also had trivial models: whenever , the symbiont-bearing hosts inevitably went extinct, since the cost of symbionts exceeded any damage the parasites could inflict and we assumed the horizontal transfer to be rare. Unless noted otherwise, the ambiguous models are included in the analyses that follow, but the trivial ones are not.
We begin by considering two simple models incorporating symbiotic protection into the Gene For Gene and Matching Alleles frameworks. We assume that the hosts' innate resistance to the parasites follows one of these two classic principles, but the symbionts may confer a partial degree of resistance to the non-resistant hosts. We further assume that the symbiotic protection is specialised: symbiont only protects against the parasite , and against . This leads to two master matrices: and . Setting , and remembering that harbouring symbionts (regardless of whether they are needed for protection) comes at the cost , we derived two pairs of host and parasite fitness matrices using equations (1) and (2). We then simulated the two models for different combinations of and , under perfect maternal inheritance of symbionts () and without horizontal transfer events ().
We found that despite the presence of symbionts, the dynamical behaviour of the models is similar to that of their classic symbiont-free counterparts. All MA models exhibit strong oscillations of host and parasite allele frequencies, while in the GFG models the allele frequencies stabilise. The balance of cost and protection quality determines the fate of symbionts in both models. In the MA model, the symbionts become fixed in the population if ; otherwise they go extinct. The corresponding condition for the GFG model, , is less strict because the symbiont-provided protection is essential against the parasite ; for the same reason the symbiont is more common than in this model. These formulae can be derived using the definition of the host fitness matrix and considering when the symbionts confer a net fitness benefit to their hosts, and were corroborated by the simulations.
We now give an intuitive specificity-based interpretation of these results. The host-parasite specificity is high in the MA models: , and therefore the negative frequency-dependent selection drives the oscillations of host and parasite alleles easily regardless of whether the symbionts are present. In the GFG models the host-parasite specificity is zero: , and thus in the absence of symbionts the allele frequencies are stable. When the symbionts are present, the moderate symbiont-parasite specificity for high () could be expected to result in oscillations (see “Allelic diversity and frequency dynamics”). However, in these models there is also the host-symbiont association that nullifies this effect of specificity because it is more resistant than any other to both parasites. In the remainder of the paper we substantiate and expand these intuitions by linking specificities to coevolutionary outcomes of models based on randomly generated master matrices.
In classical models incorporating genetic specialisation such as the MA model, pronounced oscillations of genotype frequencies often ensure indefinite maintenance of at least two genotypes. Here, we examine to what extent these effects reappear in our three-species setup. We say that a model cycles if the frequency of at least one host-symbiont association has six or more local extrema over the assessment period, and the amplitude between the extrema does not decrease; also, we declare an association lost from the population if its mean frequency over the assessment period is less than 10−3. We found that while both the host-parasite and symbionts-parasite specificities broadly promote cycling and diversity (Figure 3), they do so in qualitatively different ways. For HP specificity, the prevalence of cycling and the mean number of host-symbiont associations maintained in the model reach maximum values for master matrices with intermediate values of . For SP-specificity, both measures increase across the entire range of , with one exception (see below). In both cases, models based on non-specific matrices maintain the fewest associations on average and are the least likely to cycle.
The fidelity of vertical transmission of symbionts has a strong quantitative effect on the fate of host-symbiont associations and on their dynamics. Perfect symbiont inheritance () is often necessary to maintain symbiont infections, but it can also lead to the extinction of the symbiont-free hosts and , as these populations often depend on the influx of hosts from the infected lineages (see also [21]). Fewer models oscillate when than when , likely due to the interference of the symbiont-free hosts with the frequency-dependent selection driving the cycles. This hypothesis is consistent with the effect disappearing for master matrices with extreme , where the host-parasite relationship becomes determined by host and parasite alleles only, and no significant interference is possible from hosts differing only in their symbiont infection state. For high values of on the other hand, we found that the trend is reversed and perfect vertical transmission of symbionts results in less frequent cycling. Here, the probable explanation is that the symbiont-free hosts cannot engage in cycling because they necessarily go extinct unless they are replenished via symbiont loss (see Text S2 for more on matrices of high specificity).
The number of maintained associations and the prevalence of cycling are virtually the same for models differing only in the presence of weak horizontal transmission of symbionts ( or ).
We turn now to the question of allelic diversity, that is the maintenance of the individual host, symbiont and parasite alleles. We found that the genetic specialisation between antagonist species strongly promotes allelic diversity in these species (Figure 4). Moderate and high host-parasite specificity all but guaranteed the survival of both host and both parasite alleles. We found a similar effect of on symbiont and parasite alleles, but the mean symbiont allele diversity taken across all analysed models increases considerably more slowly due to the cost associated with harbouring symbionts that is built into most of these models (see Methods). There is no effect of the specialisation on the allelic diversity in the non-specialised species, that is and do not influence the likelihood of loss of symbiont and host alleles, respectively. Again, we found no impact of the horizontal transmission of symbionts on the allelic diversity in any of the three species.
The presence of symbionts results in novel kinds of Red Queen dynamics (Figure 5). Traditionally, this term refers to persistent oscillations of both host and parasite genotype frequencies driven by the genetic composition of the antagonist population (also known as negative frequency-dependence). Under considerable symbiont-parasite specificity, oscillations of symbiont allele frequencies may replace those of the host alleles. The result is a dynamical pattern that would be regarded as cryptic if one were unaware of the existence of symbionts: the host allele frequencies remain stable but those of the parasite oscillate. Another possibility is that one symbiont allele is lost, but the other periodically rises and falls in frequency in the host population due to its specialisation to one of the parasite alleles.
Lastly, we investigated the dependence of mean host and parasite fitnesses on the host-parasite and symbiont-parasite specificities, and on the parameters of the model. Mean fitness within the host population can be regarded as a measure of how well the hosts are adapted on average to resist infection by the parasites, and analogously for the parasites and their mean fitness. As a general rule, mean parasite fitness increases and mean host fitness decreases with increasing specificity (see Figure 6). From the host population's perspective this effect can be attributed to the specificity widening the gap between the fitnesses of well-adapted and maladapted associations (see Text S2), and the contribution of the latter to the mean fitness in the presence of efficient parasites. However, as the entries of fitness matrices become more and more polarised for high or extreme specificities, the selection against the maladapted associations becomes more and more swift. Consequently they cease to contribute to the mean fitness and the trend is halted or even reversed.
The fidelity of symbiont inheritance plays a similar role to that discussed in “Cycling and the fate of host-symbiont associations”. Imperfect inheritance maintains a small yet stable population of symbiont-free hosts even when they are severely selectively disadvantaged, e.g. for high values of , and the mean host fitness is reduced. On the other hand, when host-parasite specificity is high, symbiont-free hosts enjoy protection similar to that of their symbiont-bearing counterparts but do not pay the fitness penalty for symbiosis, and thus their influx increases the mean host fitness. We found no influence of horizontal transmission of symbionts on the mean fitness of either species throughout our analyses.
We observed the mean host fitness decrease with increasing strength of selection the hosts are under () and the costs of symbiont protection (). Similarly, mean parasite fitness is sensitive to , with stronger selection leading to lower fitness. These findings were entirely expected, since high values of , and make for low average entries in the fitness matrices. The antagonistic nature of the relationship was visible in that the increases of mean host fitness generally coincided with decreases of mean parasite fitness, and vice versa. This too can be traced back to fitness matrices, more precisely to the anti-correlation of and .
Genetic specialisation has been recognised in the literature on host-parasite interactions as a fundamental concept in its own right [22]. To the best of our knowledge, we provided here the first method of quantifying it in the context of coevolutionary modelling. By basing the construction on the concept of fitness matrices central to host-parasite theory [23], [24], we ensured that our method is applicable to a wide range of models, extant and future. For actual biological systems for which fitness matrices can be approximated experimentally, for example in factorial experiments, our index can be used directly to generate concrete predictions about the coevolutionary dynamics and genetic diversity.
Our work was inspired by that of Agrawal and Lively [12], who analysed host-parasite coevolutionary dynamics for a range of non-standard fitness matrices. Their setup was based around a single parameter so that yielded the MA model, the GFG, and intermediate values gave non-standard matrices. However, Agrawal and Lively's is not an extrinsic measure of a matrix, and therefore an independent characterisation of a relationship, but an intrinsic assumption used to construct it. In particular, if costs of resistance and virulence and the selection strength are fixed, the value of determines the matrix. Thus, our work is more general because it makes it possible to talk about the specificity of arbitrary matrices, and because we base our conclusions on a much wider range of models. In these respects it resembles a study of Engelstädter and Bonhoeffer [13], where antagonicity, another property of arbitrary fitness matrices, was developed to analyse coevolutionary dynamics.
Our specificity index is related to the notions of nestedness and matrix temperature introduced by Atmar and Patterson [25] to study the extinction of species in fragmented habitats, later used for structural analysis of plant-animal interaction networks [26], [27]. In this approach, one starts with a presence-absence matrix, that is a binary matrix where 1 denotes an existing plant-animal interaction, or the presence of a species in a habitat, and 0 the absence thereof. The matrix is rearranged so that the rows and columns corresponding to more generalist species or more hospitable habitats appear higher (rows) and farther to the left (columns). The matrix temperature is then obtained by penalising deviations of this rearranged matrix from the fully nested matrix, where ones appear only above a generalised diagonal and zeroes only below. A fully nested matrix is non-specific by our definition, but many non-specific matrices are not fully nested. Hence, matrices of high specificity will tend to have high temperature, but temperature may be different for matrices of the same specificity and vice versa. Importantly, specificity is defined for arbitrary matrices while temperature applies to binary matrices only. The similarity of the two measures suggests nevertheless that they capture facets of an essential property of biological interaction networks, and therefore that our specificity index may be applicable more widely than only to host-parasite coevolution.
Our setup explicitly included a heritable symbiotic species increasing the hosts' resistance to the parasites. Such beneficial symbionts can play fundamental roles in the the ecology and evolution of their hosts, highlighting the need for comprehensive treatment of the forces shaping symbionts' own spread and evolution [28]. Defensive symbionts are transmitted from mother to offspring with very high fidelity, with the link between protection and maternal transmission also strongly supported by theory [29]–[31], but lateral transfer appears to be relatively rare on the ecological timescale [32], [33]. Accordingly, we analysed the impact of occasional vertical loss and occasional horizontal transfer of symbionts on the coevolution of hosts and parasites. We found that a small population of symbiont-free hosts maintained exclusively by the sporadic failure of vertical transmission can disrupt the Red Queen dynamics driven by the specialisation of parasite alleles and host-symbiont associations. Sporadic lateral transfer had no effect on the results of simulations, but given the well-documented role of lateral transfer in the interspecific spread of bacterial symbionts [34], [35], we believe that better estimates of the basal rate of transfer () ought to be obtained before discounting its role in the coevolutionary dynamics of the three interacting species. Still, it seems plausible to us that horizontal transfer is important in establishing the initial symbiont infections, but not in their subsequent fate in the host populations, which is governed mainly by the cost-benefit trade-off.
The inclusion of protective symbionts as the third species highlighted the interplay of coevolutionary antagonicity and specificity. The genetic specificity between antagonist species had stronger effects on the vital properties of the system such as cycling and maintenance of alleles than the specificity of the mutualist host-symbiont relationship (Figure S1). This result dovetails with that of Engelstädter and Bonhoeffer [13], who showed that antagonicity of interaction promotes allelic diversity. We wish to point out, however, that our model is simplistic in two important respects. First, it ignores the fact that in addition to providing protection from parasites and pathogens, maternally transmitted symbionts may also manipulate the host reproductive phenotype in various ways (reviewed by Engelstädter and Hurst [36]), and thus the relationship between the host and the symbiont may be less mutualistic than envisaged here. Second, our model is fully deterministic, and as such it does not incorporate genetic drift. However, when the genotypes of two antagonist species are highly specialised to each other but not to the third one, drift can be expected to play a significant role in the evolution of the latter.
Our work may have interesting implications for the Red Queen Hypothesis (RQH)—the idea that host-parasite coevolution underlies the evolution of sex and recombination [1], [4], [24]. We assumed in our model that hosts reproduce asexually. As a consequence, defensive symbionts and host resistance genes are predominantly co-inherited, except for horizontal transmission events that we assumed to be rare. This lack of recombination, in tandem with the strong epistatic interactions between the symbionts and the nuclear genes that are implicit in many of our master matrices, can create pronounced fluctuations of the linkage disequilibrium (LD) between the nuclear locus and the symbiont “locus” when diversity is maintained at both loci (results not shown). In conventional Red Queen models considering only nuclear loci, such LD fluctuations are a prerequisite for recombination modifiers to be under positive selection. In our model, sexual reproduction would entail free recombination between the nuclear and the symbiont locus due to their different modes of inheritance (Mendelian vs. maternal). Therefore, we speculate that modifier alleles inducing sexual reproduction may be selected for under some of our host-symbiont-parasite interaction matrices. This tripartite version of the Red Queen represents an exciting avenue for future research.
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10.1371/journal.ppat.1003982 | Highly Active Antiretroviral Therapies Are Effective against HIV-1 Cell-to-Cell Transmission | HIV-1 cell-to-cell transmission allows for 2–3 orders of magnitude more efficient viral spread than cell-free dissemination. The high local multiplicity of infection (MOI) observed at cell-cell contact sites may lower the efficacy of antiretroviral therapies (ART). Here we test the efficacy of commonly used antiretroviral inhibitors against cell-to-cell and cell-free HIV-1 transmission. We demonstrate that, while some nucleoside-analog reverse transcriptase inhibitors (NRTI) are less effective against HIV-1 cell-to-cell transmission, most non-nucleoside-analog reverse transcriptase inhibitors (NNRTI), entry inhibitors and protease inhibitors remain highly effective. Moreover, poor NRTIs become highly effective when applied in combinations explaining the effectiveness of ART in clinical settings. Investigating the underlying mechanism, we observe a strict correlation between the ability of individual drugs and combinations of drugs to interfere with HIV-1 cell-to-cell transmission, and their effectiveness against high viral MOIs. Our results suggest that the ability to suppress high viral MOI is a feature of effective ART regimens and this parameter should be considered when designing novel antiviral therapies.
| HIV-1 cell-to-cell transmission has gained interest due to its potential role in AIDS pathogenesis. It has recently been suggested that antiretroviral therapies fail during cell-to-cell transmission because of the high number of particles transferred at sites of cell-cell contacts. However, these findings stand in contrast with the clinical observation that ART is successful in suppressing retroviral replication in HIV-positive patients. Consequently, many interpreted this observation to suggest that HIV-1 cell-to-cell transmission is not clinically relevant. Here we show that this interpretation is likely incorrect. By systematically testing the efficacy of commonly used antiretroviral inhibitors against cell-to-cell and cell-free HIV-1 transmission, we demonstrate that, while some NRTIs are less effective, most NNRTIs, entry inhibitors and protease inhibitors remain highly effective. Moreover, NRTIs become highly effective when combined, thus supporting the known effectiveness of HAART in clinical settings. Interestingly, the ability of individual drugs and combinations to interfere with HIV-1 cell-to-cell transmission correlates with their effectiveness against high viral MOIs. Our results suggest that the ability to suppress the high viral MOI during HIV-1 cell-to-cell transmission is a critical feature of existing ART regimens that should be tested when designing novel antiviral therapies.
| Highly active antiretroviral therapy (HAART) has significantly reduced the mortality rate and has increased the life span of HIV-infected patients by maintaining viral loads below detection levels, thus preventing the onset of AIDS [1], [2], [3], [4]. However, the presence of a stable latent reservoir, poor treatment adherence, and the emergence of drug-resistant HIV-1 variants continue to present challenges for successful treatments [5]. In order to develop more effective therapies, a detailed understanding of the pathogenesis of HIV-1 is necessary. Cell-to-cell transmission of HIV-1 has attracted significant attention as a potential factor influencing the pathogenesis of HIV-1 [6], [7].
HIV-1 cell-to-cell transmission describes efficient virus spreading via sites of cell-cell contact through formation of virological or infectious synapses [7], [8]. It provides for 2–3 orders of magnitude more efficient spread than cell-free virus dissemination and it is believed to be the main mode of viral spread in vitro [9], [10], [11], [12]. The formation of virological synapses allows the coordination of viral assembly with viral entry at sites of cell-cell contacts [13], [14], [15], [16], [17]. These supramolecular structures permit the efficient transfer of large numbers of infectious particles to target cells resulting in a higher viral MOI than cell-free infection [18], [19], [20], consistent with some in vivo observations [21], [22]. This transfer of high viral MOI can also result in bystander death of CD4+ lymphocytes [23]. Primary cells may undergo pyroptosis and/or apoptosis in response to a high load of viral DNA in the cytoplasm and/or multiple viral integration events in the nucleus [24], [25], [26]. The cell death of highly infected cells may result in the positive selection of CD4+ T cells that carry a single provirus [27], [28]. HIV-1 cell-to-cell transmission also allows HIV-1 to overcome barriers to infection and protects it from immunological and cellular restriction factors [11], [20], [29], [30], [31]. Finally, it has recently been reported that cell-to-cell transmission may protect HIV-1 from inhibition by antiretroviral therapies [32]. The transfer of large numbers of particles is thought to reduce the effective concentration of antiretroviral drugs within the cell and thus may provide a mechanism for the spread of HIV-1 in the presence of such therapies [32], [33]. A reduced effectiveness of drugs during HIV-1 cell-to-cell transmission has been reported for tenofovir (TFV), efavirenz (EFV) and zidovudine (AZT) [32], [33], [34]. However, these reports would seem to be in conflict with the clinical observation that HAART is successful at suppressing retroviral replication in millions of AIDS patients.
In this study, we tested a panel of antiretroviral drugs that include nucleoside analog reverse transcriptase inhibitors (NRTI), non-nucleoside analog reverse transcriptase inhibitors (NNRTI), entry inhibitors (Ent-I) and protease inhibitors (PI) for their ability to inhibit HIV-1 cell-to-cell transmission. We found that while some NRTI drugs lost activity when virus was transferred by cell-to-cell transmission, NNRTIs, Ent-Is and PIs remained highly effective. Importantly, we regained potent antiretroviral activity upon combining NRTIs that were ineffective towards HIV-1 cell-to-cell transmission as single therapies. These results explain the effectiveness of antiretroviral combination therapies in clinical settings. Finally, we demonstrate that the effectiveness of ART against HIV-1 cell-to-cell transmission can be recapitulated by testing their effectiveness against high viral MOI. Altogether, our results suggest that the ability to suppress high viral MOI is a defining feature of effective ART regimens and provides a valuable tool to develop novel ART that remain effective against HIV-1 cell-to-cell transmission.
To test the effectiveness of commonly used antiretroviral inhibitors against both modes of HIV-1 transmission, we established an experimental system that measures cell-free and cell-to-cell transmission with sufficient sensitivity. This system employs a Gaussia luciferase (GLuc)-based reporter genome (HIV-1inGLuc), which expresses and secretes GLuc only after splicing of an intron (inGLuc), packaging into viral particles and infection of target cells [20], [35]. To test cell-free infection, we inoculated primary CD4+ T cells with HIV-1NL4-3 carrying the spliced GLuc reporter (HIV-1NL4-3-GLuc) and measured HIV-1 infection 36 hr post-infection (Fig. 1). To measure transmission from donor T cells to primary CD4+ T target cells, we used a Jurkat cell line stably carrying the HIV-1inGLuc reporter (Jurkat-inGLuc). Jurkat-inGLuc cells were transduced with full length HIV-1NL4-3 so that donor T cells generated HIV-1NL4-3-GLuc particles and were co-cultured with primary CD4+ T cells (Fig. 1). Although, we used full length HIV-1, the level of infection in primary CD4+ T cells measured at 36 hr post-infection represents a single round of the HIV-1 life cycle (Supplementary Fig. S1A). The incubation period of 36 hr post-infection was selected since we found it to be optimal for the expression and secretion of luciferase (Supplementary Fig. S1B). Under these co-culture conditions, HIV-1 cell-to-cell transmission is 2–3 orders of magnitude more efficient, making the contribution from cell-free spread within the co-culture negligible [20]. To directly compare co-culture infection to cell-free infection, we adjusted the inoculum accordingly so that both modes of transmission resulted in equal percentage of infected target cells (Supplementary Fig. S1C). This ensured that a critical difference between both modes of transmission was the higher number of particles transferred during HIV-1 cell-to-cell transmission while target cells and infection levels remained constant [18], [19], [20]. Sorting of infected target cells, followed by Alu-PCR revealed that the average number of integration sites was ∼6-fold higher during HIV-1 cell-to-cell as compared to cell-free transmission (Supplementary Fig. S1D–F). To study the effect of PIs against cell-to-cell transmission, we adjusted the experimental design to account for the activity of this drug class within the donor cell (Supplementary Fig. S3A, Materials and Methods).
We applied these experimental conditions to systematically test the efficacy of 6 NRTIs, 4 NNRTIs, 4 Ent-Is and 4 PIs against cell-free and cell-to-cell HIV-1 transmission. The NRTI inhibitors TFV, AZT, and stavudine (d4T) were profoundly impaired in their ability to interfere with HIV-1 cell-to-cell transmission to primary human CD4+ T cells (Fig. 2A, Supplementary Fig. S2). Their dose-response curves were right-shifted indicating that ∼200–1000-fold higher drug concentrations were required to interfere with HIV-1 cell-to-cell transmission as compared to cell-free HIV-1. This observation is consistent with previous observations for TFV and AZT [32], [34] and translates into poor HIV-1 inhibition at the active drug concentrations detected in the serum of treated patients (Fig. 2A, gray bar). Interestingly, the NRTI inhibitors lamivudine (3TC), abacavir (ABC) and emtricitabine (FTC) showed a narrowing of cell-free and cell-to-cell transmission dose-response curves indicating an increased ability to interfere with HIV-1 cell-to-cell transmission relative to other NRTIs (Fig. 2A and Supplementary Fig. S2). Importantly, most NNRTIs (nevirapine (NVP), etravirine (ETR) and efavirenz (EFV)) interfered with HIV-1 cell-to-cell transmission as efficiently as with cell-free transmission. The Ent-Is enfurvitide (T20), plerixafor (AMD3100), and BMS488043 were also very effective consistent with previous results for T20 [30]. Rilpivirine (RPV) and BMS626529 exhibited intermediate effects (Supplementary Fig. S2). The PIs indinavir (IDV), darunavir (DRV), lopinavir (LPV) and saquinavir (SQV) also retained their effectiveness regardless of the mode of transmission (Fig. 2A and Supplementary Fig. S3B), consistent with recent observations [36]. The effectiveness of most NNRTIs, Ent-Is and PIs is clearly visible when the fold change in the IC90 during cell-to-cell transmission versus cell-free HIV-1 transmission is plotted for each drug (Fig. 2B). The effects could not be attributed to drug toxicity (Supplementary Fig. S4). A similar pattern was observed for a more physiologically relevant founder virus HIV-1TROJ.c (Fig. 2C and Supplementary Fig. S5) [37]. Cell-to-cell transmission of HIV-1TROJ.c was more resistant to TFV and AZT, albeit to a lesser extent than HIV-1NL4-3, and remained highly sensitive to NNRTIs, Ent-Is and PIs.
To gain a better understanding of the effectiveness of antiretroviral inhibitors in both modes of HIV-1 transmission, we calculated the instantaneous inhibitory potential (IIP) [38], [39]. The IIP incorporates both the IC50 and the slope of the inhibition curve and may provide a more accurate assessment of the effectiveness of an inhibitor. We found that the IIP in co-culture samples was dramatically weakened for TFV and AZT and significantly reduced for most other NRTIs (Fig. 3A, B and Supplementary Fig. S6). Importantly, the IIP was not affected for most NNRTIs and Ent-Is in agreement with the observations based on IC90. All data is summarized as the ratio of the IIP at the top drug dose (ICMax) for co-culture over cell-free in Figures 3B and C. All curves are shown in Supplementary Fig. S6 and S7. The IIP could not be computed for PIs because of the limited dynamic range in the signal for HIV-1 cell-to-cell transmission (data now shown). These data demonstrate that while some antiretroviral drugs such as NRTIs are less efficient against HIV-1 cell-to-cell transmission, most NNRTIs and Ent-Is remain highly effective regardless of the mode of viral transmission.
The failure of antiretroviral inhibitors such as TFV and AZT to interfere with HIV-1 cell-to-cell transmission stands in conflict with the clinical experience that they are effective in suppressing HIV-1 replication in AIDS patients [1], [2], [3], [4]. However, mono-therapy is not used for the treatment HIV-1-infected patients due to the high risk of emergence of drug-resistant mutants [40], [41]. Thus, we wondered whether drugs that fail to interfere with cell-to-cell transmission when used individually, are more effective when used in combination. To test drug combinations, we matched drug concentrations according to their IC90 values and treated co-culture and cell-free infections with serially diluted drug combinations. Strikingly, the combination of AZT and TFV potently interfered with HIV-1 cell-to-cell transmission (Fig. 4A). While each drug individually was ∼200–1000-fold less effective against HIV-1 cell-to-cell transmission, this difference was reduced to ∼4.1-fold when the drugs were combined (Fig. 4A). Furthermore, the drug combination shifted the effective dose-range required to suppress HIV-1 cell-to-cell transmission to within the drug concentrations detected in the serum of treated AIDS patients (Fig. 4A, gray bar). This observation was reproduced for three additional combinations of NRTIs including the clinically used combinations of 3TC/ABC and 3TC/AZT (Fig. 4B, Supplementary Fig. S8A) [42]. The increased effectiveness of combination therapy was also visible when the IC90 values were compared and the IIP was calculated (Fig. 4C, D and Supplementary Fig. S8B).
The effectiveness of combination therapies was surprising since drug combinations at most doubled the total drug concentration. If the effectiveness of competitive NRTI inhibitors was reduced due to a high MOI at sites of cell-cell contact [32], then doubling the drug concentration should be insufficient to inhibit all the incoming particles (Fig. 2). The observation of synergy in NRTI combination therapies can likely be explained by more efficient inhibition of reverse transcriptase. During reverse transcription, reverse transcriptase is able to excise an incorporated nucleotide analog, thus lowering the potential effectiveness of many NRTIs [43], [44], [45]. Combinations of nucleotide analogs have been observed to interfere with this excision process, thus enhancing the ability of NRTIs to terminate the growing DNA chain [46]. To test this hypothesis, we conducted our co-culture and cell-free inoculations using an HIV-1NL4-3 clone carrying the M184V mutation in RT. This mutation renders HIV-1 reverse transcriptase hypersensitive to AZT due to its inability to excise the drug [47], [48]. We predicted that AZT would efficiently interfere with HIV-1 cell-to-cell transmission of HIV-1 carrying M184V mutant RT. Indeed, the difference in IC90 between cell-free and co-culture infection was dramatically reduced compared to HIV-1 carrying wild-type RT (Fig. 4E). These results suggest that synergy between NRTIs against HIV-1 cell-to-cell transmission is, at least in part, due to a reduction of NRTI excision, which in turn causes more efficient chain termination.
Next, we asked how this drug-resistant HIV-1 mutant would behave during combination therapies in both modes of transmission. The M184V mutation was first characterized as a mutation that provides resistance against 3TC [49], [50]. We hypothesized that if this mutant were to be exposed to a combination of 3TC and TFV, it may be able to resist inhibition by TFV by cell-to-cell transmission. We found that if HIV-1 is resistant to one of the inhibitors used in the combination, the dose-response curve for cell-to-cell transmission was shifted again towards higher drug concentrations, phenocopying the behavior of NRTI mono-therapy (Supplementary Fig. S9). This suggests that drug-resistant HIV-1 mutants may gain a replicative advantage to amplify by cell-to-cell transmission in the presence of some combination therapies.
It has been suggested that the high local MOI observed at sites of cell-cell contact is responsible for the relative resistance of HIV-1 cell-to-cell transmission to antiretroviral inhibitors [32], [33]. This would suggest that the reason why most NNRTIs and all combination therapies are effective against HIV-1 cell-to-cell transmission is because they are MOI-independent, thus would remain effective despite high viral MOI. To test this hypothesis, we concentrated HIV-1NL4-3(GLuc) and used highly susceptible MT4 cells, which allowed us to use MOIs of up to 25. An MOI of 25 is close to the highest MOI that can be detected during HIV-1 cell-to-cell transmission [18], [19], [20]. We found that 3TC, TFV, FTC and AZT were indeed overpowered by increasing particle numbers (Fig. 5A, B). In other words, higher drug concentrations were required for these NRTIs to inhibit high MOIs. In striking contrast, NNRTIs and combination therapies were largely MOI-independent (Fig. 5A, C). The same drug concentration of NVP or the combination of AZT and TFV inhibited HIV-1 irrespective of the MOI. The strong correlation between non-effectiveness or effectiveness of ART against HIV-1 cell-to-cell transmission and high MOI was best seen when the change in IC90 during co-culture infection was plotted versus the change in IC90 during high MOI (Fig. 5D). This plot shows the clustering of MOI-dependent and MOI-independent treatments. Thus, we predict that those individual and combination therapies that are effective against high MOI will also efficiently interfere with HIV-1 cell-to-cell transmission.
The recent questioning of ART's effectiveness during HIV-1 cell-to-cell transmission [32] stood in conflict with the clinical experience that HAART is effective at suppressing HIV-1 replication in patients [1], [2], [3], [4]. Many clinicians may have concluded that HIV-1 cell-to-cell transmission cannot be relevant in patients and that cell-free spread must dominate. Here we showed that this interpretation is likely incorrect. Rather, we demonstrate that clinically applied ART regimens are effective against HIV-1 cell-to-cell transmission likely because they also remain effective against the high number of particles transferred at sites of cell-cell contacts. By systematically testing the efficacy of commonly used antiretroviral inhibitors against cell-to-cell and cell-free HIV-1 transmission, we demonstrate that while some NRTIs are indeed less effective against HIV-1 cell-to-cell transmission, most NNRTIs, Ent-Is and PIs remain highly effective. Importantly, upon combining of 2 NRTIs that failed as single therapies, HIV-1 cell-to-cell transmission and cell-free infection often became equally inhibited. Therefore, our findings indicate that the ability of HIV-1 cell-to-cell transmission to evade antiretroviral drug inhibition is not a universal phenomenon. Because standard treatment involves the combination of several drugs (2 NRTI+1 NNRTI or PI) it would seem unlikely that HIV-1 cell-to-cell transmission would provide a feasible mechanism for any ongoing viral replication in the presence of suppressive treatment. This observation is consistent with a large body of evidence indicating that suppressive HAART stops any measurable level of viral replication [51].
Our observations that combination therapies of NRTIs can be effective against HIV-1 cell-to-cell transmission indicates that the clinical effectiveness of HAART did not automatically imply that HIV-1 spreads by cell-free virus in patients. Rather we demonstrate that HAART effectively suppresses the high MOI observed during HIV-1 cell-to-cell transmission. The determination of the exact mechanism of HIV-1 cell-to-cell spread in vivo will require the direct in vivo visualization of viral dissemination [52], [53]. However, our results already provide evidence that HIV-1 cell-to-cell transmission can contribute to the pathogenesis of HIV-1 as a feasible mechanism of viral escape during drug mono-therapy or inadequate treatment regimens. We confirmed the original observation that some NRTIs fail to restrict HIV-1 cell-to-cell transmission during mono-therapy [32]. We also provide evidence that drug-resistant virus may gain a replicative advantage to spread by HIV-1 cell-to-cell transmission in the presence of inadequate combination therapy. Thus, HIV-1 cell-to-cell transmission may contribute to the rise of drug-resistant virus and therapy failure under conditions of poor adherence [54].
Our finding that ART similarly suppresses high viral MOIs and HIV-1 cell-to-cell transmission is consistent with the suggestion that a high viral MOI is a central feature associated with cell-cell contact mediated viral dissemination [18], [19], [20], [32]. High MOIs have been observed in infected cells in tissues in vivo [21], [22]. This observation appears to be in conflict with the finding that most circulating T cell lymphocytes carry only a single provirus [27], [28]. However, a high MOI may often result in bystander death of CD4+ lymphocytes, a hallmark of AIDS pathogenesis [23]. Primary cells have been suggested to innately sense the presence of a large number of viral DNA copies (unintegrated and/or integrated) and undergo apoptosis and/or pyroptosis [24], [25], [26]. The cell death of highly infected cells may result in the positive selection of CD4+ T cells that carry a single provirus [27], [28]. The ability of ART to suppress the high viral MOI documented in this report confirms the long standing knowledge that effective ART is able to effectively suppress bystander cell death and protect most AIDS patients from further T cell depletion [4], [55].
A high local MOI of reverse transcriptase can overwhelm drug activity by mass action [32], [33]. However, the ability of multiple drugs, particularly NNRTIs, to remain effective against the high local MOI observed during HIV-1 cell-to-cell transmission suggests that mass action alone cannot fully explain the mechanism by which antiretroviral inhibitors function under these conditions. In the case of NRTIs, our data suggest that the ability of reverse transcriptase to excise nucleotide analogs plays an important role in this phenomenon. When nucleotide excision was inhibited through mutation of the RT, mono-therapy with a nucleotide analog can inhibit both modes of viral transmission with similar efficiency. Similarly, we observed synergy in combination therapies consistent with more efficient reverse transcript chain termination and less efficient nucleotide analogue excision by RT [46]. In the case of NNRTIs, allosteric inhibition of RT also provides for synergistic effects [46]. Moreover, we hypothesize that other steps in the cellular uptake, metabolism, or secondary binding sites, determine the effective dosage of antiretroviral inhibitors. Said differently, under conditions of high MOI encountered during cell-to-cell transmission, interaction of the drug with RT is not the rate-limiting step for efficient inhibition of reverse transcription. That is, the number of incoming RT molecules alone does not define the effective dosages of drug. These considerations indicate that there is likely no single mechanism that explains whether a drug or drug combination is effective against HIV-1 cell-to-cell transmission. Thus, each drug and drug combination needs to be tested.
To this day, therapy outcome in patients has been difficult to predict. Mathematical models have been developed recently that incorporate drug IC50, and the slopes of inhibition curves as in the IIP, as well as viral fitness, mutations and treatment adherence [56], [57]. Our data indicate that the effectiveness of ART against HIV-1 cell-to-cell transmission and viral MOI are additional helpful parameters to predict drug efficacy. Moreover, we observed that all drugs effective against HIV-1 cell-to-cell transmission were effective because they are MOI-independent and can efficiently suppress the high local MOI at virological synapses. These data suggest that highly effective drug regimens, either single or in combination therapies, must exhibit MOI-independence. Testing the effectiveness of antiretroviral inhibitors against increasing MOI provides a simple assay and a valuable tool for screening existing and novel individual drugs and combination therapies prior to clinical testing.
All the cells used in this study were anonymized and were obtained from commercially available sources (ATCC, AIDS Research and Reagents Program, New York Blood Center). As such, these samples are exempt from IRB review.
Peripheral blood mononuclear cells were purified from blood enriched by leukapheresis (New York Blood Center) with the Ficoll-Paque Plus gradient (GE Healthcare Life Sciences). Following this purification step, CD4+ T cells were purified using the EasySep Human CD4+ T Cell Enrichment Kit (StemCell Technologies) and were stimulated with PHA (10 µg/mL) (Sigma-Aldrich), IL-2 (100 U/mL), and IL-7 (100 ng/mL) for 72 hr (cytokines from Miltenyi Biotec) at 37°C. After stimulation, cells were maintained in RPMI (Gibco) supplemented with 100 U/mL penicillin/streptomycin (Gibco), 2 mM of L-glutamine (Gibco), 10% FBS (Gibco), IL-2 (100 U/mL), and IL-7 (100 ng/mL) at 37°C. A subclone of Jurkat-inGLuc was selected from the population described by Zhong, et al. [20]. The cell lines Jurkat-inGLuc, MT4 (NIH AIDS Research and Reagents Program), and HEK293 (ATCC) were maintained in RPMI supplemented with 100 U/mL penicillin/streptomycin, 2 mM of L-glutamine, and 10% FBS at 37°C. TZMbl cells were obtained from the NIH Research and Reagents Program and were maintained in DMEM supplemented with 100 U/mL penicillin/streptomycin, 2 mM of L-glutamine, and 10% FBS at 37°C.
The plasmid encoding the intron-regulated HIV-based Gaussia luciferase pUCHR-inGLuc (HIVinGLuc) was kindly donated by Gisela Heidecker, National Cancer Institute. The plasmid encoding the HIV-1 molecular clones NL4-3 [58] and pTRJO.c [37] were obtained from the AIDS Research and Reagents Program. The plasmid encoding the M184V mutation in reverse transcriptase (pNL4-3ΔEnv(M184V)) was kindly donated by Robert Siliciano, Johns Hopkins University. To generate a wild type version of the M184V mutant, the construct was digested with PspOMI and AgeI (New England Biolabs). The ∼1.5 kb fragment generated was then ligated to the ∼13 kb fragment of wild type NL4-3 after digestion with the same enzymes. The plasmid encoding the vesicular stomatitis virus G-glycoprotein (VSV-G) was obtained from Michael Marks, University of Pennsylvania.
Most antiretroviral drugs tested in this study were obtained from the AIDS Research and Reagents Program. The attachment inhibitors BMS488043 and BMS626529 were donated by Mark Krystal (Bristol-Myers Squibb) [59], [60], [61].
HIV-1 pseudotyped with VSV-G was generated by co-transfecting HEK293 cells with pVSV-G and pNL4-3 or pTRJO.c at a ratio of 1∶10. HIVGLuc was generated by co-transfecting HEK293 cells with pNL4-3 (or pTRJO.c) and pHIVinGLuc at a ratio of 6∶1 or 10∶1. For inoculations of MT4 cells, HIVGLuc was generated by inoculating HEK293 cells stably carrying HIVinGLuc and collecting culture supernatant at 36 and 60 hr post-infection. Viral supernatants were concentrated using Lenti-X Concentrator (Clontech) or by ultracentrifugation (∼20,000×g) over a 20% sucrose (in PBS) cushion for 2 hr at 4°C.
Primary CD4+ T cells were incubated with serial dilutions of nucleoside analogs at 37°C for 16–24 hr prior to inoculation in a total of 1% DMSO. This is required for the accumulation of sufficient concentrations of active inhibitors within the cells. Cells were incubated at 37°C with non-nucleoside analogs and entry inhibitors for 2 hr prior to inoculation also in a total of 1% DMSO. Cell-free inoculations were conducted by spinoculating 105 primary CD4+ T cells in 96-well plates at 1,200×g and at room temperature for 2 hr with 50 µL of concentrated HIVGLuc [62]. Cultures were then incubated at 37°C for 36–40 hr.
Co-cultures were conducted by first spinoculating Jurkat-inGLuc cells with full length HIV-1NL4-3 pseudotyped with VSV-G at 1,200×g and at room temperature for 2 hr. The Jurkat-inGLuc clone was originally selected to be CD4-low cells to minimize donor-to-donor infection in co-culture experiments with target primary CD4+ T cells. Cells were then washed, stimulated with 6.25 ng/mL of PMA for 2 hr at 37°C, washed and incubated in fresh medium for 18 hr at 37°C. A brief PMA treatment was used to stimulate expression of latent HIVin-GLuc for efficient packaging by the incoming wild type HIV. Additionally, PMA treatment causes down-regulation of CD4 expression in the donor Jurkat-inGLuc cells, further preventing donor-to-donor infection [63]. Subsequently, PMA was removed from the culture so that target primary CD4+ T cells were never exposed to the drug. 105 infected Jurkat-inGLuc cells were then washed and co-cultured with 105 primary CD4+ T cells in a total of 50 µL. GLuc accumulated in the culture supernatant was detected using the BioLux Gaussia Luciferase Assay Kit (New England Biolabs) and a Berthold Technologies luminometer.
To test PIs, this protocol had to be modified to account for the activity of this drug class within the HIV-1 donor cell. To do this, HIV-1 infected Jurkat-inGLuc cells were incubated with increasing concentrations of PIs immediately following stimulation with PMA for 12 hr prior to co-culturing with primary cells (see Supplementary Fig. S3A). Co-cultures were incubated for 42 hr prior to measuring GLuc. To assess the effect of protease inhibitors on the infectivity of cell-free particles, we collected the supernatant of donor cells cultured alone in the presence of PIs 54 hr after exposure to the inhibitors. This supernatant corresponds to the total number of particles released during the co-culture. The supernatant was tittered on 105 target primary CD4+ T cells or on 2×104 TZMbl target cells at a total volume of 60 µL in 96-well plates, spinoculated and incubated at 37°C for 36 hr prior to measuring GLuc activity. TZMbl cells were used to assess the infectivity of the supernatant because they are much more susceptible to cell-free HIV-1NL4-3 than primary CD4 T cells and could detect very low titers of HIV-1NL4-3 produced by donor cells.
Prior to infection, target cells were stained with 1 µM of Cell Proliferation Dye eFluor 670 (eBioscience) in OptiMEM medium (Gibco) at 37°C for 20 min. Cells were washed and incubated in complete medium supplemented with cytokines at 37°C for 30 min, washed and prepared for drug treatment. 24 hr after infection, cultures were harvested and fixed in 100 µL of BD CytoFix/CytoPerm buffer (BD Biosciences) for at least 30 min at 4°C. The cells were then washed with BD Perm/Wash buffer (BD Biosciences) and stained for 30 min at 4°C in 100 µL of BD Perm/Wash buffer containing the anti-HIV-1 Gag antibody clone KC57 (Beckman Coulter). The cells were washed with BD Perm/Wash buffer, resuspended in PBS supplemented with 0.5% BSA and 2 mM of EDTA and analyzed by flow cytometry with a FACSCalibur (BD Biosciences). The same staining protocol was used for sorting HIV-1-positive target cells after cell-free or cell-to-cell transmission. The sort was conducted using a BD FACSAria sorter.
Following the sort, cells were spun, resuspended in 200 µL of PBS +200 µL of Buffer AL (Qiagen) +20 µL of Proteinase K (Qiagen) and incubated at 60°C for 24 h to remove paraformaldehyde. DNA was purified using the DNeasy Blood and Tissue Kit (Qiagen). HIV-1 integration was measured by Alu-PCR as previously described using 2.5 U of Platinum Taq (Life Technologies) [64].
36 hr post-infection, a sample of 10 µL of culture was collected for each drug treatment condition and mixed with 10 µL of CellTiter-Glo (Promega). Cells were incubated at 37°C for 10 min and the luciferase signal was measured using a Berthold Technologies luminometer.
Inhibitor IC90 and IIP were calculated using MATLAB software. Statistical tests were calculated using Minitab software.
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10.1371/journal.ppat.1007120 | B cell clonal lineage alterations upon recombinant HIV-1 envelope immunization of rhesus macaques | Broadly neutralizing HIV-1 antibodies (bNAbs) isolated from infected subjects display protective potential in animal models. Their elicitation by immunization is thus highly desirable. The HIV-1 envelope glycoprotein (Env) is the sole viral target of bnAbs, but is also targeted by binding, non-neutralizing antibodies. Env-based immunogens tested so far in various animal species and humans have elicited binding and autologous neutralizing antibodies but not bNAbs (with a few notable exceptions). The underlying reasons for this are not well understood despite intensive efforts to characterize the binding specificities of the elicited antibodies; mostly by employing serologic methodologies and monoclonal antibody isolation and characterization. These approaches provide limited information on the ontogenies and clonal B cell lineages that expand following Env-immunization. Thus, our current understanding on how the expansion of particular B cell lineages by Env may be linked to the development of non-neutralizing antibodies is limited. Here, in addition to serological analysis, we employed high-throughput BCR sequence analysis from the periphery, lymph nodes and bone marrow, as well as B cell- and antibody-isolation and characterization methods, to compare in great detail the B cell and antibody responses elicited in non-human primates by two forms of the clade C HIV Env 426c: one representing the full length extracellular portion of Env while the other lacking the variable domains 1, 2 and 3 and three conserved N-linked glycosylation sites. The two forms were equally immunogenic, but only the latter elicited neutralizing antibodies by stimulating a more restricted expansion of B cells to a narrower set of IGH/IGK/IGL-V genes that represented a small fraction (0.003–0.02%) of total B cells. Our study provides new information on how Env antigenic differences drastically affect the expansion of particular B cell lineages and supports immunogen-design efforts aiming at stimulating the expansion of cells expressing particular B cell receptors.
| Broadly neutralizing HIV-1 antibodies (bNAbs) display protective potentials against experimental animal infection and thus are believed to be a key component of an effective HIV vaccine. bNAbs are derived from B cells that express B cell receptors formed by specific VH/VL alleles. We report that the variable domains of recombinant HIV-1 Env immunogens activate a large number of B cell clones that give rise to many non-neutralizing antibodies, and that removing the variable domains from the immunogen reduces the number of activated B cell lineages and leads to the development of autologous neutralizing antibodies, a step towards bNAb-production. Our findings shed new light into how HIV-1 evades detection from B cells that can produce bNAbs and also provides information that is relevant for the design of optimal immunization strategies.
| Following HIV-1 infection, serum neutralizing antibody responses against the evolving autologous viral swarm are generated by the vast majority of infected subjects, usually within the first few months of infection [1–6]. In 10–30% of infected subjects, antibodies capable of neutralizing not only the autologous virus but also heterologous viruses are generated, usually following several years of infection [2, 5, 7–13]. These neutralizing antibodies are referred to as broadly neutralizing antibodies (bNAbs). Binding but non-neutralizing antibodies (nNAbs) are also present in sera from infected subjects. Broadly neutralizing monoclonal antibodies isolated from HIV-1-infected subjects protect animals from experimental infection [14–23] and thus bNAbs are expected to be an important component of the protective immune response elicited by an effective HIV-1 vaccine. The viral envelope glycoprotein (Env) is the target for both nNAbs and bNAbs and the epitopes targeted by bNAbs and nNAbs have been identified and in many cases they have been structurally characterized [24–26]. In general, nNAbs target elements of Env that are variable in sequence and are located within the more exposed regions of Env on soluble gp120s or non-stabilized soluble gp140 proteins. In contrast, bNAbs bind conserved elements of Env.
Here we examined whether the variable regions of Env stimulate the expansion of B cell lineages that differ from those expanded by more conserved Env regions, and whether such a differential B cell clonal expansion is linked to the elicitation, or not, of neutralizing antibodies. To this end, we performed immunizations studies in rhesus macaques, since they express IGH, IGK, and IGL V alleles with more than 93% homology to human alleles [27, 28], using two forms of a clade C Env (426c) whose designs we previously described [29–31]: the full length extracellular form (WT) and one where the variable immunodominant regions 1, 2, 3 (V1, V2 and V3, respectively) and three N-linked glycosylation sites (NLGS) were artificially eliminated (‘NLGS-3 Core’). We performed an in-depth analysis of serum antibodies and of isolated monoclonal antibodies as well as IGH/IGK/IGL deep sequencing analyses of the evolving immune B cell responses in the periphery, lymph nodes, and bone marrow.
Although similar serum binding antibody titers were elicited by the two immunogens, the full-length immunogen activated a larger number of B cell clonal lineages than the NLGS-3 Core immunogen. Autologous neutralizing antibodies were elicited only by the NLGS-3 Core immunogen. Binding but non-neutralizing antibodies were derived from B cell clones that became predominant in the periphery, lymph node, and bone marrow during immunization, while neutralizing antibodies were derived from infrequent B cell clonal lineages (0.003–0.02% of total B cells). Our study provides a mechanistic explanation as to how the variable regions of Env elicit high titers of non-neutralizing antibodies. As such, our results support efforts to alter the immunogenicity of non-neutralizing epitopes located in these regions. Furthermore, our approach can be used by others to assess how specific Env modifications alter the activation of particular B cell lineages, or how different adjuvant formulations may alter the activation and expansion of particular unmutated B cell receptors by a particular Env.
The immunization schedule and timing of sample collection are summarized in S1 Fig and details are presented in the Materials and Methods section. High titers of autologous binding antibody responses were generated by all animals in both immunized groups, ranging from 7568 to 11924 reciprocal EC50 for the WT immunization group and from 3036 to 7824 for the NLGS-3 Core immunization group (Fig 1A). The titers after the final immunization were not significantly different between the two immunization groups.
In both immunization groups, minimal antibody responses against the gp41 subunit were observed, an indication that the elicited binding antibody responses targeted the gp120 subunits of the immunogens used here. A sizable fraction of the antibodies elicited by the NLGS-3 Core immunized animals targeted the CD4bs, something that was not observed with the WT immunized animals (S2 Fig). Thus, the majority of the serum antibody responses elicited by the WT immunogen recognize epitopes outside the CD4bs.
Serum antibody neutralizing activities were determined following the first and second DNA/Protein (DP) booster immunizations against the autologous 426c WT virus and three NLGS derivatives: NLGS-1: lacking the NLGS in loop D (N276); NLGS-2: lacking the two NLGS in V5 (N460 and N463); and NLGS-3: lacking all three NLGS. We note that the 426c Core Env (i.e., lacking the 3 NLGS and the variable regions 1–3) is not functional and cannot be tested as a virus. Therefore, all four autologous viruses used here express the variable regions 1–3.
The 426c WT virus and these three NLGS derivatives exhibit a tier 2 neutralization phenotype when assayed with sera from chronically HIV-1-infected individuals. As additional evidence of a tier 2 phenotype, all these viruses resisted neutralization by a panel of monoclonal antibodies against the gp120 V3 loop (2219, 2557, 3074, 3869, 447-52D and 838-12D) and CD4bs (654-30D, 1008-30D, 1570D, 729-30D and F105) that are relatively specific for tier 1 viruses. We do want to emphasize that, although the three NLGS-lacking viral derivatives of the 426c virus display tier 2 overall neutralization phenotypes, they are more susceptible to neutralization by certain VRC01-class MAbs than the WT 426c virus.
Irrespective of the immunogen used, neutralization of the WT 426c virus was not recorded (Fig 1B). Only the NLGS-3 Core immunogen elicited serum neutralizing antibody responses against all three autologous NLGS viral variants (Fig 1B). The strongest neutralizing activities (reciprocal IC50s) were observed against NLGS-3 and the weakest against NLGS-1. Although 4/4 animals generated anti-NLGS-3 neutralizing antibody responses, only 2/4 animals (A13284 and A13286) generated neutralizing antibody responses against NLGS-2 (and only following the last immunization). Anti-NLGS-1 neutralizing antibodies were also elicited by 2/4 animals (A13283 and A13284), only one of which (A13284) elicited anti-NLGS-2 neutralizing antibody responses. Overall, while 4/4 animals generated anti-NLGS-3 neutralizing antibody responses, one animal (A13284) generated neutralizing antibodies against all three viruses. Interestingly, in the case of animal A13283, vaccine-elicited antibodies neutralized the virus expressing an Env that lacked the NLGS in V5 (NLGS-1), but not the virus that lacked the NLGS in Loop D (NLGS-2). Neutralizing activities against the NLGS-1 and NLGS-2 viruses were not detected following the first DNA / Protein immunization.
Thus, in animals immunized by the NLGS-3 Core immunogen, only a fraction (Log differences) of neutralizing antibodies can bypass the glycan steric blocks presented on Loop D and/or V5, but not both (i.e., the WT Env). Overall, the results suggest that: (a) the NLGS-3 Core immunogen can elicit autologous NAbs that can ‘by-pass’ the variable regions 1, 2 and 3 (which are absent from the immunogen, but present on the virus), as long as the NLGS in V5 or Loop D are absent (individually or in combination); (b) that these NAbs have a harder time ‘bypassing’ the NLGS in V5 (N460 and N463; i.e., the NLGS-1 virus) than the NLGS in loop D (N276; i.e., the NLGS-2 virus); and (c) that the autologous NAbs elicited by the NLGS-3 Core target an epitope whose relative exposure on the virus is regulated by the presence of NLGS in Loop D and V5, similar to what is known for several anti-CD4bs bNAbs [31–35].
The sera did not display neutralizing activity against several heterologous viruses (Clade A: Q168a2, Q461e2, Clade B: QHO692, SF162, Bal.26, and Clade C: 706c, 823c). Elimination of the NLGS (in Loop D and V5) from most of these viruses (Q168a2, Q461e2, Bal.26, 706c and 823) did not lead to neutralization either. Thus, the neutralizing antibodies elicited by the NLGS-3 Core in non-human primates (NHPs) using this immunization regimen target epitopes that are either absent or are present but are less accessible on heterologous viruses.
To determine whether the different abilities of these two immunogens to elicit neutralizing antibody responses were linked to a differential stimulation of B cell lineages by the two immunogens, we performed next generation Illumina MiSeq deep sequencing analysis of the variable domains of the heavy (IGHV) and light (IGKV and IGLV) chains from B cells isolated from the periphery, pre- and post-immunization.
A number of IgHV genes from circulating memory B cells became commonly enriched among the animals from the group immunized with WT 426c Env. In the IGH locus, genes belonging to the IGHV3 family underwent the most expansion (13 total genes), and to a much lesser extent we observed expansion in the IGHV1 (2 genes), IGHV4 (3 genes) and IGHV7 (2 genes) (Fig 2A, S3 Fig Top panel). Stimulation of these genes was observed after both DNA immunization and after protein plus DNA immunization (Fig 2B). This, despite the fact that IGHV1 represents <5% and IGHV3 ~20% of circulating IGHV in IgM+ B cells in NHP, while IGV4 is the most frequently expressed IGVH (~70%) in IgM+ B cells [28]. However, we previously reported [28] that the IgGHV3 family becomes more prevalent in the IgG+ B cell compartment compared to the IgM+ compartment, indicating a preferential stimulation of IGHV3 into class-switched memory B cells, in agreement with what we observed here. The particular expansion of the above-mentioned VH genes was also reported by others in studies were a different full length soluble Env was used [36, 37]. Thus, it is probable that the stimulation of IGHV1 and IGHV3 of the circulating memory B cells is due to immunization with Env.
In the light chain loci, we observed enrichment in both the IgK and IgL loci after immunization with WT 426c (Fig 2A). In the IgK locus, IgKV1 was the most enriched, followed by IgKV2, IgKV3, and IgKV4. In the IgL locus, the IgLV2 family was the most enriched after immunization, followed by IgL1, IgLV3, IgLV5, and IgLV8 families. As with the IgH locus, stimulation of the light chain families was observed after both DNA and DNA plus protein immunization (Fig 2B). IgKV1 and IgLV2 are the predominantly expressed gene families from their respective loci [28].
We did not observe any significantly enriched IGHV, IGKV, or IGLV gene families after immunization with NLGS-3 Core (Fig 2), indicating that there was not a widespread stimulation of the same V genes within the group. We determined that this finding was not due to the NGS sequence data sets themselves, as quality and Hill’s diversity analysis of all sequence sets reported here revealed all data sets to be roughly equivalent in structure and quality, no matter the chain that was amplified nor the origin of the libraries (S4–S7 Figs) [38]. These findings were confirmed by principal component analyses, which clusters large, multi-dimensional data sets by the most significant sources of variation. In the WT animals, the NGS data sets clustered by time point, indicating that the statistically significant changes in gene abundance were due to vaccination time point. In contrast, the NLGS-3 NGS data sets cluster by animal and not time point, confirming that vaccination did not drive significant changes in common gene usage among the animals in this group (S8 Fig). This stark dichotomy implies that, while the NLGS-3 is immunogenic and elicits IgG titers similar to that of WT 426c, it does not broadly stimulate a diversity of V genes during immunization. Potentially, this is a direct, measurable consequence of the elimination of the highly immunogenic variable loops.
To better characterize the B cells that produce neutralizing antibodies and those that produce binding but not neutralizing antibodies, we isolated Env-specific IgG B cells from individual animals following immunization based on their CD4bs specificity (based on the D368R and E370A mutations, DREA). Thus, two populations of B cells were isolated from animals immunized with either immunogen: CD4bs-specific cells (Env+/CD4bs-KO- B) cells and non-CD4bs-specific cells (Env+/CD4bs-KO+ B cells). The corresponding recombinant Env used to immunize the animals was used for B cell-isolation. B cells were cultured in bulk in multiple wells, each well containing ~1000 B cells, due to the high number of sorted B cells. The cell supernatants were evaluated for anti-WT 426c and anti-NLGS-3 virus neutralizing activities (Fig 3). Supernatants from wells containing B cells (irrespective of their CD4bs specificities) isolated from the WT-immunized animals did not display neutralizing activities. In contrast, supernatants from 4 of 6 wells containing non-CD4bs specific B cells isolated from the NLGS-3 Core-immunized animals neutralized the autologous NLGS-3 virus, but not the WT virus. Thus, the neutralization results obtained from B cell supernatants and those obtained from sera (Fig 1B) were in agreement. The NLGS-3 neutralizing activity was derived from non-CD4bs-specific B cells. Since these B cells were isolated from animals immunized with the NLGS-3 Core immunogen, by definition they do not bind elements of V1, V2, or V3 regions. Thus, although the majority of serum binding antibodies target the CD4bs (S2B Fig), the neutralizing activity in the sera is due to antibodies whose binding is independent of the DREA mutation that is widely used to identify anti-CD4bs antibodies in sera [5, 39–41].
To better define the characteristics of the neutralizing antibodies elicited by NLGS-3 Core and to compare them to those of non-neutralizing antibodies elicited by the same immunogen, we isolated individual CD4bs-specific and non-CD4bs-specific and peripheral IgG+ B cells from the four animals immunized with the NLGS-3 Core (S9 Fig). The CD4bs-specific B cells represented the minority of Env-specific B cells (between 4.5% and 8.6% of total Env+ B cells. Within the non-CD4bs-specific B cell population, the neutralizing B cells represented a small fraction (between 0.4% and 2.4%). Thus, only a very small fraction (0.003 to 0.02%) of total periphery IgG+ B cells display neutralization potential.
IGH and IGK/IGL genes from wells containing individual B cells that displayed neutralizing activities against the NLGS-3 virus and from wells displaying 426c NLGS-3 Core-binding, but not neutralizing activity were amplified and sequenced. Seventy-nine IGH (S10 Fig) and 32 IGK/IGL (S11 Fig) genes were fully sequenced from peripheral B cells secreting binding, but not-neutralizing antibodies. The majority of IGH (~90%) were derived from IGHV3 and IGHV4 alleles. The majority of the IGHV3 sequences (~52%) were derived from the IGHV3-Korf19 allele (human homologue is IGHV3-33*01 [28]). A majority of the IGK light chains were derived from the IGKV1 family (~28%) (S11 Fig), including IGKV1-I21 (human homologue IGKV1D-16*01) which represented 10.9% of IGK present in the peripheral B cells. The second most frequently expressed LC was derived from IGLV5. Within the IGLV5, the predominant allele was IGLV5-S28. Overall, we estimate that over 40% of the serum response was represented by these binding, but non-neutralizing antibodies, due to their allelic dominance in the peripheral B cell repertoire.
Different concepts to elicit HIV-1 bNAbs through immunization are under investigation. One concept is based on the ‘germline-targeting’ approach during which a ‘germline-targeting’ immunogen is used to initiate the activation of naïve B cells expressing specific germline (unmutated) BCRs and subsequently, booster immunizations with specifically-designed immunogens to guide the maturation (through somatic hypermutation) of these BCRs towards their broadly neutralizing forms [42].
This approach was recently shown to be effective in eliciting PGT121 bNAbs in a knock-in mouse model where the PGT121 germline BCR was expressed by every B cell [43]. On a polyclonal BCR background however, Env immunogens (including ‘germline-targeting’ immunogens) will activate not only the desired B cells, but many B cells that recognize irrelevant, but immunogenic, epitopes on the immunogen [44]. These off-target B cells will expand even further during the booster immunizations, potentially limiting the expansion of the desired B cells (although this has not yet been experimentally demonstrated). We (and others) believe that an in-depth understanding of BCR clonal lineages expanding during immunization with Env-based immunogens will be important to identify the booster immunogens for the optimal development of bNAbs [45].
The NLGS-3 Core Env immunogen used here, was engineered by introducing specific mutations on the tier 2 clade C 426c viral Env. These include deletions of the variable domains 1, 2 and 3 and the targeted elimination of three NLGS: one in Loop D (N276) and two in V5 (N460 and N463) [29, 31, 46]. These modifications were introduced so that this Env engages B cells expressing germline VRC01-class BCRs, which are derived from the human VH1-2*02 allele paired with LCs expressing infrequent 5 amino acid long CDRL3 domains.
We now know that rhesus macaques (and other animal species such as mice, rats and rabbits) do no express an exact orthologue to the human VH1-2*02 allele [28, 47, 48]. The present study was not therefore conducted to inform on the ability of the NLGS-3 Core immunogen to activate and expand B cells expressing germline VRC01-like BCRs in vivo, but to generate new mechanistic information on why the variable V1, V2 and V3 domains of Env dominate the B cell responses upon Env immunization. In this regard we note that although the observed relative changes in IGH and IGK/IGL were assessed from ‘total’ B cells and not exclusively from Env-specific B cells, we expect that these changes are due to differences in the antigenic/immunogenic properties of the two immunogens we evaluated here, as the immunogens were prepared (expressed and purified) identically, the same adjuvant was used and the immunization protocols were identical. We also note that although the NGS analyses of IGH and IGK/IGL were performed on the same samples, they were derived from bulk, but not individual B cells and thus presently we cannot assess whether the expansion of a particular IGH lineage was linked with the expansion of a particular IGK/IGL lineage.
At first glance, it is not surprising that the WT immunogen stimulated a larger number of IGH and IGK/IGL lineages, as it expresses more epitopes than the NLGS-3 Core; especially the variable V1, V2 and V3 domains which are known to be immunogenic, both in the context of HIV-1 infection and immunization with Env [39, 40, 49–55]. One would expect that the immunogenicity of epitopes present on the core part of Env to increase in the absence of the immunodominant variable domains. This was the case, as the NLGS-Core elicited similar serum antibody titer responses as the WT immunogen.
We note that both immunogens examined here are not stabilized trimers. It is anticipated that the immunogenicity of variable domains will be reduced on such constructs [56–59]. The fact however, that neutralizing antibodies against the autologous NLGS viruses were elicited by the immunogen lacking the variable regions 1, 2 and 3, indicate that neutralization epitopes are located outside these variable regions of Env; within the core components of Env. These epitopes are also present in the WT immunogen (as the two immunogens share a common amino acid sequence), but since that immunogen did not elicit neutralizing antibodies we assume that these epitopes are occluded and thus poorly immunogenic. The lack of neutralization of the WT 426c virus by the neutralizing antibodies elicited by the NLGS-3 Core immunogen supports this assumption. In part the occlusion of the epitopes is due to carbohydrates present on NLGS in Loop D and V3, as viruses lacking these NLGS are susceptible to the neutralizing antibodies elicited by the NLGS-3 Core immunogen.
One (13284) of four animals immunized with NLGS-3 Core elicited serum neutralizing antibody responses against both the NLGS-2 virus (lacking the two NLGS in V5; positions N460 and N463) and the NLGS-1 virus (lacking the NLGS in Loop D; position N276). The neutralization titers against the NLGS-2 virus were 30 fold higher than those against the NLGS-1 virus. This, combined with the fact that none of the isolated neutralizing antibodies neutralized the NLGS-1 virus, but 5/8 neutralized the NLGS-2 virus, suggests that B cells producing antibodies capable of bypassing the restrictions imposed by the V5 NLGS were less frequently expanded during immunization than the B cells producing antibodies capable of bypassing the Loop D NLGS. As the conserved NLGS in Loop D (N276) is a major block in the engagement of germline VRC01-class BCRs by Env [31–33, 35, 47, 60, 61], our results suggest that the 426c NGLS-3 Core immunogen presents CD4bs epitopes more favorably as compared to the WT Env even in animals without VRC01-like naïve B cells. An alternative possibility is that the NLGS-3 Core mutations themselves are the cause of the development of the neutralizing antibody responses discussed here. As we did not immunize animals with a 426c Env that only lacked the 3 NLGS, we do not know the relative impact these NLGS or the variable regions had on the observed B cell lineage expansions observed with the 426c NLGS-3 Core immunogen.
Regardless, our results suggest that in animals with a polyclonal naïve BCR repertoire capable of producing VRC01-class B cells immunization with the 426c NGLS-3 core immunogen may lead to activation of VRC01-like naïve B cells.
Our underline hypothesis for the observations made here, is that the epitopes targeted by the neutralizing antibodies elicited by the NLGS-3 Core immunogen are occluded on the 426c WT Env and thus are not immunogenic. It is however possible that they equally immunogenic on the 426c WT Env, but because the immunogenicity of the variable domains is so high, it overwhelms the response to the neutralizing epitopes. We previously reported that non-neutralizing anti-CD4BS antibodies can prevent the uptake of Env by B cells expressing precursors of broadly neutralizing antibodies ([29]). The outcome of the competition between ‘on target’ and ‘off target’ B cells responses to Env depends on the relative frequencies of ‘on-target’ and of ‘off-target’ B cells (that are always going to be present at some level) and on the relative affinities of the ‘on-target’ and ‘off-target’ BCRs to their respective epitopes on the same immunogen ([62, 63]). Thus, immunogen-design approaches that aim at reducing the immunogenicity of non-neutralizing epitopes on Env immunogen and at increasing the affinity of BCRs for neutralizing epitopes are fully warranted.
All Env constructs are based on the HIV-1 clade C 426c Env (GenBank: KC769518.1). Mutations that disrupt N-linked glycosylation sites (NLGS) were introduced, individually and in combination, in Loop D (N276) and V5 (N460 and N463) to generate the following single, double and triple mutants: N276D (NLGS-1), N460D+N463D (NLGS-2), and N276D+N460D+N463D (NLGS-3) [31]. Deletions of the variable regions 1, 2, and 3 were also introduced on the NLGS-3 background (this construct is referred to as ‘NLGS-3 Core’ [46]. D368R/E370A mutations that knock-out the binding of many anti-CD4-binding site antibodies (CD4-binding site KO, CD4bs-KO) were also introduced on some of the above-mentioned constructs. CD4bs-KO reagents were employed during the B cell-sorting experiments (see below). Soluble gp140 or gp120 forms of these Envs were expressed from the pTT3 vector [29, 31].
Soluble recombinant gp120 envelopes produced by transient transfection of 293E/F suspension cells and purified using a size-exclusion chromatography AKTA purifier (GE, Fairfield, CT) as described previously [64, 65]. Avi-tagged versions of WT, NLGS-3 Core, or NLGS-3 Core with CD4bs-KO mutations Envs were biotinylated overnight using the BirA enzyme in vitro biotinylation kit (Avidity, Aurora, CO) with an excess of biotin. Excess biotin was removed via Amicon Ultra-4 centrifugal membrane filtration (EMD-Millipore, Billerica, MA, USA). Streptavidin-allophycocyanin (SA-APC) or streptavidin-allophycocyanin-Cy7 (SA-APC-Cy7) was conjugated to biotinylated proteins at an optimized ratio.
Two groups (four animals each) were immunized with either 426c WT (Animals IDs: A13279, A13280, A13281, and A13282) or 426c NLGS-3 Core (Animals IDs: A13283, A13284, A13285, and A13286) (gp140 forms) (S1 Fig). The latter construct expressed the I423M / N425K / G431E mutations that reduces binding to human and macaque CD4 [66]. At weeks 0 and 4, the animals were immunized with DNA vectors expressing the gp140 Env forms. 2mg DNA in 1mL endotoxin-free water was administered intradermally in 2 sites in the back (0.2mg each) and intramuscularly in 2 sites in the quads (0.8 mg each). Protein immunizations were administered with 20% Adjuplex at weeks 12 and 20. 0.1mg protein in 0.5mL 20% adjuvant mixture was administered intramuscularly in the deltoids. Blood was collected at weeks -4, -2, 1, 2, 5, 6, 12, 13, 14, 20, 21, and 22. Lymph nodes (axillary and/or inguinal) were collected at weeks -2, 13, 21, and 39, and bone marrow collected at week 22.
PBMCs, plasma, and bone marrow were purified from freshly-collected blood using density gradient centrifugation with Ficoll-Paque and SepMate columns (StemCell Technologies, Vancouver, BC, Canada) according to adapted manufacturer’s instructions. Isolated PBMCs were resuspended (20 x 106 cells/mL) in freezing media (90% heat-inactivated FBS, 10% DMSO), placed in Mr. Frosty containers (ThermoFisher Scientific, Waltham, MA), and stored at -80°C overnight before transfer to liquid nitrogen, where they were stored until further use. Plasma was aliquoted and stored at -80°C. Lymph nodes were sliced into grindable parts and cell strained using a 40μm strainer followed by a rinse with RPMI-1640 media, and then a rinse with 10mL PBS (Thermo Fisher Scientific, Waltham, MA). Isolated lymph node cells were resuspended (10 x 106 cells/mL) in freezing media (90% heat-inactivated FBS, 10% DMSO), placed in Mr. Frosty containers, and stored in -80°C overnight before transfer to liquid nitrogen, where they were stored until further use.
All NHP studies were conducted at the Washington National Primate Research Center at the University of Washington (Seattle, WA, USA). The study was reviewed and approved by the UW Institutional Animal Care and Use Committee, Office of Animal Welfare, University of Washington under Protocol Number: 3408–04 and Protocol Title: Optimizing HIV Immunogen-BCR Interactions for Vaccine Development. Housing and care procedures were within guidelines of the National Institutes of Health (NIH) (National Research Council, Guide for the Care and Use of Laboratory Animals, 8th edition) and in compliance with federal regulations relating to animal welfare. All efforts were made to minimize suffering. Details of animal welfare and steps taken to ameliorate suffering were in accordance with the recommendations of the Weatherall report, "The use of non-human primates in research". Rhesus macaques (Macaca Mulatta) of Indian origin, approximately 3 years old, were habituated to the housing conditions (> 4 weeks) before the initiation of the study. All procedures were conducted under anesthesia (10mg/kg ketamine HCL). Animals were individually housed in suspended stainless steel wire-bottomed cages and provided with a commercial primate diet. Fresh fruit was provided once daily and water was freely available at all times. A variety of environmental enrichment strategies were employed. The animals were not terminated at the conclusion of study, and were released back into the colony.
10–20 million PBMCs, lymph nodes (LNs), or bone marrow (BM) were thawed and resuspended in 12mL complete RPMI (2% Penn-strep, 10% heat-inactivated FBS), centrifuged at 1,400rpm for 5 min, then rinsed with FACS Buffer (2% heat-inactivated FBS in sterile PBS). The cells were resuspended in 1 – 2mL RBC lysis buffer (Sigma, St. Louis, MO) for 10min at RT to lyse red blood cells according to manufacturer’s instructions. 8 – 10mL of 1x PBS was used to rinse cells. The cells were resuspended in 50–100μL PBS, and then stained with 0.5–1μL of Live/Dead Fixable Aqua Dead Cell Stain according to manufacturer’s protocol (Invitrogen/Life Technologies, Grand Island, NY). Cells were then stained with NLGS-3 Core CD4bs-KO–APC-C7 (426c.NLGS-3.D368R.E370A.MKE.DV1/2/3-APC-Cy7) for 10min on ice in the dark, then with either WT- APC or NLGS-3 Core rEnv—APC (426c.WT-APC or 426c.NLGS-3.MKE.DV1/2/3-APC) for 10min. Cells were then stained with a master mix of CD3-FITC, clone SP34 (BD Biosciences, San Jose, CA), CD14-FITC, clone MφP9 (BD Biosciences, San Jose, CA), CD19-PE, clone J3-119 (Beckman Coulter, Brea, CA), and IgG PECF594, clone G18-145 (BD Biosciences, San Jose, CA). For compensation set-up, PBMCs collected prior to immunization were used. IgG-APC, clone G18-145 was utilized for the compensation control in the APC channel due to the minimal binding of the rEnv-APC to naïve rhesus macaque PBMCs (BD Biosciences, San Jose, CA). All samples were resuspended in 1 mL media (~1M cells /mL) and filtered through 70μm Flowmi strainers (Scienceware Bel-Art, Wayne, NJ). LD Aqua- / CD3- / CD14- / CD19+ / IgG+ / WT+ or NLGS-3 Core rEnv+ / NLGS-3 Core CD4bs-KO rEnv+/- cells were bulk sorted into 100μL complete IMDM media using a FACS Aria II cell sorter (BD Biosciences, San Jose, CA). Sorted rEnv-specific cells were plated on 3T3-msCD40L feeder cells (provided by Dr. J.R. Mascola, NIH/VRC, 3T3-msCD40L are NIH 3T3 mouse embryonic fibroblast cells engineered to express the CD40 ligand) at a final dilution of 1.4 B cells/well and cultured as previously described [67]. After 12 days, supernatants were collected for ELISA and neutralization (see below) testing and the cells were lysed with 30μL of RLT supplemented with β-ME/glycogen and frozen at -80°C. Prior to single cell sorting, negative and positive populations of WT rEnv+ / NLGS-3 Core rEnv+ and NLGS-3 Core CD4bs-KO rEnv+/-, cells were sorted into 100μL of complete IMDM media supplemented with 2% Penn-strep and 10% heat-inactivated FBS then plated at 1000 cells/well on 12-well plates with 3T3-msCD40L feeder cells with conditions described above. After 12 days, supernatants were removed and were tested for neutralizing activity (see below) and the cells were lysed with RLT supplemented with β-ME/glycogen and stored at -80C.
RNA recovery, cDNA synthesis, and PCR amplification were carried out as previously described [37, 68, 69] with a few minor modifications. 45μL of RLT lysis buffer supplemented with β-ME was added to previously lysed cells for a total volume of 75μL RLT, and RNA was column-purified with RNeasy Micro Kit (Qiagen, Venlo, Netherlands). 14μL of RNA was used directly in a 20μL cDNA synthesis reaction using the High-Capacity cDNA Reverse Transcription kit according to manufacturer’s instructions (Applied Biosystems / Life Technologies, Grand Island, NY). IGH, IGK, and IGL gene transcripts were then amplified independently from cDNA using first and second round primers followed by nested PCR previously published (S2–S4 Tables) [37, 69, 70]. First and second round PCR were done using Phusion High Fidelity DNA polymerase according to manufacturer’s protocol. The first round PCR included 2 mins at 94C followed by 50 cycles of 94°C 10s, 55°C 30s, 72°C 30s with a final extension at 72°C 5mins. 3μl of primary PCR product was used in the nested second round PCR, which included 2 min at 94°C followed by 50 cycles of 90°C 30 s, 72°C 30 s, 72°C 5 min, and cooling at 4°C 15min. PCR products were evaluated on 1.2% flash gels (Lonza, Rockland, ME) and band sizes at ~450–500 bp were purified via Qiaquick purification columns (Qiagen, Venlo, Netherlands) or via Agencourt AM Pure XP beads (Beckman Coulter, Brea, CA). A third and final PCR using Accuprime pfx (Life Technologies, Carlsbad, CA) was performed to add MiSeq adaptors that were used to prime direct amplicon sequencing. The PCR program was initiated with 5 min at 95°C followed by 10 cycles of 95°C 15 s, 55°C 30 s, 68° 30 s, 68°C 5 min, and cooling at 4°C 15min. Third round PCR product was sent for Sanger sequencing (Genewiz, Plainfield, NJ or SeattleBiomed, Seattle, WA). If sequencing reads were unclear, second round nested PCR products were TOPO cloned following manufacturer’s instructions (Life technologies, Carlsbad, CA) and then sent for Sanger sequencing. Paired heavy and light chain sequences were matched against both the human genes via IGMT/V-quest and against the rhesus macaque genes via a customized IgBlast database search using an IGH/IGK/IGL database created and described previously [28, 37, 69, 71–73]. Further analysis was conducted with Geneious Alignment software (cite PMID: 22543367). Antibody sequences can be found in Genbank database with accession numbers MF346735,MF346736, MF346737, MF346738, MF346739, MF346740, MF346741, MF346742, MF346743, MF346744, MF346745, MF346746, MF346747, MF346748, MF346749, MF346750, MF346751, MF346752, MF346753, MF346754, MF346755, MF346756, MF346757, MF346758.
RNA was recovered from 1,000–100,000 sorted B cells (CD19+ / IgG+ / CD27+/-) from PBMCs using the flow staining protocol described above. RNA was recovered using the RNeasy Micro Kit, as described above. All 14μL of recovered RNA were ran through a speed vacuum for 3 min then used directly in the 12μL cDNA synthesis reaction using the Superscript III First-Strand Synthesis SuperMix kit according to manufacturer’s instructions (Invitrogen / Life Technologies, Grand Island, NY). The RT program was initiated with a pre-warmed PCR for 5 min at 65°C followed by ice for 1 min and addition of enzyme mix, then by 1 cycle of 50°C 50 min, 25°C 10 min, 50°C 50 min, and termination at 85°C 5 min followed by chilling on ice. IGH, IGK, and IGL gene transcripts were amplified independently from cDNA using adaptor PCR described above. The adaptor PCR step was performed on 5μL diluted cDNA using Accuprime pfx according to manufacturer’s protocol (Life Technologies, Carlsbad, CA). Adaptor PCR product was purified via Agencourt AM Pure XP beads as described above for 35–50 cycles (Beckman Coulter, Brea, CA). The index PCR step was performed on 5μL cleaned adaptor round PCR product using the Nextera XT DNA Library Preparation kit (Illumina, San Diego, CA) with the Kapa HiFi DNA polymerase PCR program initiated at 3 min at 95C followed by 15–25 cycles of 98°C 20 s, 55°C 15 s, 72° 15 s, 72°C 1 min, and cooling at 4 C 15min (Kapa Biosystems, Wilmington, MA). Index PCR product band size of 600 – 650bp was confirmed on a gel and purified via Agencourt AM Pure XP beads as described above (Beckman Coulter, Brea, CA).
Each library was diluted to 10nM, quantitated with Qubit and Bioanalyzer, and ran on Illumina HiSeq 2500 (Illumina, San Diego, CA) at 2 x 300 with the v3 25M kit at the Genomics Core at the Fred Hutchison Cancer Research Center (FHCRC). Sequencing of multiple libraries (limit of up to 20 per chip) were performed during every sequencing run and a single library was never sequenced alone, thus the error PCR rate was the same for all libraries per chip. Additionally, during each sequencing run, an internal control was included to ensure the proper performance of the sequencer. Illumina data was processed, as previously described (PMID: 27525066). Briefly, raw data obtained from the forward and reverse MiSeq reads were merged to reconstruct the amplicon with FLASH (ver. 1.2.11) (PMID: 21903629). The resulting amplicon sets were filtered to select only sequences containing the amplification primers (a procedure during which the primer sequences themselves were removed) using cutadapt (ver. 1.14); amplicons containing low-confidence base calls (N’s) were then removed from the set, and deduplicated using FASTX-toolkit (ver. 0.0.14) (MARTIN, Marcel. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, [S.l.], v. 17, n. 1, p. pp. 10–12, may. 2011. ISSN 2226-6089). Samples were annotated using a local IgBLAST (ver. 1.6.1) [74] installation equipped with a custom database of previously published rhesus macaque gene segments (described here [28]. The resulting annotated datasets contained assignments for the most likely matches to the database V/D/J segments and identified the CDR3 sequences for each processed amplicon. Amplicons representing productively-rearranged immunoglobulin sequences were then clustered based on three parameters: 1) V-family assignment, 2) J-family assignment, 3) CDR3 amino acid sequence. Amplicons that shared the CDR3 amino acid sequence but disagreed on the V- or J-family assignment were labeled as "chimera" and filtered out from further analysis. Only clusters containing 5 or more members were considered in further analysis. Cluster V-gene segment assignment was made based on the most abundant assignment found within each cluster; these assignments were then compiled into a gene counts table for each dataset for subsequent analysis with Bioconductor R package edgeR (ver. 3.16.5) [75]. Data sets representing WT and NLGS3-core immunizations were analyzed separately, and log fold-change (logFC) in enrichment for each gene segment as well as the associated false discovery rate (FDR) were used to identify changes associated with the immunization regimens. Structures of clustered sequence populations were analyzed using the R-package alakazam (PMID: 26069265) by calculating the values for the general Hill diversity index (encompassing representations of Shannon’s entropy, evenness, etc.).
ELISA assays were carried out using the following antigens: 426c. WT gp140, 426c.NLGS-1 gp140, 426c.NLGS-2 gp140, 426c.NLGS-3 gp140, 426c.NLGS-3.MKE Core gp140, 426c.NLGS-3.MKE CD4bs-KO gp140, 426c.WT.MKE gp120, 426c.NLGS-3.MKE gp120, and HXB2 gp41. 50ng of rEnv was adsorbed onto each well of 96-well or 384-well MaxiSorp ELISA plates (Sigma, St. Louis, MO) overnight in 0.1M NaHCO3, pH 9.4–9.6 at RT. Plates were blocked using dilution buffer, a solution of Millipore H2O, 1X PBS, 10% non-fat dry milk, and 0.03% Tween-20 (Sigma, St. Louis, MO) for 1 h at 37°C. All supernatant from sorted single B cells growing on feeder cells was diluted 1:5 and 1:10 in complete RPMI and incubated for 1 h at 37°C. Serum was diluted 1:10 in dilution buffer and serially titrated 3-fold. Antibodies were diluted to 100μg/mL in dilution buffer and titrated 3-fold. A 3-fold titration of germline and mature VRCO1 was performed as a positive control. Bound antibodies were detected at 37°C for 1h with goat-anti-human-IgG (H+L) HRP conjugate (Invitrogen, Grand Island, NY), diluted 1:6,000 with dilution buffer. Plates were developed with 30μl (384-well) or 50μl (96-well) SureBlue Reserve TMB Microwell Peroxidase Substrate (KPL, Gaithersburg, MD), stopped with an equal amount of 1N H2SO4. Absorption at 450nm was read on a Spectramax spectrophotometer (Molecular Devices, Sunnyvale, CA). ELISA positive wells were above background of 0.2nm at a 1:5 dilution. Assays were performed in triplicate.
Paired IGH and IGL/IGK sequences from isolated single B cells were fabricated into gBlocks (IDT, Coralville, IA) containing flanking regions with corresponding enzyme sites for vector-ligation. gBlocks were digested with the appropriate restriction enzymes EcoRI, NheI (γ), Bsi-WI (κ), XhoI (λ) (NEB, Ipswich, MA) and purified with the Qiagen PCR Purification Kit (Qiagen, Venlo, Netherlands). Ligation was performed for 1–2 hours in a total volume of 10–20μL with 4U T4 DNA Ligase (Invitrogen/Life Technologies, Grand Island, NY), 15ng digested and purified DNA, and 70ng linearized pt1-732 gL γ, pt1-695 κ, pt1-341 λ vectors. Chemically competent DH5α T1 E. coli cells were transformed with 2.5μL of ligation product. Single colonies were expanded for 16h at 37°C in 4mL LB Broth containing 1 μg/mL ampicillin. Plasmid DNA was purified via Qiagen Qiaprep Mini-Prep kit (Qiagen, Venlo, Netherlands) and eluted with 30μL EB Buffer. Plasmid DNA was quantified using a NanoDrop spectrometer (Thermo Fisher Scientific, Waltham, MA) and sequenced by the Sanger sequencing method (Genewiz, South Plainfield, NJ) using a pTT3 5’ forward primer. Plasmids containing confirmed sequences were expanded for 12h at 37°C in 100mL LB Broth containing 1μg/mL ampicillin. Plasmid DNA was purified with a HiSpeed Plasmid Maxi Kit (Qiagen, Venlo, Netherlands), eluted with 50μL TE Buffer, and filtered with a 0.2μM filter before co-transfection.
IgGs were produced by transient co-transfection of two plasmids: one expressing the IGH and the other the IGL/IGK. Briefly, 12.5μg of IGH plasmid DNA and 12.5μg of IGL/IGK plasmid DNA were incubated at RT for 15 m with 293F transfection reagent (EMD Millipore, Temecula, CA) in 1x PBS prior to addition to 293F cells (HEK-293F suspension cells from the American Type Culture Collection (ATCC)), these are a variation of human embryonic kidney cells derived from an unknown original patient)) at 1 million cells/mL in 50mL of FreeStyle 293 Expression Media (Life Technologies, Grand Island, NY). After 4–6 days incubation, the cell supernatants were centrifuged at 6,000rpm for 10min and the clarified supernatant was filtered through a 0.2μM filter (Millipore, Billerica, MA) before loading onto a pre-rinsed Protein-A/G agarose resin column (Thermo Fisher Scientific, Waltham, MA). After washing the agarose beads with 10x column volumes of 1x PBS, IgG was eluted from the column with 0.1 M citric acid (pH 3) in 1mL fractions into tubes containing 100μL 1M Tris-HCl (pH 9). Fractions with high IgG content were pooled and buffer exchanged into 1x PBS using Amicon Ultra-4 centrifugal units with 30kDa membrane cutoffs (Millipore, Billerica, MA). IgG concentrations were determined using a NanoDrop spectrometer (Thermo Fisher Scientific, Waltham, MA) and antibody size confirmed via SDS-PAGE/Western Blot expression.
BLI was performed on purified biotinylated IgG using an Octet Red instrument (ForteBio, Inc., Menlo Park, CA). Antibodies were biotinylated in water using the EZ-Link (NHS-PEG4-Biotin) Kit according to manufacturer’s instructions (Thermo Fisher Scientific, Waltham, MA). Biotinylated antibodies were buffer exchanged with 1x PBS and purified via Amicon Ultra-4 centrifugal units with 30kDa membrane cutoffs (Millipore, Billerica, MA). IgG concentrations were determined using a NanoDrop spectrometer (Thermo Fisher Scientific, Waltham, MA). For these assays, gp140 trimeric Env forms were resuspended at 80nM in 1x Kinetics Buffer. Streptavidin (SA) biosensors (ForteBio, Inc., Menlo Park, CA) were activated by immersion into 1x Kinetics Buffer (1x PBS, 0.1% BSA, 0.02% Tween-20, 0.005% NaN3) for 10m. Biotinylated IgGs (at 10μg/mL in 1x KB) were immobilized on SA biosensors for 300s, and then biosensors were re-immersed in 1x Kinetics Buffer for 60s to establish a ‘baseline’. Biosensors were then immersed into wells containing NLGS-3 Core rEnv gp140 or NLGS-3 Core rEnv gp140 previously incubated with saturating concentrations (160nM in 1x Kinetics Buffer) of mature VRC01, mature b12, or CD4-IgG [(CD4-IgG obtained through the National Institutes of Health (NIH) AIDS Research and Reference Reagent Program, Division of AIDS, National Institute of Allergy and Infectious Diseases, NIH (cat. no. 11780; contributors: Progenics Pharmaceuticals, Inc)]. After an association phase of 300s, SA biosensors were re-immersed into wells containing only 1x Kinetics Buffer for dissociation for 600s. Binding shift (nm) was determined by alignment to baseline, interstep correction to dissociation, and final processing with Savitsky-Golay Filtering.
Heat-inactivated serum from immunized animals, supernatants from Env-specific sorted B cells (see above), or monoclonal antibodies (MAbs), were tested for neutralizing activity using the TZM-bl (also known as JC53BL-13 cells, a Henrietta Lacks (HeLa) cell clone engineered to be CXCR4-positive was obtained from the Center for AIDS Reagents (CFAR)) cell line-based neutralization assay, as previously described [31, 76]. Briefly, MAbs (starting concentration 50μg/mL or 200μg/mL), B cell supernatant (starting at 1:2 dilution), or sera (starting at 1:10) were serially diluted 3-fold for at a final volume of 30μL. 30μL of pseudovirus, previously determined to result in ~2 x 105 luciferase units per well, was added to each well for 90 min at 37°C. 50μL of the pre-incubated mixture was added to TZM-bl cells, previously incubated with polybrene for 30min at 37°C. 72 hours later, the media were removed and Steady-Glo Luciferase reagent (Promega, Madison, WI) was added.
All assays were performed in duplicate or triplicate as indicated. Significant difference were assessed by ANOVA via Prism v6 software. Bioinformatics analysis significant differences were calculated in various R packages as indicated above. Respective p-values and FDR values are indicated in figure legends.
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10.1371/journal.pgen.1000056 | Small RNA-Directed Epigenetic Natural Variation in Arabidopsis thaliana | Progress in epigenetics has revealed mechanisms that can heritably regulate gene function independent of genetic alterations. Nevertheless, little is known about the role of epigenetics in evolution. This is due in part to scant data on epigenetic variation among natural populations. In plants, small interfering RNA (siRNA) is involved in both the initiation and maintenance of gene silencing by directing DNA methylation and/or histone methylation. Here, we report that, in the model plant Arabidopsis thaliana, a cluster of ∼24 nt siRNAs found at high levels in the ecotype Landsberg erecta (Ler) could direct DNA methylation and heterochromatinization at a hAT element adjacent to the promoter of FLOWERING LOCUS C (FLC), a major repressor of flowering, whereas the same hAT element in ecotype Columbia (Col) with almost identical DNA sequence, generates a set of low abundance siRNAs that do not direct these activities. We have called this hAT element MPF for Methylated region near Promoter of FLC, although de novo methylation triggered by an inverted repeat transgene at this region in Col does not alter its FLC expression. DNA methylation of the Ler allele MPF is dependent on genes in known silencing pathways, and such methylation is transmissible to Col by genetic crosses, although with varying degrees of penetrance. A genome-wide comparison of Ler and Col small RNAs identified at least 68 loci matched by a significant level of ∼24 nt siRNAs present specifically in Ler but not Col, where nearly half of the loci are related to repeat or TE sequences. Methylation analysis revealed that 88% of the examined loci (37 out of 42) were specifically methylated in Ler but not Col, suggesting that small RNA can direct epigenetic differences between two closely related Arabidopsis ecotypes.
| Phenotypic variation has been mainly attributed to their differences in genetic materials, i.e., the DNA sequence. The advances in Epigenetics in past decades has revealed it as a fundamental mechanism that could inheritably influence gene function without change in DNA sequence, but by modulating chemical modifications on DNA itself (methylation), or on histone proteins, which package the DNA further into nucleosome. Nevertheless, the roles of epigenetic regulation in natural variation were not explored much because of the limitation in high-throughput analytical tools. A recent study in model plant Arabidopsis showed that there are many DNA methylation polymorphisms between the two ecotypes. In plant, a subset of RNA named small interfering RNA (siRNA), is capable of triggering the epigenetic modifications on DNA or histone at their target region with complementary nucleotide sequences. Here, we took a view from the small RNA side and by applying molecular and bioinformatic approaches we showed that the same region could be led to a different epigenetic status because of the difference in their corresponding small RNA abundance and between the two closely related Arabidopsis ecotypes, suggesting that there could be small RNA-directed epigenetic differences among natural populations.
| Epigenetics, defined as the study of heritable alteration in gene expression without changes in DNA sequence, has greatly expanded our understanding of inheritance [1]. A recent study of DNA methylation by tiling array analysis of Arabidopsis Chromosome 4 in Col and Ler showed that although transposable elements (TEs) are often methylated, the methylation in the transcribed regions of genes is highly polymorphic between these two ecotypes [2]. Although epigenetic differences could potentially contribute to evolution [3]–[5], studies of evolution and natural variation have still been focused mainly on sequence variation, and little is known about the role of epigenetic machinery in these processes. This is primarily due to the lack of evidence for epigenetic natural variation between populations.
Small interfering RNAs (siRNAs), as a key player in the epigenetic machinery, have been well documented for their general role in gene silencing at both the transcriptional and post-transcriptional levels [6],[7]. In Arabidopsis, ∼24 nt siRNAs can direct DNA methylation (RNA-directed DNA methylation, RdDM) and chromatin remodeling at their target loci [8]. In the RdDM process, ∼24 nt siRNAs are incorporated into ARGONAUTE 4 (AGO4)-containing complexes and further guide the DOMAINS REARRANGED METHYLTRANSFERASE 2 (DRM2) to de novo methylate their target DNA [9],[10]; once established, the non-CG methylation could be maintained by DRM2 and/or CHROMOMETHYLASE 3 (CMT3) in a locus-specific manner, and the CG methylation by METHYLTRANSFERASE 1 (MET1) [11]. Recent advances in high-throughput sequencing techniques have enabled the thorough exploration of the small RNAs populations [12]–[16]. Therefore, together with the complete genome sequence, we are able to directly examine whether there are regions specifically matched by siRNAs that differ among ecotypes, a situation that could lead to epigenetic natural variation.
FLC, a MADS box transcription factor, is a major repressor of the transition to flowering in Arabidopsis, and many genes coordinately function in flowering time control by regulating the amount of FLC transcript [17]. In addition, allelic variation at FLC, both genetic [18]–[21] and epigenetic [22],[23], contributes to the differences in flowering time and vernalization response among accessions, which makes FLC a classic locus for the study of natural variation in Arabidopsis. Previous studies have shown that in Ler, a 1224 base pair (bp) nonautonomous Mutator-like transposable element (TE) inserted in the first intron of FLC (FLC-TE-Ler) [19] was methylated and heterochromatic under the direction of ∼24 nt siRNAs generated by homologous TEs, and mutation of HUA ENHANCER 1 (HEN1) in Ler (hen1-1), a key component in small RNA biogenesis [7], released the transcriptional silencing of FLC-Ler [22].
In this study, we discovered a cluster of ∼24 nt siRNAs that are present at high levels in the ecotype Ler and that could direct DNA methylation and heterochromatinization adjacent to FLC promoter [24]. However siRNAs matching to the same region in Col are of low abundance and cannot direct DNA methylation. Furthermore, from comparisons between Ler and Col of small RNA data produced by high-throughput sequencing, we identified at least 68 loci that are matched by significant levels of ∼24 nt siRNAs, and 88% are methylated in Ler but not Col from a set of 42 loci that were examined.. Although siRNA clusters are often heavily methylated [25] and a large proportion of the methylation polymorphisms between Col and Ler are not associated with small RNAs [2], our data reveal that there could still be considerable small RNA-directed epigenetic natural variation between two ecotypes of Arabidopsis.
In addition to the previously described Mutator-like transposable element (TE) inserted in the first intron of FLC [19] in Ler, we found that a region located adjacent to the promoter of the FLC was specifically methylated in Ler but not in Col (Figure 1A). We named this region MPF (Methylated region near Promoter of FLC). Restriction enzymes including AciI, HpyCH4 IV and Fnu4HI, which are sensitive to CpG methylation, were able to cut outside of the MPF but not within this region in Ler (Figure 1). Notably different from the TE inserted in FLC-Ler, the MPF of Ler and Col share almost identical sequences (Figure S1). Bisulfite sequencing of MPF (B1 region, Figure 2A) revealed that a small region of less than 100 bp was exhibited a very high level of asymmetric methylation (also called CHH methylation, where H represents A, C or T) (Figure 2C). This region also demonstrated extensive CpG and CNG (where N is any nucleotide) methylation (Figure 2C). In addition, no DNA methylation was found outside the MPF (the B2 and B3 regions, Figure 2A) in Ler (data not shown) or the MPF in Col (Figure 3A) by bisulfite sequencing.
Since asymmetric methylation is the hallmark of RdDM [26], we decided to verify whether there are corresponding siRNAs matching to this methylated region in Ler. Because no methylation was found at the MPF in Col, we speculated that there would be no small RNAs matching to this region. However, four 17 nt tags with very low abundances (approximately two transcripts per quarter-million, TPQ) were found in the Col-derived small RNA massively parallel signature sequencing (MPSS) datasets [12]. These small RNAs precisely matched both strands of the highly asymmetrically methylated region within MPF (Figure 2B). We performed a small RNA Northern blot hybridization to verify these small RNA in Col and Ler. By using an LNA (locked nucleic acid) modified oligonucleotide probe (Figure 2B) and a large amounts of RNA enriched for small RNAs (see materials and method for more details), we found that siRNAs complementary to this probe (MPF-siRNAs) were more abundant in Ler than in Col (Figure 2D). Published high-throughput small RNA 454 sequencing datasets from Ler [15] confirmed our RNA gel blot results. In those data, six unique 23 to 24 nt small RNAs were found matching to a region of <50 bp at the MPF, in exactly the same region as the Col-derived MPF-siRNAs (Figure 2B). Analyses of additional Col-derived 454 small RNA data [16],[27] didn't identify any MPF-matching small RNAs, possibly due to lower sequencing depth compared to that of the MPSS data. We performed chromatin immunoprecipitation (ChIP) experiments and demonstrated that the MPF in Ler was enriched in H3K9me2, a characteristic of heterochromatin, in comparison to Col (Figure 2E). These data suggest that the high levels of MPF-siRNAs in Ler could trigger DNA methylation and heterochromatinization at MPF whereas the lower levels in Col might not be sufficient.
Next, we investigated methylation at the MPF using silencing pathway mutants in either a Ler background or in lines that had been backcrossed to Ler to have the homozygote FLC-Ler allele. These mutants included hen1-1, cmt3-7, ago4-1, kryptonite-2 (kyp, a histone H3K9 methyltransferase, also known as SUVH4, can affect the DNA methylation at some loci[28]–[30], and drm2 5×Ler (homozygous drm2 backcrossed five times to Ler). Methylation at MPF was sensitive to the deficiency in the RdDM machinery: all mutants tested, with the exception of kyp-2, completely relieved methylation in all three sequence contexts at MPF (Figure 3A and Figure S2A). Although KYP has been reported to control CNG methylation together with CMT3 [26],[30], the methylation at MPF was independent of its function, perhaps because MPF at several hundred base pairs is too small for KYP to maintain the positive feed back between DNA methylation and chromatin modification [30]. Alternatively, in addition to KYP, the heterochromatic feature of this region might be redundantly controlled by other two histone H3K9 methyltransferases, SUVH5 and SUVH6 [31]. In addition, methylation of the nearby TE insertion (Figure 3B and Figure S2C) was also sensitive to ago4-1 and hen1-1 (Figure 3B). However, none of these mutants released all DNA methylation at AtSN1, a retroelement which also undergoes RdDM [26] (Figure 3C). Moreover, AGO4 complementation [15] could not restore DNA methylation at the MPF in ago4-1 (data not shown). This situation resembles the FWA locus whose methylation, once lost in ddm1(decrease in DNA methylation 1) mutant, is not recovered again even in the presence of wild type DDM1 [32]. The MPF in hen1-4, a strong hen1 allele in the Col background, had an identical methylation pattern to Col (Figure 1). Also, the identical methylation pattern of the miRNA deficient mutant dcl1-9 [7] to Ler at MPF (Figure S2B) ruled out the possibility that the restricted methylation at MPF is directed by miRNAs [33]. These observations were substantially different from prior analyses of silenced loci, at which DNA methylation was often affected in certain but never all sequence contexts by mutants in the RdDM pathway [26].
Since MPF is methylated and it is near to the TE insertion in FLC-Ler, it was of interest to investigate whether the methylation at MPF is induced by the TE. We examined the methylation status of MPF in several accessions that are also reported to contain transposable elements inserted in the first intron of FLC (Figure S3A) [19],[20]. These were tested by McrBC-PCR [34] (for Bd-0, JI-1, Stw-0, Kin-0 (CS1273), and Gr-3) and bisulfite sequencing (for Da(1)-12). Although the MPF is methylated in Bd-0, JI-1 and Kin-0 (CS1273), it remains unmethylated in Stw-0, Gr-3 and Da(1)-12 (Figure S3B, and data not shown for Da(1)-12) indicating that the TE insertions nearby are dispensable for the methylation at MPF.
A previous study using 27 Arabidopsis accessions showed that the FLC-TE in Ler was also detected in Dijon-G and Di-2 (Figure S3A) but was absent in the closely related Landsberg-0 or Di-1 [18]. McrBC-PCR analysis showed that MPF is methylated in all four of these accessions, even in those without the FLC-TE insertion (Figure S3C), which further confirmed that the methylation at MPF is independent of the TE insertion nearby.
To study the origin of the MPF-siRNAs, we found that a 220 bp sequence at MPF is absent in one Kin-0 accession (CS6755, different from the Kin-0 (CS1273) accession mentioned above that contains a methylated MPF). Further analysis revealed that this difference is caused by the insertion of a non-autonomous hAT element [35] with the typical 8 bp TSD (target site duplication) and short terminal inverted repeats (TIRs) (Figure 4 and Figure S1). However, MPF-siRNAs in Ler are probably not derived from other hAT elements because those MPF-siRNAs with the full length information from 454 sequencing in Ler [15] have only one match (at MPF) in the genome; also, genomic Southern blot hybridization revealed that Ler do not contains extra copy of this hAT element comparing to Col (Figure S4). Therefore, the MPF-siRNAs are probably generated from MPF itself.
In paramutation, the silenced paramutagenic lines are able to confer the active state of the paramutable lines, and make them become paramutagenic [36]. To test whether the methylated state at MPF in Ler is transmissible, we performed bisulfite sequencing to investigate the DNA methylation status in four F1 lines from the crosses of both Col ♀×Ler ♂ and Ler ♀×Col ♂, with the single nucleotide polymorphisms (SNPs) at MPF (Figure S1) used to distinguish the Col and Ler derived sequencing results (Figure 5A). In addition, twenty-four more lines from reciprocal crosses were tested for their MPF methylation by real-time McrBC-PCR (Figure 5B). These experiments revealed extensive diversity in the methylation status of MPF in each individual line in the F1 generation. This diversity could be summarized in the following way: 1) in some lines, the MPF-siRNAs from Ler are able to trigger the de novo methylation at Col-derived MPF; 2) in some other lines, not only the Col-derived MPF remains unmethylated, the Ler-derived MPF could even lose its methylation; 3) there are also cases in which the Ler-derived MPF remains methylated and Col-derived MPF remains unmethylated, just like their ancestors; therefore the MPF is semi-methylated in the whole plant.
The 1.2 kb FLC-TE, when inserted into a Col FLC genomic construct, is sufficient to cause reduced expression of FLC in the transgenic lines [19], therefore, it is unclear whether the MPF has any functional relevance in FLC expression. Interestingly, FLC-Ler could strongly suppress the late flowering phenotype induced by FRIGIDA (FRI) and luminidependens (ld), but remains moderately sensitive to other mutants that up-regulate FLC like fca, fve, and fpa [37]. Recently, SUPPRESSOR OF FRI4 (SUF4) has been shown to bind to the promoter of FLC and directly interact with FRI and LD [38]. Moreover, FLC-Ler is again sensitive to FRI in a hen1-1 background [22] suggesting reversible epigenetic alteration might account for this weak response.
To address the role of the epigenetic variation at MPF in flowering time control, we used an RNAi approach to artificially methylate MPF in Col, the ecotype in which MPF is originally unmethylated. All transgenic plants used for further analyses had been tested for their successful de novo methylation at MPF by McrBC PCR (data not shown). Both flowering time and FLC expression analysis showed that de novo methylation at MPF does not alter the flowering behavior of wild type Col (Figure S5). However, since Col is an early flowering ecotype and its FLC expression level is relative low, we can not rule not the possibility that MPF may play a more prominent role in some late flowering backgrounds with higher FLC levels, like FRI or ld.
The identification of MPF-siRNAs in Ler- but not Col-derived small RNA data made us wonder whether other loci are differentially and specifically matched by ∼24 nt siRNAs in these ecotypes. Because the MPSS small RNA sequencing data are not readily comparable with the 454 data (due to length differences in the sequencing reads), the small RNA datasets we used for a genome-wide identification are all 454 sequencing data, derived from two recent studies: 247,318 unique small RNA sequences from Col [16]and 25,981 unique small RNA sequences from Ler [15]. Also, to balance the enrichment of longer siRNAs in the sequencing results of AGO4 precipitated pool from Ler [15], we only selected for further analyses the siRNA reads of length no less than 23 nt, hence most of the miRNAs and short sRNAs are discarded from both the Col and Ler datasets. Since only the Col genome sequence is complete and the number of sequenced Col derived siRNAs is much greater than that of Ler, in this study, we only analyzed the regions matched by clusters of siRNAs present specifically in Ler, to exclude the interference of genetic alteration and also for higher reliability (please see materials and methods for details about the bioinformatic analysis). The unique siRNA sequences over 23 nt from both Col and Ler were mapped to the genome, respectively, and hits were counted in windows of 100 bp. Although the majority of the ∼24 nt small RNA clusters are conserved between Col and Ler (data not shown), after combining the overlapping regions, 68 unique loci were identified (including the MPF, locus #57; Table S1). These all shared the characteristic that they were matched by at least three distinct siRNAs within 300 bp in Ler but there were no hits in 1500 bp around the same region in Col (see Figure 6 for an example). Most of these loci are MPF-like, in that the siRNA matches are restricted to a small region (Figure S6), and their distribution in the genome is quite dispersed (Figure S7). Twenty-two loci are within known genes, and the other 46 are in intergenic regions (Table S2). An search of methylation data in Col (http://signal.salk.edu/cgi-bin/methylome) [25] demonstrated that all of these loci except locus #60 (located in a highly methylated region longer than several hundred kb, Table S1) were clearly lacking methylation; in addition, 28 loci contain repeat-associated sequences with one end beginning close to or within the small RNA matching region, and 15 loci had matching MPSS small RNA tags [12] (Table S1). We had also searched the website of DNA methylation information on the fourth chromosome in both Ler and Col background (http://chromatin.cshl.edu/cgi-bin/gbrowse/epivariation/) [2]. For the 13 loci (#44∼56) we identified on the fourth chromosome, six loci are found with methylation signals in their data: five loci (#46, 49, 52, 54, 55) are found specifically methylated in Ler as expected; one locus (#53) is methylated in both ecotypes but with a much higher methylation signal in Ler comparing to Col. Overall, our results are well supported by the two independent studies on epigenomics and epigenetic natural variation [2],[25].
We investigated the methylation pattern of locus #10 as an example using bisulfite sequencing. Extensive methylation was found in Ler (Figure S8), whereas the same region in Col remained unmethylated (data not shown). Other eight randomly selected loci were tested using methylation sensitive McrBC-PCR, and all of them, even those with the minimal number of three unique siRNAs, were methylated in Ler but not Col (Figure S9). Furthermore, we tested the methylation status of 44 loci (in which 42 have successful amplification results), including all the loci on Chromosome I and II,, by real-time McrBC-PCR (Figure 7A). From these analyses, 88% of the loci (37 out of 42) were found to be specifically methylated in Ler but not Col, and no locus was found only methylated in Col, strongly supporting the role of ∼24 nt siRNA in triggering epigenetic natural variation (Figure 7B).
For the features of these 68 loci showing evidence of small RNA-directed variation in DNA methylation, we looked at the genes either corresponding to or adjacent to these loci within less than 1 kb distance of flanking sequence. Among the 64 genes identified (some intergenic loci did not have flanking genes within 1 kb upstream and downstream), 22 genes were found matched by genic siRNA clusters; 18 genes contained siRNA clusters in their 5′ region and 24 genes with clusters in 3′ regions (Table S2). Among the 22 genic regions, six were transposable elements, consistent with the role of transposable element in epigenetic regulation [39]. Moreover, many of these genes are reported or predicted to have important functions (Table S2). Therefore, additional investigation of these genes may help us to understand the role of epigenetic alteration in evolution and natural variation.
Natural variation is a fundamental aspect of biology, and the implications of natural variation for deciphering the genetics of complex agricultural traits have been widely used. Recent progress in epigenetics has revealed mechanisms that can heritably regulate gene function without alteration of primary nucleotide sequences. Although the importance of epigenetic natural variation have become more and more noticed [3],[5], the role of epigenetic regulation in evolution has been less well studied due in part to limitations in the techniques used for the investigation of epigenetic variation among natural populations. Recently, substantial improvements in high-throughput analysis approaches have made it possible for the effective detection of variation in DNA methylation, histone modifications and small RNA abundances [2], [12]–[16],[25],[40]. Small RNAs that can target DNA methylation and chromatin modifications have been proposed as a potential source in inherited epigenetic differences [3], and the latest techniques offer rapid and relatively inexpensive means for the profiling of small RNAs. In this study, we discovered that a hAT element adjacent to the promoter of FLC, which we named MPF, is methylated and heterochromatic in Ler but not Col because of their differences in the abundance of corresponding siRNAs. Furthermore, by comparisons between Ler and Col of publicly available small RNA data produced by high-throughput sequencing [15],[16], we identified at least 68 loci that are matched by significant levels of ∼24 nt siRNAs, and 88% examined loci are methylated specifically in Ler but not Col. Our data reveal that there could be a considerable amount of small RNA-directed epigenetic natural variation between two ecotypes of Arabidopsis.
Although we identified dozens of loci, this analysis is still far from saturating. A Sadhu element (At2g10410), which was reported to be epigenetically silenced in Ler and other 18 strains but highly expressed in Col, did not show up among the 68 loci [41]; although bisulfite sequencing revealed that this element contains CNG and asymmetric methylation in Ler, which is presumably siRNA-directed to some extent [41]. Furthermore, hundreds of additional loci with one or two hits specifically in Ler (data not shown) may also be silent; these may be better characterized when additional Ler small RNA and genome sequence data become available.
Two examples of siRNA-associated, naturally-occurring epigenetic variation have been well studied in plants, including the phosphoribosylanthranilate isomerase (PAI) gene family in Arabidopsis and paramutation in maize [36]. In some Arabidopsis ecotypes, two PAI genes form an inverted repeats that may generate siRNAs and silence related members in the same gene family [42]. Paramutation, the allele-dependent transfer of heritable silencing state from one allele to another [36], is associated with another type of repeats, the tandem repeats. MEDIATOR OF PARAMUTATION 1 (MOP1) [43], whose deficiency disrupts paramutation, is an ortholog of the Arabidopsis RDR2 (RNA Dependent RNA polymerase 2), an essential component of RNAi machinery [6]. Notably, epigenetic variation at the MPF is quite different from these two cases: first, neither inverted- nor tandem-repeats features were found at MPF or elsewhere in the genome with similar sequence; second, the level of MPF-siRNAs is high in Ler and low in Col, instead of all-or-none; third, the restricted location of MPF-siRNAs is markedly different from the dispersed distribution of siRNAs from most inverted or tandem repeats [12].
Although paramutation phenomenon had been well documented, the details of how the silencing signal is transmitted from one allele to the other in the F1 heterozygote are still less understood. In our study, the diverse methylation status among individuals in F1 generation of the reciprocal crosses from Col×Ler indicate that there might be a reprogramming stage shortly after fertilization, in which the DNA or chromatin are open to modifiers like the MPF-siRNA containing RISC (RNA induced silencing complex) from Ler. However, this open stage must be very short, and when it is over, the epigenetic state, no matter active or silenced, will be maintained in the following developmental processes, so that the unmethylated state of Col-derived MPF and the methylated state of Ler-derived MPF could well maintained in Ler ♀×Col ♂line #2 (Figure S5A).
Thus far, the function of ∼24 nt siRNAs in plants has mainly been ascribed a role in silencing transposable elements and repeat-associated sequences [39]. Thus, it is unclear how Ler and Col, both with the functional RNAi machinery, might acquire many siRNA-directed epigenetically variable loci. One characteristic of MPF-siRNAs, their very restricted location (all matching to a region less than 50 bp), may confer on them more flexibility than other, larger silent loci.
Genetic variability (due to insertion, deletion and point mutation) occurs stochastically, at very low frequency, primarily irreversibly and is often recessive. In contrast, heritable epigenetic variability may be more appropriate to regulate, rather than disrupt or create, gene function, and thus may be an ideal or more dynamic force for evolutionary change of gene regulation.
The Bd-0 (CS962), JI-1 (CS1248), Stw-0 (CS1538), Gr-3 (CS1202), Kin-0 (CS1273, CS6755), Da(1)-12 (CS917), Dijon-G (CS910), Di-1 (CS1108), Di-2 (CS1110), and La-0 (CS1299) accessions of Arabidopsis were acquired from ABRC; hen1-1 (Ler background), hen1-4 (Col background), and dcl1-9 mutants were described before [22]; cmt3-7, kyp-2, ago4-1, and drm2 5×Ler were generous gifts from Steve Jacobsen at UCLA. The AGO4 complementation lines were kindly provided by Gregory J. Hannon at CSHL and Yijun Qi at NIBS.
RNAs were extracted from 20-day-old, soil-grown plants. 32P end-labeled LNA probe was used for hybridization. Total RNAs were extracted using Trizol solution (Invitrogen) from 20-d-old soil-grown plants and dissolved in RNase free water. Small sized RNAs were enriched by adding the same volume of 8M LiCl and centrifuging at 12,000rpm for 30 min at 4°C. RNA filter hybridizations were carried out as previously described [44]. LNA probe [45] was used for hybridization (5′- cgagcAgtGgcGgatCcaaga-3′; uppercases represent modified nucleotides).
The ChIP assays were performed using 20-d-old soil-grown plants and as previously described [46]. Antibodies against H3K9me1 (07-450), H3K9me2 (07-441) and H3K9me3 (07-442) were from Upstate Biotechnology.
The genomic DNA from Col was used as a template for PCR amplification using the primer pairs (CX2004: ctcgagATTTTTGTGGTAATATATATATA and CX2005: agatctACATCAATCCAAGTTCAAGC, carrying the XhoI and BglII sites, respectively). The PCR products were sequentially inserted into pUCC-RNAi vector using the XhoI/BglII and BamHI/SalI sites for both the sense and antisense orientations. The stem-loop structured fragment was cut off and further cloned into a modified pCambia1302 vector (pCambia1302-LX-1) and used for plant transformation (XF718). All transgenic plants used for further analyses had been tested for their successful de novo methylation at MPF.
Genomic DNA was isolated from rosette leaves of 4-week-old, soil-grown plants. Southern blots was performed as previously described [22] using PCR products amplified from FLC promoter as the probe (Figure 1). Bisulfite sequencing experiments were performed as previously described [47]. Primers with one end in FLC-TE and the other in FLC were designed to specifically amplify the FLC-TE and exclude other TEs in the genome. Only the cytosines within TE were counted for methylation analysis of FLC-TE in Figure 3. McrBC-PCR experiments were performed as previously described [34],[47], Equal amounts of McrBC-digested and non-digested DNA were used for PCR amplification. Real-time McrBC-PCR was performed to quantitatively measure the methylation level. The primer information for these experiments could be found in Supporting Information (Text S1).
After discarding smaller (<23 nt) and redundant sequences, 247,318 unique small RNA sequences in Col and 25,981 unique small RNA sequences in Ler were used for further analysis. All these siRNAs were mapped to the Col genome by BLAST [48] and PERL scripts, and the numbers of perfect matches were counted per 100 bp. Next, regions contain more than 3 hits within 300 bp in Ler but no hits in 1.5 kb at the same region in Col (Figure 6) were filtered out and overlapping regions were artificially combined. Col derived small RNA dataset was downloaded from NCBI GEO (GSE5228), and Ler derived small RNA sequences from NCBI GenBank (DQ927324-DQ972825). The Arabidopsis genome (Col) information was provided by TIGR (release version 5). Gene positions were annotated according to TAIR's SeqViewer data. Tandem gene duplication information was provided by TIGR (tandem_gene_duplicates.Arab_R5). |
10.1371/journal.pgen.1005124 | MAPK Signaling Pathway Alters Expression of Midgut ALP and ABCC Genes and Causes Resistance to Bacillus thuringiensis Cry1Ac Toxin in Diamondback Moth | Insecticidal crystal toxins derived from the soil bacterium Bacillus thuringiensis (Bt) are widely used as biopesticide sprays or expressed in transgenic crops to control insect pests. However, large-scale use of Bt has led to field-evolved resistance in several lepidopteran pests. Resistance to Bt Cry1Ac toxin in the diamondback moth, Plutella xylostella (L.), was previously mapped to a multigenic resistance locus (BtR-1). Here, we assembled the 3.15 Mb BtR-1 locus and found high-level resistance to Cry1Ac and Bt biopesticide in four independent P. xylostella strains were all associated with differential expression of a midgut membrane-bound alkaline phosphatase (ALP) outside this locus and a suite of ATP-binding cassette transporter subfamily C (ABCC) genes inside this locus. The interplay between these resistance genes is controlled by a previously uncharacterized trans-regulatory mechanism via the mitogen-activated protein kinase (MAPK) signaling pathway. Molecular, biochemical, and functional analyses have established ALP as a functional Cry1Ac receptor. Phenotypic association experiments revealed that the recessive Cry1Ac resistance was tightly linked to down-regulation of ALP, ABCC2 and ABCC3, whereas it was not linked to up-regulation of ABCC1. Silencing of ABCC2 and ABCC3 in susceptible larvae reduced their susceptibility to Cry1Ac but did not affect the expression of ALP, whereas suppression of MAP4K4, a constitutively transcriptionally-activated MAPK upstream gene within the BtR-1 locus, led to a transient recovery of gene expression thereby restoring the susceptibility in resistant larvae. These results highlight a crucial role for ALP and ABCC genes in field-evolved resistance to Cry1Ac and reveal a novel trans-regulatory signaling mechanism responsible for modulating the expression of these pivotal genes in P. xylostella.
| Biopesticide and transgenic crops based on Bacillus thuringiensis (Bt) Cry toxins are widely used worldwide, yet the development of field resistance seriously threatens their sustainability. Unraveling these resistance mechanisms are of great importance for delaying insect field resistance evolution. The diamondback moth was the first insect to evolve field resistance to Bt biopesticides and it is an excellent model for the study of Bt resistance mechanisms. In this work, we present strong empirical evidence supporting that (1) field-evolved resistance to Bt in P. xylostella is tightly associated with differential expression of a membrane-bound alkaline phosphatase (ALP) and a suite of ATP-binding cassette transporter subfamily C (ABCC) genes, and (2) a constitutively transcriptionally-activated upstream gene (MAP4K4) in the MAPK signaling pathway is responsible for this trans-regulatory signaling mechanism. These findings identify key resistance genes and provide the first comprehensive mechanistic description responsible for the field-evolved Bt resistance in P. xylostella. Given that expression alterations of multiple receptor genes result in Bt resistance in many other insects, it can now be tested to determine whether the previously unidentified trans-regulatory mechanism characterized in this study is also involved in these cases.
| The Gram-positive entomopathogen Bacillus thuringiensis (Bt) is the most widely used biopesticide due to its highly specific activity and environmental safety [1]. The insecticidal activity of Bt is largely attributed to diverse δ-endotoxins (Cry toxins) produced during sporulation [2]. Transgenic crops harboring Bt toxin genes (Bt crops) are the most successful insecticidal biotechnology, with >75 million hectares planted worldwide [3]. However, high adoption of Bt crops and concurrent use of Bt pesticides represent high selection pressure for insect resistance evolution. To date, cases of field-evolved resistance to Bt sprays or Bt crops have been reported in at least seven insect species [4,5]. The economic and environmental importance of Bt insecticides highlight the significance of clarifying the molecular mechanisms of insect field-evolved resistance to Bt.
The mode of action of Bt Cry toxins includes a critical binding step to receptors in the insect midgut, which is conducive to formation of a toxin pore on the enterocyte membrane that leads to osmotic cell death [6]. The importance of this binding step is further highlighted by high levels of resistance to Bt Cry toxins being almost exclusively associated with alterations in receptor genes [7]. While a number of insect midgut proteins have been proposed as functional receptors for diverse Cry toxins [8], high levels of resistance to Cry1 toxins due to reduced toxin binding have been genetically linked to mutations or expression alterations of receptor genes such as cadherin, aminopeptidase-N (APN) and alkaline phosphatase (ALP) [6]. Recently, mutations in ATP-binding cassette transporter subfamily C member 2 (ABCC2) gene [9–13] have been reported to be linked to high levels of resistance to Bt Cry toxins in diverse lepidopteran insects, and it has been proved to be a functional receptor for Bt Cry toxins in Bombyx mori [14]. Although expression alterations of ABCC genes have been reported to result in chemical insecticide resistance in many insects [15,16], whether the expression alterations of ABCC genes can be involved in insect Bt resistance is unclear. In addition, although altered ALP gene expression seems to be commonly associated with lepidopteran resistance to Cry1 toxins [17,18], there is currently no available functional or genetic evidence for these ALP proteins in Bt resistance.
The diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae), is a global notorious pest that can rapidly evolve resistance to insecticides and cause US $4–5 billion in management costs annually [19]. Thus far, field-evolved resistance to Bt sprays has only been described in P. xylostella [20] and greenhouse populations of Trichoplusia ni [21]. In both cases, resistance was monogenic and transmitted as an autosomal recessive trait associated with reduced toxin binding to the midgut [22,23]. In T. ni, this reduced Cry1Ac toxin binding is associated with reduced expression of a midgut aminopeptidase gene (APN1) [24] possibly trans-regulated by an unidentified gene located in a resistance locus containing the ABCC2 gene [10]. In P. xylostella, cis-acting mutations in putative toxin receptor genes are not linked to field-evolved resistance to Cry1Ac [25,26]. As in T. ni, resistance to Cry1Ac in P. xylostella also mapped to a multigenic resistance locus (BtR-1) containing the ABCC2 gene [10]. However, the detailed genetic makeup of this resistance locus and the potential trans-acting regulatory effect on Cry toxin receptors by resident genes remain unknown.
High-level resistance phenotype to Bt Cry toxins in insects is often autosomal recessive and controlled by a single gene, however, the fact that many players responsible for resistance suggests there may be a common pathway that links all these receptor genes. Mitogen-activated protein kinase (MAPK) signaling pathway has been described to control immune defensive responses to Bt Cry toxins in nematodes [27,28] and insects [29,30]. The MAPK signaling pathway consists of a four-kinase cascade module (MAP4K, MAP3K, MAP2K and MAPK) that can positively or negatively regulate expression of diverse functional genes via different transcription factors [31,32]. Therefore, it is plausible that the MAPK signaling pathway may be the common pathway that can regulate the expression of diverse receptor genes to result in insect resistance to Bt Cry toxins.
In this study, we identify a novel major mechanistic pathway that the MAPK signaling cascade trans-regulating differential expression of ALP and ABCC genes confers high-level resistance to Cry1Ac in both field-evolved and laboratory-selected strains of P. xylostella. This discovery greatly advances our comprehensive understanding of insect resistance mechanisms to Bt Cry1Ac toxin and provides new insights into how insects evolve resistance to Bt entomopathogen.
Bioassays confirmed high-level Bt resistance in diamondback moth strains originally collected from Florida (DBM1Ac-R, >3500-fold), Shenzhen (SZ-R, 458-fold Cry1Ac) and Shanghai (SH-R, 1890-fold, Bt var. kurstaki) (S1 Table). A fourth near-isogenic strain (NIL-R) was generated to control for variation in genetic backgrounds that may be observed between strains, and was highly resistant to both Cry1Ac (>3900-fold) and Btk (>2800-fold). Despite their diverse origins, reduced Cry1Ac toxin binding to midgut BBMV proteins was a common phenotype observed in all resistant samples compared to a Bt susceptible reference strain DBM1Ac-S (Fig 1), suggesting midgut receptor alterations as a likely resistance mechanism.
Multiple previously reported midgut receptors for Bt Cry toxins including cadherin, APN and ALP were first investigated. Recently, we have determined that the midgut cadherin is not involved in Cry1Ac resistance in all of our Cry1Ac/Btk resistant P. xylsotella strains [33]. In this study, we further detected that reduced Cry1Ac toxin binding was significantly associated with reduced ALP enzymatic activity in BBMV from larvae of all resistant strains (P < 0.05; Holm-Sidak’s test; n = 3), while APN activity did not differ (Fig 2A). In contrast, we did not detect significant differences in ALP or APN enzymatic activity when comparing gut luminal contents (Fig 2B), supporting a reduction of membrane-bound ALP (mALP) might be responsible for reduced toxin binding, but not soluble ALP (sALP).
As ALP activity was significantly reduced in resistant strains, we cloned the full length cDNA of a novel mALP gene (PxmALP, GenBank accession no. KC841472) from the DBM1Ac-S larval midgut tissue (S2 Table). The deduced PxmALP protein sequence displays typical structure features of a mALP protein (S1 Fig), and GenBank database search with the PxmALP protein sequence detected high identity to mALPs in diverse insect species. Phylogenetic analysis (S2 Fig) showed that PxmALP is clearly grouped into the same cluster of a clade containing many other lepidopteran mALPs reportedly involved in Bt resistance, suggesting the PxmALP may be a functional Cry1Ac toxin receptor as other lepidopteran mALPs [34,35].
Full length PxmALP sequencing using larval midgut cDNA from four resistant P. xylostella strains didn’t identify any constant non-synonymous substitutions or indels, suggesting PxmALP mutations are not linked with Cry1Ac resistance. However, qPCR analysis confirmed a significant reduction (>50%) in PxmALP expression in larvae from all the resistant strain (Fig 2C), which is also reflected by our RNA-Seq transcriptome profiling data [36] (S3 Table). Moreover, the reduced expression level of PxmALP gene is congruent with reduced ALP activity in the resistant strains.
To determine whether PxmALP can serve as a functional Cry1Ac receptor in P. xylostella, heterologous expression of recombinant PxmALP was conducted in Spodoptera frugiperda Sf9 cell culture, and we detected it by Western blotting and ALP activity assays using cells transfected with an empty bacmid or a bacmid containing the Arabidopsis thaliana β-glucuronidase (GUS) gene as controls (S3A Fig). Localization of PxmALP to the surface of transfected cells was demonstrated by releasing GPI-anchored proteins through cleavage with phosphatidylinositol-specific phospholipase C (PI-PLC) and detecting most of the recombinant ALP activity in the supernatant (S3B Fig). When expressed on the surface of the Sf9 cells, PxmALP bound Cry1Ac toxin as detected by confocal fluorescence microscopy (Fig 3A) and ELISA assays (S3C Fig), while no Cry1Ac toxin binding was detected in untransfected or cells expressing the GUS gene. As expected from the GPI-attachment to the membrane of the recombinant PxmALP, release of PxmALP from the surface of Sf9 cells by PI-PLC treatment resulted in Cry1Ac toxin binding being localized to aggregates in the media, probably containing the released PxmALP, rather than to the surface of the Sf9 cells (Fig 3A, compare panels 2F and 3F). Moreover, binding of Cry1Ac toxin to the Sf9 cells expressing PxmALP was conducive to cytotoxicity (Fig 3B), while cell viability was unaffected in untransfected and Sf9 cells expressing the GUS gene.
To further test PxmALP as functional Cry1Ac receptor, we silenced PxmALP gene expression and detected larval susceptibility to Cry1Ac protoxin. Both dsRNA concentration and timing of silencing were optimized in preliminary experiments (S4 Fig). As negative controls, we used non-injected or buffer-injected larvae, while to control for unintended off-target effects, we used larvae injected with dsRNA targeting the PxmALP ortholog in Helicoverpa armigera (HamALP1, GenBank accession no. EU729322.1). Sequence similarity in the dsRNA fragments targeting PxmALP or HamALP1 reached to about 56%, but no consensus motifs were longer than 19 bp to avoid possible off-target effects [37]. Microinjection of dsRNA targeting an internal region of PxmALP (nucleotides 510 to 883) resulted in about 80% reduction in expression levels compared to controls 48 h post-injection, whereas no significant changes in expression were detected when injecting dsRNA targeting HamALP1 (Fig 3C). Subsequent bioassays at 48 h post-injection for 72 h demonstrated that PxmALP silencing resulted in significantly decreased larval susceptibility to Cry1Ac protoxin (P < 0.05; Holm-Sidak’s test; n = 3) compared to controls (Fig 3D). Specifically, about 55% mortality was observed in control larvae treated with 1.0 μg/ml of Cry1Ac, while only 19% mortality was observed in larvae injected with dsPxmALP (mortality in non-injected larvae fed control diet was < 5%). When the toxin concentration was increased to 2.0 μg/ml, 95% mortality was observed in controls while only 48% mortality was detected in larvae injected with dsPxmALP.
While resistance to Cry1Ac in the DBM1Ac-R (previously called Cry1Ac-R) and NO-QA P. xylostella strains maps to the same BtR-1 resistance locus [38], the PxmALP gene is located on a separate chromosome [26]. We assembled the approximately 3.15 Mb chromosome region representing BtR-1 locus (Fig 4) assisted by linkage mapping data [10], genomic data of B. mori [39] and P. xylostella [40], and the genetic synteny between P. xylostella and B. mori. The BtR-1 locus contains four P. xylostella genome scaffolds and more than 130 annotated genes (S4 Table). Presence of seven known genetic mapping marker genes [10] were confirmed and ten candidate resistance genes, including two P450 genes (CYP18A1 and CYP18B1), five ABCC genes (ABCC1-5) and three genes involved in the MAPK signaling pathway (two MAPK genes and one MAP4K gene), were identified (Fig 4). Considering previous reports suggesting their involvement in Bt Cry toxins intoxication [9,29], we focused our subsequent work on potential alterations in the ABCC and MAPK genes in BtR-1.
Previous study showed that a 30-bp deletion in exon 20 of ABCC2 gene is linked to Cry1Ac resistance in the NO-QAGE strain of P. xylostella [10], however, we did not detect any indels or constant non-synonymous substitutions in this region in our resistant strains, which led us to further study all the five ABCC genes in the BtR-1 resistance locus. We assembled the full-length coding sequences of PxABCC1-5 by in silico analysis, PCR cloning and sequencing, and the bona fide full-length cDNA sequences of these five ABCC genes (GenBank accession nos. KM245560–KM245564) as they were incorrectly annotated in the draft P. xylostella genome. Cloning and comparison of the full-length PxABCC1-5 cDNA sequences from midgut pools of susceptible or resistant larvae detected sequence variations in the region encompassing exons 4 to 11 of PxABCC1-3, which did not result in changes in the number or size of amplified bands in PCR assays targeting PxABCC1 (S5A Fig, left figure), but led to additional amplicons observed for PxABCC2 and PxABCC3 (S5B and S5C Fig, top figures). Sequencing of these amplicons from each strain allowed us to detect one PxABCC1 isoform, eleven PxABCC2 isoforms and four PxABCC3 isoforms, probably resulting from alternative splicing of the PxABCC1-3 mRNA precursor (S6 Fig). Some of the identified alternative splicing isoforms contained premature stop codons in the transmembrane domain (TMD) or the subsequent nucleotide binding domain 1 (NBD1), which would result in truncated and possibly non-functional proteins. However, their relative distribution was similar (S5A Fig, right figure; S5B and S5C Fig, bottom figures) among untreated individual susceptible (DBM1Ac-S) larvae and larvae of the NIL-R resistant strain surviving exposure to an extremely high dose of Cry1Ac protoxin (10000 μg/ml, causes 100% mortality in DBM1Ac-S larvae), supporting no associations between ABCC isoforms in that region and resistance to Cry1Ac. The lack of large inversions or deletions was also tested and confirmed using one-step amplification of the full-length PxABCC1-5 cDNA followed by nested PCR with overlapping primer sets (S5–S9 Tables).
Since no association between mutations in PxABCC genes and resistance was observed, we subsequent compared levels of expression for all five ABCC genes in the BtR-1 locus in susceptible and resistant strains using qPCR (Fig 5A). These analyses revealed that three of the five ABCC genes (PxABCC1-3) showed significant differences in gene expression between susceptible and resistant strains, whereas no obvious expression alteration for PxABCC4 or PxABCC5 was found. Interestingly, while PxABCC2 and PxABCC3 were significantly down-regulated, PxABCC1 was dramatically up-regulated in all the resistant compared to susceptible P. xylostella strains (P < 0.05; Holm-Sidak’s test; n = 3). These observations (except PxABCC2 differences) were also detected after re-analyzing of our RNA-Seq transcriptome profiling data [36] (S3 Table).
The observed association between differential PxABCC gene expression and resistance led us to investigate the relationship among the three PxABCC genes. Protein sequence analysis showed that these genes share typical structural features of ABCC family members, including two transmembrane domains (TMDs) and two nucleotide binding domains (NBDs). The genomic structure showed that these three PxABCC genes display high protein sequence similarity (about 59%), extremely similar exon size and number, and the same intron phase (S10 Table; see also S7A Fig), which indicating they are paralogs derived from an ancient gene duplication event. Phylogenetic analysis showed that the P. xylostella ABCC1-3 genes share high sequence identity with homologs from Spodoptera exigua (S7B Fig), which have been recently proved to be involved in resistance to Cry1 toxins [12].
To determine the effect of PxABCC2 and PxABCC3 down-regulation on susceptibility to Cry1Ac toxin, we silenced their expression by RNAi and tested susceptibility (LC50 and LC90) in silenced larvae. Expression levels for both genes were significantly reduced at 24 h after dsRNA injection, with lowest expression levels detected after 48 h and lasting at least 72 h in both cases (Fig 5B and 5C). Silencing was specific to each ABCC gene and did not affect PxmALP expression levels (Fig 5B and 5C). Bioassays performed at 48 h post-injection for 72 h showed a marked decrease in susceptibility to Cry1Ac toxin in both dsPxABCC2- and dsPxABCC3-treated larvae compared to the buffer- or dsEGFP-injected larvae. Moreover, silencing of multiple genes simultaneously (combinational RNAi) by injection of a combination of dsRNAs targeting PxmALP, PxABCC2 and PxABCC3 (dsMultigenes) resulted in a comparatively higher reduction in susceptibility to Cry1Ac (P < 0.05; Holm-Sidak’s test; n = 3) (Fig 5D). Specifically, about 50% mortality was observed in control larvae treated with 1.0 μg/ml of Cry1Ac (LC50 value), while only 12%, 15% and 6% mortality was observed in larvae injected with dsPxABCC2, dsPxABCC3 and dsMultigenes, respectively (mortality in non-injected larvae fed control diet was <5%). When using 2.0 μg/ml (LC90 value), 92% mortality was observed in controls while only 32%, 37% and 15% mortality was detected in larvae injected with dsPxABCC2, dsPxABCC3 and dsMultigenes. All three silencing treatments had significant effects (LSD test; P < 0.05; n = 3) on fitness components, including decreased pupation rate, reduced pupal weight, shortened pupal time and lower eclosion rate when compared to control treatments, and no differences (LSD test; P > 0.05; n = 3) were detected among control treatments (S11 Table).
We further performed genetic linkage analysis to test non-cosegregation of alternative ABCC1-3 gene splicing isoforms or cosegregation of differentially altered expression of the PxmALP and PxABCC1-3 genes with resistance to Cry1Ac toxin in the NIL-R strain. Reciprocal F2 backcross families from crossing the near-isogenic NIL-R (resistant) and DBM1Ac-S (susceptible) strains were generated and selected on cabbage with or without a lethal dose of Cry1Ac protoxin (S8 Fig). Comparison of distribution and sequencing of PxABCC1-3 cDNA isoforms among individual larvae from backcross families exposed or not to Cry1Ac toxin demonstrated no association between PxABCC1-3 isoforms and resistance to Cry1Ac (S9 Fig).
Quantification of PxmALP and PxABCC1-3 expression levels in individual larval midguts from backcross families not exposed to Cry1Ac selection showed two distinct groups (Fig 6). One group demonstrated significantly reduced expression levels of PxmALP (< 0.4-fold), PxABCC2 (< 0.15-fold) and PxABCC3 (< 0.25-fold), while the other group displayed expression levels similar to larvae from the susceptible parental strain (DBM1Ac-S) or the F1 generation from NIL-R × DBM1Ac-S crosses (Fig 6A, 6C and 6D). The ratio between the numbers of individuals in each group, 8:10, 9:9 and 9:9 in backcross family a and 9:9, 9:9 and 10:9 in backcross family b, for the PxmALP, PxABCC2 and PxABCC3 genes, respectively, were statistically validated to follow the 1:1 random assortment ratio (P > 0.10 or P = 1.0; χ2 test). Rearing of neonates from both backcross families on Cry1Ac resulted in about 50% mortality (55% backcross family a, 47.5% in family b), consistent with the expected Mendelian inheritance of the recessive resistance trait. All the surviving larvae in both backcross families had reduced PxmALP (< 0.4-fold), PxABCC2 (< 0.15-fold) and PxABCC3 (< 0.25-fold) expression levels compared to larvae from the DBM1Ac-S strain or the F1 generation, demonstrating tight linkage (cosegregation) with resistance to Cry1Ac in NIL-R (P < 0.001, χ2 test). However, the expression levels for the PxABCC1 gene in both Cry1Ac-selected and non-selected larvae were similarly up-regulated (4- to 14-fold) compared to susceptible larvae (Fig 6B). Although the unselected backcross individuals exhibited two distinct groups with differing PxmALP, PxABCC2 and PxABCC3 expression levels, both groups of larvae had similar PxABCC1 expression levels (t test, P > 0.10), supporting no correlation between down-regulation of PxmALP, PxABCC2, or PxABCC3 and up-regulation of PxABCC1 expression in the larvae.
Analysis of the BtR-1 locus found three genes (two MAPK genes and one MAP4K gene) involved in MAPK signaling pathways (S4 Table; see also Fig 4). Unlike the two MAPK genes, the PxMAP4K4 gene locates extremely close to the three PxABCC genes within the core BtR-1 locus and shows perfect genetic synteny between P. xylostella and B. mori (S4 Table; see also S10 Fig). Using specific primers (S12 Table), we cloned and corrected the full-length cDNA sequence of the incorrectly annotated PxMAP4K4 gene in the P. xylostella genome (DBM-DB, Gene ID Px002422), the bona fide full-length cDNA sequence has been deposited in the GenBank database (accession no. KM507871). Sequence alignment of the deduced amino acid sequences showed conserved N-terminal kinase (Serine/threonine kinase catalytic domain, STKc) and the C-terminal regulatory (citron/NIK homology domain, CNH) domains of PxMAP4K4 gene in homologs from different species (S11 Fig).
Comparisons of PxMAP4K4 expression levels between susceptible and resistant strains by qPCR showed that this gene was constitutively up-regulated in larvae from all resistant strains compared to the susceptible strain (Fig 7A). Moreover, toxin induction assays showed that the expression level of PxMAP4K4 was significantly increased (P < 0.05; Holm-Sidak’s test; n = 3) in the susceptible strain DBM1Ac-S but didn’t alter (P > 0.05; Holm-Sidak’s test; n = 3) in the resistant strain NIL-R when treated with respective median lethal concentration of Cry1Ac in both strains, suggesting that the high expression levels of PxMAP4K4 in resistant strain was constitutive rather than induced (S12 Fig).
To confirm the significance of this observation, we silenced PxMAP4K4 expression by RNAi in resistant NIL-R larvae and tested larval susceptibility to Cry1Ac post-RNAi. Microinjection of dsRNA targeting the CNH domain region of the PxMAP4K4 mRNA resulted in about 55% reduction (0.45-fold) in expression levels at 48 h post-injection, with expression returning to control levels at 120 h post-injection (Fig 7B). Correspondingly, the expression levels of PxmALP, PxABCC2 and PxABCC3 were significantly increased by 2.1-, 4.8- and 2.5-fold at 48 h post-injection, whereas the expression level of PxABCC1 was dramatically reduced 0.28-fold (77% reduction) at this time point (Fig 7B). Subsequent bioassays performed at 48 h post-injection for 72 h demonstrated that silencing of PxMAP4K4 gene expression resulted in a significant increase in larval susceptibility to Cry1Ac protoxin (P < 0.05; Holm-Sidak’s test; n = 3) when compared to the buffer- or dsEGFP-injected larvae (Fig 7C). Specifically, about 3% and 12% mortality was observed in respective control larvae untreated or treated with 1000 μg/ml of Cry1Ac (LC10 value), while approximately 72% mortality was observed in larvae injected with dsPxMAP4K4, and mortality in dsPxMAP4K4-treated larvae not exposed to toxin was < 4%.
Field insect populations can develop resistance to entomopathogens used as biopesticides, such as B. thuringiensis (Bt), limiting their potential efficacy for pest management. Multiple Cry1A midgut receptors have been reported in Lepidoptera, which should theoretically make resistance evolution difficult, however, genetic analysis has commonly shown resistance to be a single autosomal locus [41]. Data in this study provides a comprehensive mechanistic description of resistance to Cry1Ac and a Btk biopesticide in larvae from diverse P. xylostella strains. Although previous reports supported that mutations in the PxABCC2 gene localized to the BtR-1 locus are responsible for resistance to Cry1Ac in P. xylostella [10], we did not detect any mutations in the PxABCC2 or other PxABCC genes in BtR-1 associated with resistance in any of our tested strains. In contrast, our findings clearly support that differential expression of a midgut membrane-bound alkaline phosphatase (PxmALP) gene and a suite of PxABCC genes (including PxABCC2) is associated with high levels of resistance to Cry1Ac and Btk in P. xylostella. This is the first report showing that expression alterations, not gene mutations, of ABCC2 and other ABCC genes can be involved in insect Bt resistance. More importantly, for the first time, we identify a transcriptionally-activated upstream gene in the MAPK signaling pathway (PxMAP4K4) within the BtR-1 locus can trans-regulate differential altered expression of the PxmALP and PxABCC genes in BtR-1 to result in Cry1Ac resistance. This novel molecular mechanism of Cry1Ac resistance in P. xylostella is summarized in Fig 8.
Resistance to Cry1Ac in our field-evolved strain DBM1Ac-R [42] and the near isogenic strain NIL-R [43] fits the “Mode 1” type characterized by high levels of resistance to at least one Cry1A toxin, recessive inheritance, reduced binding of at least one Cry1A toxin, and lack of cross-resistance to Cry1C toxin [44]. Reduction in Cry1Ac binding is associated with “Mode 1” type resistance in nearly all cases of field-evolved P. xylostella resistance [7]. This observation, coupled with the Cry toxin binding site model developed for P. xylostella [45] and our Cry1Ac toxin binding data, clearly suggested that “Mode 1” resistance in our strains was due to alterations in at least one Cry1A toxin receptor. While a number of putative Cry receptors have been proposed [8], only alterations in cadherin, APN, ALP and ABCC2 genes have been found to associate with high resistance to Cry1Ac in Lepidoptera [6]. Dramatically reduced ALP enzymatic activity in BBMV samples of resistant P. xylostella larvae prompted us to clone the full-length cDNA of this gene for further functional studies. Sequence analysis showed that the cloned PxmALP is identical to the partial ALP1 gene sequence (GenBank accession no. EF579960, a partial genomic sequence including an intron). In agreement with Cry1Ac resistance not genetically mapping to ALP1 [26], we did not detect any mutations in PxmALP associated with resistance among our P. xylostella strains, however, the PxmALP expression levels were significantly reduced. This observation is also supported by our prior reports demonstrating down-regulation of ALP1 in the DBM1Ac-R (Cry1Ac-R in that report) strain [46] and a more detailed analysis of our recent RNA-Seq survey (S3 Table) [36]. In agreement with these observations, current data support ALPs as relevant Cry1A toxin binding proteins [47,48]. Importantly, altered ALP levels were described as associated with “Mode 1” type resistance to Cry1Ac in lepidopteran hosts [17,18], yet no mechanistic or linkage evidence were provided. Based on the functional and linkage data here, we propose a model for “Mode 1” resistance in P. xylostella in which PxmALP serves as a “lethal receptor” for Cry1Ac toxin. Down-regulation of PxmALP expression in resistant larvae results in reduced toxin binding to the midgut cells and survival. Our definition of PxmALP as a “lethal receptor” is based on its effectiveness as Cry1Ac receptor in cell assays, and the similar susceptibility in heterozygous and homozygous susceptible larvae, as previously suggested in Cry1Ac-resistant H. virescens [49].
Previous complementation tests have suggested that the same resistance locus (BtR-1) containing the mutant PxABCC2 gene [10] is responsible for resistance to Cry1Ac in P. xylostella strains from the continental US (PEN from Pennsylvania, SC1 from South Carolina and DBM1Ac-R originally collected from Florida), Hawaii (NO-QA and NO-QAGE), and China (SZBT) [22,25,38]. Since that the PxmALP gene is not located in the BtR-1 locus [26], our first hypothesis to reconcile the available data on P. xylostella resistance was that mutations or altered expression of PxABCC2 or other PxABCC genes in BtR-1 could result directly or indirectly in reduced expression of PxmALP. In support of this observation, resistance to a Bt pesticide in S. exigua [12,50] and to Cry1Ac in T. ni [10,24] was linked to mutations in ABCC2 and a concomitant down-regulation of an APN gene. However, unlike previous reports [10], we did not detect any mutations in PxABCC2 associated with resistance. Instead, we detected alternative splicing of ABCC subfamily genes, as previously reported in other insects [51–53]. Some of the splicing isoforms can lead to truncated ABCC proteins, which in heterozygotes would be masked by the susceptible allele, as previously proposed for cadherin gene [49]. Thus, these alternative splicing isoforms may represent a natural system to generate recessive gene mutation pools for Bt resistance selection. Intriguingly, alternative ABCC splicing was limited to the exon 4–11 region, suggesting that this cDNA region is more prone to allow mutations. This mechanism would explain rapid appearance of field-evolved resistance to Bt sprays in P. xylostella.
Although gene mutations of PxABCC genes were excluded, our further study corroborated differential expression alterations of these PxABCC genes were associated with Cry1Ac intoxication in P. xylsotella. However, our RNAi results showed that silencing of PxABCC2 or PxABCC3 genes did not affect PxmALP expression, which suggests PxABCC genes can’t regulate the expression level of PxmALP gene. Then, an independent role of PxABCC and PxmALP genes in Cry1Ac susceptibility is suggested as our second hypothesis. In support of this observation, similar effects on Cry1Ac susceptibility were detected when silencing PxABCC or PxmALP genes separately, and the comparatively higher reduction in Cry1Ac susceptibility was observed after performing combinatorial RNAi to simultaneous silencing of PxmALP, PxABCC2 and PxABCC3 genes. Therefore, it is plausible to postulate that an uncharacterized trans-regulatory gene in the BtR-1 locus could potentially control differential altered expression of both PxABCC and PxmALP resistance genes. Not incidentally, a similar hypothesis was proposed to explain allelic expression alteration of ABCC2 and APN3 genes in B. mori [54], and APN down-regulation in Cry1Ac-resistant T. ni [24] and Cry1Ab-resistant Ostrinia nubilalis [55].
While genes modulating ALP and ABCC expression in insects have not been reported, genes in the MAPK signaling pathway have been shown to modulate mammalian ALP [56–58] and human ABCC gene expression [59–62]. The MAPK signaling pathway can be activated as a defensive response to Bt Cry toxins [27–30]. Consequently, it is plausible that altered PxmALP and PxABCC gene expression in resistant P. xylostella may result from a primary enhanced defensive response involving activation of a trans-acting gene in the MAPK signaling pathway located in the BtR-1 locus. As expected, we found three MAPK genes in BtR-1 and identified the PxMAP4K4 gene in proximity to the three ABCC genes (S10 Fig). Of particular note, homologs of this PxMAP4K4 gene in mammals, Caenorhabditis elegans, and Drosophila are all upstream components of the MAPK signaling pathway and play important physiological roles in these species [63–65]. Accordingly, we found that the expression level of this gene can be induced in the susceptible strain when challenged by low concentration of Cry1Ac toxin, and functional data in this study demonstrated that this gene is constitutively transcriptionally-activated in resistant larvae to trans-regulate PxmALP and PxABCC expression levels thereby dramatically affecting Cry1Ac susceptibility, which finally attests to the involvement of the MAPK signaling pathway in P. xylostella Cry1Ac resistance. Since that cadherin can mediate the intracellular MAPK signaling pathway in mammal or insect cells [32,66,67], and considering that alteration of cadherin gene expression is associated with resistance to Cry toxins in several other lepidopteran insects [68,69], it will be very interesting to examine the possible feedback regulation of MAPK signaling in cadherin gene expression regulation. Moreover, since diverse upstream cytokines in the MAPK signaling pathway can regulate expression of APN genes in mammals [70,71], and considering that alteration of APN gene expression is associated with resistance to Cry toxins in several other lepidopteran insects [24,50,54,55,72], it will be very interesting to examine the possible involvement of MAPK signaling in APN gene expression regulation. Recently, we have found that down-regulation of a novel ABC transporter gene (PxABCG1 or Pxwhite) possibly trans-regulated by the MAPK signaling pathway can also be involved in P. xylostella Cry1Ac resistance, suggesting the MAPK signaling pathway may trans-regulate numerous ABC transporters from different subfamilies [73]. Therefore, it is plausible that this novel trans-regulatory mechanism might be a common regulation event of diverse Bt receptor genes in all of these cases.
Duplication or amplification of functional genes is thought to be a major driving force for adaptive evolution of insect response to environmental stress [74] and development of insecticide resistance [75,76]. Whole genomic analyses support that the ABC transporter superfamily has undergone apparent gene duplication in the P. xylostella genome [40], and this duplication may allow for functional redundancy. Based on their extremely similar genomic structure and high sequence similarity, it is highly possible that the PxABCC1-3 genes in BtR-1 locus may have been generated through gene duplication and share similar functions. Considering that PxABCC1 gene up-regulation was not linked to Cry1Ac resistance, we speculate that PxABCC1 may have lost the ability to bind Cry1Ac toxin but retained substrate transport function to functionally rescue the reduced PxABCC2 and PxABCC3 phenotype in resistant larvae. Likewise, transcriptionally-activated MAPK signaling induced by Cry5B intoxication in C. elegans up-regulated a target cation efflux transporter gene (ttm-1) possibly involved in removing cytotoxic cations generated by toxin-induced pore formation [27]. This phenomenon would also resemble up-regulation of APN6 in Cry1Ac-resistant T. ni with down-regulated APN1 expression [24]. Since silencing of PxABCC2 and PxABCC3 genes result in obvious fitness costs in P. xylostella larvae, rescue of ABCC gene function in resistant P. xylostella by PxABCC1 would also help explain lack of fitness costs in the DBM1Ac-R strain [77]. These data suggest that functional redundancy in ABCC genes can reduce fitness costs and thus increase the probability of resistance evolution in the field, which may threaten the continued effectiveness of Bt sprays/Bt crops and the currently adopted refuge strategy. In this case, we should attach great importance to this observation and perform continuous field monitor of insect resistance in such form.
In summary, the present study shows that alteration in expression of multiple putative Cry1Ac receptors is linked to “Mode 1” type resistance in P. xylostella, and that this altered gene expression is trans-regulated by the MAPK signaling pathway. Although the data presented do not directly address the participation of additional elements and the full repertoire of the MAPK signaling pathway, they do provide strong evidence for an important role of this signaling pathway in insect susceptibility to Cry1Ac toxin. Further work is needed to identify additional toxin receptor genes (e.g. cadherin and APN) that may also be controlled by this pathway and other MAPK genes or downstream transcription factors involved in Bt resistance in insects. The present data deepens our understanding of how insect target cells counter Cry intoxication through functionally sophisticated intracellular responses to result in Bt resistance. Moreover, the identified pivotal genes and their expression regulation mechanism responsible for resistance to Cry toxins in this study are critical for sensitive and efficient monitoring and management practices to delay field-evolved insect resistance to Bt pesticides and Bt crops.
The susceptible DBM1Ac-S and resistant DBM1Ac-R (previously referred to as Cry1Ac-R) strains of P. xylostella were originally provided by Drs. J. Z. Zhao and A. Shelton (Cornell University, USA) in 2003. The DBM1Ac-R strain originated from insects with field-evolved resistance to Javelin (Bt var. kurstaki) from Loxahatchee (Florida, USA) [78] that were crossed with the DBM1Ac-S (Geneva 88) strain (originated from Geneva, NY, USA) and further selected with Cry1Ac-expressing broccoli [79]. Resistance to Cry1Ac in DBM1Ac-R is autosomal, incompletely recessive and mostly monogenic [77]. The SZ-R (previously referred to as T2-R) and SH-R strains were originated from moths collected in China at Shenzhen (2003) and Shanghai (2005), respectively. The SZ-R strain was generated by selection in the laboratory with Cry1Ac while the SH-R strain was selected with a Bt var. kurstaki (Btk) formulation (WP with potency of 16000 IU/mg, provided by Bt Research and Development Centre, Agriculture Science Academy of Hubei Province, China). The DBM1Ac-S strain was kept unselected while the DBM1Ac-R and SZ-R strains have been kept under constant selection with a Cry1Ac protoxin solution killing 50–70% of the larvae sprayed on cabbage leaves. The near-isogenic NIL-R strain has been generated at the time this study was carried out and has been described elsewhere [43]. For this work, all strains were reared on JingFeng No.1 cabbage (Brassica oleracea var. capitata) without exposure to any Bt toxins or chemical pesticides at 25°C, 65% RH and 16D:8L photoperiod. Adults were fed with a 10% sucrose solution.
The Cry1Ac protoxin was extracted and purified from Bt var. kurstaki strain HD-73 as previously described [80]. Both purified Cry1Ac protoxin and trypsin-activated toxin were quantified by densitometry as described elsewhere [81].
Toxicity of Cry1Ac toxin or Btk formulation in 72 h bioassays with larvae from five different strains of P. xylostella using a leaf-dip method as described elsewhere [33]. Ten third instar P. xylostella larvae were tested for each of seven toxin concentrations and bioassays replicated four times. Mortality data were corrected using Abbott’s formula [82] and experiments with control mortality exceeding 10% were discarded and repeated. The LC50 values were calculated by Probit analysis [83].
Fourth-instar larval midguts (about 2000) from each P. xylostella strain were dissected in cold MET buffer [17 mM Tris–HCl (pH 7.5), 5 mM EGTA, 300 mM mannitol] plus protease inhibitors (1mM PMSF). Midgut brush border membrane vesicles (BBMV) were prepared as described elsewhere [84]. Purified BBMV proteins were quantified using the method of Bradford [85] with bovine serum albumin (BSA) as standard, and then flash frozen and kept in aliquots at -80°C until used. Between 5–8 fold enrichment in specific APN activity using L-leucine-p-nitroanilide (Sigma) was detected when comparing to initial midgut homogenates.
Midgut luminal contents were obtained as described elsewhere [18] by homogenization of pools of dissected midguts of actively feeding fourth-instar P. xylostella larvae with an electric pestle in a 1.5 ml centrifuge tube containing 100 μl of phosphate-buffered saline (PBS) buffer (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2.0 mM KH2PO4, pH 7.4). Homogenates were vigorously vortexed and centrifuged at 4°C (10 min at 16000×g). Supernatants were used for subsequent enzymatic assay measurements or flash frozen and kept in aliquots at -80°C until used.
Specific APN and ALP activity assays were performed using L-leucine-p-nitroanilide and p-nitrophenyl phosphate disodium (pNPP) as substrates, respectively, as described elsewhere [86]. Enzymatic activity was detected as the changes in optical density (OD) at 405 nm for 5 min at room temperature in a SpectraMax M2e (Molecular Devices) microplate reader. One enzymatic unit was defined as the amount of enzyme that would catalyze production of the chromogenic product from specific substrate per min and mg of BBMV protein at 37°C. Data shown are the means from triplicate measurements from three independent BBMV preparations and were analyzed for significance with a one-way ANOVA using Holm-Sidak’s tests (overall significance level = 0.05) with the SPSS Statistics (ver. 17.0) software (SPSS Inc.).
The seven mapped genes in Baxter et al. [10] were used as markers to find P. xylostella genome scaffolds within the chromosome region of the BtR-1 resistance locus in the Diamondback moth Genome Database (DBM-DB, http://iae.fafu.edu.cn/DBM), and their locations determined the final jointed pattern of all P. xylostella genome scaffolds. Homologs of the genes within this locus between P. xylostella and B. mori were identified through Blastp searches in each genome database. The detailed genetic makeup of the BtR-1 resistance locus is listed in S4 Table.
Fourth-instar larvae from different P. xylostella strains were anesthetized on ice and the midgut tissues were immediately dissected in RNase-free water containing 0.7% NaCl. Total RNA was extracted from single or pool of these dissected midguts using TRIzol reagent (Invitrogen) according to different experiments. Integrity of the RNA was determined using 1% TBE agarose gel electrophoresis, and then quantified by a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific Inc.). For gene cloning, the first-strand cDNA was prepared using 5 μg of total RNA with the PrimeScript Ⅱ 1st strand cDNA Synthesis Kit (TaKaRa) following manufacturer’s recommendations. For qPCR analysis, the first-strand cDNA was prepared using 1 μg of total RNA with the PrimeScript RT kit (containing gDNA Eraser, Perfect Real Time) (TaKaRa) following manufacturer’s recommendations. The synthesized first-strand cDNA was immediately stored at -20°C until used.
For PxmALP cloning, degenerate primers (S2 Table) were designed to target two highly conserved regions among selected lepidopteran mALP sequences (Bombyx mori, BAB62745; Helicoverpa armigera, ACF40806; Heliothis virescens, ACP39712; and Ostrinia furnacalis, AEM43806). The PCR parameters were as follows: one cycle of 94°C for 3 min; 35 cycles of 94°C for 30 s, 59°C for 45 s and 72°C for 1 min; a final cycle of 72°C for 10 min. The generated 536 bp midgut cDNA fragment was sequenced and used in Rapid Amplification of cDNA Ends (RACE) to obtain the full length ALP cDNA with 3′-Full RACE Core Set Ver.2.0 (TaKaRa) and SMARTer RACE cDNA Amplification (Clontech) kits following manufacturer’s protocols. Once a full-length cDNA for PxmALP was obtained the full coding sequence was validated by PCR amplification. Large-scale sequencing and comparing of the full-length midgut PxmALP was performed among all the susceptible and resistant P. xylostella strains. The full length midgut PxmALP cDNA sequence has been deposited in the GenBank (accession no. KC841472).
To first detect the reported PxABCC2 mutation (30 bp deletion in Exon 20) correlated with Bt resistance in P. xylostella strain NO-QAGE [10], one specific primer pair covering this region was designed based on the published partial PxABCC2 cDNA sequence (GenBank accession no. JN030490) (S6 Table). The PCR amplification of all the cDNA sequences of five ABCC genes in the BtR-1 resistance locus was performed using the strategy described elsewhere [87]. We first in silico assembled and corrected the full-length sequences of ABCC1-5 based on their putative coding sequences in the DBM-DB database (http://iae.fafu.edu.cn/DBM, Gene ID: Px002418+19, Px002416, Px002414+15, Px009835 and Px009834) and their unigenes in our P. xylostella transcriptome database [88] (S3 Table), then their cDNA was amplified by designing full-length primer pairs with four or five overlapping fragments (S5–S9 Table). The full length ABCC1-5 cDNA sequences have been deposited in GenBank (accession nos. KM245560–KM245564). In addition, the occurrence of large inversions or deletions was tested in a second amplification strategy by amplifying the whole cDNA sequence with specific full-length primers (S5–S9 Table) in a first step, followed by nested PCR amplification using the same full-length primer pairs with four or five overlapping fragments as described above. Amplicons were sequenced and the distribution of alternatively spliced transcripts of ABCC1-3 was compared among samples from untreated and larvae surviving Cry1Ac exposure. Using the same strategy as for ABCC genes, we cloned and obtained the full-length cDNA sequence of the PxMAP4K4 gene (GenBank accession no. KM507871).
The PCR reactions (25 μl total volume) contained 18.5 μl of double-distilled H2O (ddH2O), 2.5 μl of 10×LA Taq or Ex Taq Buffer, 2 μl of dNTP Mix, 5 μM of each specific primer, 1 μl of first-strand cDNA template, and 0.25 μl LA Taq HS or Ex Taq HS polymerase (TaKaRa). Reactions (35 cycles) were then performed in an S1000 or C1000 Thermal Cycler PCR system (BioRad) with the following parameters: one cycle of 94°C for 6 min; 35 cycles of 94°C for 30 s, 50–59°C for 45 s and 72°C for 5 min; a final cycle of 72°C for 15 min. The nested PCR parameters were as follows: one cycle of 94°C for 6 min; 35 cycles of 94°C for 30 s, 59°C (PxABCC2 and PxMAP4K4)/54°C (other four PxABCC genes) for 45 s and 72°C for 2 min; a final cycle of 72°C for 10 min.
All the cloning primers for each gene were designed in the Primer Premier 5.0 software (Premier Biosoft). Amplicons of the expected size were excised from 1.5–2.5% agarose gels, purified using the Gel Mini Purification Kit (Generay), and subcloned into the pEASY-T1 (Transgen) or pMD18-T vectors (TaKaRa) before transformation into Escherichia coli TOP10 competent cells (Transgen) for sequencing.
Gene sequence assembling, multiple sequence alignment and exon-intron analysis were carried out with DNAMAN 7.0 (Lynnon BioSoft). The open reading frame of the target nucleotide sequence is found by the ORF Finder tool at NCBI website (http://www.ncbi.nlm.nih.gov/gorf/gorf.html). The nucleotide sequence-similarity analyses were performed through BLAST tool at NCBI website (http://blast.ncbi.nlm.nih.gov/). The deduced protein sequence was obtained by an ExPASy translate tool Translate (http://web.expasy.org/translate/) from the Swiss Institute of Bioinformatics. The N-terminal signal peptide was determined using the SignalP 4.0 server (http://www.cbs.dtu.dk/services/SignalP/). The transmembrane region and membrane topology was analyzed by the TOPCONS online software (http://topcons.cbr.su.se/). Protein specific motif was searched and analyzed using the Myhits software (http://myhits.isbsib.ch/cgi-bin/motif_scan), the Prosite software (http://www.expasy.ch/prosite/) and CDD (conserved domain database) at NCBI. Two GPI modification site prediction servers (big-PI Predictor: http://mendel.imp.ac.at/sat/gpi/gpi_server.html and GPI-SOM: http://gpi.unibe.ch/) were used to predict the GPI-anchor signal sequence and GPI anchoring site. Presence of N- and O-glycosylation sites on the predicted protein sequence were tested using the NetNGlyc 1.0 (http://www.cbs.dtu.dk/services/NetNGlyc/) and NetOGlyc 4.0 server (http://www.cbs.dtu.dk/services/NetOGlyc/), respectively.
Protein sequences of the ALP and ABCC genes used for phylogenetic analyses were extracted from different databases: GenBank (http://www.ncbi.nlm.nih.gov/), SilkDB (http://silkworm.genomics.org.cn/), DBM-DB (http://iae.fafu.edu.cn/DBM) and Manduca Base (http://agripestbase.org/manduca/), and sequence alignment was carried out after eliminating vast redundant ALP or ABCC sequences. All the selected insect ALP and ABCC amino acid sequences were subjected to analysis through Clustal W alignment using Molecular Evolutionary Genetic Analysis software version 5.0 (MEGA 5) [89], then the phylogenetic tree was constructed using the neighbor-joining (NJ) method with “p-distance” as amino acid substitution model, “pairwise deletion” as gaps/missing data treatment and 1000 bootstrap replications.
Gene-specific primers to the PxmALP gene were selected and used in PCR reactions (25 μl) containing 11.95 μl of ddH2O, 11.25 μl of 2.5×SYBR Green MasterMix (TIANGEN), 4 μM of each specific primer, and 1 μl of first-strand cDNA template. The qPCR program included an initial denaturation for 6 min at 94°C followed by 40 cycles of denaturation at 94°C for 30 s, annealing for 30 s at 61°C, and extension for 35 s at 72°C. Gene-specific primers for PxABCC and PxMAP4K4 genes were designed in the cDNA regions without alternative splicing and used in PCR reactions (25 μl) containing 9.5 μl of ddH2O, 12.5 μl of 2×SuperReal PreMix Plus (TIANGEN), 7.5 μM of each specific primer, 1 μl of first-strand cDNA template and 0.5 μl 50×ROX Reference Dye (TIANGEN). The qPCR program included an initial denaturation for 15 min at 95°C followed by 40 cycles of denaturation at 95°C for 15 s, annealing for 30 s at 53°C (PxABCC2)/55°C (other four PxABCC genes)/63°C (PxMAP4K4), and extension for 32 s at 72°C. For melting curve analysis, an automatic dissociation step cycle was added. Reactions were performed in an ABI 7500 Real-Time PCR system (Applied Biosystems) with data collection at stage 2, step 3 in each cycle of the PCR reaction. Amplification efficiencies were calculated from the dissociation curve of quadruplicate replicates using five 2-fold serial dilutions (1:1, 1:2, 1:4, 1:8, and 1:16). Only results with single peaks in melting curve analyses, 95–100% primer amplification efficiencies, and >0.95 correlation coefficients were used for subsequent data analysis. Negative control reactions included ddH2O instead of cDNA template, which resulted in no amplified products. The amplified fragments were sequenced to confirm that potential expression differences were not due to sequence mutations in the targeted genes. Relative quantification was performed using the 2-ΔΔCt method [90] and normalized to the ribosomal protein L32 gene (GenBank accession no. AB180441) as validated elsewhere [40,91]. Four technical replicates and three biological replicates were used for each treatment. One-way ANOVA with Holm-Sidak’s tests (overall significance level = 0.05) were used to determine the significant statistical difference between treatments.
The Bac-to-Bac Baculovirus Expression System (Invitrogen) was used to express the recombinant PxmALP protein in Spodoptera frugiperda Sf9 cell cultures. The full-length PxmALP cDNA was cloned and amplified by high fidelity PCR with specific primers (S2 Table). Amplicons were purified, subcloned and sequenced as described above. Recombinant plasmids with correct insertion were verified by endonuclease digestion, PCR and sequencing. The verified positive clone was digested with EcoRI and XbaI for 3 h and then ligated into the pFastBac TH B donor plasmid vector to generate the recombinant pFastBac HT B-PxmALP bacmid. The recombinant plasmids (pFastBac HT B-PxmALP) were then transformed into DH10Bac competent cells (Invitrogen) and positive recombinant bacmid DNAs were detected by antibiotic selection and confirmed by PCR amplification.
For heterologous expression, transfections were performed in sterile six-well plates (Costar). Briefly, Sf9 cultures (8×105 cells/well) with >97% viability were cultured in Grace’s insect medium supplemented with 10% fetal bovine serum and transfected with 1 μg of pFastBac HT B-PxmALP in Cellfectin II Reagent (Invitrogen) following manufacturer’s instructions. Cells were incubated at 27°C until the viral infection was clear (3 days post-infection) and then the P1 viral stock was harvested by centrifugation at 480×g for 5 min at room temperature. The viral titer was determined using absolute quantification with standard curve by qPCR. The optimized viral stock with multiplicity of infection (MOI) of 0.1 was used to infect 2.0×106 Sf9 cells/well, and the supernatant at 72 h post-infection representing the P2 viral stock was collected and used to infect Sf9 cells (2.0×106 cells/well) at an optimized high MOI value (3–5). Non-infected cells and Sf9 cells infected with either an empty bacmid or a bacmid containing the Arabidopsis thaliana β-glucuronidase (GUS) gene (pFastBac-GUS) were used as controls.
Transfected Sf9 cell pellets were harvested 3 days post-infection, washed three times with PBS buffer (pH 7.4) and lysed using the I-PER Insect Cell Protein Extraction Reagent (Thermo Fisher Scientific Inc.) plus 1 μg/ml aprotinin (Sigma) with gentle agitation at 4°C for 10 min. After centrifugation at 15000×g at 4°C for 15 min, the supernatants containing recombinant proteins were quantified as described above, and stored at -80°C until used.
Binding of Cry1Ac to BBMV proteins was tested as described elsewhere [92] in 100 μl (final volume) reactions containing 10 nM Cry1Ac toxin and 10 μg P. xylostella BBMV in PBS binding buffer (PBS, pH 7.4 containing 0.1% BSA and 0.1% Tween-20). After electrophoresis with a constant current of 300 mA at 4°C for 1 h and then incubation with blocking buffer (PBS, 0.1% Tween-20, 3% BSA) for 1 h with constant shaking, bound toxin was detected with rabbit anti-Cry1Ac polyclonal antisera (1:100000 dilution) followed by goat anti-rabbit secondary antibody conjugated to horseradish peroxidase (HRP) (1:5000 dilution, CWBIO). The bound Cry1Ac was visualized using the SuperSignal West Pico (Pierce) reagent. Relative Cry1Ac binding was quantified using densitometry with the ImageJ v.1.47 software (http://rsbweb.nih.gov/ij/) with intensity in the DBM1Ac-S BBMV sample considered 100% binding. Data presented are the means and standard errors from assays using three independent BBMV experiments per strain.
Immunolocalization of Cry1Ac toxin binding to Sf9 cells expressing PxmALP was tested as previously [93] with slight modifications. Transfected cell cultures were incubated in 300 μl of PBS (pH 7.4) alone or with 1 U of phosphatidylinositol-specific phospholipase C (PI-PLC) (Invitrogen) at 4°C for 2 h with gentle agitation, and then washed thrice with PBS and incubated with Cry1Ac toxin (100 μg/ml) at 27°C for 2 h. Cultures were washed and then fixed in ice-cold 4% paraformaldehyde for 15 min. After washing and blocking with 1% BSA for 1 h at room temperature, the cells were probed sequentially with primary rabbit polyclonal anti-Cry1Ac antibody and FITC-conjugated goat anti-rabbit secondary antibody, each with 1:100 dilution and incubation for 1 h at 27°C. Finally, the cells were pipetted onto glass slides, mounted with coverslips and examined immediately under a LSM 700 confocal laser scanning microscope (Carl Zeiss) using excitation at 488 nm and 20× objective with additional zooming. Image acquisition of the controls (Non-infected Sf9 cells and GUS-infected Sf9 cells) and data processing were performed under the same conditions.
Enzyme Linked Immunosorbent Assays (ELISA) were performed as described elsewhere [47,94]. To test for Cry1Ac binding to PxmALP, 10 nM trypsin-activated Cry1Ac toxin was fixed into ELISA plates (Costar) overnight at 4°C, followed by five washes with 200 μl PBST buffer (PBS, pH 7.4; 0.05% Tween-20). The plates were then blocked by incubating with 100 μl 1% BSA at 37°C for 1.5 h, and washed five times with 200 μl PBST. After incubating with 0.5 μg of solubilized Sf9 cell culture proteins transfected with empty bacmid, expressing the GUS protein or PxmALP, bound PxmALP to Cry1Ac was detected using a 1:5000 dilution of anti-His antibody coupled to horseradish peroxidase (HRP) and subsequent 1:5000 dilution of anti-mouse antibody (CWBIO). Finally, the plates were incubated with 150 μl TMB (3,3′,5,5′-tetramethylbenzidine) Horseradish Peroxidase Color Development Solution (Beyotime), and the enzymatic reaction was stopped with 50 μl 2M H2SO4 and absorbance values (OD values) were read at 450 nm in microplate reader. As controls, wells coated with Cry1Ac but incubated without any of the three expressed proteins and revealed with the same antibodies above. The OD values of controls were all below 0.2 and subtracted from the experimental OD values. The experiments were repeated for three times using protein samples from independent batches and each with three replications.
Susceptibility to Cry1Ac in transfected Sf9 cell cultures was assessed by counting cells stained by trypan blue using an IX-71 Inverted Microscope (Olympus). Cells at 3-day post-infection were washed twice with PBS and then incubated with 100 μg/ml of Cry1Ac toxin in Sf9 cell medium. After 3 h at 27°C with gentle agitation, cells were washed once with 1 ml of PBS and then resuspended in 1 ml of a 0.4% trypan blue solution in PBS. The numbers of live (unstained) and dead (stained blue) cells in three replicates for each cell type and from three independent transfections were counted in a hemocytometer. Relative percentage mortalities were calculated using the total cell number in each replicate. Mortality data from diverse treatments were tested for significant differences using two-way analysis of variance (ANOVA) and Holm-Sidak’s multiple pairwise comparison tests (overall significance level = 0.05).
Toxin induction assays of the PxMAP4K4 gene were performed using third instar larvae from the susceptible strain DBM1Ac-S and the near-isogenic resistant strain NIL-R. We selected the DBM1Ac-S or NIL-R larvae with 1 or 3500 μg/ml Cry1Ac protoxin (respective LC50 value in each strain) for 72h as the leaf-dip method used in bioassay, the unselected larvae from both strains were used as control groups. After 72h, larval midguts were dissected from survivors, and subsequent total RNA extraction, cDNA synthesis, qPCR analysis of the PxMAP4K4 gene expression were as described above. Three independent experiments were conducted, and one-way ANOVA with Holm-Sidak’s tests (overall significance level = 0.05) were used to determine the significant statistical difference between control and treatment groups.
The expression of PxmALP, PxABCC2, PxABCC3 and PxMAP4K4 genes was silenced using injection of dsRNA in early 3rd instar P. xylostella larvae. Specific primers containing a T7 promoter sequence at the 5′ end to generate dsRNA targeting PxmALP (GenBank accession no. KC841472) and mALP1 from H. armigera (GenBank accession no. EU729322.1), or EGFP (GenBank accession no. KC896843) were designed using the SnapDragon tool (http://www.flyrnai.org/cgi-bin/RNAi_find_primers.pl). Primers to generate dsRNA to PxABCC2 (GenBank accession no. KM245561) and PxABCC3 (GenBank accession no. KM245562) were designed to the specific transmembrane region lacking alternative splicing and not in the intergenic conserved nucleotide binding domain (NBD) to avoid potential off-target effects. Primers for dsRNA of PxMAP4K4 (GenBank accession no. KM507871) were designed to the constant C-terminal CNH domain region lacking alternative splicing (S11 Fig). After amplification from P. xylostella or H. armigera total larval midgut RNA and confirmation by sequencing, the amplicons (438 bp for dsPxmALP, 538 bp for dsHamALP1, 469 bp for dsEGFP, 603 bp for dsPxABCC2, 531 bp for dsPxABCC3, and 582 bp for dsPxMAP4K4) were used as template for in vitro transcription reactions to generate dsRNAs using the T7 Ribomax Express RNAi System (Promega). The synthesized dsRNAs were suspended in injection buffer (10 mM Tris–HCl, pH 7.0; 1 mM EDTA), and then they were subjected to 1% agarose gel electrophoresis and quantified spectrophotometrically prior to microinjection. To increase dsRNA stability and facilitate dsRNA delivery, injection was carried out with a 1:1 volume ratio of Metafectene PRO transfection reagent (Biontex) after incubation for 20 min at 25°C. A combinatorial RNAi approach involving simultaneous knockdown of PxmALP, PxABCC2 and PxABCC3 genes was performed by mixing equal amounts (300 ng each) of the corresponding dsRNAs for microinjection of larvae from the susceptible DBM1Ac-S strain. In contrast, silencing of PxMAP4K4 was performed in the near-isogenic Cry1Ac resistant strain NIL-R displaying increased PxMAP4K4 expression. None of the larvae were exposed to Cry1Ac toxin before dsRNA microinjection to avoid detection of transcriptome changes due to exposure to the toxin. Optimal time to detect silencing and dsRNA amounts were optimized for PxmALP in preliminary experiments (S4 Fig).
Microinjection was carried out under a SZX10 microscope (Olympus). The volume of sample microinjected into each larvae was determined to result in <20% larval mortality 5 days post-injection. The Nanoliter 2000 microinjection system (World Precision Instruments Inc.) with sterilized fine glass capillary microinjection needles pulled by P-97 micropipette puller (Sutter Instrument) were used to deliver 70 nanoliters of injection buffer (containing Metafectene PRO solution) or dsRNAs (300 ng) into the hemocoel of early 3rd instar DBM1Ac-S or NIL-R P. xylostella larvae.
Larvae were starved for 6 h and anesthetized for 30 min on ice before microinjection. More than twenty or fifty larvae were injected for each treatment and three independent experiments performed. Injected larvae were allowed to recover for about 3 h at room temperature and then returned to normal rearing conditions for the subsequent qPCR assays to determine gene silencing and bioassays.
Effectiveness of RNAi was tested by qPCR 0–120 h post-injection using cDNA prepared from isolated total midgut RNA as described above. Leaf-dip bioassays were performed for 72 h using larvae at 48 h after dsRNA injection and Cry1Ac protoxin concentrations representing approximately the LC50 (1 μg/ml) and LC90 (2 μg/ml) values for non-injected DBM1Ac-S larvae and LC10 (1000 μg/ml) values for non-injected NIL-R larvae. Bioassays were performed with forty larvae per RNAi treatment and toxin concentration, and each bioassay replicated three times. Mortality in control treatments was below 5% and bioassay data processing was as described above. One-way or two-way ANOVA with Holm-Sidak’s tests (overall significance level = 0.05) were used to determine the significant statistical difference between qPCR and bioassay treatments, respectively.
Effects of RNAi on fitness costs were analyzed by comparing biological parameters, including pupation percentage, pupal weight, pupation duration and eclosion percentage. Larvae injected with buffer containing Metafectene PRO transfection reagent were used as a negative control. All the larvae used in the test were fed on fresh cabbage leaves without exposure to Cry1Ac toxin. Each treatment was replicated three times with ten larvae per replicate. Least squared difference (LSD) tests (overall significance level = 0.05) were used to determine statistical significance of differences in biological parameters between control and treated groups.
The near-isogenic NIL-R (resistant) and DBM1Ac-S (susceptible) strains were used for genetic linkage analysis as described elsewhere [24]. A single-pair cross was prepared between a male from the NIL-R and a female from the DBM1Ac-S strain to generate an F1 progeny. A diagnostic Cry1Ac toxin dose killing 100% of the F1 (heterozygous) larvae was determined in bioassays as described above. Reciprocal crosses between an F1 and NIL-R moths were made to generate two backcross families (S8 Fig). The progenies from each backcross family (total of 40 larvae per family) were reared on control (cabbage) or experimental (cabbage with 20 μg/ml of Cry1Ac toxin) diets.
Purified RNA from single backcross family individuals surviving no treatment or exposure to Cry1Ac was used for cDNA synthesis. Linkage between the existence of multiple PxABCC gene isoforms or differential alteration of PxmALP and PxABCC gene expression and resistance to Cry1Ac was tested using PCR amplification and qPCR conditions as described above.
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